Search Results (271)

A sustainable pathway for converting low-value solid waste (Coal gangue, CG) into high-performance thermal insulation materials through a green synthesis strategy has been demonstrated. The SiO₂ was successfully and efficiently extracted from CG in the form of sodium silicate. The subsequent sol–gel process of sodium silicate solution utilized an innovative CO₂ carbonation method, which replaced the conventional use of strong acids, thereby reducing the carbon footprint and enhancing process safety. Hydrophobic SiO₂ aerogel was subsequently prepared via ambient pressure drying, exhibiting a high specific surface area of 750.4 m²/g, a narrow pore size distribution ranging from 2 to 15 nm and a low thermal conductivity of 0.022 W·m⁻¹·K⁻¹. Furthermore, the powdered aerogel was shaped into a monolithic form using a simple molding technique, which conferred appreciable compressibility and resilience, maintaining the low thermal conductivity and hydrophobicity of the original aerogels, ensuring its functional integrity for practical applications. Practical thermal management tests including low and high temperature, conclusively demonstrated the superior performance of the prepared aerogel material. This work presents a viable and efficient waste-to-resource pathway for producing high-performance thermal insulation materials. Full article

In this study, we present the design and analysis of a stacked inverter-based millimeter-wave (mmWave) power amplifier (PA) in 90 nm CMOS-targeting wideband Q-band operation. The PA employs two PMOS and two NMOS devices in a fully stacked inverter topology to distribute device stress, remove the need for an RF choke, and increase effective transconductance while preserving compact layout. A resistor ladder biases the stack near $V_{\mathrm{DD}}/4$ per device, and capacitive division steers intermediate-node swings to enable class-E-like voltage shaping at the output. Closed-form models are developed for gain, output power, drain efficiency/PAE, and linearity, alongside a small-signal stacked-ladder formulation that quantifies stress sharing and the impedance presented to the matching networks; L/T network synthesis relations are provided to co-optimize bandwidth and insertion loss. Post-layout simulation in 90 nm CMOS shows $|S_{21}|$ = 10 dB at 39.84 GHz with 3 dB bandwidth from 36.8 to 42.4 GHz, peak PAE of 18.38% near 41 GHz, and saturated output power $P_{\mathrm{sat}}=8.67$ dBm at $V_{\mathrm{DD}}=4$ V, with $S_{11}<-15$ dB and reverse isolation $\approx -16$ dB. The layout occupies $1.6\times 1.6$ mm² and draws 31.08 mW. Robustness is validated via a 200-run Monte Carlo showing tight clustering of $P_{\mathrm{sat}}$ and PAE, sensitivity sweeps identifying sizing/tolerance trade-offs ($\pm 10\%$ devices/passives), and EM co-simulation of on-chip passives indicating only minor loss/shift relative to schematic while preserving the target bandwidth and efficiency. The results demonstrate a balanced gain–efficiency–power trade-off with layout-aware resilience, positioning stacked-inverter CMOS PAs as a power- and area-efficient solution for mmWave front-ends. Full article

The very flexible Weibull (VF-W) distribution is formulated by expressing its cumulative risk function as a logarithmic composite of auxiliary cumulative risks, making the model particularly well-suited for modeling heterogeneous life behaviors. This model admits a remarkably flexible hazard structure, capable of generating monotone increasing, unimodal (increase-then-decrease), and multi-turning-point shapes, thereby capturing complex failure behaviors far beyond those allowed by the classical Weibull distribution. This paper presents a comprehensive inferential study of the VF-W model through the unified progressive hybrid (UPH) censoring framework for modeling shock-type lifetime data. The UPH scheme integrates the advantages of Type-II, generalized hybrid, and progressive hybrid censoring mechanisms into a unified structure that ensures efficiency and adaptability in reliability testing. Classical inference is developed through maximum likelihood estimation with asymptotic interval construction, while Bayesian inference is performed using independent gamma priors and a Markov iterative algorithm. Extensive Monte Carlo experiments are conducted to evaluate the finite-sample performance of both approaches under various censoring intensities, revealing that the Bayesian MCMC-based estimators and their highest posterior density intervals provide superior precision, coverage, and robustness. The proposed VF-W model using UPH-based strategy is further validated through the analysis of a real shocks dataset, where it demonstrates a comparative performance improvement over existing models. The VF-W model exhibits stable parameter estimation under diverse censoring levels, indicating robustness in incomplete-data scenarios. Furthermore, the model maintains analytical tractability, offering closed-form expressions for key reliability measures, which facilitates practical implementation in different scenarios. The results confirm the VFW model’s strong potential as a unifying and computationally stable tool for reliability modeling, particularly in complex engineering and physical systems operating under stochastic shock environments. Full article

