Search Results (7,569)

Accurate, automated delineation of adult diffuse gliomas from multi-parametric magnetic resonance imaging (mpMRI) is central to quantitative neuro-oncology. Volumetric 3D networks dominate the BraTS leaderboard but require expensive GPUs, long training cycles, and provide diminishing returns relative to their compute budget. Slice-wise 2D models, by contrast, discard inter-slice context that is informative for thin tumor rims and small enhancing foci. We introduce GDNet, a 2.5D multimodal MRI segmentation framework for adult glioma evaluated on the BraTS 2024 cohort. GDNet consumes a stack of three adjacent axial slices from the four standard BraTS modalities (T1, T1ce, T2, FLAIR) as a 12-channel input to a compact U-shaped encoder–decoder with Group Normalization and predicts whole tumor (WT), tumor core (TC), and enhancing tumor (ET) masks for the central slice. The training pipeline pairs the 2.5D backbone with: (i) Exponential Moving Average (EMA) of model weights with decay 0.999, (ii) mixed tumor-aware slice sampling (p_tumor = 0.50), (iii) a compound Cross-Entropy + Soft-Dice loss, and (iv) AdamW with warm-up plus cosine annealing under Automatic Mixed Precision. We performed a systematic, step-by-step ablation covering a 2D baseline, EMA + mixed sampling, tumor-centered crop fine-tuning, a GDNet-inspired architectural integration, a region-aware loss, 3-slice and 5-slice 2.5D inputs, and connected-component post-processing, and we report multi-seed results to quantify reproducibility. On the held-out BraTS 2024 test partition, the final 3-slice 2.5D GDNet achieved positive-only Dice scores of 0.791 ± 0.000 (WT), 0.736 ± 0.003 (TC), 0.654 ± 0.004 (ET), and a mean foreground positive-only Dice of 0.820 ± 0.000 across seeds; the all-slice mean foreground Dice exceeded 0.927 ± 0.000. Validation positive-only scores were 0.805 ± 0.002 (WT), 0.757 ± 0.004 (TC), 0.683 ± 0.009 (ET). The inter-seed standard deviation was small for every region (≤0.01 Dice points), indicating low inter-seed variance across the two seeds evaluated; with only two seeds, we regard this as preliminary evidence of training stability rather than a strong reproducibility claim. The ablation isolated EMA + mixed tumor sampling and the 2.5D context window as the dominant sources of improvement; notably, a GDNet-style architectural integration with a region-aware loss did not outperform the simpler 2.5D U-Net on positive-only WT/TC/ET, and light post-processing improved only all-slice Dice. A failure-mode audit found that the residual catastrophic predictions are concentrated on a small minority of diffuse, infiltrative tumors with mass effect. Conclusions: Carefully engineered training strategies, tumor-aware sampling, EMA stabilization, and a modest 2.5D context window recover a substantial fraction of the accuracy of much heavier 3D networks at a fraction of the compute, are reproducible across seeds, and outperform a heavier GDNet-inspired architectural variant on the same data. GDNet is therefore a practical and, pending external validation, potentially clinically deployable framework for multimodal glioma segmentation on workstation-class GPU hardware. Full article

