Search Results (65,829)

Adult neural stem cells (NSCs) maintain lifelong neurogenesis, a fundamental process for neuroplasticity, memory and brain homeostasis. Despite decades of research, translating basic NSC biology into effective clinical therapies remains a central challenge. Here we present a narrative review that provides a comprehensive update on the current landscape of adult NSC research, associating molecular mechanisms with the emerging translational technologies. First, we analyze the biological features and neurogenic sequences within canonical niches such as the subventricular lateral zone and the subgranular zone, emphasizing phylogenetic and migratory differences between rodent models and humans. Second, we integrate these mechanisms with the influence of environmental and pathological modulators, describing how aging, metabolic changes, chronic stress and neuroinflammation disrupt NSC quiescence and lineage progression. Finally, we highlight recent technological advances driving the field toward clinical applications. By examining current NSC isolation strategies, induced pluripotent stem cell modeling, direct somatic reprogramming and the use of CRISPR-Cas9-based gene-editing therapies, this review delineates the pathways to overcome existing methodological limitations. Ultimately, we provide an integrated context that connects the modulation of the neurogenic niches with advanced in vitro technologies, offering new perspectives for regenerative medicine and the treatment of neurological disorders. Full article

In contemporary scholarship, pilgrimage is increasingly analysed as a practice associated with personal transformation, spiritual reflection, and psychological well-being. Among the most popular contemporary pilgrimage routes, the Camino de Santiago attracts hundreds of thousands of participants each year, many of whom describe their journey in explicitly therapeutic terms. This article examines the Camino experience through the theological category of penance understood as a form of spiritual therapy within the Christian tradition. The main argument of the study is that the early Christian understanding of penance as spiritual medicine provides a meaningful interpretative framework for analysing the therapeutic experiences reported by contemporary pilgrims. Early Christian authors such as Hermas, Tertullian, and Cyprian described sin as a spiritual illness and penance as a process of healing and restoration. Within this perspective, practices involving physical effort, repentance, prayer, and moral transformation functioned as forms of spiritual therapy (gr. θεραπεία). The article combines theological and empirical approaches. Analyses the concept of penance as spiritual healing in early Christian sources and traces its historical connection with penitential pilgrimage. Presents qualitative research based on semi-structured interviews conducted with pilgrims on the Camino de Santiago. While the participants rarely framed their experiences explicitly in penitential terms, their testimonies reveal recurring themes of inner purification, emotional reconciliation, coping with illness or personal crisis, and the search for meaning. The findings suggest that these experiences can be meaningfully interpreted through the lens of the Christian understanding of penitential practice, particularly as a process of transformation and restoration. Rather than demonstrating a direct continuity, the study proposes an interpretative perspective that highlights structural similarities between historical theological models and contemporary experiential narratives. By integrating theological reflection with empirical data, the article contributes to debates on how historical religious concepts can illuminate contemporary experiences of healing, meaning, and well-being. Full article

Inflammatory bowel disease (IBD) is characterized by chronic intestinal inflammation, epithelial barrier disruption and immune dysfunction. Alleviating and curing these pathological manifestations is the goal of IBD treatment. Despite substantial advances in targeted immunotherapies and anti-inflammatory strategies, achieving sustained intestinal mucosal healing remains a major clinical challenge. Dynamin-related protein 1 (Drp1) is a GTPase that mediates mitochondrial fission and plays a crucial role in maintaining the dynamic balance of mitochondrial morphology and function. In IBD, Drp1 expression is frequently upregulated and continuously activated, resulting in excessive fission and fragmentation of mitochondria. This mitochondrial dysregulation contributes to ATP depletion and excessive reactive oxygen species (ROS) production, thereby exacerbating disease progression and amplifying inflammatory signaling. This review highlights the distinctive role of Drp1 as an integrative node in IBD. Specifically, we connect mitochondrial dynamics with epithelial barrier failure, immune dysregulation, inflammatory cell death, and intestinal microenvironment remodeling. We further emphasize the potential relevance of Drp1 for biomarker-based patient stratification and mechanism-informed therapeutic targeting, thereby distinguishing this review from more descriptive accounts of mitochondrial dysfunction in intestinal inflammation. Full article

