Search Results (3,496)

Conventional multispectral photodetectors typically rely on multiple electrical terminals to discriminate different wavelengths, which inevitably increases structural complexity. Here, we break this paradigm by demonstrating a dual-color visible–infrared photodetector based on a simple two-terminal Au/MoS₂/Te heterostructure. The device operates through a bias-switching mechanism: reversing the voltage polarity selectively activates either the MoS₂/Au Schottky junction for visible-light detection (520 nm) or the Te/MoS₂ heterojunction for infrared detection (1550 nm). This bias-controlled wavelength selectivity is unambiguously verified by scanning photocurrent mapping. Beyond dual-color discrimination, an adaptive convolutional neural network is employed to decode the nonlinear current–voltage characteristics and enable precise spectral identification, achieving a reconstruction error of approximately 4.5%. Furthermore, high-fidelity dual-color imaging is demonstrated at room temperature. These results establish a hardware–algorithm co-design strategy based on a minimalist two-terminal architecture, providing a viable route toward compact and intelligent spectral-sensing systems. Full article

Cloud-based intrusion detection systems (IDSs) increasingly rely on deep learning classifiers to identify malicious traffic; however, this reliance exposes them to adversarial evasion attacks in which adversaries craft near-imperceptible perturbations to bypass detection. Existing defenses based on conventional adversarial training often recover robustness against known perturbation patterns at the cost of degraded detection accuracy on canonical attack categories—a robustness–accuracy trade-off that remains an open challenge in the field. In this paper, we propose GT-CSAT (Game-Theoretic Cost-Sensitive Adversarial Training), a novel defense framework tailored for cloud security environments. GT-CSAT couples an improved Wasserstein GAN with Gradient Penalty (WGAN-GP) threat generator—conditioned on attack semantics to simulate functionally consistent and highly covert traffic variants—with a minimax adversarial training loop governed by a game-theoretic cost-sensitive loss function. The proposed loss function assigns asymmetric misclassification penalties derived from a two-player zero-sum payoff matrix, enabling the detector to maintain vigilance over both novel adversarial variants and well-characterized conventional threats simultaneously. Specifically, misclassifying an adversarially perturbed attack as benign incurs a strictly higher penalty than the symmetric cross-entropy baseline, while the cost weights are dynamically adapted via a Nash equilibrium-inspired update rule during training. We conduct comprehensive experiments on the Cloud Vulnerabilities Dataset (CVD), CICIDS-2017, and UNSW-NB15, which encompass diverse cloud-specific attack scenarios including denial-of-service, port scanning, brute-force, and SQL injection traffic. Under six representative evasion strategies—FGSM, PGD, C&W, BIM, DeepFool, and IDSGAN-style black-box perturbations—GT-CSAT achieves an average robust accuracy of 94.3%, surpassing standard adversarial training by 6.8 percentage points and the undefended baseline by 21.4 percentage points, while preserving clean-traffic detection at 97.1%. These results confirm that the game-theoretic cost structure effectively decouples robustness from accuracy, yielding a Pareto-superior detection profile relative to competing baselines across all evaluated threat models. The source code and experimental configurations have been publicly released to facilitate reproducibility. Full article