This article analyzes the construction of the Digital Extended Self of digital influencers from the travel channel Travel Channel, drawing on R. W. Belk’s theory. The study employs a qualitative exploratory–descriptive approach, using a case study as its methodological strategy. Data collection involved analyzing five podcast interviews with the channel’s founders, along with videos published between 2022 and 2024. In addition, viewer comments on these videos were extracted and examined. All materials were analyzed using Bardin’s content analysis. The results reveal a strong presence of the Extended Self dimensions, co-construction, and sharing, showing that interaction with the audience actively shapes the influencers’ identity and content. The dimensions of dematerialization (e.g., cloud storage) and distributed memory (the use of digital records as extensions of memory) were also evident. Reincarnation (the use of avatars or personas) was the least observed dimension, a finding attributed to the influencers’ authentic style and focus on real-life experiences. Overall, the Digital Extended Self of the Travel Channel emerges as a genuine and organically constructed entity, resulting in an aggregated Self that reflects a strong connection with its audience. This research provides valuable insights into how Belk’s theory can be applied to the in-depth analysis of digital materials. Full article

Blue-green space patterns and meteorological conditions jointly influence PM_2.5 concentrations. However, the nonlinear mechanisms and interactions among these key drivers remain insufficiently studied. To address this gap, this study applied an interpretable machine learning approach (XGBoost-SHAP) to detect seasonal nonlinearities, thresholds, and interaction effects of meteorological and landscape metrics on PM_2.5 distribution in Jiangsu Province, China. Structural Equation Model was further employed to quantify the direct and indirect effect pathways among these factors. Model explanatory power showed distinct seasonal variations, with the highest performance in summer (R² = 0.615) and the lowest in winter (R² = 0.316). Meteorological factors exerted stronger influences than blue-green space pattern metrics, with wind speed being the most critical meteorological factor across all seasons. Among landscape metrics, the proportion of green space and water body (G_PLAND and W_PLAND) was the key driver of PM_2.5 concentrations in spring, autumn, and winter, while its influence became insignificant in summer, replaced by the number and shape complexity of green space patches. This study further revealed that in spring, autumn, and winter, G_PLAND and W_PLAND not only exerted direct effects on PM_2.5 but also significantly influenced it indirectly by modulating land surface temperature. Additionally, green space shape complexity and land surface temperature were found to interact with other meteorological and landscape factors during these seasons; once exceeding specific thresholds, they reversed the direction of other factors’ effects on PM_2.5. No significant interactions were detected in summer, indicating that dominant factors primarily exerted independent effects during this season. Collectively, our findings provide important insights for formulating seasonally adaptive planning strategies to advance sustainable urban development and long-term air quality management. Full article

Long-endurance hypersonic vehicles face the dual challenge of withstanding extreme aerodynamic heating while meeting onboard power requirements. Integrating thermoelectric generators within thermal protection systems offers a solution by converting thermal loads into electrical power. However, accurate prediction requires resolving coupled multiphysics, where three-dimensional simulations are computationally prohibitive and existing one-dimensional models lack accuracy. This study develops a quasi-two-dimensional distributed thermal network incorporating shape-factor corrections for rapid, high-fidelity prediction. Multi-objective optimization is performed to balance specific power, thermal expansion mismatch, and thermal margin. Analysis reveals fundamental trade-offs: a maximum-power design achieves 28.1 W/kg but only a 0.8% thermal margin, whereas a balanced design delivers 24.5 W/kg with a 5.1% thermal margin and significantly reduced thermal stress. Despite geometric variations, peak conversion efficiency converges to approximately 13%. This indicates that efficiency is primarily governed by material properties, while geometric optimization effectively tunes temperature and thermal strain distributions, providing guidelines for reliable system development. Full article