The engineering, resource, and financial constraints in space and spacecraft so far have not allowed the incorporation of biological components into a closed-loop bioregenerative life support system (BLSS), despite decades of research. The expected increase in deep-space exploration and planetary bases with limited access to Earth-based resources necessitates the development of self-sustaining hybrid BLSS technology. The created physicochemical systems, together with photosynthetic organisms and bacteria, aim to revitalize the air, produce food, and recycle nutrients and water in mutually beneficial mini-ecosystems. While plants are best in the function of food production and bacteria in waste recycling, the incorporation of microalgae would add immense benefits in optimizing the life support system (LSS) and increasing the degree of closure. Microalgal photobioreactors (PBRs) could perform wastewater treatment (WWT), removing the nitrogen (N) and phosphorus (P) in the human-derived wastewater (WW), and couple it with converting carbon dioxide (CO₂) from the cabin to oxygen (O₂) and food production. As microalgal WWT on Earth is an emerging field with engineering hurdles, power, mass, volume, microgravity fluid dynamics, and other constraints have also prevented their operations in space. However, in space vehicles, there is no need for large upscaling of a laboratory prototype system, and the WW effluent is easier to predict, facilitating microalgal extraplanetary use in comparison to Earth treatment plants. These factors, combined with the qualities of microalgae such as surface-to-volume efficiency, fast growth rate, high yield, and tolerability to WW, etc., have led to many preliminary testbeds, prototypes, and ground demonstrations from space agencies, space centers, and academia, which show promising results. Microalgal participation in space WWT is beyond current operational practice; however, PBRs are on the space agenda, and the scientific community is elaborating the technologies that would allow their successful implementation. Full article

Background/Objectives: Cholera remains an important global health problem. Inactivated oral cholera vaccines (OCVs) are essential in the WHO/GTFCC (World Health Organization)/Global Task Force on Cholera Control strategy to end cholera by 2030; however, global supply is insufficient, they require partial cold-chain storage, and their formulation and antigen contents leave room for improvement. We describe here the development and preclinical evaluation of DuoChol OCV, a next-generation thermostable oral vaccine designed to address these gaps. Methods: DuoChol is a lyophilized dry-powder formulation in enteric capsules containing formalin-inactivated Vibrio cholerae O1 El Tor Ogawa and Inaba isogenic bacteria, recombinant cholera toxin B subunit (rCTB), and sucrose as stabilizer. Methods describe the construction of the novel vaccine strains, processes for the preparation and characterization of vaccine components, and the final dry formulation in enteric capsules, and in vitro and in vivo vaccine stability analyses. Results: The newly engineered vaccine strains, together with a high-yield mixed-mode chromatography process for rCTB purification, enabled efficient and cost-effective vaccine production. Stability studies demonstrated complete preservation of O1 LPS and rCTB antigens for at least 21 months across temperatures of 4–40 °C. Moreover, regardless of storage duration or temperature, oral immunization of mice with DuoChol elicited strong serum and mucosal antibacterial and antitoxin responses that were similar to those induced by the licensed Dukoral^® OCV. Conclusions: Its heat stability, practical enteric capsule formulation, and potential for improved efficacy compared to inactivated whole-cell only OCVs support positioning DuoChol as a promising next-generation OCV, suitable for national cholera control programs and particularly advantageous for outbreak response, where rapid deployment and early, robust protection are essential. Full article

The increasing demand for sustainable materials has intensified efforts to enhance the performance of recycled polymers for engineering applications. This study investigates the effect of nanoclay reinforcement on the mechanical properties of recycled high-density polyethylene (rHDPE). Nanoclay was incorporated into rHDPE at varying loadings through melt blending, and the resulting composites were evaluated in terms of tensile, flexural, impact, and hardness properties. The tensile strength and tensile modulus improved significantly with increasing nanoclay content, reaching maximum values of 31.27 MPa and 2.39 GPa, respectively, at 1.5 wt% nanoclay, corresponding to increases of 23.11% and 47.53% relative to unreinforced rHDPE. Similarly, the flexural strength and flexural modulus attained peak values of 25.88 MPa and 1105.08 MPa at 1.5 wt% nanoclay, representing improvements of 12.57% and 15.49%, respectively. Impact strength exhibited a different trend, achieving a maximum value of 73.58 kJ/m² at 0.5 wt% nanoclay before decreasing at higher loadings, indicating a transition towards more brittle behaviour. Hardness increased progressively with nanoclay addition and reached a maximum value of 68.06 Shore D at 1.5 wt%, exceeding both unreinforced rHDPE and virgin HDPE. The overall results demonstrate that nanoclay effectively compensates for the mechanical degradation associated with recycling by enhancing stiffness, strength, and surface hardness. Among the investigated formulations, 1.5 wt% nanoclay provided the optimum balance of mechanical performance, while higher loadings led to reduced reinforcement efficiency due to particle agglomeration. These findings highlight the potential of nanoclay-reinforced rHDPE as a sustainable, high-performance material for applications in packaging, construction, and automotive components, thereby supporting circular economy initiatives and resource-efficient material development. Full article

