Search Results (32,544)

Background: Proton radiation is both a core component of the space radiation environment and a pivotal modality for precision clinical tumor radiotherapy. Proton exposure can impair cognitive, learning and memory functions, representing a common concern in the fields of deep space aerospace medicine and clinical radiotherapy. Nevertheless, the cellular and molecular regulatory mechanisms underlying proton-induced cognitive impairment remain incompletely elucidated. Methods: In vivo and in vitro injury models were established via 100 MeV proton radiation. Specifically, an in vivo model of proton radiation-induced cognitive injury was constructed using C57BL/6J mice (male), and an in vitro radiation injury model was established with HT22 hippocampal neuronal cells to systematically investigate the molecular mechanism of cognitive impairment caused by proton radiation. Results: Proton radiation induced a persistent decline in learning and memory capacity in mice, triggered systemic oxidative stress, caused metabolic disturbances of multiple neurotransmitters in the hippocampus, and induced structural mitochondrial damage and decreased mitochondrial membrane potential in hippocampal neurons, suggesting that mitochondria are the key subcellular target of its neurotoxicity. Proton radiation induced oxidative stress in hippocampal neurons. Hyperactivation of mTORC1 inhibited the kinase activity of ULK1 by phosphorylating its Ser757 residue and downregulated ULK1 protein levels, resulting in mitophagy dysfunction and mitophagic flux blockade. Persistent severe oxidative stress prevented damaged mitochondria from being cleared via mitophagy and activated the apoptotic cascade, ultimately leading to hippocampal neuronal death and cognitive impairment. Conclusions: This study reveals the molecular mechanism by which proton radiation induces mitophagy dysfunction and hippocampal neuronal apoptosis and subsequently triggers cognitive impairment, providing a new theoretical basis and potential intervention targets for the prevention and treatment of neurocognitive complications associated with space radiation exposure and clinical radiotherapy. Full article

The growing demand for large, lightweight, heat-resistant, and multifunctional aerospace structures has raised the requirements for metal matrix composites in terms of defect minimization, performance enhancement, and near-net-shape manufacturing. Additive friction stir deposition of composites enables feedstock delivery, reinforcement mixing, and layer-by-layer consolidation in a thermoplastic state below the melting point of the matrix, thereby mitigating porosity, hot cracking, elemental segregation, reinforcement degradation, and excessive interfacial reactions commonly encountered in fusion-based additive manufacturing. This review summarizes recent advances in the application of this technology to the fabrication of metal matrix composites, elucidates the mechanisms of material flow, interlayer bonding, microstructural evolution, and defect formation during deposition, and discusses the effects of tool design, process parameters, and reinforcement characteristics on interfacial bonding, microstructure control, and mechanical properties. Remaining challenges include the uniform delivery and quantitative control of reinforcements, characterization of interfacial bonding and load transfer, forming stability of complex components, and evaluation of in-service performance; accordingly, thermo-mechanical-flow multiphysics models, multisensor closed-loop control systems, and unified quality-assessment methods should be developed to promote the engineering application of large-scale, multimaterial graded aerospace components. Full article

Women’s entrepreneurship is embedded in family, community, and gender relations in marginalized contexts. This convergent mixed-methods study examined predefined business persistence and development status, subjective business success, and relational support among Bedouin women entrepreneurs. The quantitative component included 175 matched woman–man dyads who independently evaluated the businesses. The qualitative component included 24 linked dyads, with women’s and men’s interviews analyzed separately and then compared. Unadjusted comparisons showed higher subjective business-success ratings among both women and men associated with continuing/developed businesses; after demographic adjustment, the group difference remained significant for women’s ratings but not for men’s ratings. Women in the continuing/developed group also reported greater entrepreneurial empowerment, socioeconomic benefits, and immediate family support after adjustment, whereas the unadjusted group difference in men’s positive attitudes toward women’s entrepreneurship did not persist. Stronger endorsement of the importance of husband support was positively associated with subjective success among continuing/developed businesses, whereas the corresponding association was absent or reversed among early-closed businesses. Qualitative accounts portrayed family and male support as providing assistance, legitimacy, and resources while also operating through conditional approval, supervision, and restrictions on autonomy. Active-business accounts emphasized interconnected family, community, professional, and institutional resources, whereas closed-business accounts emphasized accumulating personal, familial, market, and structural vulnerabilities. Participants’ accounts portrayed support as more enabling when it expanded women’s autonomy and capacity to act, and as potentially constraining when tied to permission, monitoring, or conditional access to resources. Full article

