Search Results (21,306)

Polymers such as PLA, PLA reinforced with carbon fiber (PLA + CF), and PETG are widely employed in utensils, structural components, and biomedical device housings where load-bearing capability and chemical resistance are desirable. This is particularly relevant for reusable applications in which sterilization with hydrogen peroxide (HP) or ethylene oxide (EO) is often required. In this study, the impact of HP and EO sterilization processes on the mechanical, thermal, and structural properties of PLA, PLA + CF, and PETG was evaluated. The mechanical properties assessed included elongation at break, elastic modulus, and tensile strength after sterilization. The thermal properties examined comprised thermal stability and the coefficient of thermal expansion (CTE). Additionally, Fourier Transform Infrared Spectroscopy (FTIR) was performed to detect potential alterations in functional groups. For PLA, sterilization with HP and EO resulted in a 22% increase in ultimate tensile strength (UTS) and a 21% increase in elastic modulus, accompanied by a noticeable reduction in ductility and the appearance of more brittle fracture surfaces. PLA + CF exhibited greater stability under both sterilization methods due to the reinforcing effect of carbon fibers. In the case of PETG, tensile strength and stiffness remained stable; however, HP sterilization led to a remarkable increase in elongation at break (294%), whereas EO sterilization reduced it. Regarding thermal properties, glass transition temperature (Tg) showed variations: PLA presented either an increase or decrease in Tg depending on the sterilization treatment, PLA + CF displayed a Tg reduction after EO sterilization, while PETG exhibited a moderate Tg increase under HP sterilization. CTE decreased at lower temperatures but increased after EO treatment. FTIR analysis revealed only minor chemical modifications induced by sterilization. Overall, HP and EO sterilization can be safely applied to additively manufactured medical components based on these polymers, provided that the structures are not subjected to high mechanical loads and do not require strict dimensional tolerances. Full article

In recent years, deep learning has been increasingly applied in the field of fault diagnosis, but it currently faces two challenges: (1) data privacy issues prevent the aggregation of data from different users to form a large training dataset; (2) the limited memory of edge devices or handheld detection devices restricts the application of some larger structural models. To address these issues, this article proposes a lightweight federated learning method with transformer network for intelligent fault diagnosis. A federated learning architecture is constructed to achieve distributed learning of different user data, which not only ensures the privacy and security of user data, but also enables feature learning of different user data. In addition, the lightweight transformer network is built locally for different users to achieve the applicability of the model on different devices. An experimental case was implemented to demonstrate the effectiveness of the proposed method, and the results showed that the proposed method can achieve effective fault diagnosis while preserving data privacy. Compared with other methods, the proposed diagnostic model requires less computing resources. In addition, even under noisy conditions, the method maintains significant robustness against acoustic interference. Full article

While digital transformation (DT) promises significant advancements in the construction sector, many firms report a disconnect between technological investment and realized project performance. This study investigates the mechanisms through which DT drives project management optimization (PMO), hypothesizing that organizational agility (OA) and knowledge management capability (KMC) serve as critical mediating factors. We propose and test a conceptual model in which DT directly enhances PMO and also exerts indirect influence through the parallel pathways of OA and KMC. Data from a survey of 312 construction professionals were analyzed using structural equation modeling. The results confirm a significant direct effect of DT on PMO. Furthermore, both OA and KMC are identified as complementary and significant partial mediators. This finding underscores that the efficacy of digital technologies is contingent upon supportive organizational structures and systematic knowledge processes. The study provides a nuanced theoretical framework explaining how DT translates into improved project outcomes and offers strategic guidance for practitioners: to fully capitalize on digital investments, construction firms must concurrently cultivate adaptive capabilities and robust knowledge management systems. Full article

The rapid proliferation of devices on the Internet of Things (IoT) in smart city environments enables autonomous decision-making, but introduces challenges of scalability, coordination, and privacy. Existing reinforcement learning (RL) methods, such as Multi-Agent Actor–Critic (MAAC), depend on centralized critics and recurrent structures, which limit scalability and create single points of failure. This paper proposes a Federated Decision Transformer (FDT) framework that integrates transformer-based sequence modeling with federated learning. By replacing centralized critics with self-attention-driven trajectory modeling, the FDT preserves data locality, enhances privacy, and supports decentralized policy learning across distributed IoT nodes. We benchmarked the FDT against MAAC in a mobile edge computing (MEC) environment with identical hyperparameter configurations. The results demonstrate that the FDT achieves superior reward efficiency, scalability, and adaptability in dynamic IoT networks, although with slightly higher variance during early training. These findings highlight transformer-based federated RL as a robust and privacy-preserving alternative to critic-based methods for large-scale IoT systems. Full article

