Search Results (9,416)

Density functional theory (DFT) was used to investigate how bisubstitution doping in graphene alters its electronic structure and interfacial stability with two model lignocellulosic binders, carboxymethylcellulose (CMC), and a representative aromatic fragment (LCmA). The properties were evaluated at the ωB97X-D/LANL2DZ level for pristine graphene and its bisubstitution-doped variants with nitrogen (graphene-2N) and sulfur (graphene-2S), integrating frontier orbitals, electrostatic potential (ESP) maps, electronic localization functions (ELF/LOL), and QTAIM topology. Doping with 2N markedly reduces the HOMO–LUMO gap from 0.16052 eV (graphene) to 0.10560 eV (−34.2%), while 2S reduces it to 0.14222 eV (−11.4%), evidencing different electronic activation mechanisms. The interaction energies show doping-controlled selectivity: In pristine graphene, adsorption strongly favors LCmA ($\mathsf{\Delta }{E}_{int}$ = −99.3 kcal·mol⁻¹) over CMC (−23.7 kcal·mol⁻¹); in graphene-2N, CMC coupling intensifies (−93.7 kcal·mol⁻¹) while maintaining a high interaction with LCmA (−74.3 kcal·mol⁻¹); and in graphene-2S, CMC remains favorable (−71.9 kcal·mol⁻¹) while LCmA falls to a practically marginal regime (−4.1 kcal·mol⁻¹). QTAIM the presence of confirms closed-layer interactions in all complexes (∇²Pc > 0, H > 0, |V|/G < 1), with |V|/G close to unity for graphene–LCmA (0.994) and less compaction when doped with 2N (0.760 for 2N–LCmA). The bisubstitution modulates the electronic heterogeneity of the basal plane and redefines the binder–surface compatibility, favoring the multipoint anchoring of polar ligands in 2N and penalizing efficient aromatic stacking in 2S. Full article

Oral squamous cell carcinoma (OSCC) therapy frequently produces acute and chronic injury to the oral mucosa, including surgical lining defects and radiochemotherapy-associated oral mucositis (OM). Beyond pain and ulceration, these injuries compromise nutrition, speech, oral hygiene, and feasibility of dental/implant rehabilitation, and may disrupt oncologic treatment delivery. The oral cavity imposes stringent constraints on regenerative biomaterials—continuous salivary flow, high microbial load, and repeated mechanical shear—such that clinical success depends on reliable mucoadhesion/wet adhesion, barrier function, mechanical compliance, and safe, spatially confined bioactivity. This PRISMA-informed evidence-mapped structured narrative review provides an evidence map and structured qualitative synthesis of hydrogel and scaffold platforms relevant to post-OSCC care, spanning clinically used mucoadhesive barrier formulations through emerging wet-adhesive multifunctional patches, acellular matrices, and tissue-engineered oral mucosa (TEOM) constructs. Clinically, the strongest evidence base remains barrier-forming gels and liquids that reduce OM pain and improve oral function during active therapy, establishing performance benchmarks for intraoral retention and patient-reported benefit. Preclinical studies are rapidly expanding toward multifunctional designs that integrate antimicrobial, anti-inflammatory, pro-epithelialization, and pro-angiogenic cues. However, a pervasive limitation is the inconsistent use of OSCC-relevant models (e.g., irradiated/xerostomic tissue beds), standardized functional endpoints (e.g., oral intake, durability under mastication, and neurosensory outcomes), and explicit oncologic safety evaluation, which severely compromises translational validity. For reconstructive applications, dermal matrices and early TEOM reports suggest feasibility for selected defects, but controlled comparative trials and scalable manufacturing pathways remain limited. Translational priorities include oncologic-by-design bioactivity (time-limited, locally confined cues), clinically anchored outcome reporting, and quality-by-design manufacturing aligned with device/combination/advanced-therapy regulatory requirements. Full article

