Search Results (16,329)

RMB₆-TMB₂ (RM = rare earth elements, TM = transition metal elements) composites retain superior field emission properties of RMB₆ while addressing its inherent mechanical limitations by constructing a eutectic structure with TMB₂. Herein, an in situ route for synthesizing RMB₆-TMB₂ composite nanopowders with homogeneous phase distribution using reduction reactions was proposed. The LaB₆-ZrB₂ composite nanopowders were synthesized in situ for the first time using sodium borohydride (NaBH₄) as both a reducing agent and boron source, with lanthanum oxide (La₂O₃) and zirconium dioxide (ZrO₂) serving as metal sources. The effects of the synthesis temperature on phase compositions and microstructure of the composites were systematically investigated. The LaB₆-ZrB₂ system with a eutectic weight ratio exhibited an accelerated reaction rate, achieving a complete reaction at 1000 °C, 300 °C lower than that of single-phase ZrB₂ synthesis. The composite phases were uniformly distributed even at nanoscale. The composite powder displayed an average particle size of ~170 nm when synthesized at 1300 °C. With the benefit of the in situ synthesis method, LaB₆-TiB₂, CeB₆-ZrB₂, and CeB₆-TiB₂ composite powders were successfully synthesized. This process effectively addresses phase separation and contamination issues typically associated with traditional mixing methods, providing a scalable precursor for high-performance RMB₆-TMB₂ composites. Full article

Thin-walled structures have been extensively adopted as energy absorbers in various engineering fields. The energy accumulated in the coal and rock is released instantly, resulting in varying degrees of damage and failure to support equipment. To improve the crushing performance of underground support equipment, a metal thin-walled tube with high-bearing capacities is placed in the column as an energy-absorbing column. Based on the characteristics of non-dimensional parameters governing the crashworthiness of thin-walled tubes by the author’s team, a type of high-performance bi-tubular tube (HPBT) with mixed multicellular configurations is innovatively proposed. First, the finite element models of the HPBTs are established in LS-DYNA, and the accuracy of the FE model is verified by crushing tests. Second, the theoretical model of the mean crushing force (MCF) is derived. Moreover, the effects of the cross-sectional shapes and the wall thickness gradient distribution on the deformation modes and crashworthiness are investigated. The results show that the design strategies of the bi-tubular structures mixed multicellular configurations significantly improve the values of ω. The MCF of HPBT_C2 is 4458.0 kN, which is 28% and 56% higher than those of the conventional circular tube and square tube. The theoretical MCF is consistent with the simulated MCF, with a maximum discrepancy of 6.0%. The gradient distribution (k) of wall thickness significantly affects the crushing behaviors of the HPBT. Considering the energy absorption efficiency, the crushing stability, and the wall thickness gradient distribution, the HPBT_C2 with k = 0.6 has the best overall performance. The results can provide insights and guidelines for designing energy absorption devices with superior crashworthiness for support equipment. Full article

In this study, Ni-Co-Y₂O₃ composite coating was prepared by electrodeposition, and the effect of Y₂O₃ particle size on the microstructure and properties of the coating was investigated. The samples were analyzed by XRD, SEM, AFM, EDS, cyclic voltammetry, XPS, hardness, and corrosion resistance test. The results indicate that the diffraction peak of the coating prepared with 50 nm particles exhibits reduced intensity and broadening, whereas the coating prepared with 100 nm particles displays a sharper and more pronounced peak. The onset reduction potential and the performance of the reduction reaction are influenced by particle size. When the particle size is 50 nm, the reduction process is less favorable, with an onset reduction potential of −0.9 V; in contrast, when the particle size is 100 nm, the reduction occurs more readily, with an onset reduction potential of −0.8 V. XPS analysis reveals that the chemical environment of elements varies with particle size. Regarding hardness, the coating prepared by combining different Y₂O₃ particle sizes exhibits higher hardness compared to that prepared using a single particle size, which can be attributed to the synergistic effect. In terms of corrosion resistance, the coating prepared with 100 nm Y₂O₃ particles demonstrates superior corrosion resistance, whereas the coating prepared with mixed particle sizes shows reduced stability and is more susceptible to corrosion. The coating prepared by mixing Y₂O₃ with particle size of 50 nm and 100 nm has a small friction coefficient. In summary, the particle size of Y₂O₃ has a significant influence on the microstructure, hardness, and corrosion resistance of Ni-Co-Y₂O₃ composite coatings. Full article