Background: While vaccines are designed to elicit targeted immune responses, in some cases, the immunogenic molecules employed can inherently interact with broader host cellular pathways as a secondary consequence. This phenomenon can be exemplified by COVID-19 vaccines. COVID-19 vaccines, including mRNA platforms, use the SARS-CoV-2 spike protein as an immunogen to induce the production of neutralizing antibodies. The spike protein binds the ACE2 (angiotensin-converting enzyme 2) receptor on human cells, mediating viral entry and infection. ACE2 is widely expressed across multiple tissues and is a key component of the renin–angiotensin–aldosterone system (RAAS) that acts as a homeostatic regulator of systemic and local blood flow, blood pressure, cardiac function, fluid balance and immunity. Some studies have proposed the interaction between the spike protein and ACE2 as a possible contributing factor to rare adverse effects observed following COVID-19 vaccination, including myocarditis, pericarditis, thrombosis, and reported alterations in blood pressure, though these mechanisms remain to be fully elucidated. Objectives: As a proof-of-concept approach in vaccine antigen development, we engineered SARS-CoV-2 spike mutants with impaired binding to the host receptor ACE2. Methods: By rational design, we produced and validated in vitro and in vivo spike point mutants that do not effectively bind ACE2. Results: The engineered spike mutants do not effectively bind the human entry receptor ACE2 while retaining the immunogenic properties equal to or better than the wild type spike and thus generate a protective response in animals when used as a vaccination agent. Conclusions: By establishing a straightforward molecular strategy for rational vaccine design, this work demonstrates the feasibility of limiting specific antigen–host receptor interactions while maintaining immunogenicity. This approach may be applicable to future vaccination strategies where antigen interaction with host cells could potentially interfere with physiological pathways. Full article

To resolve the discrepancy between the measured strain and the actual surface strain of wind turbine blades when using rubber-encapsulated fiber Bragg grating (FBG) sensors for strain monitoring, this study establishes a surface-bonded strain transfer model for such sensors. The total strain transfer efficiency of the sensor is decomposed into two components: the strain transfer efficiency from the rubber substrate to the FBG core (encapsulated grating strain transfer efficiency) and that from the wind turbine blade to the rubber substrate (strain transfer efficiency between the rubber substrate and the blade). Based on the theory of mechanics of materials, the strain transfer equation is derived, and the key factors influencing strain transfer efficiency—FBG bonding length and rubber substrate thickness—are analyzed via the control variable method. Three ethylene propylene diene monomer (EPDM)-encapsulated FBG sensors with rubber substrate thicknesses of 3 mm, 4 mm, and 6 mm were fabricated. Tensile strain transfer tests were conducted using fiber-reinforced plastic (FRP) strips to simulate the material properties of wind turbine blades, so as to validate the effectiveness of the proposed model. The experimental results demonstrate that the strain transfer efficiency of the sensor increases with the extension of FBG bonding length and decreases with the increase in rubber substrate thickness, with 4 mm determined as the optimal substrate thickness for EPDM-encapsulated FBG sensors. On the basis of the aforementioned findings, an EPDM-encapsulated FBG strain rosette sensor was developed, which can effectively measure the complex stress of a wind turbine blade model. This study provides a theoretical foundation for the structural design and engineering application of rubber-encapsulated FBG sensors in the strain monitoring of wind turbine blades. Full article

