Search Results (12,536)

Fabric flattening is a critical pre-processing step for automated garment manufacturing. Most existing approaches employ single-arm robotic systems that act at a single contact point. Due to the nonlinear and deformable dynamics of fabric, such systems often require multiple actions to achieve a fully flattened state. This study introduces a dual-arm fabric-flattening method based on a cascaded Proposal–Action network with a hybrid training framework. The PA network is first trained through imitation learning from human demonstrations and is subsequently refined through reinforcement learning with real-world flattening feedback. Experimental results demonstrate that the hybrid training framework substantially improves the overall flattening success rate compared with a policy trained only on human demonstrations. The success rate for a single flattening operation increases from 74% to 94%, while the overall success rate improves from 82% to 100% after two rounds of training. Furthermore, the learned policy, trained exclusively on baseline fabric, generalizes effectively to fabrics with varying thicknesses and stiffnesses. The approach reduces the number of required flattening actions while maintaining a high success rate, thereby enhancing both efficiency and practicality in automated garment manufacturing. Full article

Electrified powertrains in the transportation sector have increased significantly in recent years, thanks to the need for decarbonization of the on-the-road transport means. However, management of powertrains still deserves particular attention to assess necessary improvements for reducing electric consumption and increasing the mileage of the vehicles. In this regard, electric motor cooling is essential for maintaining optimal performance and longevity. In fact, as electric motors operate, they generate heat due to electric and magnetic phenomena as well as mechanical friction. If not properly managed, this heat can lead to decreased efficiency, accelerated wear, or even failure of critical components. Effective cooling systems ensure that the motor runs within its ideal temperature range, reducing the occurrence of the mentioned concerns. This improves operational reliability and, at the same time, contributes to energy savings and reduced maintenance costs over the components’ life. In this study, the cooling of the rotor of a 130-kW electric motor via refrigerating fluid circulating inside the shaft has been investigated. Two configurations of fluid passages have been considered: a direct-through flow crossing the shaft along its axis and a hollow shaft with recirculating flow, with three types of rotating helical configurations at different pitches. The benefits when using nanofluids as a cooling medium have also been evaluated to enhance the heat transfer coefficient and decrease temperature values. Compared with the baseline configuration using standard fluids (water), the proposed solution employing nanofluids demonstrates effectiveness in terms of heat transfer coefficients (up to 28% higher than pure water), with limited impact on pressure losses, thus reducing rotor temperature by up to 30 K with respect to the baseline. This study opens the possibility of integrating the cooling of the rotor with whole electric motor cooling for electric and hybrid powertrains. Full article

The fall armyworm (Spodoptera frugiperda) is an invasive pest of global concern, posing a significant threat to food security. It can be divided into two biotypes: the rice strain (RS) and the corn strain (CS). These two biotypes are nearly indistinguishable morphologically but differ significantly in host adaptability. The two biotypes can hybridize, but the efficiency of their hybridization has not been conclusively determined. Our research has found that the two biotypes of fall armyworm exhibit significant differences in weight gain when feeding on different host plants and artificial diets. The rice strain has a broader diet range, showing notably stronger adaptability to rice and ryegrass compared to the corn strain, suggesting that the rice strain may possess more robust detoxification metabolism. Under laboratory conditions, the two biotypes can hybridize effectively, and their offspring exhibit certain hybrid advantages. The host adaptability traits of the rice strain are dominantly inherited. Our findings provide an important foundation for understanding the biological basis of host adaptability in the rice strain of fall armyworm. Full article

Selecting an appropriate Multi-Criteria Decision-Making (MCDM) algorithm for optimizing marine diesel engine operation presents a complex challenge due to the diversity in mathematical formulations, normalization schemes, and trade-off resolutions across methods. This study systematically evaluates fourteen MCDM algorithms, which are grouped into five primary methodological categories: Scoring-Based, Distance-Based, Pairwise Comparison, Outranking, and Hybrid/Intelligent System-Based methods. The goal is to identify the most suitable algorithm for real-time performance optimization of two stroke marine diesel engines. Using Diesel-RK software, calibrated for marine diesel applications, simulations were performed on a variant of the MAN-B&W-S60-MC-C8-8 engine. A refined five-dimensional parameter space was constructed by systematically varying five key control variables: Start of Injection (SOI), Dwell Time, Fuel Mass Fraction, Fuel Rail Pressure, and Exhaust Valve Timing. A subset of 4454 high-potential alternatives was systematically evaluated according to three equally important criteria: Specific Fuel Consumption (SFC), Nitrogen Oxides (NO_x), and Particulate Matter (PM). The MCDM algorithms were evaluated based on ranking consistency and stability. Among them, Proximity Indexed Value (PIV), Integrated Simple Weighted Sum Product (WISP), and TriMetric Fusion (TMF) emerged as the most stable and consistently aligned with the overall consensus. These methods reliably identified optimal engine control strategies with minimal sensitivity to normalization, making them the most suitable candidates for integration into automated marine engine decision-support systems. The results underscore the importance of algorithm selection and provide a rigorous basis for establishing MCDM in emission-constrained maritime environments. This study is the first comprehensive, simulation-based evaluation of fourteen MCDM algorithms applied specifically to the optimization of two stroke marine diesel engines using Diesel-RK software. Full article