Despite the growing adoption of Ti6Al4V-ELI made by Laser Powder Bed Fusion (LPBF) in tribologically demanding applications, the influence of hybrid nanoparticle additives on its lubrication behavior under starved contact conditions remains insufficiently explored. The tribological performance of Ti6Al4V was investigated under starved boundary-to-mixed lubrication conditions using engine oil modified with Ag-doped TiO₂ nanoparticles. Double-scan LPBF-fabricated discs were tested in a ball-on-disc configuration against AISI 52100 bearing steel using a TRB³ tribometer. Nanolubricants were prepared by dispersing TiO₂ and Ag–TiO₂ nanopowders with different Ag⁺/Ti⁴⁺ ratios (0.5%, 1.5%, and 2.5%) in SAE 10W-40 engine oil at a constant nanoparticle concentration of 0.05 wt%. Comprehensive physicochemical characterization of the nanopowders and nanolubricants was performed through structural, chemical, optical, morphological, rheological, and stability analyses. Tribological experiments were conducted following a full-factorial design combining three normal loads (5–15 N), three sliding speeds (0.10–0.20 m·s⁻¹), and four lubricant formulations. The steady-state coefficient of friction ranged between 0.281 and 0.359, while the specific wear rate varied from 2.81 × 10⁻⁴ to 4.83 × 10⁻⁴ mm³·N⁻¹·m⁻¹. The contact temperature rise remained relatively moderate, within the interval of 1.9–9.4 °C. Among the investigated formulations, the lubricant containing 1.5% Ag–TiO₂ exhibited the lowest friction coefficient, whereas the formulation with the highest Ag content showed improved stability of tribological performance across the investigated operating domain. These results indicate that Ag-modified TiO₂ nanoparticles are consistent with the formation of protective tribofilms and contribute to the stabilization of friction, wear, and thermal behavior under starved lubrication conditions. ANOVA confirmed that sliding speed and the load–lubricant interaction are the dominant factors governing friction and wear, while normal load controls the thermal response. These findings support the use of Ag–TiO₂ nanolubricants as a viable strategy for stabilizing interfacial behavior in LPBF-fabricated titanium components operating under starved lubrication conditions. Full article

Accurate assessment of mitral valve (MV) and pulmonary vein (PV) flow velocities is important for left ventricular diastolic function testing. This study investigated the scan–rescan reproducibility of 4D Flow MRI-assessed MV and PV flow velocities in 21 healthy volunteers (25 ± 4 years). Participants underwent repeated whole-heart 3T 4D Flow MRI involving repositioning and different respiratory compensation strategies (motion-uncompensated free-breathing vs. respiratory motion-compensated navigator gating). MV parameters (net flow volume (NFV), E-wave velocity, A-wave velocity, E/A ratio, E deceleration time (DT), annular e’ velocity, E/e’ ratio) and PV parameters (NFV, S-wave velocity, D-wave velocity, S/D ratio, atrial reversal (AR) wave velocity) were derived from velocity–time curves and compared using intraclass correlation coefficients (ICCs), Bland–Altman analysis, and Pearson’s correlation (r). Results showed significant moderate-to-strong scan–rescan agreement and correlation for most MV and PV parameters (ICC = 0.51–0.92; r = 0.51–0.92; all p < 0.05), except E DT, e’ velocity, E/e’ ratio, PV NFV, and AR velocity (ICC = −0.13–0.47; r = −0.14–0.47). Subanalysis of respiratory motion strategies showed moderate-to-strong agreement and correlation for MV and PV parameters (ICC = 0.61–0.99; r = 0.52–0.99; all p < 0.05 excluding E DT), except E DT (ICC = 0.44) and PV NFV (ICC = 0.46; r = 0.46). While intraobserver agreement was mostly moderate-to-excellent (ICC = 0.58–0.97; ICC = 0.41 for E DT), interobserver agreement was poor for E DT and PV parameters (ICC = −0.12–0.34). Overall, 4D Flow MRI shows acceptable reproducibility for selected diastolic flow parameters, particularly mitral inflow indices, but substantial variability and limited robustness for key indices currently restrict its clinical applicability. Full article

To address the demand for wide-swath, high-resolution short-wave infrared (SWIR) imaging on resource-constrained spaceborne platforms, this study presents the design and on-orbit validation of a compact dual-channel push-broom (line-scanning) imaging system. The system adopts a transmissive optical architecture and a centralized, compact electronic control unit (ECU) configuration. By interleaving and mosaicking sixteen InGaAs linear array detectors, the system achieves an imaging swath of approximately 187 km and a nominal ground sampling distance of about 24 m, while maintaining a total instrument mass of 10.62 kg and a power consumption of approximately 12 W, thereby demonstrating a high level of integration and efficient resource utilization. To address focal plane consistency issues arising from multi-detector mosaicking, a closed-loop leveling method was developed using the modulation transfer function (MTF) as the primary performance metric. Through defocus estimation and quantitative correction of protrusions on a SiC substrate, convergence toward a unified confocal focal plane among multiple detectors was achieved. On-orbit image quality assessment indicates that the full width at half maximum (FWHM) of the line spread function (LSF) for both channels is approximately 1.38 pixels, with favorable signal-to-noise ratio (SNR) performance. These results validate the effectiveness of the proposed focal plane leveling strategy as well as the opto-mechanical-thermal design of the system. The proposed approach provides a practical pathway for the engineering implementation and consistency control of multi-detector mosaicked SWIR payloads under stringent resource constraints. Full article