Aero-engine defects often exhibit micro-scale and high-frequency characteristics under complex metallic textures, which makes precise segmentation difficult. Most existing pixel-level methods rely on spatial-domain modeling and lack frequency-domain decoupling. As a result, high-frequency details are easily hidden by low-frequency background information. In addition, repeated downsampling weakens the representation of fine-grained structures, leading to inaccurate boundary localization and limited robustness. To address these issues, a spectrum-driven hierarchical learning network is proposed for aero-engine defect segmentation. First, a dual-band spectral module is constructed using the discrete cosine transform to separate high-frequency and low-frequency components, providing stable and physically meaningful frequency-domain priors for the network. Second, a detail-guided module is designed where high-frequency features adaptively guide skip connections, compensating information loss during encoding and improving boundary recovery. Furthermore, a low-frequency-driven region-aware modeling module is developed. The internal defect regions, boundary areas, and background regions are modeled hierarchically. A dynamic hyper-kernel generation mechanism performs region-sensitive convolutional modeling, improving adaptation to complex structural variations. Extensive experiments on the Turbo19 and NEU-Seg datasets demonstrate that the proposed method produces accurate defect boundaries and achieves mIoU scores of 89.82% and 91.44%, improving over the second-best method by 5.22% and 4.42%, respectively. Full article

In deep geothermal engineering, concrete slabs are prone to thermal cracking. The aggregates and pores are the core influencing factors for this failure behavior. However, existing research methods are unable to accurately capture the microscopic evolution process of thermal cracking and cannot clarify the intrinsic mechanism of how the characteristics of aggregates and pores affect the initiation and propagation of cracks. This limitation restricts the in-depth understanding of the laws of concrete thermal cracking. To address this deficiency, this study employs the discrete element method (DEM) and combines the particle flow program PFC2D to construct a microscopic model of concrete disks. By setting reasonable temperature parameters and thermal load boundaries, a numerical simulation system matching the actual deep geothermal high-temperature environment is established. Three sets of quantitative variables were designed: aggregate particle size (0.003, 0.004, 0.005, 0.006), aggregate volume fraction (0.35, 0.40, 0.45, 0.50), and porosity (0.11, 0.12, 0.13, 0.14). Through controlled variable simulations, the influence laws of each variable on the formation, propagation path, and time evolution of concrete thermal cracks were explored. The quantitative research results show that an increase in aggregate particle size significantly accelerates the generation and propagation of cracks. When the particle size is 0.006, the number of cracks is the highest and the propagation rate is the fastest. The aggregate volume fraction is negatively correlated with the final number of cracks, and 0.50 is the optimal fraction, at which the number of cracks is the smallest. A decrease in the fraction will lead to intensified stress concentration in the cement paste and a sudden increase in the number of cracks. An increase in porosity significantly disrupts the material continuity. When the porosity is 0.14, the bifurcation and connection of cracks are the most significant, while a low porosity of 0.11 can effectively inhibit the overall development process of thermal cracks. In addition, compared with traditional experimental methods and continuous medium numerical simulation techniques, the discrete element method has unique advantages in revealing the internal mechanism of concrete thermal cracking at the microscopic level. It can achieve real-time tracking of the evolution of discrete micro-cracks and the internal stress distribution characteristics. This study enriches the microscopic theoretical system of concrete thermal cracking and provides reliable quantitative references and technical support for the design of thermal crack resistance of concrete in deep geothermal engineering and the optimization of material composition. Full article