As a central regulator of nutrient absorption, immune homeostasis and overall health, the gastrointestinal tract has become a major focus of biomedical research. However, developing in vitro models that accurately reproduce human gastrointestinal architecture and physiological conditions remains a major challenge. Gut-on-chip (GoC) systems, which integrate microfluidics with cell cultures, have emerged as a promising solution in the past decade. By recreating dynamic microenvironments that incorporate fluid flow, peristalsis-like mechanical stimulation and co-culture with microorganisms, GoC systems enable more physiologically relevant investigation of host–microbe and host–microbiome interactions. This systematic review provides a comprehensive overview of available GoC technology used in host–microbe research, including their structural and cellular components. A systematic search of PubMed, Embase and Google Scholar databases up to May 2026 identified forty-eight studies evaluating interactions between the host and probiotic strains, postbiotics, commensal microorganisms, pathogenic bacteria, fungi, viruses, or faecal-derived microbiota using GoCs. Common features, distinctive characteristics and application for modelling host–microbiome interactions and pathogenic infections are summarized. The available literature is characterized by heterogeneous study designs, variable microbiome compositions and analytical approaches, and limited cross-platform standardization, which should be considered when interpreting findings. Lastly, current limitations and future perspectives are discussed, highlighting the potential of GoC models to support biotic characterization and preclinical evaluation while underscoring the need for further validation and standardization. Full article

Timber structures constitute the primary form of ancient architecture in China and possess immense historical, artistic and scientific value. Having withstood the ravages of time over hundreds or even thousands of years, timber beams—one of the main load-bearing components in such structures—have largely sustained varying degrees of damage and require repair and reinforcement. Superelastic shape memory alloy (SMA) wires offer numerous advantages, including high strength, corrosion resistance, and stable recovery stress via stress-induced martensitic transformation, and are currently widely used in the field of concrete; however, research into their application for reinforcing timber beams remains limited. This study experimentally investigated the flexural performance of diagonally cracked Korean pine beams reinforced with prestressed Ni–Ti SMA wires. The results demonstrate that SMA reinforcement significantly enhances ultimate load-bearing capacity, with prestressing proving substantially more effective than simply increasing the number of wires. The optimal configuration—two SMA wires at 3% prestress—yielded the greatest improvement of 38.02% in ultimate capacity. Additionally, SMA reinforcement improved flexural stiffness, reduced compressive strain under equivalent loads, and lowered the neutral axis position, thereby enlarging the compression zone. These findings confirm that prestressed superelastic SMA wires offer a highly effective solution for strengthening diagonally cracked timber beams in existing structures. Full article

Four-dimensional (4D) printing is a cutting-edge additive manufacturing technique that enables dynamic deformation of three-dimensional printed components under external stimuli. Polymer-derived ceramics (PDCs) feature excellent design flexibility, corrosion and wear resistance, but ceramic 4D printing still faces critical drawbacks including inferior deformability, low forming precision, poor slurry-photopolymerization compatibility, and single-response actuation limits. This study proposes an innovative strategy for high-resolution, programmable multi-responsive ceramic 4D printing to tackle these issues. We integrated stereolithography (SLA) with thermosetting shape memory polymers (SMPs) and Fe₃O₄/Al₂O₃ ceramic composites with magnetothermal conversion effects. The prepared composite slurries were systematically characterized, and an integrated process of stepwise curing and shape programming was constructed. Molecular dynamics simulations clarified the interfacial bonding between polymers and inorganic particles. The ceramic precursors achieve autonomous and precise shape recovery under magnetic or thermal stimulation, and the programmed geometries can be stably maintained as designed configurations. This work establishes a complete technical framework that synergistically integrates SLA-based high-precision forming, magnetothermal dual-responsive actuation, and programmable reconfigurability, enabling complex ceramic precursor structures with tunable shape memory effects. The strategy offers a viable route for smart ceramic fabrication and opens promising perspectives for applications in aerospace deployable structures and non-contact biomedical devices. Full article