Carbonate reservoirs in the Shunbei area develop pronounced fracture networks after acidized hydraulic fracturing and thus have the potential to be repurposed as underground gas storage (UGS) after hydrocarbon depletion. Characterizing their mechanical behavior is essential for safe UGS operation; however, deep to ultra-deep natural cores are difficult to obtain, and conventional macroscopic tests often cannot provide parameters that meet engineering requirements. To address this issue, nanoindentation combined with QEMSCAN (Quantitative Evaluation of Minerals by Scanning Electron Microscopy) was employed to quantify microscale mineral distributions and the mechanical properties of the major constituents. The investigated rock is calcite-dominated (89.62%), with minor quartz (9.89%) and trace feldspar-group minerals (1.89%). Minerals are randomly embedded, and soft–hard phase boundaries are widely distributed. A finite–discrete element method (FDEM) model was then constructed and calibrated in ABAQUS. The discrepancies in uniaxial compressive strength and elastic modulus relative to laboratory results were 6.51% and 9.91%, respectively, indicating good agreement in both mechanical response and failure mode. Parametric analyses using three additional models with different mineral proportions show that damage preferentially initiates at mineral phase boundaries and stress concentration zones induced by end constraints. Microcracks then propagate and coalesce into a dominant compressive–shear band, and final failure is mainly governed by slip along the shear band with localized tensile cracking. With increasing quartz and feldspar contents, enhanced heterogeneity and a higher density of phase boundaries lead to a higher density of crack nucleation sites and increased crack branching, and the failure pattern transitions from a single shear-band–controlled mode to a more network-like fracture system. Moreover, macroscopic strength is not determined solely by the intrinsic strength of individual minerals; heterogeneity and phase-boundary characteristics strongly govern microcrack behavior, such that higher hard-phase contents may result in a lower peak strength. Full article

Monocular 3D human–object interaction (HOI) reconstruction requires jointly recovering articulated human geometry, object pose, and physically plausible contact from a single RGB image. While recent token-based methods commonly employ dense self-attention to capture global dependencies, isotropic all-to-all mixing tends to entangle spatial-geometric cues (e.g., contact locality) with channel-wise semantic cues (e.g., action/affordance), and provides limited control for representing directional and asymmetric physical influence between humans and objects. This paper presents HOIMamba, a state-space sequence modeling framework that reformulates HOI reconstruction as bidirectional, multi-scale interaction state inference. Instead of relying on symmetric correlation aggregation, HOIMamba uses structured state evolution to propagate interaction evidence. We introduce a multi-scale state-space module (MSSM) to capture interaction dependencies spanning local contact details and global body–object coordination. Building on MSSM, we propose a spatial-channel grouped SSM (SCSSM) block that factorizes interaction modeling into a spatial pathway for geometric/contact dependencies and a channel pathway for semantic/functional correlations, followed by gated fusion. HOIMamba further performs explicit bidirectional propagation between human and object states to better reflect asymmetric reciprocity in physical interactions. We evaluate HOIMamba on two public benchmarks, BEHAVE and InterCap, using Chamfer distance for human/object meshes and contact precision/recall induced by reconstructed geometry. HOIMamba achieves consistent improvements over representative prior methods. On the BEHAVE dataset, it reduces human Chamfer distance by 8.6% and improves contact recall by 13.5% compared to the strongest Transformer-based baseline, with similar gains observed on the InterCap dataset. Ablation studies on BEHAVE verify the contributions of state-space modeling, multi-scale inference, spatial-channel factorization, and bidirectional interaction reasoning. Full article

