Search Results (7,094)

Warm forming of heat-treatable aluminium alloys can induce significant changes in their initial heat treatment, affecting both the forming process and the final in-service properties. This work aims to systematically investigate the influence of heat-holding time on the thermo-mechanical behaviour and post-forming properties of Al–Mg–Si alloys (EN AW 6016-T4 and EN AW 6061-T6), with a focus on optimizing process parameters to enhance formability and minimize springback. The study combines uniaxial tensile tests, cylindrical cup forming, hardness measurements, and springback evaluation, at room temperature (RT) and 200 °C, for different heat-holding times. The results show that short heat-holding times improve formability and reduce springback, while longer times promote artificial ageing, increasing strength and hardness but reducing ductility, especially in the EN AW 6016-T4 alloy. The EN AW 6061-T6 alloy exhibits greater thermal stability. The findings provide practical guidelines for industrial warm forming of Al–Mg–Si alloys, highlighting the critical role of heat-holding time in balancing formability, strength, and dimensional accuracy. Full article

Unmanned aerial vehicle Synthetic Aperture Radar (UAV SAR) is inevitably affected by hardware performance and complex electromagnetic environments, resulting in noise in the radar echo signal. This causes image blurring and loss of detail, severely limiting the detection performance and imaging quality of UAV SAR. High-repetition-rate UAV SAR can achieve high signal-to-noise ratio (SNR), but the SAR data volume grows exponentially, posing a challenge for large-scale data processing. Furthermore, in the case of high repetition rate, downsampling methods are needed to reduce the amount of raw data, which leads to a decrease in the echo SNR, thus significantly affecting SAR image details. Existing SAR signal processing methods typically involve a series of processing steps on the raw echo data, such as azimuth and range direction processing. However, these traditional methods still have limitations in improving the SNR, especially in complex environments or when the target signal is weak, where their effectiveness is often unsatisfactory. To address these issues, this paper first analyzes the SNR gain in SAR echo data processing and proposes a phase-compensated parameter-adjusted Chebyshev filtering algorithm to improve the SNR of SAR echoes. The algorithm first utilizes azimuth Chebyshev filtering to avoid spectral aliasing during downsampling and fully leverages navigation information provided by the airborne platform to accurately compensate for phase changes between pulses. Then, it employs parameter-adjusted Chebyshev filtering and coherent superposition techniques to combine multiple adjacent pulses into a single pulse with a higher SNR. Finally, the enhanced pulses are combined into a new two-dimensional matrix for subsequent pulse compression and imaging processing. This method can improve the echo SNR while reducing the amount of echo data, minimizing the loss of the original echo SNR and reducing the memory footprint of subsequent imaging processing, thus effectively improving data processing efficiency. The effectiveness of the algorithm is verified through simulation and actual measurement data. Full article

Alkali-activated materials can significantly reduce carbon dioxide emissions compared with cement. However, their durability remains insufficiently understood. This study investigated the effects of calcium hydroxide (Ca(OH)₂, CH), an expansion agent (calcium sulfoaluminate, CSA), and a shrinkage-reducing agent (SRA) on the compressive strength and length change and determined the optimal content levels for each agent. Experiments were conducted to evaluate the compressive strength and length change of 17 mortar mixtures containing CH, CSA, and SRA. The substitution ratios of CH, CSA, and SRA were fixed at three predefined levels for each factor. The microstructural changes induced by the use of each agent were analyzed using pH measurements, porosity analysis, and X-ray diffraction. In addition, the water desorption behaviors associated with CSA and SRA were assessed. Experimental and statistical analyses demonstrated that the optimal contents of CH, CSA, and SRA for simultaneously improving the compressive strength and length change were 8.54, 10.0, and 0.76 wt.%, respectively. The use of CSA significantly enhanced the compressive strength development and dimensional stability of the mortar. This improvement was associated with a reduction in the porosity, which was attributed to ettringite formation. Furthermore, while the SRA slightly reduced the compressive strength, it significantly improved the dimensional stability. Full article

