Search Results (3,003)

Surface roughness, critically measured by the Arithmetical Mean Roughness (Ra), is a vital determinant of workpiece functional performance. Traditional contact-based measurement methods are inefficient and unsuitable for online inspection. While machine vision offers a promising alternative, existing approaches lack robustness, and pure deep learning models suffer from poor interpretability. Therefore, MTFE-Net is proposed, which is a novel deep learning framework for surface roughness classification. The key innovation of MTFE-Net lies in its effective integration of traditional texture feature analysis with deep learning within a dual-branch architecture. The MTFE (Multi-dimensional Texture Feature Extraction) branch innovatively combines a comprehensive suite of texture descriptors including Gray-Level Co-occurrence Matrix (GLCM), gray-level difference statistic, first-order statistic, Tamura texture features, wavelet transform, and Local Binary Pattern (LBP). This multi-scale, multi-perspective feature extraction strategy overcomes the limitations of methods that focus on only specific texture aspects. These texture features are then refined using Multi-Head Self-Attention (MHA) mechanism and Mamba model. Experiments on a dataset of Q235 steel surfaces show that MTFE-Net achieves state-of-the-art performance with 95.23% accuracy, 94.89% precision, 94.67% recall and 94.74% F1-score, significantly outperforming comparable models. The results validate that the fusion strategy effectively enhances accuracy and robustness, providing a powerful solution for industrial non-contact roughness inspection. Full article

Shaftless Pump-jet Thrusters (SPTs), which integrate the propulsion motor directly with impellers, provide a compact design and high propulsion efficiency. Despite this, their performance is significantly hampered by eddy current losses in conductive stator sleeves. This study introduces Titanium Matrix Composites (TMC) as superior alternatives to conventional titanium alloys (Ti-6Al-4V, Ti64), leveraging their tailorable anisotropic electromagnetic properties to effectively suppress eddy current losses. Through simulations and experimental validation, the electromagnetic performance of an SPT equipped with a TMC stator sleeve is systematically investigated. Electromagnetic simulations predict a dramatic reduction in eddy current loss of 53.5–79.8% and an improvement in motor efficiency of 5.8–8.5% across the 1500–2900 rpm operational range compared to the Ti64 baseline. Experimental measurements on prototype motors confirm the performance advantage, demonstrating a 3.5–5.7% reduction in input power under equivalent output conditions across the same speed range. After accounting for manufacturing tolerances and control strategies, the refined model demonstrated a markedly improved agreement with the experimental results. This research conclusively establishes TMCs as a high-performance containment sleeve material, which is promising not only for SPTs but also for a broad range of canned motor applications, where an optimal balance between electromagnetic and structural performance is critical. Full article

Fiber-reinforced laminates composed of a thermoset matrix have seen widespread use in industries such as the aerospace, wind power, and automotive industries, due to their strength-to-weight ratios and ease of formability. For optimal performance, the instantaneous cure state must be sufficient such that the component will not deform during or after molding, a state that can vary based on many manufacturing-related factors. Thus, monitoring the cure process non-destructively in situ is key to manufacturing composite laminates to achieve the as-designed properties while balancing the cycle time reduction. The current work presents a pulse-echo ultrasound method to correlate the acoustic waveform to the thermoset resin cure state and the instantaneous structural properties, specifically the resin storage and loss moduli. This latter information provides a fabricator knowledge of when a part can be successfully demolded, allowing for optimizing part cycle times. The present paper provides the results for the neat resin specimen and fiberglass specimen impregnated with the same resin system. The results provide a direct correlation between the acoustic and the viscoelastic properties. Interestingly, it is noted that there is a direct correlation between the peak signal attenuation and the peak gelation of the material, thus providing a means to predictively schedule the demolding time while maintaining proper curing cycles. Full article