This study provides a comprehensive assessment of 12 metal(loid)s in the muscle tissue of the commercially vital shrimp, Penaeus semisulcatus, from four stations (Bozyazi, Silifke, Karatas, and Iskenderun) along the Northeastern Mediterranean. Metal concentrations were evaluated separately for males and females, utilizing Estimated Weekly Intake (EWI), Target Hazard Quotient (THQ), Carcinogenic Risk (CR), and Selenium Health Benefit Value (HBV_Se) indices. While the species is generally safe for consumption across the region, a striking, localized bioaccumulation of Chromium (Cr) was identified specifically in Iskenderun Bay, where male shrimps exhibited concentrations (1.209 mg/kg wet weight) approximately 10-fold higher than females, highlighting a sex-specific sensitivity likely linked to metabolic and physiological differences. By adopting a precautionary risk assessment framework—considering the region’s intense industrial profile—this localized spike resulted in a Total Carcinogenic Risk (∑CR = 5.15 × 10⁻⁴) for this group, exceeding the priority threshold. Furthermore, widespread Lead (Pb) contamination was detected across all stations, with several samples surpassing EU maximum levels (0.50 mg/kg). Regarding Arsenic (As), while high total concentrations led to THQ values > 1 across the regional gradient, this was characterized as a conservative modeling artifact rather than a physiological threat, as Arsenic in crustaceans is predominantly in the non-toxic organic form. Conversely, any potential risk from Mercury (Hg) was conclusively mitigated by an overwhelming molar excess of Selenium (Se) at all locations, confirmed by consistently positive HBV_Se values (0.312–0.658). In conclusion, our findings demonstrate that seafood safety is conditional and region-specific. The study underscores that localized contamination “hotspots” can be easily masked by non-sex-specific sampling and emphasizes the necessity of moving beyond simplistic risk models by incorporating selenium-mercury antagonism and precautionary risk assumptions for industrial pollutants. Full article

Using advanced optical modelling, we quantify how sinusoidal corrugation and emitter dipole orientation jointly govern light extraction from OLED thin-film stacks into a glass substrate for red, green, and blue emission. Irrespective of emission colour, the corrugation aspect ratio (AR = height/period) is the dominant geometric parameter controlling extraction, with absolute period and height playing secondary roles, as periods of 600–1000 nm deliver similar gains across all colours. Extraction peaks at AR ≈ 0.2 for predominantly horizontal dipoles, AR ≈ 0.5 for vertical dipoles, and AR ≈ 0.3 for isotropic orientations. For the isotropic case, extraction improves by up to 40%, 34%, and 20% relative to flat red, green, and blue devices, respectively. Absorption analysis attributes the principal gains to suppression of surface-plasmon-polariton losses of vertical dipoles, supported by local dipole reorientation, waveguide disruption, and scattering. Because practical texturing can alter dipole orientation, optimum conditions must be re-evaluated; if orientations follow the sinusoidal profile, an AR of approximately 0.2–0.3 is favoured for isotropic to moderately horizontal orientations, whereas higher ARs benefit strongly vertical orientations. The results provide guidelines for co-optimising corrugation geometry and dipole orientation for high-efficiency OLEDs. Full article

Purpose: Castration-resistant prostate cancer (CRPC) responds poorly to conventional chemotherapy. We evaluated a cell-based enzyme–prodrug therapy using adipose-derived stem cells (ADSCs) engineered to express cytosine deaminase (CD) or carboxylesterase 2 (CE2), paired with their respective prodrugs 5-fluorocytosine (5-FC) or irinotecan (CPT-11), to compare their antitumor efficacy. Materials and Methods: Human telomerase reverse transcriptase (hTERT)-immortalized ADSCs were transduced with CD or CE2, and transgene expression and stem cell phenotype were confirmed. CD expression was verified at the transcript level and by functional 5-FC-to-5-fluorouracil (5-FU) conversion, whereas CE2 expression was verified by transcript analysis and immunoblotting. Tumor tropism toward PC3 prostate cancer cells was tested using migration assays and analysis of chemoattractant ligand/receptor expression. Prodrug-induced self-killing and bystander tumor cell killing were assessed through viability assays and co-culture with PC3 cells. For the CE2/CPT-11 system, SN-38 was not directly quantified; functional activity was inferred from prodrug-dependent cytotoxicity and in vivo efficacy. In vivo efficacy was evaluated in nude mice with PC3 tumors treated systemically with engineered ADSCs plus prodrug. Results: CD- and CE2-expressing ADSCs were successfully established and retained mesenchymal stem cell (MSC) characteristics. Both cell types exhibited significant migration toward PC3 cells. The CE2/CPT-11 system produced stronger prodrug-mediated cytotoxicity than CD/5-FC, with CE2-modified ADSCs showing higher sensitivity to CPT-11 and inducing greater apoptosis in co-cultured PC3 cells. In vivo, both treatments suppressed tumor growth, but CE2/CPT-11 achieved greater inhibition (tumor volume ~26% of control vs. ~32% for CD/5-FC at day 14). No overt clinical toxicity was observed based on body weight and daily clinical monitoring; however, hematology/serum chemistry were not assessed. Conclusions: Engineered ADSCs home to CRPC tumors and enable local prodrug activation, producing significant antitumor effects. Within the constraints of our in vitro assays and subcutaneous xenograft model, CE2/CPT-11 demonstrated stronger efficacy outcomes than CD/5-FC. Mechanistic attribution to intratumoral SN-38 exposure should be confirmed by direct metabolite measurements in future studies. Full article