Solid-state nanopore sequencing, a key third-generation sequencing technology, offers considerable potential for genomics and diagnostics due to its long read lengths, real-time detection, and amplification-free operation. The technology identifies DNA sequences by measuring characteristic changes in ionic current as single-stranded DNA translocates through a nanoscale pore. However, its practical development faces challenges including limited spatiotemporal resolution, pore clogging from nonspecific adsorption, and significant electrical noise. This review systematically examines strategies developed to address these limitations. We discuss the use of ultrathin two-dimensional materials such as graphene and molybdenum disulfide to improve spatial resolution, and methods to modulate DNA translocation through optimized solution conditions, pore geometry, surface charge engineering, and bio-solid hybrid pore designs. Furthermore, we detail noise suppression strategies targeting key sources like thermal noise, 1/f noise, and dielectric noise. These approaches encompass careful material selection, surface coatings, innovations in chip and amplifier design, and machine learning–based signal processing. The review also outlines surface functionalization techniques that reduce clogging and enhance analytical specificity. While challenges remain, continued convergence of materials science, nanofabrication, and data science is advancing solid-state nanopore technology toward reliable, high-precision sequencing platforms, promising to significantly impact personalized medicine and biological research. Full article

Ensuring efficient nutrient delivery and waste removal within the interior of three-dimensional (3D) cultures remains a major challenge in tissue engineering. Here, we demonstrate a proof-of-concept methodology that creates internally distributed driving sources to enhance diffusion and perfusion within 3D constructs. Iron microparticles or iron-containing microtubes were incorporated into collagen gels used for the 3D culture of dermal or cardiac fibroblasts, and cyclic dynamic magnetic fields were applied to the constructs. Oscillatory motion of the iron particles enhanced diffusion within the gels, as evidenced by increases in the fast diffusion coefficient of more than threefold and the slow diffusion coefficient of more than tenfold under conditions suitable for cell culture. In cardiac fibroblast cultures, this enhancement significantly increased proliferation by approximately twofold and reduced cytotoxicity by half compared with controls. In contrast, no significant effects were observed in dermal fibroblast cultures. Cyclic compression of microtubes within the collagen gels induced by dynamic magnetic fields primarily resulted in cellular morphological changes, including a reduction in cell area to approximately 0.8-fold of the control values, increased cell polarization with the cellular aspect ratio rising from 1.4 to 1.9, and preferred cell orientations either parallel or perpendicular to the microtube axis. Together, these results suggest that this methodology has the potential to be developed as an effective strategy for improving diffusivity in 3D metabolic environments and for promoting angiogenesis in hydrogel-based cultures. Full article

For decades, zirconia- and ceramic-based materials have dominated dental crown fabrication due to their durability and aesthetic appeal. However, a fundamental shift is occurring as polymeric alternatives emerge with notable advantages: better adhesive bonding, versatile aesthetics, lower costs, and a lighter weight. The advances in polymer chemistry and additive manufacturing have significantly impacted prosthodontics, allowing the rapid creation of highly customized, patient-specific restorations with a precision previously impossible (achieved through advanced Computer-Aided Design software and standardized 3D-printing equipment) with traditional methods. This review provides a detailed analysis of 3D-printed polymeric dental crowns from various angles. It explores the materials science behind different polymers, compares manufacturing methods, and evaluates the mechanical performance and biocompatibility. Despite the progress, polymeric materials still fall short of matching the mechanical properties of advanced ceramics, especially in compressive strength and wear resistance. Moreover, there is limited long-term clinical data over five to ten years. The lack of standardized testing protocols complicates cross-study comparisons, and the regulatory pathways for patient-specific 3D-printed devices are still developing, creating uncertainty for manufacturers and clinicians. The future prospective looks promising in many ways such as innovations like four-dimensional printing, where materials respond dynamically to environmental stimuli, which could enable crowns that adapt to changing oral conditions. Nanocomposites with functionalized nanoparticles might enhance mechanical properties while maintaining printability. AI-driven design optimization could automate and improve the crown morphology, occlusal contacts, and fit. Incorporating bioactive materials could turn crowns into active therapeutic devices that promote remineralization and combat bacterial colonization. This review summarizes the current knowledge, highlights the key gaps, and suggests steps toward establishing polymeric 3D-printed crowns as viable long-term alternatives capable of competing with or surpassing traditional ceramic options. Full article