With the advancement of educational informatization, vast amounts of Chinese text are generated across online platforms and digital textbooks. Effectively classifying such text is essential for intelligent education systems. This study conducts a systematic comparative evaluation of three Transformer-based models—TinyBERT-4L, BERT-base-Chinese, and RoBERTa-wwm-ext—for Chinese educational text classification. Using a balanced four-category subset of the THUCNews corpus (Education, Technology, Finance, and Stock), the research investigates the trade-off between classification effectiveness and computational efficiency under a unified experimental framework. The experimental results show that RoBERTa-wwm-ext achieves the highest effectiveness (93.12% Accuracy, 93.08% weighted F1), validating the benefits of whole-word masking and extended pre-training. BERT-base-Chinese maintains a balanced performance (91.74% Accuracy, 91.66% F1) with moderate computational demand. These findings reveal a clear symmetry–asymmetry dynamic: structural symmetry arises from the shared Transformer encoder and identical fine-tuning setup, while asymmetry emerges from differences in model scale and pre-training strategy. This interplay leads to distinct accuracy–latency trade-offs, providing practical guidance for deploying pre-trained language models in resource-constrained intelligent education systems. Full article

This paper proposes a novel adversarial patch-generation method for infrared images, focusing on enhancing the robustness and transferability of infrared adversarial patches. To improve the flexibility and diversity of the generation process, a Bernoulli random dropout strategy is adopted. The loss function integrates multiple components, including target hiding loss, smoothing loss, structural similarity loss, and patch pixel value loss, ensuring that the generated patches maintain low texture complexity and natural visual features. During model training, the Grad-CAM algorithm is employed to identify the critical regions of interest in the target detector, where adversarial patches are applied to maximize the attack effectiveness. Furthermore, affine transformations and random erasing operations are introduced to increase the diversity and adaptability of patches, thereby enhancing their effectiveness across different scenarios. Experimental results demonstrate that the proposed GADP (Generative Adversarial Patch based on Bernoulli Random Dropout and Loss Function Optimization) algorithm achieves a high Attack Success Rate of 75.8% on various target detection models, significantly reducing the average precision (AP). Specifically, the AP of the YOLOv5s model drops from 81.3% to 15.1%. Compared with existing adversarial attack methods such as advYOLO Patch and QR Attack, GADP exhibits superior transferability and attack performance, reducing the Average Precision of multiple detection models to around 40%. The proposed method is not only theoretically innovative but also shows potential practical value, particularly in tasks such as unmanned aerial vehicle (UAV) detection and ground security under low-visibility environments. This study provides new insights into adversarial attack research for infrared target recognition. Full article

Ethnic villages are a multidimensional interactive space between cultural inheritance and modernization; analyzing their spatial reconstruction is fundamental for promoting agricultural and rural modernization and sustainable ethnic development. This study examined ethnic villages in Yongcong Township, Liping Country, from 2016 to 2022, focusing on changes in function and suitability under relocation through a function and suitability evaluation index. Case comparisons were made between administrative villages with high functional and suitability levels and those with resettlement sites. In 2016, ethnic villages followed a growth pattern of Yongcong–Dundong–Guantuan, with low patch density, dispersed distribution, and simple shapes. By 2022, functionality and suitability significantly improved, with an increase in village patches and larger patch areas shifting toward spatial aggregation. Horizontally, land use within reconstruction boundaries diversified by function, whereas vertically, housing structures were reorganized: non-settlement villages retained traditional and modern types while settlement villages combined both, leading to a shift from functional singularity to multifunctionality. Relocation-induced reconstruction may lag local knowledge systems and reduce well-being. Initially, government-led suitability enhancement dominates; gradually, villages increasingly internalize regional identity and competitiveness. By analyzing post-relocation village reconstruction, this study supports the integration of ethnic and regional dynamics, achieving high-quality sustainable development in minority regions. Full article