Background/Objectives: Chronic lower-limb ulcers of mixed etiology are characterized by impaired microcirculation and persistent inflammation, leading to delayed healing, frequent hospitalizations, and a high risk of limb loss. While infrared thermography reflects local perfusion status and systemic inflammatory markers capture whole-body immune activation, these dimensions are usually assessed separately. The objective of this study was to develop and internally evaluate a composite Thermal–Inflammatory Index (TI) integrating wound-bed thermography with systemic inflammatory markers to stratify disease severity and prognosis in patients with chronic lower-limb ulcers. Methods: In this prospective observational study, 82 adults with chronic lower-limb ulcers underwent baseline infrared thermographic assessment of wound-bed temperature using a standardized protocol. Concurrently, neutrophil-to-lymphocyte ratio (NLR) and C-reactive protein (CRP) were measured. The Thermal–Inflammatory Index was constructed as a standardized composite of inverted wound-bed temperature, NLR, and CRP. A simplified TI score (0–3) was derived using predefined clinical thresholds. The primary endpoint was a composite adverse outcome defined as amputation or failure to achieve complete wound healing within 12 weeks. Secondary outcomes included a prolonged hospital stay (>7 days). Discriminative performance was assessed using receiver operating characteristic analysis, and associations were examined using correlation and logistic regression models. Results: Higher TI values were associated with colder wound beds, elevated systemic inflammatory markers, and increased disease burden. The TI demonstrated moderate discrimination for the composite adverse outcome (AUC 0.75) and prolonged hospitalization (AUC 0.71), performing comparably to the strongest single component (−T_bed, AUC 0.77) while integrating local and systemic information. Each one-standard-deviation increase in TI was independently associated with higher odds of the composite adverse outcome and a prolonged hospital stay. The simplified TI score showed clear stepwise gradients in adverse outcomes and length of hospitalization. Conclusions: The Thermal–Inflammatory Index integrates thermographic and inflammatory signals into a single, clinically interpretable biomarker of severity and prognosis in chronic lower-limb ulcers. TI and the simplified TI score may support early risk stratification using low-cost, bedside-accessible data. Full article

Underwater imaging is generally subject to complex degradation issues such as color distortion, contrast degradation, and detail blurring due to the selective absorption and scattering of light wavelengths by water. Existing deep learning methods have limitations in the collaborative optimization of local details and global color. To address this issue, this paper proposes a multi-scale enhancement network based on global and local feature fusion. By integrating the advantages of CNN and Transformer, it achieves joint optimization of global color correction and local detail enhancement. Specifically, MGLFNet extracts global and local features of the image through the global and local feature fusion block in the core component of the multi-scale convolution–Transformer block and performs dynamic fusion. Meanwhile, to extract features at different scales to enhance performance, we design a multi-scale convolution feed-forward network. Through the action of the fusion module and the feed-forward network, a color-rich and detail-clear enhanced image is obtained. A large number of experimental results show that MGLF-Net outperforms comparison methods in both qualitative and quantitative evaluations of visual quality, with PSNR and SSIM values of 25.37 and 0.918 on the UIEB dataset, respectively, as well as low memory usage and computational resource requirements. In addition, detailed ablation experiments prove the effectiveness of the core components of the model. Full article