Energy shortage is a significant global concern due to the heavy reliance of industrial and residential sectors on energy. As fossil fuels diminish, there is a pressing shift towards alternative energy sources such as solar and wind. However, the intermittent nature of these renewable resources, such as the absence of solar energy at night, necessitates robust energy storage solutions. This study focuses on enhancing the performance of a thermal storage unit by employing multiple fin configuration with solar salt (NaNO₃-KNO₃) as a phase change material (PCM) and Duratherm 630 as a heat transfer fluid (HTF). Notably, W-shaped and trapezoidal fins achieved reductions in melting time from 162 min to 84 min and 97 min, respectively, while rectangular fins were the least effective, albeit still reducing melting time to 143 min. Reduction in thermal gradients due to well-developed thermal mixing significantly reduced phase transition duration. Impact of fins geometries on localized vortexes generation within the unit was identified. W-shaped and trapezoidal fins were notably efficacious because of greater heat transfer area and better heat distribution through conduction and convection. Full article

A mark–recapture experiment was conducted off Mazatlán in the entrance of the Gulf of California to evaluate the growth of juvenile spiny lobsters (Panulirus inflatus). A total of 40 post larvae of spiny lobster were captured, marked, and maintained in plastic cages for 180 days in seawater off Mazatlán, Sinaloa, Mexico (23°12′32.4″ N 106°25′33.2″ W). Carapace length (CL in mm) was measured. Growth was estimated using the Schnute model, which encompasses four primary cases. In this study, two additional variants equivalent to the von Bertalanffy and Logistic models were also incorporated. These six models were parametrized using normal and log-normal distributions of errors. The selection of the best distribution and best model was based on the Akaike information criterion (AIC). The AIC selected the normal distribution of error and sigmoid-shaped curve as best representative of the growth pattern of juvenile spiny lobster P. inflatus. By identifying the asymptotic sigmoidal curve as the best descriptor of juvenile growth, this study offers a reliable foundation for subsequent assessments of population dynamics, resource management, and aquaculture development of P. inflatus. Full article

Nickel–tungsten carbide (Ni/WC) multi-pass fused cladding layers with different cerium (IV) oxide (CeO₂) contents were applied to Cr12MoV cold work tool steel surfaces using the coaxial powder feeding method for laser cladding. Scanning electron microscopy, energy spectrum analysis, X-ray diffraction, and wear experiments were conducted to study how adding CeO₂ to change the properties of WC-reinforced Ni-base composite coatings in turn alters the microstructure and properties of Cr12MoV cold work tool steel. The results show that laser cladding is effective when the process parameters are as follows: a power of 1500 W, a 24 mm defocusing distance, a 6 mm/s scanning speed, a 5 mm spot diameter, and a powder delivery of 0.1 g/s. Laser-fused cladding coatings are mainly composed of dendrites, crystalline cells, strips, and bulk microstructures. The addition of CeO₂ is effective at improving the microstructure and morphology of the coating—the size and distribution of the reinforcing phase change very significantly, and the shape changes from irregular and lumpy to spherical. With a 2% CeO₂ content, the enhanced phase, now spherical and white, is more diffusely distributed in the tissue. The maximum microhardness of the composite-coated specimen after the addition of CeO₂ is about 986 HV, which is approximately 20% higher than the hardness of the composite coating with no CeO₂ added. Full article