This study investigates the evolution of public sentiment and discourse surrounding artificial intelligence through a comprehensive multi-method analysis of 28,819 Reddit comments spanning March 2015 to May 2024. Addressing three research questions—(1) what dominant topics characterize AI discourse, (2) how has sentiment changed over time, particularly following ChatGPT 5.2’s release, and (3) what linguistic patterns distinguish positive from negative discourse—we employ 28 distinct analytical techniques to provide validated insights into public AI perception. Methodologically, the study integrates VADER sentiment analysis, Linguistic Inquiry and Word Count (LIWC) analysis with regression validation, dual topic modeling using Latent Dirichlet Allocation and Non-negative Matrix Factorization for cross-validation, four-dimensional tone analysis, named entity recognition, emotion detection, and advanced NLP techniques including sarcasm detection, stance classification, and toxicity analysis. A key methodological contribution is the validation of LIWC categories through linear regression (R² = 0.049, p < 0.001) and logistic regression (61% accuracy), moving beyond the descriptive statistics typical of prior linguistic analyses. Results reveal a pronounced decline in positive sentiment from +0.320 in 2015 to +0.053 in 2024. Contrary to expectations, sentiment decreased following ChatGPT’s November 2022 release, with negative comments increasing from 31.9% to 35.1%—suggesting that direct exposure to powerful AI capabilities intensifies rather than alleviates public concerns. LIWC regression analysis identified negative emotion words (β = −0.083) and positive emotion words (β = +0.063) as the strongest sentiment predictors, confirming that affective rather than technical engagement drives public AI attitudes. Topic modeling revealed nine coherent themes, with facial recognition, algorithmic bias, AI ethics, and social media misinformation emerging as dominant concerns across both LDA and NMF analyses. Network analysis identified regulation as a central hub (degree centrality = 0.929) connecting all major AI concerns, indicating strong public appetite for governance frameworks. These findings contribute to theoretical understandings of technology risk perception, provide practical guidance for AI developers and policymakers, and demonstrate validated computational methods for tracking public opinion toward emerging technologies. Full article

Multiphoton endomicroscopy (MPEM) has recently become a key development in optical biomedical diagnostics, providing histologically relevant in vivo images that are eliminating both the need for tissue damage during biopsy sampling and the need for dye injections. Due to its ability to visualize structures at the epithelial, extracellular matrix, and subcellular levels, MPEM offers a promising diagnostic method for precancerous conditions and early forms of gastrointestinal (GI) cancer. The high specificity of multiphoton signals—the two-photon fluorescence response of endogenous fluorophores (NADH, FAD), the second-harmonic generation signal from collagen, and others—makes this method a promising alternative to both traditional histology and confocal endoscopy, enabling real-time assessment of metabolic status, intestinal epithelial cell status, and stromal remodeling. Despite the promising prospects of multiphoton microscopy, its practical implementation is progressing extremely slowly. The main factors here include the difficulty of delivering ultrashort pulses with high peak power, which is necessary for multiphoton excitation (MPE), and obtaining these pulses at the required wavelengths to activate the autofluorescence mechanism. One of the most promising solutions is the use of specialized multimode optical fibers that can both induce beam self-cleaning (BSC), which allows for the formation of a stable beam profile close to the fundamental mode, and significantly broaden the optical spectrum, which can ultimately cover the entire region of interest. This review presents the biophysical foundations of multiphoton microscopy of GI tissue, existing endoscopic architectures for MPE, and an analysis of the potential for using novel nonlinear effects in multimode optical fibers, such as the BSC effect and supercontinuum generation. It is concluded that the use of optical fibers in which the listed effects are realized in the tracts of multiphoton endomicroscopes can become a key step in the creation of a new generation of high-resolution instruments for the early detection of malignant neoplasms of the GI tract. Full article

The maximum principle is a widely used qualitative property of linear (and not only) elliptic boundary value problems. A natural goal for developing numerical methods is for the approximate solution to have a similar property. In this case, we say that a discrete maximum principle holds. In many cases, such a requirement is critical to ensuring the reliability of computational models. Here, we consider multidimensional linear elliptic problems with diffusion and reaction terms. Such problems have been studied and analyzed for many decades. Since relatively recently, scientists have faced conceptually new challenges when considering anomalous (fractional) diffusion. In the present paper, we concentrate on the case of spectral fractional diffusion. Discretization was carried out using the finite difference method and the finite element method with a lumped mass matrix. In large-scale multidimensional problems, the computational complexity of dense matrix operations is critical. To overcome this problem, BURA (best uniform rational approximation) methods were applied to find the efficient numerical solutions of emerging dense linear systems. Thus, along with the need to satisfy the discrete maximum principle associated with the mesh method applied for discretization of the differential operator, the issue of the monotonicity of BURA numerical solution arises. The presented results are three-fold and include the following: (i) maximum principles for fractional diffusion–reaction problems; (ii) sufficient conditions for discrete maximum principles; and (iii) sufficient conditions for monotonicity of the investigated BURA- or BURA-like approximation methods. A novel, systematic theoretical analysis is developed for sub-diffusion with a fractional power $\alpha \in (1/2,1)$ and a constant reaction coefficient. The theoretical findings are further supported by numerical examples. Full article