Background/Objectives: Calcaneal spurs are pathological bone formations at entheseal attachment sites with clinical implications but limited forensic anthropological applications. While entheseal changes have been proposed as age estimation markers in forensic contexts, empirical validation remains insufficient, particularly for Southeast Asian populations. This study evaluated calcaneal spur utility for forensic age estimation in Thai skeletal remains while establishing population-specific osteological reference data for forensic individuation. Materials and Methods: The 3516 dry calcanei from 1758 Northeastern Thai skeletons (1031 males, 727 females; age 22–106 years) were examined. Spurs were classified by anatomical location as dorsal (D-type), plantar (P-type), or combined plantar–dorsal (P–D type). The morphometric measurements were performed bilaterally. Age-associated patterns were analyzed across four age cohorts (≤40, 41–50, 51–60, ≥61 years), and Random Forest machine learning classification tested forensic age estimation capacity using 10-fold cross-validation. Results: Overall prevalence reached 67.63% with distinctive P–D type predominance. While age-stratified prevalence increased from 24.56% (≤40 years) to 74.77% (≥61 years), Random Forest modeling explicitly demonstrated overall classification accuracy of 62.5%. Compared between sexes, the maximum length of calcaneal spurs was significantly longer in males. Dimensional analyses revealed weak age correlations and substantial inter-individual morphological variation precluded reliable age prediction. Interestingly, the unique P–D type distribution pattern (77.5% among spur-bearing individuals) may serve as an auxiliary marker for Thai population affinity assessment in forensic contexts. Conclusions: This study established the first comprehensive Thai-specific osteological reference for calcaneal spurs, revealing distinctive plantar–dorsal type predominance valuable for forensic population affinity assessment and provided population-specific baseline data for forensic individuation. Full article

Background/Objectives: We evaluated a 6-day repeated resveratrol exposure regimen in a three-dimensional (3D) culture model of human renal cell carcinoma (Caki-1) spheroids to examine phenotypic responses and changes in CIP2A abundance and epithelial–mesenchymal transition (EMT)-associated marker expression. Methods: Over 6 days, we assessed morphology and 2D cell viability and quantified CIP2A, fibronectin, and α-SMA by immunoblotting and immunofluorescence. Results: Resveratrol reduced 2D viability and increased cytoplasmic vacuoles, consistent with a stress-associated morphological response. In 3D spheroids, resveratrol treatment was associated with reduced CIP2A protein levels and decreased fibronectin and α-SMA, consistent with attenuation of a mesenchymal marker profile. Conclusions: These proof-of-concept data link 6-day resveratrol exposure to CIP2A reduction and decreased mesenchymal marker expression in a human 3D RCC spheroid system; however, PP2A activity and downstream signaling, AMPK/SIRT1 activation, and EMT-relevant functional assays were not assessed, and validation across additional RCC models will be required. Full article

Byzantine fault tolerance (BFT) protocols serve as the cornerstone of data consistency in permissioned blockchains; however, their scalability is inherently constrained by stochastic leader-centric bottlenecks and rigid, non-adaptive timeout mechanisms. Existing rule-based heuristics often fail to capture high-entropy and time-varying network latency, leading to frequent view changes and severe performance degradation under network volatility. To mitigate this epistemic uncertainty, this paper proposes TimesNet-BFT, a novel entropy-aware optimization framework. By leveraging TimesNet’s transformation of one-dimensional time series into two-dimensional tensors for multi-periodicity analysis, the framework accurately characterizes stochastic nodal latency patterns to facilitate entropy-minimized dynamic leader election and adaptive timeout strategies. Extensive evaluations conducted on simulated and real-world trace-driven Internet of Vehicles (IoV) scenarios validate the proposed approach, achieving a prediction MAPE below 5% alongside robust zero-shot generalization. Notably, under high-entropy network conditions, the framework demonstrates up to a 191.9% increase in throughput and mitigates latency variance by 73.3%, effectively neutralizing the structural bottlenecks inherent to traditional information-agnostic protocols. Crucially, by mathematically decoupling consensus safety from AI prediction errors, the system introduces an aggressive liveness paradigm that maintains minimal control plane overhead while significantly enhancing the entropic stability of the consensus process. Full article