The global construction industry is facing pressure to reduce environmental impact by improving energy efficiency amid rising energy demands and growing concerns about climate change. Consequently, sustainable building practices, like the Passive House (PH) design, prioritize minimizing building energy demand. In Saudi Arabia, where cooling loads dominate electricity use, implementing PH could significantly lower energy demand. However, research on PH challenges in the Saudi climate is limited, which highlights the importance of investigating the barriers and potential solutions for PH adoption in this context. This study investigates barriers to PH adoption and proposes context-specific solutions for Saudi Arabia. A mixed-methods approach, including a literature review and structured questionnaires among construction professionals, was used. Thematic analysis and importance–performance analysis (IPA) helped prioritize barriers and identify strategies. By combining evidence from the literature and practitioner surveys, this study uniquely prioritizes PH adoption barriers and proposes tailored solutions for Saudi Arabia’s hot climate. The results showed that the most critical barriers include a lack of supportive building codes, the absence of government incentives, low awareness, contractor resistance, and limited material availability. At the same time, key strategies were identified, such as revising building regulations, offering tax incentives, and adapting PH design with improved shading and HVAC systems. Overall, these findings indicate that removing the identified barriers and implementing the suggested strategies can reduce energy demand and demonstrate the feasibility of PH in Saudi Arabia’s hot climate, thereby supporting the Kingdom’s broader sustainability goals. Full article

To enhance passive mixing in microchannels, T-shaped double-spiral and serpentine microchannels with identical curvature radii were designed and numerically analyzed across a Reynolds number (Re) range of 1 to 300. The double-spiral microchannel exhibited superior mixing performance at Re ≤ 200, which is primarily attributed to the efficient utilization of Dean vortices. In contrast, the serpentine microchannel showed better performance at Re ≥ 250, benefiting from the early formation of four-vortex structures induced by periodic curvature reversals. To further enhance the performance of the serpentine microchannel at low Re, groove structures with varying orientation angles were incorporated. The introduction of the groove structures generated lateral secondary flows that not only increased flow disturbances but also disrupted the symmetry of the Dean vortices. Among these configurations, Structure 2, with a 45° angle between the groove direction and centrifugal force, exhibited the most pronounced enhancement in vortex intensity, as the secondary flows induced by the grooves synergistically interacted with the Dean vortices. This configuration resulted in the highest mixing enhancement (>50%). This study provides valuable insights into geometry-driven mixing mechanisms and offers design guidelines for high-efficiency micromixers across a wide range of Re. Full article

In recent years, a type of biochip known as a Digital Microfluidic Biochip (DMFB) has been actively researched in the field of life sciences. DMFBs perform dilution operations by mixing reagent solutions and buffer solutions at a 1:1 ratio to generate droplets with the desired concentration. One of the challenges of DMFBs is that droplets may not always be evenly split during the droplet division process. To address this issue, an error correction method utilizing error cancellation has been proposed. This method modifies the dilution graph to minimize the impact of division errors on the target node. However, this approach has a significant drawback: when large division errors occur in nodes close to the target node, they can introduce substantial concentration errors at the target node. Full article