The fast adoption of electric vehicles (EVs) and the integration of renewable distributed generators (DGs) provide significant operational issues for radial distribution networks (RDNs), notably in terms of power losses, voltage variations, and system stability. This paper investigates the optimal placement and sizing of EV charging stations (EVCSs) and DGs under varying EV hosting factors (EV-HFs). An impedance matrix-based load flow method is developed, and a derived analytical formula for power loss calculation is proposed to improve computational efficiency. A weighted multi-objective function is developed to reduce active power losses and voltage variations while optimizing the voltage stability index and the yearly cost savings from energy loss. The optimization is performed using a deterministic heuristic procedure that incrementally adjusts the location and size of EVCSs and DGs until no further improvement in the fitness function is achieved. This stepwise approach provides fast convergence with low computational effort compared to population-based metaheuristics. The methodology is used on the IEEE 33-bus system under different loading conditions and EV-HFs. The results reveal that for 40% and 60% EV-HFs, active power losses decreased by about 57% compared with the basic case, while the minimum bus voltage improved from 0.9148 pu to 0.9654 pu and 0.9641 pu. The economic analysis demonstrates annual savings of up to USD 473,550, with a payback period between 7 and 8 years. These findings emphasize the need of integrated EVCS and DG planning in improving future distribution systems’ technical and economic performance. Full article

In high-precision optical systems such as laser optics, astronomical observation, and semiconductor lithography, anti-reflection coatings are crucial for light transmittance, imaging quality, and stability, but traditional designs face modeling challenges in balancing ultralow reflectivity, high wavefront quality, and manufacturability amid multi-dimensional parameter coupling and multi-objective constraints. This study addresses these by proposing a unified mathematical modeling framework integrating a Symmetric five-layer high-low refractive index alternating structure (V-H-V-H-V) with dual-scale nanostructures, employing a constrained quasi-Newton optimization algorithm (L-BFGS-B) to minimize reflectivity, wavefront root-mean-square (RMS) error, and surface roughness root-mean-square (RMS) in a six-dimensional parameter space. The Sellmeier equation is adopted to calculate wavelength-dependent material refractive indices, the model uses the transfer matrix method for the Symmetric five-layer high-low refractive index alternating structure’s reflectivity, incorporates nano-surface height function gradient correction, sub-wavelength modulation, and radial optimization, applies Zernike polynomials for low-order aberration correction, quantifies surface roughness via curvature proxies, and optimizes via a weighted objective function prioritizing low reflectivity. Numerical results show the spatial average reflectivity at 632.8 nm reduced to 0.13%, the weighted average reflectivity across five representative wavelengths in the 550–720 nm range to 0.037%, the reflectivity uniformity to 10.7%, the post-correction wavefront RMS to 11.6 milliwavelengths, and the surface height standard deviation to 7.7 nm. This framework enhances design accuracy and efficiency, suits UV nanoimprinting and electron beam evaporation, and offers significant value for high-power lasers, lithography, and space-borne radars. Full article