Bone regeneration is governed by a tightly coordinated interplay between skeletal cells, immune cells, vascular components, and signaling networks within a dynamic microenvironment. Increasing evidence from osteoimmunology demonstrates that immune regulation is not merely supportive but mechanistically determinative of regenerative outcomes. Dysregulated or persistent inflammation can impair osteogenesis, whereas timely immune resolution promotes angiogenesis and matrix deposition. In this context, nanotechnology has enabled the development of nanoparticles (NPs) that function not only as delivery vehicles but also as active modulators of the bone immune microenvironment. Immunomodulatory NPs can be engineered to deliver bioactive agents, regulate cytokine networks, and influence immune cell phenotypes, particularly macrophage polarization, at defined stages of healing. Through tailored surface chemistry, targeting ligands, and stimuli-responsive release mechanisms, NPs can achieve spatially localized and temporally controlled modulation of inflammatory and reparative phases, thereby enhancing osteogenesis and vascular integration. This review provides a comprehensive overview of organic, inorganic, and hybrid NP platforms applied to bone regeneration, with emphasis on their mechanisms of immune modulation, strategies for cell-specific targeting, and approaches for sequential regulation of inflammatory resolution and tissue repair. By integrating advances in materials science and immunology, NP-enabled platforms have the potential to transform bone regeneration from passive structural repair into precision immune-guided healing. Full article

Monitoring dam deformation is critical for ensuring structural integrity and identifying long-term settlement trends. However, traditional InSAR techniques often face limitations in tropical environments due to severe temporal decorrelation. This study addresses these challenges at Jatiluhur Dam, Indonesia, by implementing an integrated framework using Sentinel-1 InSAR, in situ leveling, GNSS, and reservoir water-level data from 2019 to 2024. To overcome the observation bottlenecks, Tracy–Widom-guided PSI (TW-PSI) was employed and compared against SBAS and conventional PSI. The TW-PSI approach successfully increased on-structure measurement point density by approximately 40%, supporting a first-order ascending–descending decomposition into east–west and quasi-vertical components. The analysis reveals a persistent settlement bowl at the central crest (C7–C12), consistent with long-term leveling observations and supported by regional GNSS trend checking. While the 2022 Mw 5.6 Cianjur earthquake showed no statistically significant co-seismic crest deformation, a strong correlation (r = −0.709) was identified between crest deformation and reservoir water-level variations, suggesting an observational association between reservoir level and crest settlement tendency. Furthermore, the application of the Annual Structural Deformation Tolerance Ratio (ASDTR) identified specific priority monitoring zones. These findings demonstrate that the proposed integrated framework can support operational dam deformation monitoring by linking satellite-derived measurements with in situ observations and engineering-oriented interpretation. Full article

To support drilling gas influx control, saline aquifer CO₂ sequestration and CCUS development under the dual carbon goals, this study proposes a high-precision calculation method for the solubility of high-temperature, high-pressure, CO₂-rich natural gas in formation water. An activity–fugacity coupling model is established: fugacity coefficients of gas components are solved via the dimensionless Helmholtz free energy equation of state, and liquid-phase activity coefficients are characterized by the Pitzer electrolyte model. Comparative experiments with three natural gas and three formation water samples are carried out at 393.15–453.15 K and 5–100 MPa to analyze the influences of temperature, pressure, salinity and CO₂ content on solubility for model verification. The overall relative error between calculated and experimental data is below 10% (max 4.5%). Solubility rises rapidly with pressure then plateaus, declines with salinity, and grows with CO₂ content; CO₂ solubility far exceeds that of alkanes. This efficient, widely applicable model cuts engineering costs and guides safe oil-gas exploitation and CCUS deployment. Full article