Background and Clinical Significance: Patients with a neurodegenerative condition known as posterior cortical atrophy (PCA) can present with attention impairments across a variety of cognitive contexts, but the consequences of these are little explored in example of single word reading. Case Presentation: We present a detailed single-case study of KL, a local resident of South Wales, a patient diagnosed with posterior cortical atrophy (PCA) in 2018, whose reading and letter-naming abilities are selectively disrupted under non-canonical visual presentations. In particular, KL shows significantly impaired accuracy performance when reading words presented in tilted (rotated 90°) format. By contrast, his reading under conventional horizontal (canonical) presentation is nearly flawless. Whilst other presentation formats including, mixed-case text (e.g., TaBLe) and vertical (marquee) format led to only mild performance decrements—even though mixed-case formats are generally thought to increase attentional ‘crowding’ effects. Discussion: These findings indicate that impairments of word reading can emerge in PCA when visual-attentional demands are sufficiently high, and access to ‘top down’ orthographic information is severely attenuated. Next, we explored a cardinal feature of attentional dyslexia, namely the word–letter reading dissociation in which word reading is superior to letter-in-string naming. In KL, a similar dissociative pattern could be provoked by non-canonical formats. That is, conditions that similarly disrupted his word reading led to a pronounced disparity between word and letter-in-string naming performance. Moreover, different orientation formats revealed the availability (or otherwise) of distinct compensatory strategies. KL successfully relied on an oral (letter by letter) spelling strategy when reading vertically presented words or naming letters-in-strings, whereas he had no ability to engage compensatory mental rotation processes for tilted text. Thus, the observed impact of non-canonical presentations was moderated by the success or failure of alternative compensatory strategies. Conclusions: Importantly, our results suggest that an attentional ‘dyslexia-like’ profile can be unmasked in PCA under sufficiently taxing visual-attentional conditions. This approach may prove useful in clinical assessment, highlighting subtle reading impairments that conventional testing might overlook. Full article

Linpan ecosystems, distinct to western Sichuan, China, are integral to regional biodiversity and carbon cycling. However, comprehensive biomass estimation for these systems has not been thoroughly investigated. This study seeks to fill this gap by enhancing the accuracy and precision of biomass estimation in these ecologically vital landscapes through the application of multi-source remote sensing techniques, specifically by integrating the strengths of optical and radar remote sensing data. The focus of this research is on the forest biomass of Linpan, encompassing the tree layer, which includes the trunk, branches, leaves, and underground roots. Specifically, the research focused on the Linpan ecosystems in the Wenjiang District of western Sichuan, utilizing an integration of Sentinel-1 SAR, Sentinel-2 multispectral, and GF-2 high-resolution data for multi-source remote sensing-based biomass estimation. Through the preprocessing of these data, Pearson correlation analysis was conducted to identify variables significantly correlated with the forest biomass as determined by field surveys. Ultimately, 19 key modeling factors were selected, including band information, vegetation indices, texture features, and phenological characteristics. Subsequently, three algorithms—multiple stepwise regression (MSR), support vector machine (SVM), and random forest (RF)—were employed to model biomass across mixed-type, deciduous broadleaved, evergreen broadleaved, and bamboo Linpan. The key findings include the following: (1) Sentinel-2 spectral data and Sentinel-1 VH backscatter coefficients during the summer, combined with vegetation indices and texture features, were critical predictors, while phenological indices exhibited unique correlations with biomass. (2) Biomass displayed a marked north–south gradient, characterized by higher values in the south and lower values in the north, with a mean value of 161.97 t ha⁻¹, driven by dominant tree species distribution and management intensity. (3) The RF model demonstrated optimal performance in mixed-type Linpan (R² = 0.768), whereas the SVM was more suitable for bamboo Linpan (R² = 0.892). The research suggests that integrating multi-source remote sensing data significantly enhances Linpan biomass estimation accuracy, offering a robust framework to improve estimation precision. Full article

This exploratory study investigates the usability of performance metrics for generative adversarial network (GAN)-based models for speech-driven facial animation. These models focus on the transfer of speech information from an audio file to a still image to generate talking-head videos in a small-scale “everyday usage” setting. Two models, LipGAN and a custom implementation of a Wasserstein GAN with gradient penalty (L1WGAN-GP), are examined for their visual performance and scoring according to commonly used metrics: Quantitative comparisons using FID, SSIM, and PSNR metrics on the GRIDTest dataset show mixed results, and metrics fail to capture local artifacts crucial for lip synchronization, pointing to limitations in their applicability for video animation tasks. The study points towards the inadequacy of current quantitative measures and emphasizes the continued necessity of human qualitative assessment for evaluating talking-head video quality. Full article