This study investigates date palm fiber (DPF) and sheep wool hybrid polyester composites with fiber loadings of 0%, 10%, 20%, and 30% by weight, fabricated by compression molding, to develop a sustainable and reliable material system. Experimental data from prior work were modeled using Weibull analysis for reliability evaluation and response surface methodology (RSM) for multi-objective optimization. Weibull statistics fitted a two-parameter distribution to tensile strength and fracture toughness, extracting shape (η) and scale ($\beta$) parameters to quantify variability and failure probability. The analysis showed that 20% hybrid content achieved the highest scale values ($\beta$ = 28.85 MPa for tensile strength and $\beta$ = 15.03 $\mathrm{M}\mathrm{P}\mathrm{a}\sqrt{\mathrm{m}}$ for fracture toughness) and comparatively low scatter (η = 10.39 and 9.2, respectively), indicating superior reliability. RSM quadratic models were developed for tensile strength, fracture toughness, thermal conductivity, acoustic attenuation, and water absorption, and were combined using desirability functions. The RSM optimization was found at 18.97% fiber content with a desirability index of 0.673, predicting 25.89 MPa tensile strength, 14.23 $\mathrm{M}\mathrm{P}\mathrm{a}\sqrt{\mathrm{m}}$ fracture toughness, 0.08 W/m·K thermal conductivity, 20.49 dB acoustic attenuation, and 5.11% water absorption. Overlaying Weibull cumulative distribution functions with RSM desirability surfaces linked probabilistic reliability zones (90–95% survival) to the deterministic optimization peak. This integration establishes a unified framework for designing natural fiber composites by embedding reliability into multi-property optimization. Full article

Background: Visual impairment remains a critical public health challenge, and diabetic retinopathy (DR) is a leading cause of preventable blindness worldwide. Early stages of the disease are particularly difficult to identify, as lesions are subtle, expert review is time-consuming, and conventional diagnostic workflows remain subjective. Methods: To address these challenges, we propose a novel Pixel-Attention W-shaped (PAW-Net) deep learning framework that integrates a Lesion-Prior Cross Attention (LPCA) module with a W-shaped encoder–decoder architecture. The LPCA module enhances pixel-level representation of microaneurysms, hemorrhages, and exudates, while the dual-branch W-shaped design jointly performs lesion segmentation and disease severity grading in a single, clinically interpretable pass. The framework has been trained and validated using DDR and a preprocessed Messidor + EyePACS dataset, with APTOS-2019 reserved for external, out-of-distribution evaluation. Results: The proposed PAW-Net framework achieved robust performance across severity levels, with an accuracy of 98.65%, precision of 98.42%, recall (sensitivity) of 98.83%, specificity of 99.12%, F1-score of 98.61%, and a Dice coefficient of 98.61%. Comparative analyses demonstrate consistent improvements over contemporary architectures, particularly in accuracy and F1-score. Conclusions: The PAW-Net framework generates interpretable lesion overlays that facilitate rapid triage and follow-up, exhibits resilience under domain shift, and maintains an efficient computational footprint suitable for telemedicine and mobile deployment. Full article

Molybdenum-rhenium (Mo-Re) alloys, especially those with low Re content, have great potential in fabricating nuclear components. However, the extremely high melting point and high brittleness of Mo-Re alloys make them difficult to weld. In this study, laser welding was used to prepare single-pass remelted joint of Mo-5Re alloy with welding parameters of laser power 2800 W, welding speed 2 m·min⁻¹ and argon gas flow rate 20 L·min⁻¹. The microstructure of the remelted joint was investigated by the optical microscopy and the scanning electron microscopy. The microhardness distribution of the joint was analyzed. In addition, the temperature field, residual stress, and angular distortion of the joint were investigated by both numerical and experimental methods. The results show that columnar grains grew from the fusion boundary toward the center of the weld pool, and equiaxed grains formed in the central region of the fusion zone (FZ). In the heat-affected zone (HAZ), the grains transformed from initial elongated into equiaxed grains. The electron backscatter diffraction (EBSD) results revealed that high-angle grain boundaries (HAGBs) dominated in FZ. Oxide/carbide particles at grain boundaries and inside the grains can be inferred from contrast results. The average microhardness of FZ was 170 ± 5 (standard deviation) HV, which was approximately 80 HV lower than that of the base metal (250 ± 2 HV). Softening phenomenon was also observed in HAZ. The calculated weld pool shape showed high consistency with the experimental observation. The peak temperature (296 °C) of the simulated thermal cycling curve was ~8% higher than the measured value (275 °C). The residual stress calculation results indicated that FZ and its vicinity exhibited high levels of longitudinal tensile residual stresses. The simulated peak longitudinal residual stress (509 MPa) was ~30% higher than the measured value (393 MPa). Furthermore, both the simulation and experimental results demonstrated that the single-pass remelted joint of Mo-5Re alloy produced only minor angular distortion. The obtained results are very useful in understanding the basic phenomena and problems in laser welding of Mo alloys with low Re content. Full article