The development of electric bus superstructures requires an integrated engineering approach combining structural design, numerical simulation, experimental validation and durability assessment. This need is particularly important for electric buses, where heavy roof-mounted battery systems and auxiliary components influence structural load paths, fatigue durability and rollover crashworthiness. This paper presents a measurement-supported workflow for the development of a virtual electric bus superstructure model, including finite element analysis, multibody dynamics simulations, operational load assessment, fatigue-oriented evaluation and rollover crashworthiness analysis. The finite element model is used to assess static load cases, modal properties and structural response under selected design conditions. A multibody vehicle model with nonlinear suspension characteristics is applied to simulate representative operating scenarios and to support the definition of dynamic load cases. Operational measurement data from previous work are used as a basis for realistic load characterization. Experimental torsional stiffness and modal tests are used to validate the numerical model. The main contribution of the study is the integration of these numerical, experimental and operational-data-based activities into a consistent early-stage verification process. The proposed workflow supports early identification of critical structural regions, assessment of design modifications and reduction in prototype-based design iterations. Full article

To address the challenge of maintenance decision-making for critical components in electro-hydraulic servo material fatigue testing machine, characterized by weak state observability and difficulty in degradation prediction, a multi-component joint maintenance decision-making method based on multi-head deep reinforcement learning is proposed. Considering the heterogeneity of the degradation mechanisms and observation methods for the four components—bearing beam, fixture, main machine sensors, and hydraulic oil tank—a continuous-discrete hybrid state Markov decision process (HS-MDP) is constructed. To account for differences in maintenance strategies across components, a differentiated discrete action space for each component is designed, and engineering feasibility constraints are explicitly integrated into the policy through action masking. A data-quality loss term, determined by the degradation level of the sensors, is introduced into the reward function to align the optimization objective with the metrological properties of the fatigue testing machine. Based on the Branching Dueling DQN framework, a Q-network structure is constructed, incorporating a shared encoder, an inter-component attention mechanism, and multi-head branched outputs. Taking a 100 kN electro-hydraulic servo fatigue testing machine as a case study, comparisons with baseline strategies such as periodic maintenance, threshold-based condition-based maintenance (CBM), independent DQN, and PPO indicate that the proposed method reduces the average annual total cost by 60.3% compared to periodic maintenance and by 42.6% compared to threshold-based CBM. The number of failures decreases from 9.8 times/year to 1.4 times/year, while data efficiency increases from 82.1% to 96.2%. Ablation experiments and robustness tests further verify the critical contributions of three key design elements: action masking, inter-component attention, and data-quality loss. Full article

Titanium alloys are irreplaceable in aerospace, biomedical and marine industries due to their low density, high specific strength and excellent biocompatibility. Conventional manufacturing methods suffer from low material utilization and difficulty in fabricating complex components, while additive manufacturing (AM) realizes near-net-shape forming of customized structures but introduces unique non-equilibrium microstructures and defects, which significantly alter the corrosion behavior and limit the long-term service reliability of additively manufactured (AMed) titanium alloys. This work systematically analyzes the corrosion behavior of titanium alloys fabricated by four mainstream AM processes: LPBF (laser powder bed fusion)/SLM (selective laser melting), EBM (electron beam melting), DED (directed energy deposition) and WAAM (wire arc additive manufacturing). It quantitatively summarizes the key electrochemical parameters and discusses the regulatory effects of matrix composition, post-treatment and service environment on their corrosion behaviors. The universal corrosion mechanisms—namely, passive film breakdown, micro-galvanic corrosion, and defect-induced localized corrosion—as well as process-specific corrosion mechanisms inherent to AMed titanium alloys are systematically elucidated. This study offers theoretical foundations for optimizing corrosion resistance and ensuring the reliable engineering implementation of AMed titanium alloys. Full article