In this paper, we propose a deep learning-based surrogate model for Multivariate Empirical Mode Decomposition (MEMD) using Long Short-Term Memory (LSTM) networks, aimed at efficiently extracting Intrinsic Mode Functions (IMFs) from electroencephalographic (EEG) signals. Unlike traditional data-driven methods, our approach leverages temporal sequence modeling to learn the decomposition process in an end-to-end fashion. We further enhance the decomposition targets by employing Noise-Assisted MEMD (NA-MEMD), which stabilizes mode separation and mitigates mode mixing effects, leading to better supervised learning signals. Extensive experiments on synthetic and real EEG data demonstrate the superior performance of the proposed LSTM surrogate over conventional feedforward neural networks and standard MEMD-based targets. Specifically, the LSTM trained on NA-MEMD outputs achieved the lowest mean squared error (MSE) and the highest signal-to-noise ratio (SNR), significantly outperforming the feedforward baseline, even when compared using the Power Spectral Density (PSD). These results confirm the effectiveness of combining LSTM architectures with noise-assisted decomposition strategies to approximate nonlinear signal analysis tasks such as MEMD. The proposed surrogate model offers a fast and accurate alternative to classical empirical methods, enabling real-time and scalable EEG analysis. Full article

Deep reinforcement learning (DRL)-based HVAC control has shown clear advantages over rule-based and model predictive methods. However, most prior studies remain limited to HVAC-only optimization or simple coordination with operable windows. Such approaches do not adequately address buildings with fixed glazing systems—a common feature in high-rise offices—where the lack of operable windows restricts adaptive envelope interaction. To address this gap, this study proposes a multi-zone control framework that integrates HVAC systems with electrochromic windows (ECWs). The framework leverages the Q-value Mixing (QMIX) algorithm to dynamically coordinate ECW transmittance with HVAC setpoints, aiming to enhance energy efficiency and thermal comfort, particularly in high-consumption buildings such as offices. Its performance is evaluated using EnergyPlus simulations. The results show that the proposed approach reduces HVAC energy use by 19.8% compared to the DQN-based HVAC-only control and by 40.28% relative to conventional rule-based control (RBC). In comparison with leading multi-agent deep reinforcement learning (MADRL) algorithms, including MADQN, VDN, and MAPPO, the framework reduces HVAC energy consumption by 1–5% and maintains a thermal comfort violation rate (TCVR) of less than 1% with an average temperature variation of 0.35 ${}^{\circ }\mathrm{C}$ Moreover, the model demonstrates strong generalizability, achieving 16.58–58.12% energy savings across six distinct climatic regions—ranging from tropical (Singapore) to temperate (Beijing)—with up to 48.2% savings observed in Chengdu. Our framework indicates the potential of coordinating HVAC systems with ECWs in simulation, while also identifying limitations that need to be addressed for real-world deployment. Full article

Material extrusion (MEX) is an additive manufacturing process used for 3D printing thermoplastic-based polymers, including single polymers, blends, and reinforced polymer composites (RPCs). RPCs are highly valued in various industries for their exceptional properties. The surface finish of RPC MEX-printed parts is high due to the process-related layering nature and the materials’ properties. This study explores RPC development for MEX printing and the potential of dry milling post-processing to enhance the MEX-printed part’s surface quality. RPC MEX filaments were developed by incorporating graphene nanoplatelets (GNPs) and/or recycled-carbon fibers (rCFs) into a polylactic acid (PLA) matrix. The filaments, including pure PLA and various GNPs-PLA composites, rCF-PLA, and rCF-GNPs-PLA, were developed through ball mill mixing and melt extrusion. Tensile tests were performed to assess the mechanical properties of the developed materials. Dry milling post-processing was carried out to assess the machinability, with the aim of enhancing the MEX-printed part’s surface quality. The results revealed that adding GNPs into PLA showed no considerable enhancements in the tensile properties of the fabricated RPCs, which is contrary to several existing studies. Dry milling showed an enhanced surface quality of MEX-printed parts in terms of surface roughness (Sa and Sz) and the absence of defects such as delamination and layer lines. Adding GNPs into PLA facilitated the dry machining of PLA, resulting in reduced surface asperities compared to pure PLA. Also, there was no observation of pulled-out, realigned, or naked rCFs, which indicates good machinability. Adding GNPs also suppressed the formation of voids around the rCFs during the dry milling. This study provides insights into machining 3D-printed polymer composites to enhance their surface quality. Full article