Traditional Chinese papercutting is an important form of intangible cultural heritage characterized by intricate structures, repeated motifs, and prominent symmetrical organization. Existing digital studies of traditional art commonly emphasize classification, restoration, style transfer, or visual reconstruction, while computational frameworks that combine interpretable structural characterization with generative exploration remain comparatively limited. This study therefore presents an exploratory framework integrating image standardization, quantitative structural-feature analysis, Principal Component Analysis (PCA), and Deep Convolutional Generative Adversarial Network (DCGAN)-based synthesis. The image corpus was standardized by grayscale conversion, Gaussian filtering, Otsu thresholding, morphological processing, resizing, and normalization. Foreground occupancy, edge density, connected-component complexity, and horizontal and vertical symmetry were extracted as interpretable structural descriptors. PCA was applied to the resulting feature matrix to examine variation among samples. The DCGAN used a 100-dimensional latent vector and generated 64 × 64 single-channel outputs; training progression was documented through generator/discriminator loss curves and fixed-latent-vector samples up to epoch 2000. The generated examples qualitatively exhibited repeated motifs and symmetrical organization also observed in the source corpus. Because no FID, IS, SSIM, Dice, or other formal generative-quality metric, model benchmark, raw-versus-preprocessed ablation, or expert cultural-authenticity assessment was performed, the generative findings are interpreted as proof-of-concept observations rather than quantitative validation. The framework provides an exploratory basis for linking measurable structural characteristics with generative modeling in the computational study of traditional Chinese papercutting. Full article

Accurate multi-step traffic-flow forecasting requires dynamic spatial modeling and adaptation to heterogeneous temporal and node-level patterns. This study proposes FAMoE-ST, a hierarchically frozen attention network with hybrid-memory experts. Historical flow, temporal context, and node identity are encoded as sensor tokens and processed by a six-layer Transformer initialized from GPT-2. To avoid dependence on arbitrary sensor indexing, the causal mask is replaced by all-to-all bidirectional spatial attention and every sensor uses the same GPT position ID. The lower four blocks are frozen, while the upper two blocks are adapted and their feed-forward networks are replaced by four-expert modules. A linear router is fused with a 32-slot memory router; each token retrieves four slots and activates two experts. With 12 observations predicting the next 12 steps, FAMoE-ST achieves MAE/RMSE/MAPE of 18.55/30.35/12.91% on PEMS04 and 14.57/24.08/9.71% on PEMS08. Relative to ST-LLM, MAE decreases by 6.97% and 7.39%, respectively. Component and capacity-matched controls support the roles of restricted adaptation, sparse expert capacity, and memory routing. A separate PEMS04 initialization control finds no advantage from GPT-2 pretraining over random initialization; the contribution is therefore attributed to the proposed structural adaptation rather than to transferred linguistic knowledge. Full article

The development of sustainable soil amendment materials derived from natural and recycled resources represents an important strategy for improving soil functionality and mitigating the impacts of land degradation and climate change. This pot study evaluated the effects of biochar (BC), poultry litter (PL), smectite-silica clay (SSC), and two composted combinations, C(PL+BC) and C(PL+BC+SSC), on soil biochemical activity, pore structure, and water retention. Seven treatments included an unfertilized control, mineral fertilization (MF), and MF combined with each amendment. The highest cumulative oxygen demand was recorded in MF+SSC (1.805 mg O₂ g⁻¹ DM), whereas the lowest occurred in MF+C(PL+BC) (1.356 mg O₂ g⁻¹ DM). Mineral fertilization produced the highest dehydrogenase activity. Compared with CTR, MF+BC had a larger volume of storage pores (0.5–50 μm) and produced the highest field capacity (0.385 cm³ cm⁻³) and available water content (0.265 cm³ cm⁻³). MF+SSC had the highest bulk density (1.309 g cm⁻³) and permanent wilting point (0.120 cm³ cm⁻³), but its available water content did not differ significantly from CTR. The first two principal components explained 58.0% of the variance and represented gradients associated with water retention and with bulk density and fine porosity. Among the tested materials, BC and C(PL+BC+SSC) provided the most balanced improvements in soil functionality by simultaneously enhancing water retention and maintaining moderate biological activity. The results indicate that biochar-, clay-, and compost-based amendment materials have potential for improving soil quality and supporting sustainable land management. Full article

Electrode-level compressibility may not imply transferable hardware redundancy in biomimetic myoelectric interfaces. This study tested whether sensor-count sufficiency discovered by trial-level analysis survives participant-grouped evaluation. Dataset A comprised 398 archived Myo Armband trials from eight gestures. Dataset B contained 864 one-second trials from 36 participants and six gestures. A timestamp audit identified extensive repeated channel values; Dataset B was therefore analyzed on a conservative 100 Hz grid with 20–45 Hz filtering. Sensor subsets and RBF-SVM parameters were selected exclusively within grouped training data using repeated nested validation. Electrode-level NMF, all 28 fixed six-sensor layouts, cyclic re-indexing, channel-block ablation, participant-cluster bootstrap, PCA, and time-domain-only sensitivity analyses were evaluated. Dataset A yielded 97.74% accuracy with six sensors and 97.93% with eight. In Dataset B, accuracy was 75.96% ± 7.47% with six sensors and 77.93% ± 6.49% with eight; the paired difference was −1.97 percentage points (corrected 95% CI, −5.50 to 1.56). The participant-cluster bootstrap interval was −3.70 to −0.31 points. Active-gesture accuracy was 71.67% and 74.35%, respectively. All fixed six-sensor layouts averaged 74.59%. Three NMF components reconstructed 89.95% ± 1.78% of held-out-participant normalized RMS patterns, with no nonconverged folds. One-position cyclic re-indexing reduced accuracy to 40.28%; channel-block ablation caused losses of 0.62–6.71 points. Low-dimensional electrode-level RMS structure did not establish removable sensors across unseen users. Compact biomimetic interfaces require registration, adaptation, or equivariant processing before physical sensor reduction. Full article