Despite its potential to enhance construction quality, efficiency, and sustainability, modular construction continues to experience defects that hinder its broader adoption. Understanding and mitigating defects is essential for maximising the sustainability benefits of modular construction by reducing material waste, minimising rework and improving lifecycle performance. Existing research remains fragmented, with limited synthesis integrating defects with their root causes across the project lifecycle. To address this gap, this study investigates defect types, lifecycle-based causes, and mitigation strategies in modular building projects through a PRISMA-guided systematic literature review of 61 peer-reviewed journal articles published between 2015 and 2025 and retrieved from Scopus and Web of Science. Six major defect categories were identified: geometric and dimensional; material and component; joint and connection integrity; envelope performance and durability; structural; and mechanical, electrical, and plumbing (MEP) defects, with geometric and dimensional defects emerging as the most prevalent, accounting for 26.7% of reported cases. Lifecycle root-cause mapping indicates that poor workmanship during on-site assembly is the dominant contributor, accounting for 44.1% of identified root causes, with manufacturing errors (26.8%) and design limitations (13.4%) acting as critical upstream sources. Mitigation strategies cluster into three groups: general recommendations (39% of reported strategies), mainly focusing on low-cost organisational measures such as logistics coordination and workforce training; structured risk-management frameworks (9.1%), including assembly sequencing and tolerance planning; and digital and data-driven technologies (51.9%), such as laser scanning, AI-based inspection, and digital twins, enabling proactive quality assurance across the lifecycle. The study proposes an integrated lifecycle–defect–mitigation framework to strengthen quality governance and advance sustainable modular delivery. Full article

Background/Objectives: Non-sedate magnetic resonance imaging (MRI) can be challenging for young children with neuromotor disabilities, often resulting in motion-degraded images that complicate interpretation in the context of underlying neuropathology. This study aimed to characterize tolerance factors and barriers related to awake MRI of the pediatric brain and to examine additional considerations in analyzing structural scans affected by motion and pathology. Methods: 10 children (mean age 5y9m; 5 girls; GMFCS level IV) with cerebral palsy (CP) underwent non-sedate 3T MRI of the brain. Tolerance factors and challenges were documented. MRI quality and automated structural preprocessing with Freesurfer (FS) v.8.0 were reviewed by a pediatric neuroradiologist and neurologist. To assess the impact of motion, automated basal ganglia segmentation was compared with manual segmentation. Segmentation accuracy was characterized using Dice Coefficient (D). Results: Five participants (50%) tolerated non-sedate structural MRI, although two of them were unable to remain still. Factors affecting MRI tolerance included sensitivity to scanner noise (n = 4), hyperkinetic movement (n = 2), difficulty with positioning/padding (n = 4), fear of clinical environment (n = 2) or confined scanner interior (n = 2), and earbud discomfort (n = 3). Automated structural preprocessing with FS yielded discrepancies in gray-white matter boundaries in motion-degraded scans, necessitating manual correction. Automated segmentation of motion-compromised scans closely agreed with manual delineation of the caudate (D ≥ 0.85) and putamen (D ≥ 0.78), while the pallidum was least reproducible (D = 0.58). Conclusions: Tailored acquisition and processing strategies are necessary to support non-sedate MRI in children with CP, preserve downstream neuroimaging analyses, and promote inclusion of underrepresented populations in research. Full article