Background/Objectives: A combination of sympathoexcitation and parasympathetic withdrawal contributes to the occurrence of ventricular arrhythmias, sudden cardiac death, and progression of heart failure after myocardial injury. As a result, vagal nerve stimulation has been under investigation as a potential option to increase cardiac vagal tone, but the results of clinical trials have been mixed. Prior studies have suggested that the vagal ganglia and nerves may contain sympathetic neurons that express tyrosine hydroxylase (TH), which, if stimulated, could potentially mitigate the effects of vagal nerve stimulation. The goal of the current study was to better characterize these neurons. Methods: Immunohistochemical staining was performed to evaluate for the presence of TH-expressing neurons in the inferior vagal (nodose) ganglia from six pigs. Additional staining was performed for dopamine beta-hydroxylase (DBH), which is required for the production of norepinephrine (NE), to determine if these neurons are indeed sympathetic and capable of releasing NE. Analysis of stellate ganglia was also performed, given that these ganglia are known to provide sympathetic innervation to the heart and release NE in the myocardium. Results: While nearly all TH-expressing neurons in the stellate ganglia expressed DBH, confirming that they can produce or release NE, none of the TH-expressing neurons in the vagal ganglia expressed DBH, demonstrating that these are dopaminergic but not noradrenergic neurons. Conclusions: TH-expressing neurons in the vagal ganglia previously reported to be potentially “sympathetic” do not express DBH and are, therefore, not capable of synthesizing NE. Full article

This paper investigates federated learning (FL) for cross-site scripting (XSS) detection under out-of-distribution (OOD) drift. Real-world XSS traffic involves fragmented attacks, heterogeneous benign inputs, and client imbalance, which erode conventional detectors. To simulate this, we construct two structurally divergent datasets: one with obfuscated, mixed-structure samples and another with syntactically regular examples, inducing structural OOD in both classes. We evaluate GloVe, GraphCodeBERT, and CodeT5 in both centralised and federated settings, tracking embedding drift and client variance. FL consistently improves OOD robustness by averaging decision boundaries from cleaner clients. Under FL scenarios, CodeT5 achieves the best aggregated performance (97.6% accuracy, 3.5% FPR), followed by GraphCodeBERT (96.8%, 4.7%), but is more stable on convergence. GloVe reaches a competitive final accuracy (96.2%) but exhibits a high instability across rounds, with a higher false positive rate (5.5%) and pronounced variance under FedProx. These results highlight the value and limits of structure-aware embeddings and support FL as a practical, privacy-preserving defence within OOD XSS scenarios. Full article

The effective use of nursing assistant robots requires an understanding of key acceptance factors. The study examined the differences in attitudes among 58 nursing students while performing ambulation tasks with and without an Adaptive Robotic Nursing Assistant (ARNA) robot. An ARNA is driven by tactile cues from the patient through a force–torque-measuring handlebar, whose signals are fed into a neuro-adaptive controller to achieve a specific admittance behavior regardless of patient strength, weight, or floor incline. Ambulation tasks used two fall-prevention devices: a gait belt and a full-body harness. The attitude toward the robot included perceived satisfaction, usefulness, and assistance, replacing the perceived ease-of-use construct found in the standard technology acceptance model. The effects of external demographic variables on those constructs were also analyzed. The modified technology acceptance model was validated with the simultaneous estimation of the effects of perceived usefulness and assistance on satisfaction. Our analysis employed an integrated hierarchical linear mixed-effects regression model to analyze the complex relationships between model variables. Our results suggest that nursing students rated the ARNA’s performance higher across all model constructs compared to a human assistant. Furthermore, male subjects rated the perceived usefulness of the robot higher than female subjects. Full article