In synthetic aperture radar (SAR) imagery of built-up areas, multipath effects generate false targets that closely resemble genuine structural features, severely hindering refined interpretation of building structures. Existing methods based on interferometric SAR, tomographic SAR, or full-angle circular SAR (CSAR), while effective in 3D information extraction, impose stringent requirements on radar systems, data acquisition conditions, and prior information, rendering them less applicable to time-critical scenarios with limited observation constraints. To address this issue, this paper proposes a multipath suppression and detailed feature extraction method for buildings based on projection offset discrepancies across adjacent sub-aperture images. First, a projection offset model for elevated target points and a multipath effect model between elevated targets are established, theoretically revealing that the projections of elevated targets and multipath ghosts are offset to opposite sides of the target in successive sub-aperture images. Building upon this theoretical foundation, a complete image-domain processing pipeline is developed: an improved iterative watershed algorithm for robust building region segmentation, non-edge Hough transform combined with Thresholded Connected Component Analysis clustering for wall line extraction, multi-dimensional feature-based Hungarian algorithm for wall matching and tracking across sub-apertures, and normalized cross-correlation (NCC) for pixel-level offset estimation. Based on the distinct offset characteristics, building structures are categorized into three classes—stationary walls, elevated structures, and multipath ghosts—enabling simultaneous multipath suppression and structural extraction. Experimental results on Ku-band UAV-borne circular SAR data demonstrate that the proposed method requires only a small number of sub-aperture images with narrow angular spans to effectively distinguish different scattering structures, suppress multipath ghosts, and extract major structural details, providing a viable solution for building interpretation in SAR imagery under observation-constrained scenarios. Full article

Accurate estimation of the state of health (SOH) of lithium-ion batteries is essential for ensuring the safety, reliability, and longevity of electric vehicles, battery energy storage systems, and other energy applications. This paper presents a comprehensive review of capacity-based SOH estimation algorithms, focusing on methods suitable for real-time battery management system (BMS) implementation. Existing approaches are categorized into model-based and data-driven methods. Model-based techniques, including equivalent circuit models, electrochemical models, and filtering algorithms such as extended Kalman filters, unscented Kalman filters, and particle filters, provide strong interpretability and compatibility with embedded systems. Data-driven approaches, including machine learning, deep learning, transfer learning, and feature-driven statistical methods, offer improved accuracy and adaptability by learning degradation patterns directly from operational data. Methods that fuse physical modelling with data-driven learning are reviewed within both categories, according to which component forms the structural core of the estimator. The paper compares these approaches using key criteria relevant to BMS deployment, including estimation accuracy, robustness, interpretability, computational complexity, and implementation feasibility. Emerging trends such as physics-informed learning, cloud-edge collaboration, and digital twinning are identified as promising directions for developing scalable, adaptive, and practical next-generation SOH estimation algorithms. Full article

Background/Objective: Clinical leadership is essential for improving care quality and patient safety in complex orthopedic emergency settings. This study examined perceptions of clinical leadership among Orthopedic and Trauma Clinical Nurse Specialists (OTCNSs) at a major regional trauma center in Romania. The objective was to apply and contextually adapt a previously developed methodological approach to identify candidate components for a clinical leadership development intervention tailored to the specific clinical demands of orthopedic trauma care and the Romanian legislative environment. Methods: A qualitative approach was utilized, systematically applying and contextually adapting the two-phase methodological architecture developed by Palermo et al. Semi-structured interviews were conducted with seven OTCNSs and analyzed using hybrid deductive–inductive thematic analysis. Following this, a consensus meeting was convened with nine OTCNSs, one Head Nurse, and one Clinical Researcher to co-construct a locally tailored intervention prototype. Results: Five overarching themes emerged from the interviews: individual attributes and professional capabilities, interprofessional collaboration and relational dynamics, team frameworks and organizational culture, role-based leadership and functional autonomy, and patient-centered trajectories. Based on these findings and stakeholder consensus, a four-component intervention prototype was co-constructed: (1) clinical orientation pathway and strategic onboarding, (2) core competency curriculum for advanced orthopedic nursing, (3) point-of-care mentorship and reflective clinical support, and (4) collaborative peer circles and case-based reflection. Conclusions: This study identified perceived leadership-development needs among OTCNSs and generated a preliminary, contextually tailored intervention prototype. The findings suggest that clinical leadership is shaped by a dynamic interplay of personal, relational, institutional, and patient-centered variables. The candidate intervention components require feasibility testing, refinement, and evaluation before they can be recommended for wider implementation. Full article