Industrial chains in agriculture are currently fragmented and do not support developing resource-based competitive advantages. This is true under the Big Food Framework’s strategic orientation. This research seeks to develop a new analytical framework for evaluating pathways to the integration of agricultural industrial chains and their impact on the performance of companies engaged in food processing in Xinjiang. A mixed-method approach, employing both an exploratory and sequential design, will be used to do this. The primary method of data collection for this study is the case study method, along with the questionnaire method involving 145 agricultural enterprises. From these data, structural equation modeling (SEM) will be used to test the paths of causation among cognitive managers of firms who have implemented the BFF. Evidence will be presented to demonstrate the relationship among three types of integration (vertical, horizontal, and lateral) in the agricultural industrial chain, dynamic capabilities, and company performance. Additionally, network topology and optimization simulations will be conducted to determine how effectively structures are organized in training the respective companies. Important findings revealed in this research include the following: The managerial cognition constructs offered by BFFs play a key role in enhancing the depth and structural balance of industry chain integration. There were complementary performance effects found, and they are related to vertical integration achieving operational efficiency and financial efficiency; horizontal integration improving market competitiveness and brand competitiveness; and lateral integration facilitating innovative growth. Dynamic capabilities are a significant mediating mechanism linking institutional support and digital capability with the depth of integration across different modes of integration. The findings from network optimization suggest that there is a positive effect of balanced connectivity across the different dimensions of integration on overall system efficiency and reduced structural inefficiencies. Based on these findings, the authors recommend that organizations establish governance mechanisms that facilitate coordinated connectivity; strengthen adaptive capabilities within the firm; and promote balanced integration across industrial networks. Future researchers should consider applying these findings to conducting longitudinal studies on network evolution; integrating sustainability measures as part of their analysis; and conducting comparative validation studies across regions or industry systems. Full article

We introduce a minimal relational network model in which superconducting-like behavior emerges as a collective phase of constrained connectivity and phase coherence, without assuming microscopic electrons, phonons, or material-specific interactions. The model is formulated as a concrete instantiation of a previously introduced axiomatic relational–informational framework for emergent geometry and effective spacetime, in which geometry and effective forces arise from constrained information flow rather than from a background manifold. Mathematically, this construction is realized on a finite weighted graph with binary edge-activation variables and compact vertex phase variables, sampled through a Gibbs ensemble generated by an additive informational action. The system is represented as a finite weighted graph with weighted edges encoding transport or informational costs, augmented by dynamically activated low-cost channels and compact phase degrees of freedom defined at vertices. The effective edge costs induce a weighted shortest-path metric, providing an operational notion of emergent relational geometry. Using Monte Carlo simulations on two-dimensional periodic lattices, we show that the same informational action supports three distinct emergent regimes: a normal resistive phase, a fragile low-temperature–like superconducting phase characterized by noise-sensitive coherence, and a noise-robust high-temperature–like superconducting phase in which global phase coherence persists under substantial fluctuations. These regimes are identified using purely relational observables with direct graph-theoretic and statistical-mechanical interpretation, including percolation of low-cost channels, phase correlation functions, an operational phase stiffness (helicity modulus), and a geometric diagnostic based on relational ball growth. In particular, we extract an effective geometric dimension from the scaling of low-cost accessibility balls, using a ball-growth relation of the form $⟨B\left(r\right)⟩~r^{d_{eff}}$, revealing a clear monotonic hierarchy between normal, fragile superconducting, and noise-robust superconducting—like regimes. This demonstrates that superconducting-like behaviour in the present framework corresponds not only to percolation and phase alignment, but also to a qualitative reorganization of relational geometry. Robustness is tested via finite-size comparison between 8 × 8, 12 × 12 and 16 × 16 lattice realizations. Within this framework, normal and superconducting-like behavior arise from the same underlying relational mechanism and differ only in the structural stability of connectivity, coherence, and geometric accessibility under fluctuations. The aim of this work is structural rather than material-specific: we do not reproduce detailed experimental phase diagrams or microscopic pairing mechanisms, but identify minimal relational conditions under which low-dissipation, phase-coherent transport can emerge as a generic organizational regime of constrained relational systems. Full article