There is an enormous rise in the amount of user-generated content on social media. That makes it easier for hateful and fake messages to spread, and threatens both societal stability and public trust in institutions. Most of current solutions have fundamental limitations due to modal limitations (i.e., each solution only uses one type of data at a time), lack of user context integration, poor synchronization across different types of data, and poor resilience to manipulation by adversaries. As a result, most solutions are subject to compound loss in terms of their ability to generalize well, classify correctly, or remain reliable when deployed in real-world environments. To address all of the above challenges, we propose a comprehensive and modular analytical framework consisting of five interconnected components that integrate contextual representation learning, multimodal semantic alignment, graph-based propagation modeling, adaptive inference, and consistency validation for hate speech and fake post detection. First is our Context-Driven Social Vector Extraction methodology, which provides enriched contextual embeddings by extracting and combining text-based metadata, image-based metadata, temporal metadata, and behavioral metadata. We use those embeddings in our second module, Multimodal Label Fusion via Mutual Co-Attention (CMF-MCA). Our CMF-MCA module incorporates two transformers with co-attention mechanisms that can mutually annotate text and images. In our third methodology, Semantic Propagation Graph for Hate and Fake Correlation (SPG-HFC), we implement a relational graph attention mechanism that captures both the influence of semantics and how communities propagate information about hate and fake posts. The fourth module, Adaptive Modality Routing via Reinforcement (AMR-R), routes based on the modality of the input and whether the input is simple enough to be classified using machine learning or complex enough to require deep learning. Finally, our Counterfactual Consistency Validation Engine (CCVE) is used after prediction to validate that the model’s predictions are consistent with the output data by creating counterfactuals and validating them. Therefore, in addition to improving the overall accuracy of hate speech and fake post detections, our proposed framework also improves its scalability and inference reliability. Additionally, because our framework allows multimodal classifications that include both context and behavior, it enables the scalable and trustworthy development of content moderation systems. Full article

Optics is the science of light, which supports disciplines like biology, medicine, engineering, materials science, chemistry, physics and more. Optics helps to improve diagnostic speed, portable and user-friendly devices, cost efficiency, and sensitivity. Through time, optical components have been made with hard and non-deformable materials. However, traditional optical elements can no longer meet the needs of the market, and new optical elements are needed, such as materials with higher degrees of freedom. A candidate that has been proposed to replace traditional optical materials is polydimethylsiloxane (PDMS or silicone) because it presents suitable characteristics like biocompatibility, nontoxicity, flexibility, non-biodegradability, high transparency in the UV–visible range, low scattering and absorption, easy fabrication, cost-effective relation and more. Many articles have reported the fabrication of optical components with silicone and the use of these components in optical devices. Unfortunately, there is no review that comprehensively covers the field of optics in relation to the application of silicone. The present work is intended as a descriptive overview to provide a clear and accessible review of the topic, rather than a comparative analysis. Articles describing the use of silicone in the fabrication of optical components during the past 20 years were reviewed. Full article

Light-converting polymer coatings and films are emerging passive photonic materials for spectral engineering in sustainable and protected agriculture. By absorbing ultraviolet or weakly used spectral components and re-emitting in visible bands that overlap with photosynthetic pigments and plant photoreceptor action regions, these materials can modify the radiation environment without additional electrical energy input. This critical narrative review analyses light-converting polymer films and coatings from a materials and coatings perspective, with emphasis on photophysical mechanisms, polymer matrices, luminophore families, coating fabrication routes, optical transparency, photoluminescence, aggregation phenomena, photostability and scalability. The photobiological background is included as a concise framework that justifies the spectral targets of the conversion process. Rare-earth complexes, inorganic phosphors, quantum dots, aggregation-induced-emission systems and organic dyes are compared as candidate luminophores. Particular attention is devoted to the general challenges associated with organic luminescent coatings, including dispersion, aggregation, optical transparency, photostability, and scalability. A PMMA/PDI coating system is discussed only as an illustrative case study demonstrating these broader materials-design considerations. Extrusion, solution casting, spin-coating, dip-coating and sol–gel processing are evaluated as fabrication strategies for laboratory and large-area greenhouse applications. The work concludes by identifying the main gaps that must be addressed before practical deployment: quantitative UV–Vis and photoluminescence characterization, absolute quantum yield, haze and scattering, thickness and morphology mapping, accelerated UV aging, weathering resistance, toxicity assessment and crop-specific validation. Full article