This study explores the use of construction robots with collaborative path planning and coordination in complex building construction tasks. Current construction processes involving robots are often fragmented due to their single-task focus, with limited research focused on employing multiple construction robots to collaboratively perform tasks. To address such a challenge, this research proposes an improved A* algorithm for global path planning and obstacle avoidance, combined with the development of a BIM-based grid map of the construction site. The leader–follower method is utilized to guide the robot group in maintaining an optimal formation, ensuring smooth collaboration during construction. The methodology includes formalizing building construction site environments into BIM-based grid maps, path planning, and obstacle avoidance, which allows robot groups to autonomously navigate and complete specific tasks such as concrete, masonry, and decoration construction. The results of this study show that the proposed approach achieves significant reductions in pathlength and operational time of approximately 9% and 10%, respectively, while maintaining safety and efficiency compared with traditional manual methods. This research demonstrates the potential of collaborative construction robot groups to enhance productivity, reduce labor costs, and provide a scalable solution for the intelligent transformation of the construction industry; extends the classical A* algorithm by incorporating obstacle density into the heuristic function; and proposes a new node simplification strategy, contributing to the literature on robot motion planning in semi-structured environments. Full article

Research on surfaces generated by curves plays a central role in linking differential geometry with physical applications, especially following Hasimoto’s transformation and the development of Hasimoto-inspired surface models. In this work, we introduce a new class of such surfaces, referred to as RM Hasimoto surfaces, constructed by employing the rotation-minimizing (RM) Darboux frame along both timelike and spacelike curves in Minkowski 3-space ${\mathbb{E}}_1^3$. In contrast to the classical Hasimoto surfaces defined via the Frenet or standard Darboux frames, the RM approach eliminates torsional difficulties and reduces redundant rotational effects. This leads to more straightforward expressions for the first and second fundamental forms, as well as for the Gaussian and mean curvatures, and facilitates a clear classification of key parameter curves. Furthermore, we establish the associated evolution equations, analyze the resulting geometric invariants, and present explicit examples based on timelike and spacelike generating curves. The findings show that adopting the RM Darboux frame provides greater transparency in Lorentzian surface geometry, yielding sharper characterizations and offering new perspectives on relativistic vortex filaments, magnetic field structures, and soliton behavior. Thus, the RM framework opens a promising direction for both theoretical studies and practical applications of surface geometry in Minkowski space. Full article

This conceptual paper presents a framework for understanding how technology can help transform formal education by blurring five foundational boundaries: time and space, knowledge, pedagogy, hierarchy, and community. It is grounded in the interactions between technology and key schooling components, namely, learners, instructors, peers, and content, and promotes thinking about technology integration in schools not merely as instrumental, but as a driver for educational change. We apply the framework to three reported cases of technology integration in different educational contexts, analyzing each in terms of its potential to disrupt traditional boundaries. Through this analysis, we illustrate how certain uses of technology may enable deeper pedagogical shifts and foster more equitable, flexible, and collaborative learning settings. This illustrates the power of the proposed framework in allowing a nuanced understanding of technology integration, and the entanglement of pedagogy and technology for meaningful changes to occur. The paper concludes with recommendations for educators, policymakers, researchers, and designers seeking to promote boundary-blurring innovation. Ultimately, we advocate for a shift in discourse—from using technology to optimize education, to using it to reimagine its foundational structures. Full article

The traditional DC distribution grid is evolving into a meshed structure to create additional energy exchange paths and integrate the rapidly growing renewable energy sources. However, existing converter stations lack sufficient power flow controllability, necessitating the development of multiport power electronic transformers to address potential power flow congestion and high loss issues. This paper proposes a compact multi-terminal modular-multilevel-converter-based power electronic transformer (M³C-PET). This device enables flexible power flow regulation of the connected feeders through adopting two small-capacity power flow control modules (PFCMs). The simple structure and reduced switching count make the proposed PET more competitive and prominent and more cost-effective. Furthermore, this paper elaborates on the operational principle of the proposed device and presents a multilayer power balancing control strategy along with a power flow control scheme. These control strategies are designed based on the internal and external energy distribution mechanism of the proposed PET. The feasibility and effectiveness of the proposed topology and control schemes are rigorously validated through both a MATLAB/Simulink simulation model and a scaled-down experimental prototype. Full article