As rural revitalization advances, the age-friendliness of public spaces directly impacts the well-being of left-behind elderly populations. However, the spatial and social marginalization of these vulnerable groups in tourism-driven peri-urban villages remains critically underexplored. To bridge this gap, this study proposes a quantitative evaluation framework integrating space syntax and multiple linear regression to investigate the matching mechanism between physical spatial layout and elderly activity needs. Focusing on Shixia Village in Beijing, surveys and satisfaction assessments were conducted with 30 elderly residents (representing a rigorous 27.3% of the permanent population). Space syntax analysis revealed a distinct “core-periphery” spatial differentiation. Despite a moderate spatial intelligibility (0.586), the rapid decay of integration in peripheral clusters acts as the primary physical bottleneck restricting the elderly’s social radius. Furthermore, regression results indicate that public facility accessibility (β = 0.703) and residential environment quality (β = 0.779) are the core positive drivers of satisfaction (p < 0.001). Conversely, road connectivity exhibited an unexpected negative correlation (β = −0.308). This highlights a crucial “double-edged sword” effect: in traditional villages with tourism development, excessive spatial permeability diminishes the elderly’s territorial sense of security due to external traffic interference. Finally, targeted optimization strategies—including traffic-calming interventions and hierarchical node layouts—are proposed, providing an operational evaluation model and design reference for age-friendly environmental construction in similar peri-urban villages. Full article

Voltage stability assessment of a transmission interface is carried out by continuation power flow (CPF) using a fixed post-contingency operating condition. However, if legacy renewable energy sources (RESs) in the sink area are tripped during or following a fault, the actual post-fault operating point can differ from that assumed in the CPF study. This paper examines the effect of sink-area RES tripping on transmission interface voltage stability. The shift in the post-fault operating point caused by the loss of sink-area active power injection is explained using a two-bus equivalent, and the effect of reactive power support from connected RES on the transfer limit is also discussed. The proposed analysis is verified using a modified SAVNW test system in PSS/E. Two contingency scenarios were studied by applying a three-phase fault at the receiving-end bus and tripping one transmission interface line at fault clearing. The results show that sink-area RES tripping moves the post-fault operating point toward the nose point and reduces the voltage stability margin. The results also show that reactive power support from connected RES increases the transfer limit and leads to a larger margin. These effects should be considered in voltage stability assessment of transmission interfaces with legacy RES. Full article

Background: The epiligament (EL) of the medial collateral ligament (MCL) has recently attracted increasing attention as a biologically active structure. Emerging evidence suggests that it may contribute to ligament healing by providing progenitor cells, vascular components, and signaling mediators. However, its cellular composition and possible regional variability remain insufficiently characterized. Aim: This study evaluated the expression of vimentin, S100 protein, and epithelial membrane antigen (EMA) to better characterize the EL compared with the ligament proper (LP). Methods: Twelve human MCLs obtained from twelve deceased donors were divided into proximal, middle, and distal segments. Thirty-six paraffin blocks were prepared, from which 180 sections were obtained and equally assigned for immunohistochemical staining of vimentin, S100 protein, and EMA (60 slides for each marker). Systematic quantification of seven to eight non-overlapping microscopic fields per slide generated 900 standardized observations for each investigated marker. This sampling strategy provided 150 measurements for each sub-region (EL and LP across the three anatomical segments). Immunoreactivity was quantified using ImageJ software. Statistical differences were analyzed using a robust two-way analysis of variance (ANOVA), while biological associations between markers were assessed using Spearman’s rank correlation analysis. Results: Vimentin and S100 expression were consistently higher in the EL than in the LP across all anatomical regions (p < 0.0001). The highest vimentin values were observed in the proximal region (median 17.34 vs. 10.14) and distal region (median 19.34 vs. 11.23), whereas S100 showed the greatest expression in the proximal (median 16.9 vs. 7.2) and distal regions (median 14.1 vs. 8.9). EMA expression was generally lower overall; however, it remained significantly higher in the EL than in the LP within the proximal (median 6.87 vs. 5.77) and middle regions (median 4.80 vs. 3.26). No significant difference was identified in the distal region. Spearman rank correlation analysis demonstrated significant positive associations among all investigated markers (p < 0.001), with the strongest relationship observed between vimentin and S100 protein (Spearman correlation coefficient = 0.430). Conclusions: The EL of the MCL is a structurally and biologically distinct component, characterized by significantly higher expressions of vimentin, S100, and EMA than the LP. The significant positive correlations observed among these markers support the concept that the EL functions as an integrated biological microenvironment with clear regional heterogeneity, particularly within the proximal and distal segments. Further studies are warranted to clarify the functional relevance of these findings and their potential implications for clinical management and ligament healing strategies. Full article