Sample-wise detection of P-, R-, and T-peaks in electrocardiograms (ECGs) is challenging because each peak type is sparsely represented (≈1:500 samples in a typical 10-s, 500-Hz ECG at 60 bpm), such that even a small number of false-positives (FPs) can markedly degrade positive predictive value (PPV) and limit the practicality of classifier-only approaches. This study proposes a lightweight ECG peak detection framework that combines binary classifiers with physiological temporal constraints (PTC) to address extreme sample-level class imbalance. Local morphological features are first evaluated using lightweight machine-learning models, among which XGBoost (XGB) exhibited the most stable score-ranking performance. Rather than directly thresholding classifier outputs, prediction scores are interpreted within the framework, which encodes physiological timing relationships. R-peaks are detected using score ranking combined with a refractory-period constraint, and the detected R-peaks serve as temporal landmarks for subsequent P- and T-peak detection within physiologically plausible time windows reflecting the P–QRS–T sequence. Quantitative evaluation was conducted using the Lobachevsky University Electrocardiography Database, hereafter referred to as LUDB. With a temporal tolerance of ±20 ms, the XGB-based system achieved an F1-score of 0.87 for R-peak detection (sensitivity 0.96, PPV 0.79), corresponding to approximately 9–10 true R-peaks with only 2–3 FP samples per 10-s segment. For P- and T-peaks, F1-scores of 0.70 and 0.69 were obtained, respectively. Additional evaluation on arrhythmic LUDB records demonstrated robust R-peak detection across rhythm types. In AF-related rhythms, where organized P waves are physiologically absent, the framework appropriately suppressed P-peak detections, with false-positive rates remaining below 0.31%. Qualitative application to ECG recordings from the PTB-XL database further demonstrated physiologically consistent behavior. These results indicate that reliable and interpretable ECG peak detection under extreme class imbalance can be achieved by integrating lightweight classifiers within the proposed framework, without reliance on complex deep learning architectures. Full article

In today’s digital era, cyberattacks pose a critical threat to networks of all scales, from local systems to global infrastructures. Intrusion detection systems (IDSs) are essential for identifying and mitigating such threats. However, existing machine learning-based IDS often suffer from low detection accuracy, heavy reliance on manual feature extraction, and limited coverage of attack categories. To address these limitations, we propose a modular, deployment-ready intrusion detection framework that integrates multiple heterogeneous datasets through a hybrid transformer–multilayer perceptron (Transformer–MLP) architecture. The system employs three parallel Transformer–MLP models, each specialized for a distinct dataset, whose probabilistic outputs are fused using a weighted decision-level strategy. Unlike traditional feature-level fusion, this strategy ensures module independence, eliminates the need for global retraining when adding new components, and provides seamless modular scalability. The framework accurately identifies twenty-one traffic categories, including one benign and twenty attack classes, derived from a unified mapping across multiple heterogeneous sources to ensure a consistent cross-dataset taxonomy. By combining advanced contextual representation learning with ensemble-based probabilistic fusion, the framework demonstrates high detection accuracy and practical applicability in real-world network environments. The Transformer module captures complex contextual dependencies, while the MLP performs final classification. Class imbalance is mitigated via adaptive synthetic sampling (ADASYN), synthetic minority over-sampling technique (SMOTE), edited nearest neighbor (ENN), and class weight adjustments. Empirical evaluation demonstrates the framework’s high effectiveness: for binary classification, it achieves 99.98% on CICIDS2017, 99.19% on NSL-KDD, and 99.98% on NF-BoT-IoT-v2; for two-stage multi-class classification, 99.56%, 99.55%, and 97.75%; and for one-phase multi-class classification, 99.73%, 99.07%, and 98.23%, respectively. Moreover, the framework enables real-time deployment with 4.8–6.9 ms latency, 9800–14,200 fps throughput, and 412–458 MB memory. These results outperform existing multi-dataset IDS approaches, highlighting the architectural effectiveness, robustness, and practical applicability of the proposed framework. Full article

Synthetic dataset generation in Computer Vision, particularly for industrial applications, is still underexplored. Industrial defect segmentation, for instance, requires highly accurate labels, yet acquiring such data is costly and time-consuming. To address this challenge, we propose a novel diffusion-based pipeline for generating high-fidelity industrial datasets with minimal supervision. Our approach conditions the diffusion model on enriched bounding-box representations to produce precise segmentation masks, ensuring realistic and accurately localized defect synthesis. Compared to existing layout-conditioned generative methods, our approach improves defect consistency and spatial accuracy. We introduce two quantitative metrics to evaluate the effectiveness of our method and assess its impact on a downstream segmentation task trained on real and synthetic data. Our results demonstrate that diffusion-based synthesis can bridge the gap between artificial and real-world industrial data, fostering more reliable and cost-efficient segmentation models. Full article