This study presents an experimental investigation of the influence of dopant type and calcination temperature on BaTiO₃-based piezoceramics synthesized by a solid-state calcination process. The effects of Mn, Nb, La, and Ce dopants on the structural, morphological, and piezoelectric characteristics of powders calcined at 1000 °C and 1100 °C were systematically evaluated. In addition, two co-doped BaTiO₃ compositions, namely Mn–Nb and La–Nb, calcined at 1000 °C, were investigated in order to assess the combined effect of acceptor–donor and donor–donor doping strategies on microstructural evolution and structural stability. The synthesized powders were characterized by scanning electron microscopy (SEM), particle size analysis, energy-dispersive X-ray spectroscopy (EDS), elemental mapping, and X-ray diffraction (XRD), in comparison with a commercial BaTiO₃ reference powder. The piezoelectric response was assessed by correlating the structural modifications induced by doping with the estimated piezoelectric coefficient d₃₃, calculated as a function of the tetragonality ratio (c/a) and further correlated with the crystallite size. The results reveal significant variations in grain growth, dopant distribution, and crystallographic stability, highlighting the critical role of dopant chemistry and calcination temperature in tailoring the functional properties of BaTiO₃ for piezoelectric applications. Full article

Chiller systems account for a substantial proportion of building energy consumption, where their operational efficiency and start–stop cycling frequency directly influence overall system energy use and equipment lifespan. In practical applications, load fluctuations and improper control settings often cause chillers to experience frequent cycling, leading to decreased efficiency and increased mechanical wear. While existing studies predominantly focus on real-time control or model predictive approaches, fewer investigations systematically identify stable operating regions and optimal control thresholds using historical operational data. This study proposes a data-driven method for identifying an operational threshold. Long-term historical data are analyzed to establish a start–stop event detection mechanism. A normalized power index is introduced, and multi-scenario classification—incorporating seasonal conditions and peak/off-peak periods—is employed to evaluate system behavior across different contexts. Furthermore, a quantile scanning approach combined with hysteresis simulation is utilized to identify optimal operational threshold intervals. Stability evaluation indices, based on cycling frequency, power variation rate, and load deviation magnitude, are constructed to quantify stability performance. To verify the robustness of these thresholds, K-fold cross-validation is performed. Results indicate that the identified thresholds effectively reduce cycling frequency and power fluctuations, thereby enhancing system stability. Specifically, the start–stop cycling frequency is reduced by approximately 75–90%, and the power variation rate decreases by up to 85% across various scenarios. This study provides an offline decision-support framework to assist operators in optimizing control parameters and strategies. These outcomes serve as a reference for chiller energy management and provide empirical evidence for the future design of control strategies. Full article

Reliable change detection in indoor environments remains a challenge for autonomous robotic systems using 3D LiDAR. Existing methods often require manual annotation, computationally intensive architectures, or focus on outdoor scenes. This paper presents SPICD-Net, a lightweight Siamese PointNet framework for indoor 3D change detection trained exclusively on synthetically generated anomalies, eliminating manual labeling. The framework offers three deployment-oriented contributions: a three-class Siamese formulation separating no-change, changed, and geometrically inconsistent tile pairs; a pre-FPS anomaly injection strategy that aligns synthetic training with inference-time preprocessing; and a stochastic-gated Chamfer-statistics branch that complements learned embeddings with explicit geometric cues under consumer-grade hardware constraints. Evaluated on 14 controlled simulation experiments in an indoor corridor dataset, SPICD-Net achieved aggregated Precision = 0.86, Recall = 0.82, F1-score = 0.84, and Accuracy = 0.96, with zero false positives in the no-change baseline and mean inference time of 22.4 s for a 172-tile map on a single consumer GPU. Additional robustness experiments identified registration accuracy as the main operational prerequisite. A limited real-world validation in one unseen room (four scans, 67 tiles) achieved Precision = 0.583, Recall = 1.000, and F1 = 0.737. Full article