Series-connected lithium-ion battery packs are widely used in electric vehicles (EVs). However, inevitable inconsistency among cells can cause charge imbalance, accelerated aging, and reduced system safety. To improve the consistency of series-connected battery packs under complex EV operating conditions, this study proposes a hierarchical active balancing system. Bidirectional Buck–Boost converters are employed for intra-group balancing, and distributed flyback converters are used for inter-group balancing. A multi-stage coordinated balancing control strategy is further developed to reduce control complexity and improve balancing efficiency. A 16-cell series-connected battery pack model is established in MATLAB R2024a /Simulink and evaluated under resting, charging, and discharging conditions. The results show that, compared with the conventional single-layer Buck–Boost balancing topology, the proposed method reduces the balancing time by 58.09%, 57.97%, and 58.06%, respectively. These results indicate that the proposed system can effectively improve the consistency and balancing performance of series-connected battery packs, providing a scalable solution for EV battery management systems. Full article

Owing to the intricate mechanical coupling characteristics and the considerable difficulty in extracting synergistic spatio-temporal features from high-dimensional sensor data under fluctuating alternating loads, this study proposes a robust anomaly detection framework that combines Normalized Mutual Information (NMI) and Spatio-Temporal Graph Neural Networks (STGNN). First, NMI is utilized to quantify the nonlinear physical coupling intensity among multi-source sensors, thereby filtering out weakly correlated noise and reconstructing the spatial topological structure of the coupler system. Subsequently, a deep learning architecture incorporating Graph Convolutional Networks (GCN), Gated Recurrent Units (GRU), and one-dimensional convolutional residual connections is developed to capture the dynamic evolutionary characteristics of equipment states across both spatial interactions and temporal sequences. Finally, based on the model’s health-state predictions, a moving average algorithm is introduced to smooth the residual sequences, and an anomaly early-warning baseline is established in conjunction with the 3σ criterion. Experimental validation conducted using field service data from heavy-haul trains demonstrates that, compared to conventional serial CNN and Long Short-Term Memory (LSTM) models, the proposed method exhibits superior fitting performance and robustness against noise, effectively reducing the false alarm rate within normal working intervals. In a real-world case study, the method successfully identified variations in spatial linkage features induced by local damage and triggered timely alerts. Notably, the spatial alarm nodes were highly consistent with the fatigue crack initiation sites identified through on-site magnetic particle inspection. This study provides a viable data-driven analytical framework for the condition monitoring and anomaly identification of critical load-bearing components in heavy-haul trains. Full article