Mechano-Organ-on-Chip (Mechano-OoC) platforms are emerging as powerful microphysiological systems that place mechanical cues at the center of tumor modeling, providing a scalable and human-relevant approach to recapitulate the biophysical complexity of the tumor microenvironment. Mechanical factors such as matrix stiffness, viscoelasticity, solid stress, interstitial flow, confinement, and shear critically regulate cancer progression, metastasis, immune interactions, and treatment response, yet remain poorly captured by conventional in vitro models and are often studied separately in tumor-on-chip and mechanobiology research. In this review, we summarize recent advances in mechano-OoC technologies for cancer research, highlighting strategies that integrate engineered mechanical cues with microfluidics, tunable extracellular matrices, vascular and stromal interfaces, and dynamic loading to model tumor invasion, vascular transport, immune trafficking, and drug delivery. We also discuss emerging approaches for real-time, multimodal readouts, including sensor-integrated platforms and artificial intelligence-assisted data analysis, and outline key challenges that limit translation, such as device complexity, limited throughput, insufficient standardization, and inadequate validation against in vivo and clinical data. By organizing progress across platform engineering, sensing and readout, data standardization, and AI-driven analytics, this review provides a unified framework for advancing mechanobiology-aware tumor models and guiding the development of predictive preclinical platforms for precision cancer therapy. Full article

Particulate Matter (PM) is a type of air pollution that poses risks to human health, the environment, and property. Among the various PM types, PM₁₀ is particularly significant, as it acts as a vector for numerous hazardous trace elements that can negatively impact human health and the ecosystem. Identifying potential sources of PM₁₀ and quantifying their impact on ambient concentrations is crucial for developing efficient control strategies to meet threshold values. Receptor modeling, which identifies sources using chemical species information derived from PM samples, has been widely used for source apportionment. In this study, PM₁₀ samples were collected over three periods (April, May, and June 2021), each lasting 16 days, using semi-automatic dust sampling systems at two sites in Biga, Canakkale, Turkiye. The relative contributions of different source types were quantified using EPA PMF (Positive Matrix Factorization) based on 35 elements comprising PM₁₀. As a result of the analysis, five source types were identified: crustal elements/limestone/calcite quarry (64.9%), coal-fired power plants (11.2%), metal industry (9%), sea salt and ship emissions (8.5%), and road traffic emissions and road dust (6.3%). The distribution of source contributions aligned with the locations of identified sources in the region. Full article

This study investigates the two-dimensional (2D) steady-state frictional contact behavior of functionally graded piezoelectric material (FGPM) coatings under a high-speed rigid cylindrical punch. An electromechanical coupled contact model considering inertial effects is established, while a layered model is employed to simulate arbitrarily varying material parameters. Based on piezoelectric elasticity theory, the steady-state governing equations for the coupled system are derived. By utilizing the transfer matrix method and the Fourier integral transform, the boundary value problem is converted into a system of coupled Cauchy singular integral equations of the first and second kinds in the frequency domain. These equations are solved semi-analytically, using the least squares method combined with an iterative algorithm. Taking a power-law gradient distribution as a case study, the effects of the gradient index, relative sliding speed, and friction coefficient on the contact pressure, in-plane stress, and electric displacement are systematically analyzed. Furthermore, the contact responses of FGPM coatings with power-law, exponential, and sinusoidal gradient profiles are compared. The findings provide a theoretical foundation for the optimal design of FGPM coatings and for enhancing their operational reliability under high-speed service conditions. Full article

The growing miniaturization of electronic systems and the expansion of sustainable, autonomous IoT technologies emphasize the need for efficient, ultra-low-power energy harvesting devices. This study evaluates fifteen devices from five industry leaders for use in small-scale autonomous seed germination systems. Its novelty lies in applying a competitive profile matrix within a flexible multicriteria evaluation framework based on the simple additive weighting (SAW) method that uses a comprehensive set of competitive technology factors (CTFs). The results demonstrate that a transparent and structured methodology can generate prioritized lists of suitable energy harvesters while accounting for technical, economic, and environmental trade-offs. The study also shows that device rankings depend on the scope and objectives of the project. If these change, then the CTF selection, classification, and weighting adjust accordingly. Therefore, the relevance of this study lies in the adaptability, replicability, and audibility of the proposed framework, which supports the selection of informed technology for autonomous, IoT-based germination systems and other technological projects. Full article