This study presents a comparative morphological and elemental characterization of organomineral coatings composed of polydopamine (PDA) and nanocrystalline carbonate-substituted hydroxyapatite (nano-cHAp), formed on model Si(111) substrates and human enamel specimens. Three technologically distinct routes were investigated: sequential PDA/HAp/PDA deposition without an external field (NF), one-step PDA/HAp co-deposition without applied voltage (F0, U = 0 V), and one-step PDA/HAp co-deposition under applied voltage (F, U = 50 V). X-ray diffraction was employed to verify the apatite-like structure of the initial nanocrystalline HAp and to identify drying-related contributions from the TRIS-containing medium; however, it was not used as direct evidence of the HAp phase or texture in the final coatings. SEM segmentation of selected enamel areas showed that the area fraction assigned to the morphologically continuous surface-layer class was approximately 85% for NF, 73% for F0, and 95.5% for F. Representative AFM analysis on Si(111) revealed a rough, topographically heterogeneous F0 layer (S_q ≈ 68 nm, height range ≈ 440–490 nm), a pronounced island-like NF morphology (S_q ≈ 45 nm), and a lower-amplitude F morphology (S_q ≈ 4 nm). For the F coating, scratch-edge profiling indicated an apparent step height of approximately 88 nm, while a preliminary ultrasonic retention challenge in water for 5 min at a 30 mm sample-to-probe distance preserved this apparent step height and reduced S_q from approximately 12 to 5 nm. Point-EDX-derived visualization on Ca/P-free Si(111) supported the presence of Ca/P-containing domains and indicated different spatial organization of the mineral component across the NF–F0–F sequence. FTIR and micro-Raman spectroscopy were performed for the two terminal routes, NF and F, and were interpreted as qualitative evidence of PDA- and HAp-related spectral features rather than as a comprehensive quantitative comparison of all deposition modes. Overall, the data support a transition from island-like or domain-heterogeneous coatings toward a more laterally continuous, lower-amplitude surface layer in the one-step field-assisted route. The results remain morphology-oriented and do not demonstrate clinical remineralization, HAp texture, long-term oral stability, or quantitative adhesion strength. Full article

AI and machine learning deployments in regulated decision contexts face an intolerance for inadmissible outputs (“hallucinations” when the model is generative) that current explainability methods address only after the fact. Bounded AI denotes prevention by architectural design. This study establishes five conditions (C1–C5) under which a bounded artificial intelligence (AI) architecture transfers from one regulated decision domain to another. Conditions C1 through C4 adapt or combine previously established principles. The most significant contribution is the discrete joint-state topology condition, C5, for which no precedent was found in this role. The claim is that these five conditions are jointly necessary for the closure property to survive an architectural transfer—while sufficiency is not claimed. Two of the five conditions are structural prerequisites governing whether the architecture’s operators can be constructed in a destination at all. The remaining three provide warrant conditions governing whether it is the appropriate instrument or not. In existing runtime-assurance architectures, the constraint acts after inference, on the output of the learned component. In contrast, the pattern developed in this paper reverses the assurance steps via a deterministic-first/learned-second approach. Namely, the assurance architecture acts before inference on the input domain: a deterministic filter admits only rule-compliant objects, and the trigger fires non-discretionarily on joint-state cell occupancy rather than on the learned score. The architecture’s domain-neutral type signatures are formalized, and three structural transfers are developed in depth: predictive maintenance, energy-grid management, and credit underwriting, each concluding with a closure proof. All three transfers remain conceptual and report no deployment outcomes. The proofs are conditional on three stated premises that establish soundness with respect to a rule set rather than a safety case. The strongest evidence of transferability is a market-surveillance destination classified as admissible in advance and later realized on a live venue. C5 is what discriminates the transferability. It is shown that the autonomous-vehicle perception case satisfies C1 through C4, but fails C5 because the required distinctions are absent from the representation at every granularity—which is a failure that no additional compute can resolve. The architecture becomes domain-neutral through the act of transfer, not before it. Full article