Bearing fault diagnosis is an important task for condition monitoring and predictive maintenance of rotating machinery. Nevertheless, many existing deep learning-based methods have difficulty in jointly modeling multi-scale fault characteristics, adaptively highlighting informative features, and maintaining robustness under noisy measurement conditions. To address these issues, this study presents MDCAD-Net, a multi-dilated convolution attention denoising network that integrates multi-scale temporal feature extraction, attention-based feature refinement, and explicit noise suppression within an end-to-end learning framework. Parallel dilated convolutions with different dilation rates are employed to capture short-duration transient impulses as well as long-range periodic patterns in vibration signals. Channel-wise feature recalibration using squeeze-and-excitation networks and spatial-temporal attention via a convolutional block attention module are combined to enhance informative representations. In addition, a denoising block with gated attention and residual connections is introduced to reduce noise interference while retaining fault-related signal components. Experiments conducted on the Case Western Reserve University bearing dataset show that the proposed method achieves a classification accuracy of 98.93% and yields competitive performance compared with several commonly used deep learning models. Ablation studies and feature visualization results further illustrate the contributions of the individual components and the separability of the learned feature representations under noisy conditions. The results indicate the potential of the proposed framework for practical bearing fault diagnosis under noisy operating conditions. Full article

The Industrial Internet of Things (IIoT) has become a cornerstone of modern industrial automation, enabling real-time monitoring, intelligent decision-making, and large-scale connectivity across cyber–physical systems. However, the growing scale, heterogeneity, and dynamic behavior of IIoT environments significantly expand the attack surface and challenge the effectiveness of conventional security mechanisms. In this paper, we propose DeepSense, a hybrid and adaptive anomaly and intrusion detection framework specifically designed for resource-constrained and heterogeneous IIoT deployments. DeepSense integrates three complementary components: DataSense, a realistic data pipeline and experimental testbed supporting synchronized sensor and network data processing; RuleSense, a lightweight rule-based detection layer that provides fast, deterministic, and interpretable anomaly screening at the edge; and NeuroSense, a learning-driven detection module comprising an adaptive ensemble of 22 machine learning and deep learning models spanning classical, neural, hybrid, and Transformer-based architectures. NeuroSense operates as a second detection stage that validates suspicious events flagged by RuleSense and enables both coarse-grained and fine-grained attack classification. To support rigorous and practical assessment, this work further introduces a comprehensive performance evaluation framework that extends beyond accuracy-centric metrics by jointly considering detection quality, latency, resource efficiency, and detection coverage, alongside an optimization-based process for selecting Pareto-optimal model ensembles under realistic IIoT constraints. Extensive experiments across diverse detection scenarios demonstrate that DeepSense exhibits strong generalization, lower false positive rates, and robust performance under evolving attack behaviors. The proposed framework provides a scalable and efficient IIoT security solution that meets the operational requirements of Industry 4.0 and the resilience-oriented objectives of Industry 5.0. Full article

Structured radiology reporting can improve clinical decision support by standardizing clinical findings into hierarchical formats. However, thousands of questions in structured report templates about clinical findings are prohibitively time-consuming, which can limit clinical adoption. Furthermore, early medical VQA datasets primarily focused on free-text and independent question–answer pairs while a recent dataset, Rad-ReStruct, introduced a hierarchical VQA, but the accompanying model still relies heavily on flattened embedding representations and single-path text–image fusion mechanisms that inadequately handle complex hierarchical dependencies in responses. In this paper, we propose DPA-HiVQA (Dual-Path Cross-Attention for Hierarchical VQA), addressing these limitations through two key contributions: (1) multi-scale image embedding representing global semantic embeddings with patch-level spatial features from domain-specific BioViL encoder; (2) dual-path cross-attention mechanism enabling simultaneous holistic semantic understanding and fine-grained spatial reasoning. Evaluated on the Rad-ReStruct benchmark, the model substantially outperforms the established benchmark baseline with an overall F1-score and Level 3 F1-score improvement by 21.2% and 31.9%, respectively. The proposed model demonstrates that dual-path cross-attention architectures can effectively connect holistic semantic understanding and fine-grained spatial detail, paving the way for practical AI-assisted structured reporting systems that reduce radiologist burden while maintaining diagnostic accuracy. Full article