This research investigates the effects of corona plasma treatment on the mechanical properties of jute/epoxy-reinforced composites, particularly within biomedical application contexts. Plant Fibre Composites (PFCs) are attractive for medical devices and scaffolds due to their environmental friendliness, renewability, cost-effectiveness, low density, and high specific strength. However, their applications are often constrained by inferior mechanical performance arising from poor bonding between the plant fibre used as the reinforcement and the synthetic resin or polymer serving as the matrix. This study addresses the challenge of improving the weak interfacial bonding between plant fibre and synthetic resin in a 2/2 twill-weave-woven jute/epoxy composite material. The surface of the jute fibre is modified for better adhesion with the epoxy resin through plasma treatment, which exposes the jute fibre to controlled plasma energy and utilises dry air (plasma only), argon (Ar) (argon gas with plasma), and nitrogen (N₂) (nitrogen gas with plasma) at two different distances (25 mm and 35 mm) between the plasma nozzle and the fibre surface. In this context, “equilibrium” refers to the optimal combination of plasma power, treatment distance, and gas environment that collectively determines the degree of fibre surface modification. The results indicate that all plasma treatments improve the interlaminar shear strength in comparison to untreated samples, with treatments at 35 mm using N₂ gas showing a 35.4% increase in shear strength. Conversely, plasma treatment using dry air at 25 mm yields an 18.3% increase in tensile strength and a 35.7% increase in Young’s modulus. These findings highlight the importance of achieving an appropriate equilibrium among plasma intensity, treatment distance, and fibre–plasma interaction conditions to maximise the effectiveness of plasma treatment for jute/epoxy composites. This research advances sustainable innovation in biomedical materials, underscoring the potential for improved mechanical properties in environmentally friendly fibre-reinforced composites. Full article

Perfluorosulfonic acid (PFSA) membranes suffer from severe conductivity decay caused by dehydration at elevated temperatures, hindering their application in medium- to high-temperature proton exchange membrane fuel cells (MHT-PEMFCs). To address this, Nafion/SiO₂ composite membranes with systematically varied filler contents were fabricated via a sol–gel-assisted casting strategy to enhance hydration stability and proton transport. Spectroscopic and microscopic analyses reveal a homogeneous nanoscale dispersion of SiO₂ within the Nafion matrix, along with strong interfacial hydrogen bonding between SiO₂ and sulfonic acid groups. These interactions effectively suppress polymer crystallinity and stabilize hydrated ionic domains. Thermogravimetric analysis confirms markedly improved water retention in the composite membranes at intermediate temperatures. Proton conductivity measurements at 50% relative humidity (RH) identify the Nafion/SiO₂-3 membrane as exhibiting optimal transport behavior, delivering the highest conductivity of 61.9 mS·cm⁻¹ at 120 °C and significantly improved conductivity retention compared to Nafion 117. Furthermore, single-cell tests under MHT-PEMFC conditions (120 °C, 50% RH) demonstrate the practical efficacy of these membrane-level enhancements, with the Nafion/SiO₂-3 membrane exhibiting an open-circuit voltage and peak power density 11.2% and 8.9% higher, respectively, than those of pristine Nafion under identical MEA fabrication and operating conditions. This study elucidates a clear structure–property–transport relationship in SiO₂-reinforced PFSA membranes, demonstrating that controlled inorganic incorporation is a robust strategy for extending the operational temperature window of PFSA-based proton exchange membranes toward device-level applications. Full article

Conversational agents are transforming digital interactions across various domains, including healthcare, education, and customer service, thanks to advances in large language models (LLMs). As these systems become more autonomous and ubiquitous, understanding what constitutes high-quality interaction from a user perspective is increasingly critical. Despite growing empirical research, the field lacks a unified framework for defining, measuring, and designing user-perceived interaction quality in human–artificial intelligence (AI) dialogue. Here, we present an integrative review of 125 empirical studies published between 2017 and 2025, spanning text-, voice-, and LLM-powered systems. Our synthesis identifies three consistent layers of user judgment: a pragmatic core (usability, task effectiveness, and conversational competence), a social–affective layer (social presence, warmth, and synchronicity), and an accountability and inclusion layer (transparency, accessibility, and fairness). These insights are formalised into a four-layer interpretive framework—Capacity, Alignment, Levers, and Outcomes—operationalised via a Capacity × Alignment matrix that maps distinct success and failure regimes. It also identifies design levers such as anthropomorphism, role framing, and onboarding strategies. The framework consolidates constructs, positions inclusion and accountability as central to quality, and offers actionable guidance for evaluation and design. This research redefines interaction quality as a dialogic construct, shifting the focus from system performance to co-orchestrated, user-centred dialogue quality. Full article