The enzymatic saccharification of cellulose remains a key step in biomass conversion processes, often influenced by enzyme stability, distribution, and accessibility at solid–liquid interfaces. Immobilization of cellulolytic enzymes on nanostructured supports has been proposed as a strategy to modulate catalytic behavior; however, the relationship between enzyme loading and catalytic response remains insufficiently understood. In this study, CuO-based nanobiocatalysts were prepared through controlled cellulase immobilization and systematically evaluated under defined experimental conditions. Structural and physicochemical characterization was performed using X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), and integrated thermal analysis (TGA–DTG–DSC), enabling a comparative assessment of the analyzed systems. SEM analysis showed that the average particle diameter increased from 39.5 ± 14.8 nm (CuO nanoparticles) to 95.6 ± 21.8 nm (NPI10), 106.6 ± 27.7 nm (NPI15), and 113.5 ± 23.1 nm (NPI20), indicating progressive variations in particle organization with increasing enzyme loading. Catalytic performance was evaluated through enzymatic hydrolysis of cellulose filter paper as a model substrate, with products quantified by HPLC at a representative reaction time. The system prepared at lower enzyme loading (NPI10) exhibited product formation comparable to that of the free enzyme, with apparent average glucose formation values of 1.054 and 1.047 mg·mL⁻¹·h⁻¹, respectively. In contrast, higher immobilization levels were associated with reduced catalytic output. Across all systems, glucose was the predominant product, with negligible accumulation of intermediate oligomers under the evaluated conditions. These results indicate that increasing enzyme loading does not correspond to proportional increases in product formation and highlight the influence of enzyme distribution and accessibility within the system. The combined structural and catalytic observations provide a controlled framework for evaluating how immobilization conditions influence system behavior in nanobiocatalytic systems. Full article

There is a strong and growing need for low environmental impact, fluorine-free finishes that deliver durable water repellency and stain resistance to leather while preserving its original appearance. This work successfully addresses this need by introducing a simple, robust, and scalable two-step coating strategy that endows leather surfaces with excellent hydrophobic and self-cleaning properties. The process relies on a straightforward spray application of functionalized silica nanoparticles followed by a hydrophobic silane, namely hexadecyltrimethoxysilane (HDTMS), enabling precise control over surface properties through the number of applied layers. Comprehensive characterization by Fourier transform infrared spectroscopy (FTIR) and scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM–EDS) confirmed the effective formation and uniformity of the coating. Performance testing demonstrated excellent functional outcomes: the optimized coating achieved a water contact angle (WCA) of 128° and maintained values above 125° even after abrasion, highlighting its durability. Treated leather exhibited resistance to common liquid stains such as tea and coffee, maintaining a clean surface. These functional gains were achieved without compromising the leather’s natural look or soft feel, even after multiple coating cycles. This work delivers a fluorine-free solution offering an effective route to high-value water- and stain-resistant leather finishes that respect both environmental and aesthetic requirements. Full article

Ribulose-1,5-bisphosphate carboxylase/oxygenase (Rubisco) efficiency constrains carbon fixation, making it a high-value target in biotechnology. The core task of this work is a supervised regression and ranking problem on Rubisco: given a numerical representation of a protein sequence (a PLM embedding), we predict continuous phenotypic scores such as an enzyme kinetic proxy or fitness value. The predictions then guide which variants to test next. Engineering Rubisco is a point of focus but remains challenging due to selection forces in vivo and the combinatorial space of potential mutants for ex vivo uses. We combine protein language model (PLM) embeddings with tabular learning to model Rubisco variant landscapes in two regimes. First, we analyze deep mutational scanning data providing inferred kinetic proxies, including K_m for CO₂ and V_max. Second, we model a cyanobacterial screening dataset measuring mutant fitness under differing oxygen and nitrogen regimes, enabling an oxygen tolerance objective. Across tasks, a tabular foundation (TabPFN-2.5) model outperforms gradient-boosted trees on rank-based criteria for variant prioritization, including Spearman correlation and top 5% hit recovery. We then simulate active-learning campaigns initialized with 200 measured variants and iteratively acquiring batches of 48. Model-guided selection recovers more top-performing mutants than random sampling at fixed experimental budgets, even with a conservative XGBoost surrogate. We also demonstrate that Rubisco large-subunit embeddings predict cyanobacterial doubling time and cross-species kinetic parameters, suggesting that Rubisco representation remains meaningful across organisms even with multi-objective cellular constraints. Together, these results support a practical, data-efficient workflow for enzyme engineering and motivate objective-aware design strategies that complement directed evolution. Full article