We propose a phase-aware complex-spectrogram autoencoder (AE) for preprocessing raw vibration signals of rotating electrical machines. The AE reconstructs normal components and separates fault components as residuals, guided by an input-phasor phase-orthogonality regularization that defines parallel/orthogonal residuals with respect to the local signal phase. We use a U-Net-based AE with a mask-bias head to refine local magnitude and phase. Decisions are based on residual features—magnitude/shape, frequency distribution, and projections onto the normal manifold. Using the AI Hub open dataset from field ventilation motors, we evaluate eight representative motor cases (2.2–5.5 kW: misalignment, unbalance, bearing fault, belt looseness). The preprocessing yielded clear residual patterns (low-frequency floor rise, resonance-band peaks, harmonic-neighbor spikes), and achieved an area under the receiver operating characteristic curve (ROC-AUC) = 0.998–1.000 across eight cases, with strong leave-one-file-out generalization and good calibration (expected calibration error (ECE) ≤ 0.023). The results indicate that learning to remove normal structure while enforcing phase consistency provides an unsupervised front-end that enhances fault evidence while preserving interpretability on field data. Full article

In the pursuit of zero-emission mobility, hydrogen represents a promising fuel for internal combustion engines. However, its low volumetric energy density poses challenges, especially for high-performance applications where compactness and lightweight design are crucial. This study investigates the feasibility of an innovative hydrogen-fueled two-stroke opposed-piston (2S-OP) engine, targeting a specific power of 130 kW/L and an indicated thermal efficiency above 40%. A detailed 3D-CFD analysis is conducted to evaluate mixture formation, combustion behavior, abnormal combustion and water injection as a mitigation strategy. Innovative ring-shaped multi-point injection systems with several designs are tested, demonstrating the impact of injector channels’ orientation on the final mixture distribution. The combustion analysis shows that a dual-spark configuration ensures faster combustion compared to a single-spark system, with a 27.5% reduction in 10% to 90% combustion duration. Pre-ignition is identified as the main limiting factor, strongly linked to mixture stratification and high temperatures. To suppress it, water injection is proposed. A 55% evaporation efficiency of the water mass injected lowers the in-cylinder temperature and delays pre-ignition onset. Overall, the study provides key design guidelines for future high-performance hydrogen-fueled 2S-OP engines. Full article

With the rise of 6G, the exponential growth of data traffic, the proliferation of emerging applications, and the ubiquity of smart devices, the demand for spectral resources is unprecedented. Terahertz communication (100 GHz–3 THz) plays a key role in alleviating spectrum scarcity through ultra-broadband transmission. In this study, terahertz optical carrier-based systems are employed, where fiber-optic components are used to generate the optical signals, and the signal is transmitted via direct detection in the receiver side, without relying on fiber-optic transmission. In these systems, deep learning-based equalization effectively compensates for nonlinear distortions, while probability shaping (PS) enhances system capacity under modulation constraints. However, the probability distribution of signals processed by PS varies with amplitude, making it challenging to extract useful information from the minority class, which in turn limits the effectiveness of nonlinear equalization. Furthermore, in IM-DD systems, optical multipath interference (MPI) noise introduces signal-dependent amplitude jitter after direct detection, degrading system performance. To address these challenges, we propose a lightweight neural network equalizer assisted by the Synthetic Minority Oversampling Technique (SMOTE) and a clustering method. Applying SMOTE prior to the equalizer mitigates training difficulties arising from class imbalance, while the low-complexity clustering algorithm after the equalizer identifies edge jitter levels for decision-making. This joint approach compensates for both nonlinear distortion and jitter-related decision errors. Based on this algorithm, we conducted a 3.75 Gbaud W-band PAM4 wireless transmission experiment over 300 m at Fudan University’s Handan campus, achieving a bit error rate of 1.32 × 10⁻³, which corresponds to a 70.7% improvement over conventional schemes. Compared to traditional equalizers, the proposed new equalizer reduces algorithm complexity by 70.6% and training sequence length by 33%, while achieving the same performance. These advantages highlight its significant potential for future optical carrier-based wireless communication systems. Full article