Urban environments face increasing thermal stress from climate change and the Urban Heat Island effect, with significant implications for livability, public health, and energy sustainability. Outdoor thermal comfort is defined as the state in which conditions are perceived as acceptable, depends on interactions among meteorological, morphological, physiological, and behavioral factors. This review synthesizes the application of machine learning (ML) to outdoor thermal comfort assessment into a practice-oriented taxonomy. Research spans diverse climates and urban forms, using inputs across environmental and human domains. Supervised learning dominates. Regression approaches (linear regression, support vector regression, random forest, gradient boosting) and classification algorithms (decision trees, support vector machines, K-nearest neighbors, Naïve Bayes, random forest classifiers) are widely used to predict thermal indices such as the Physiological Equivalent Temperature and Universal Thermal Climate Index, or to classify subjective responses including thermal sensation, comfort, and acceptability. Unsupervised learning (clustering, principal component analysis) supports identification of microclimatic zones and perceptual clusters, while deep learning (multilayer perceptrons, convolutional and recurrent neural networks, generative adversarial networks) achieves superior accuracy for complex, high-dimensional, and spatiotemporal data. Algorithms such as random forests, support vector machines, and gradient boosting consistently show strong performance for both indices and subjective responses when integrating multi-domain inputs. Semi-supervised and reinforcement learning remain underexplored but offer promise for leveraging large-scale sensor data and enabling adaptive, real-time comfort management. The review concludes with a roadmap emphasizing explainable artificial intelligence, scalable surrogate modeling, and integration with simulation-based optimization and parametric design tools. Full article

Background: Burn assessment traditionally relies on visual inspection and 2D estimation, which introduces substantial variability in determining wound size and healing progression. Three-dimensional (3D) surface scanning offers a more objective alternative, yet the clinical utility of area-based metrics obtained from 3D surface data remains insufficiently defined. This pilot study aimed to evaluate structured-light 3D scanning for objective longitudinal quantification of the burn wound surface area and a description of area-based healing dynamics derived from repeated measurements. Methods: Eighteen patients with 43 acute thermal burns underwent serial structured-light scanning, followed by manual segmentation of wound regions and the calculation of absolute and percentage area reduction as well as TBSA-normalized metrics. Longitudinal monitoring was performed by comparing sequential 3D surface models acquired at defined clinical follow-ups, enabling the calculation of absolute area change (ΔA), percentage reduction, daily healing rate, and ΔTBSA%. Results: Baseline wound areas ranged from 7.27 to 2137.98 cm². Percentage area reduction ranged from 5.25% to 92.30%. The overall reduction in burn burden (ΔTBSA) ranged from 0.07% to 12.94%. Large wounds tended to show rapid absolute area reduction (>100–300 cm²/day) during early follow-up, while small superficial burns frequently achieved >80% reduction within 10–15 days. Conclusions: These findings suggest that 3D surface scanning may support the objective longitudinal assessment of burn wound healing. This pilot provides a basis for future studies evaluating additional topographic parameters and broader clinical applications. Full article

Background/Objectives: Lower extremity peripheral artery disease (PAD) is recognized as a significant public health issue, particularly due to its strong association with adverse cardiovascular events. Despite this, little attention has been given to its influence on left ventricular (LV) and left atrial (LA) function in patients with chronic heart failure (CHF). This study aims to examine the relationship between femoral plaque burden and structural and functional properties of the LV and LA in patients with CHF. Methods: Study design: cross-sectional observational single-center study. A total of 89 patients with CHF underwent comprehensive assessments, including duplex ultrasonography of lower extremity arteries and two-dimensional echocardiography. Analysis focused on evaluating femoral plaque burden, left ventricular deformation, and ventricular–arterial coupling. Results: Findings indicated that increased femoral plaque burden was associated with reductions in LA deformation and increases in LA stiffness. Similarly, there was evidence of impaired LV mechanics and elevated arterial loading, suggesting impaired ventricular–arterial coupling in patients with CHF and significant lower extremity atherosclerosis. Conclusions: Femoral plaque burden is closely linked to detrimental changes in LA and LV function, as well as disturbances in ventricular–arterial coupling, underscoring the importance of addressing lower extremity atherosclerosis in managing CHF patients. Full article