Dolostone is an important reservoir for hydrocarbons, and significant hydrocarbons have been produced in the Middle Permian Qixia Formation dolostone reservoirs in the southeastern Sichuan Basin. The origin and formation process of the dolomite reservoir in the research area are studied through thin-section, geochemical, and sedimentary cycle analyses and U-Pb geochronology. Three types of dolomites were identified, including stratiform fine-crystalline dolomite (D1), patchy fine-crystalline dolomite (D2), and saddle dolomite cement (SD). D1 and D2 exhibit a range of δ¹³C values from 3.39‰ to 4.21‰ and a range of δ¹⁸O values from −6.06‰ to −5.75‰, indicating a mild depletion of δ¹⁸O relative to coeval seawater while maintaining seawater-equivalent δ¹³C signatures. Their ⁸⁷Sr/⁸⁶Sr ratios and REE patterns indicate seawater-derived fluids for D1 and D2 (both test results showed a U-Pb age of ≈274 Ma) and hydrothermal origin for SD. Sedimentary cycle analysis found that the regression process in the fourth-order sequence is conducive to the formation of dolomite under the background of regression in the third-order sequence. Exposure of bioclastic shoals enabled evaporated seawater reflux, forming penecontemporaneous D1 in fluid-saturated settings. Selective dolomitization occurred in the bioturbation structure with good porosity and permeability, forming D2. In the burial stage, the hydrothermal fluid had a slight transformation on the dolomite and formed SD. This model highlights transgressive–regressive cycle controls on reservoir development, providing exploration criteria for analogous carbonate systems. Full article

The poetic turn—a significant shift in the rhetorical and/or dramatic trajectory of a poem—is a considerably under-recognized aspect of poetry. Despite being a crucial element in the working of so many poems, no large-scale critical conversation on the turn, such as a monograph or a collection of essays, exists. Despite its supposed innovations, the criticism of and about hybrid poetry has done little to change this. While many accomplished poets, including a number of hybrid poets, are alert to the structural dynamics of their poems—that is, they are aware of and significantly engage turns in their work—hybrid poet Jorie Graham is relatively unique in that her poetics and poems foreground turns to an extent and in ways that few others do. Specifically, Graham tropes the turn so that it represents even as it enacts transformative power. Such troping is at work in much of Graham’s oeuvre; however, it reveals itself especially clearly in her most recent collection, To 2040, in which the turn itself becomes one more natural, if sublime, element that is being lost to the ravages of unremitting environmental degradation. In To 2040, the turn becomes a hopeless craning—“we feel our soul turn frantic/in us, craning this way and that”—a desperate gesture that is ultimately empty as it has no safe place to land, no alternative position on which to rest or from which to relaunch. This powerful, if frightening, trajectory in Graham’s work can be difficult to recognize, especially given hybridity’s continued missing of the turn, but it is important to see: doing so can help to recenter the turn in discussions about poetry while also leading to an improved comprehension of Graham’s prophetic, apocalyptic vision. Full article

Photothermal catalysis has emerged as a promising approach for the efficient and cost-effective removal of volatile organic compounds (VOCs). Pt@MnO₂ catalysts have demonstrated excellent performance in the photothermal catalytic oxidation of VOCs. However, current research has predominantly focused on the interaction between Pt and MnO₂, while often overlooking the influence of the MnO₂ crystal phase. Therefore, in this study, we synthesized Pt supported on four crystal phases (α, β, γ, and δ) of MnO₂ and established the structure–activity relationships through performance evaluation and characterization. Among the prepared catalysts, Pt@Mn[δ] exhibited excellent performance and possessed outstanding stability. Crystal structure characterization revealed that the larger specific surface area and lower crystallinity of Pt@Mn[δ] exposed more active sites. XPS analysis indicated the transformation of Mn⁴⁺ to Mn³⁺ on Pt@Mn[δ], leading to the formation of oxygen vacancies. O₂-TPD and H₂-TPR further confirmed the activation of lattice oxygen and the promoted redox cycle of Pt@Mn[δ]. UV-Vis DRS and electrochemical measurements demonstrated that Pt@Mn[δ] exhibited the most pronounced visible-light absorption, the highest photocurrent density, the lowest charge transfer resistance and superior charge carrier mobility. TD-GC-MS analysis indicated that o-xylene underwent alkylation and isomerization, with subsequent oxidation following the Mars–van Krevelen (MvK) mechanism. Full article

This study investigates the dynamic magneto-optical properties of ferrofluid thin films, focusing on how magnetic fields induce light–matter interactions using a device known as Ferrocell. Our findings reveal that incident light interacts with self-assembled, anisotropic nanoparticle structures, transforming the ferrofluid into a highly responsive optical medium. Monochromatic laser experiments confirmed the direct correlation between laser color and diffracted light color offering direct insights into particle orientation and aggregate morphology. We observed significant chromatic shifts, especially in regions under strong perpendicular magnetic fields, which provide compelling evidence of structural colors. This phenomenon stems from wavelength-selective interference and diffraction, reminiscent of photonic crystal behavior, yet characterized by short-range order, classifying the material as a photonic glass. Full article