Parameter estimation for biochemical reaction networks is computationally demanding, especially for systems with oscillatory nonlinear dynamics, where standard iterative optimization strategies, including genetic algorithms, often struggle with prohibitive computational costs. We introduce an efficient parameter estimation framework that combines a real-coded genetic algorithm with a novel adaptive simulation termination strategy. This strategy defines a time-dependent termination boundary based on population quantiles, which is permissive during early transients and becomes progressively stricter as simulations advance, explicitly accounting for the temporal structure of oscillatory behavior. Crucially, this mechanism facilitates the efficient identification and early simulation termination of poor parameter candidates, thus avoiding the computational expense of full-horizon simulations. The framework further integrates global exploration with the modified Powell method for rapid local refinement. Numerical experiments on two benchmark oscillatory models—the Lotka–Volterra and Goodwin oscillators—demonstrate that the framework reduces computational cost by approximately 30–50% compared to a baseline GA without this strategy. For the parameter-sensitive Goodwin model, the framework efficiently identifies candidates evolving toward damped oscillations caused by subtle parameter variations. Sensitivity analysis also confirms robustness across diverse hyperparameter settings, indicating that adaptive simulation termination provides a practical acceleration mechanism for inverse problems in systems biology where iterative objective function evaluation dominates runtime. Full article

Atomic charges are widely used to analyze molecular electronic structure and substituent effects, yet their numerical values and interpretations are inherently dependent on the adopted density partitioning scheme. Here, we adapt the Equivariant Atomic Contribution framework to molecular systems (EAC-qm), enabling prediction of atom-resolved continuous charge densities from which atomic charges are obtained as spatial moments. The predicted densities reproduce reference density functional theory results with high accuracy and preserve global charge conservation. To assess chemical interpretability, we examine charge responses in monosubstituted aromatic systems using Hammett substituent constants as external empirical references. Atomic charges derived from EAC-qm exhibit a strong linear association with Hammett parameters, compared with values obtained from traditional density partitioning approaches applied to the same electronic structures. These correlations indicate that density-derived charges respond systematically to established substituent electronic trends. Beyond scalar charges, atom-resolved dipole moments can be evaluated as first-order moments of the same continuous density representation. Illustrative examples for formaldehyde (H₂CO) and formamide (HCONH₂) show that local dipole vectors provide directional information about intra-atomic polarization that is not captured by point-charge models. Overall, the results suggest that machine-learned continuous electron densities provide a representation-consistent basis for constructing atom-centered electronic descriptors with chemical interpretability. Full article

Facial occlusion induces sample-wise reliability shifts in facial expression recognition (FER), where the usefulness of global context and local discriminative cues varies dramatically with the amount of visible facial information. Existing occlusion-robust FER studies often evaluate under limited or homogeneous occlusion settings and commonly adopt static fusion strategies, which are insufficient for complex and heterogeneous real-world occlusions. In this work, we establish a rigorous occlusion robustness evaluation protocol by constructing a fixed offline test benchmark with diverse synthetic occlusion patterns (e.g., masks, sunglasses, texture blocks, and mixed occlusions) on top of public FER test splits. We further propose a Dual-Stream Adaptive Weighting Mixture-of-Experts framework (DS-AW-MoE) that fuses a global contextual expert and a local discriminative expert via an occlusion-aware weighting network. Crucially, we introduce a facial visibility assessment as a task-agnostic prior to explicitly regulate expert contributions, enabling dynamic re-allocation of model capacity according to input-dependent feature reliability. Extensive experiments on public datasets and the constructed occlusion benchmark demonstrate that DS-AW-MoE achieves more stable recognition under complex occlusions, characterized by a smaller and more consistent performance drop. To support reproducibility under dataset license constraints, we will release an anonymous, fully runnable repository containing the complete occlusion synthesis pipeline, evaluation protocol, and configuration files, allowing researchers to reproduce the benchmark after obtaining the original datasets. Full article