This paper deals with several equations of mathematical physics written in explicit form with their solutions. In Theorem 1, an oblique derivative problem for the string equation is studied. More precisely, the initial-boundary value problem for the string equation is investigated. The corresponding vector field on the boundary is non-vanishing and does not have a characteristic direction, but can be tangential to some part of the boundary, and it is allowed to change sign. A classical solution exists with suitable compatibility conditions at the corner points. The picture changes significantly in the case of the wave equation with several (say two: 2D) space variables in a circular cylinder. The initial-boundary value problem turns out to be underdetermined with an infinite-dimensional kernel if the boundary vector field is orthogonal to the time axis. By prescribing extra conditions on the generatrices of the cylinder where the vector field is tangential to the cylinder, we obtain a unique classical solution. In Theorem 2, we consider the Cauchy problem in the interior of the parabola of the Lorentzian-type eikonal equation and find its unique classical solution in $\{0\le x_2\le 1/2\}\cap \{x_2\ge {\displaystyle \frac{x_1^2}{2}}\}$. Propagation of singularities for the D and 3 D hyperbolic (Klein–Gordon) equations in ${\mathbf{R}}^4$, ${\mathbf{R}}^8$ is studied in Theorem 3. In the double characteristic points, the wave front propagates either along the surface of the characteristic cone, or in the solid cone starting from $(t_0,x_0)$. Full article

Single-phase ground faults account for more than 80% of total faults in distribution networks. However, the introduction of distributed generation complicates power grid topology, leading to strong nonlinearity and non-stationarity in the zero-sequence current. This limits the accuracy of traditional faulted line selection methods. To address this problem, a CNN–ResNet-based method for faulted line selection for single-phase ground faults in distribution networks is proposed. Firstly, a 10 kV arc ground fault simulation test platform is built to analyze the nonlinear distortion characteristics of fault current. The WOA–VMD algorithm, optimized by permutation entropy, is used to denoise the zero-sequence current signal. The Gram Angular Difference Field (GADF) is then adopted to convert the one-dimensional signal into a two-dimensional image that retains its temporal characteristics. A hybrid deep learning model is constructed by fusing the one-dimensional time-domain features extracted by CNN and the two-dimensional time-frequency image features extracted by ResNet34. Matlab/Simulink simulations and physical experimental verification demonstrate that the proposed method achieves a training accuracy of over 97%, with zero misjudgments recorded in 15 arc grounding fault tests, representing a significant improvement in accuracy compared with existing diagnostic algorithms. It can adapt to complex scenarios such as high-resistance grounding and changes in neutral point grounding mode, effectively improving the accuracy and robustness of faulted line selection and providing technical support for the safe operation of distribution networks. Full article

Developing free-standing electrodes without the need of metal current collectors, binders, and conductive additives are essential for promoting the development of sodium-ion batteries (SIBs) to attain higher energy density. In this study, we developed and effectively synthesized a novel three-dimensional free-standing sodium-ion battery anode material with the composition of Bi@MoS₂@C carbon nanofibers by cleverly utilizing the energy storage advantages of each material. By growing MoS₂ nanospheres on Bi carbon nanofibers and coating them with a carbon layer, this free-standing system achieves both structural optimization and synergistic performance enhancement. Experimental results show that this composite electrode has a remarkably high initial specific capacity of 275.31 mA h g⁻¹ at a current density of 0.5 A g⁻¹, significantly exceeding that of Bi carbon nanofibers (150.6 mA h g⁻¹). Furthermore, it retains a capacity retention of 96.07% after 800 cycles, which significantly exceeds that of pristine MoS₂ (72.33 mA h g⁻¹) as a sodium-ion battery anode. The significant performance improvement originates from the free-standing structural design and synergistic effects of Bi carbon nanofibers, MoS₂ nanospheres and carbon layer, which not only provide 3D electron transport pathways and improved conductivity but also effectively accommodate volume changes during the charging and discharging processes. This work offers a promising and practical strategy for designing high-performance free-standing energy storage electrodes through hybrid mechanisms and synergistic effects. Full article