Carbon fiber-reinforced polymers (CFRP) are widely employed in advanced manufacturing sectors such as aerospace, wind energy, and new energy vehicles owing to their high specific strength and stiffness. The growing demand for lightweight, high-performance, and multifunctional materials has accelerated the development of structurally and functionally integrated CFRP. Introducing functional interlayers between composite laminates is an effective strategy to impart additional functionalities; however, such interlayers are often multi-component and structurally complex. A critical challenge remains to integrate functionality without compromising, and preferably enhancing, the load-bearing capability of CFRP, particularly their resistance to interlaminar delamination. In this study, electrically heated CFRP incorporating a sandwich-structured interlayer composed of glass fiber mesh fabric/CNT veils doped with carbon nanotubes/glass fiber mesh fabric (GF/CNTs-CNTv/GF) was investigated. The effects of interlayer architecture and CNT loading on the Mode II interlaminar fracture toughness were systematically examined. Delamination failure modes and interlaminar toughening mechanisms were analyzed using scanning electron microscopy and ultra-depth-of-field three-dimensional microscopy. The results demonstrate that an optimal CNT pre-impregnation concentration of 1.0 wt% yielded a maximum G_IIC of 1644.8 J/m², corresponding to a 103.06% increase relative to the reference laminate. The enhanced performance is attributed to simultaneous optimization of interfacial “nano-engineering” effects, including matrix toughening and a pronounced “nano-anchoring” mechanism induced by CNT. These effects promote a transition in failure mode from weak interfacial debonding to a mesh-block composite delamination pattern, thereby activating multiple energy-dissipation mechanisms such as crack deflection, fiber pull-out, rupture, and bridging. This work highlights the effectiveness of CNT-modified sandwich interlayers in improving delamination resistance and provides both theoretical insight and experimental validation for the design of multifunctional CFRP with superior interlaminar fracture toughness. Full article

Background: Mechanical subgingival instrumentation remains the gold standard in periodontitis treatment; however, it may leave residual debris and induce surface alterations. Adjunctive strategies such as air polishing and ethylenediaminetetraacetic acid (EDTA) might optimize root surface conditions. Objective: To evaluate, by scanning electron microscopy (SEM), the effects of scaling and root planing (SRP) combined with EDTA, with or without adjunctive erythritol- or glycine-based air polishing, on root surface alterations and smear layer formation. Materials and Methods: Ten extracted human teeth affected by periodontitis (yielding twenty samples) were included. Two teeth served as descriptive controls. The remaining teeth were allocated to four treatment groups. The first three groups included samples obtained from the middle portion of the roots: S (SRP + EDTA), Se (SRP + erythritol air polishing + EDTA), and Sg (SRP + glycine air polishing + EDTA). The fourth group, Js, consisted of samples from the cementoenamel junction (CEJ) treated with SRP + EDTA. SEM images were appreciated qualitatively and assessed using ordinal scores (0–3) for marks, cracks (×100), and smear layer (×1000). Non-parametric statistics were applied. Results: A significant difference in mark scores was found among S, Se, and Sg samples (H = 13.411, p = 0.001), with Se samples showing lower mark scores than S (p = 0.001). Crack scores also differed among groups (H = 12.038, p = 0.002), with higher values observed in Se compared to S (p = 0.001). Smear layer scores did not differ among groups (H = 0.102, p = 0.950). Compared with S samples, Js differed only in marks (p = 0.009), with no significant differences in cracks or smear layer. Conclusions: Within the limitations of this in vitro study, root surface alterations and smear layer formation showed variable responses across treatment protocols, with comparable smear layer scores. Similar effects were observed for CEJ and mid-root samples; however, these findings should be interpreted with caution. Further studies are needed to clarify the potential clinical relevance of these observations. Full article