This paper presents a qualitative exploratory study based on the analysis of a representative sample of 35 projects carried out during the period 2020–2022 by six Spanish newspapers: elDiario.es, ABC, IDEAL, El Correo, ElConfidencial.com and El País. This study aims to detect and analyze the main elements of multimedia, crossmedia and transmedia content in the selected projects using an original analysis sheet designed for this research. In relation to the categories proposed in the categorization model, in this work we will focus on analyzing two in particular: authorship and information architecture. The projects were selected based on criteria of appropriateness, quality and innovation, as well as the results of semi-structured interviews with the heads and innovation managers (laboratories) of the media included in the framework of the projects ‘NEWSNET: News, Networks, and Users in the Hybrid Media System: Transformation of the Media Industry and the News in the Post-Industrial Era’ and ‘IAMEDIA: Impact of Artificial Intelligence and Algorithms on Online Media, Journalist and Audiences’. The aim of the qualitative analysis is to propose a list of aspects, characteristics, and fundamentals in the ideation, elaboration, and distribution of these types of products. We conclude that the results of applying the designed analysis sheet help us to understand these processes and also to propose alternatives and improvements in its design and implementation Full article

This study presents the modeling and analysis of a composite structure incorporating an embedded macro fiber composite (MFC) patch. MFC actuators are available in several variants, with types P1 and P2 being the most commonly used. In this paper, an electromechanical model of the hybrid structure is developed, and experimental procedures are outlined for identifying selected system parameters. In the first phase of the study, two separate cantilever beam specimens are investigated—one with an embedded P1 patch and the other with a P2 patch. Their behaviors are tested and compared to identify and critically assess the advantages and limitations associated with each MFC type. In the second phase, a more complex system—a bistable cantilever shell—is examined. The choice of the appropriate MFC type (P1 or P2) for this structure is based on the findings obtained in the first phase. For the system incorporating the selected MFC patch, the dynamic response is analyzed in the vicinity of both stable equilibrium states, which are characterized by significantly different levels of pre-strain and pre-stress. The study concludes with highlights for the design of smart composite structures with integrated MFC patches. Full article

In this study, SweAMI FISH fluorescence in situ hybridization and immunofluorescence were used to analyze the spatiotemporal expression characteristics of the TBXT gene and Brachyury protein in 16-day-old Hulunbuir sheep embryos and 19-day-old Hu sheep embryos and to explore their regulatory effects on the development of sheep tails and related organs. The study showed the following: At 16 days of embryonic age, the TBXT gene was concentratedly expressed in the heart, somites, neural tube, and mesonephros of both sheep breeds; at 19 days of embryonic age, it was concentratedly expressed in the limb ectoderm and tail bud of Hulunbuir sheep, and in the midgut and tail bud of Hu sheep. At 16 days of embryonic age, the BRACHYURY protein was concentratedly expressed in the neural tube, somites, brain vesicles, and mesonephros of both sheep breeds; at 19 days of embryonic age, it was concentratedly expressed in the heart and hindgut of Hulunbuir sheep, and in the tail bud and hindgut of Hu sheep. In summary, this shows that there are differences in the temporal and spatial expressions of the TBXT gene and BRACHYURY protein between the two sheep breeds. There are also site-specific and time-specific differences in the regulation of the above genes and proteins during tail and related organ development between the two breeds, which confirms that the molecular regulation pathways of tail and related organ development are different between the two breeds. This study provides an experimental basis for screening molecular markers related to goat tail development and breed improvement. Full article

We synthesized the current evidence from the literature and conducted a 2 × 3 factorial experiment to quantify the impact of thermocycling on the Vickers microhardness (HV) of dental CAD/CAM materials: VITA ENAMIC (VE, polymer-infiltrated ceramic network) and CERASMART (CS, nanofilled resin-matrix). Sixty polished specimens (n = 10 per Material × Cycles cell; 12 × 2 × 2 mm) were thermocycled at 5–55 °C (0, 10,000, 20,000 cycles; 30 s dwell, ≈10 s transfer) and tested as HV0.3/10 (300 gf, 10 s; five indentations/specimen with standard spacing). Assumptions regarding the model residuals were met (Shapiro–Wilk W ≈ 0.98, p ≈ 0.36; Levene F(5,54) ≈ 1.12, p ≈ 0.36), so a two-way ANOVA (Type II) with Tukey’s HSD post hoc (α = 0.05) was applied. VE maintained consistently higher HV than CS at all cycle levels and showed a smaller drop from baseline: VE (mean ± SD): 200.2 ± 10.8 (0), 192.4 ± 13.9 (10,000), and 196.7 ± 9.3 (20,000); CS: 60.8 ± 6.1 (0), 53.4 ± 4.7 (10,000), and 62.1 ± 3.8 (20,000). ANOVA revealed significant main effects from the material (η²p = 0.972) and cycles (η²p = 0.316), plus a Material × Cycles interaction (η²p = 0.201). Results: Thermocycling produced material-dependent changes in microhardness. Relative to baseline, VE varied by −3.9% (10,000) and −1.7% (20,000), while CS varied by −12.2% (10,000) and +2.1% (20,000); from 10,000→20,000 cycles, microhardness recovered by +2.2% (VE) and +16.3% (CS). Pairwise comparisons were consistent with these trends (CS decreased at 10,000 vs. 0 and recovered at 20,000; VE only showed a modest change). Conclusions: Thermocycling effects were material-dependent, with smaller losses and better retention in VE (PICN) than in CS. These results align with the literature (resin-matrix/hybrids are more sensitive to thermal aging; polished finishes mitigate losses). While HV is only one facet of performance, the superior retention observed in PICN under thermal challenge suggests the improved preservation of superficial integrity; standardized reporting of aging parameters and integration with wear, fatigue, and adhesion outcomes are recommended to inform indications and longevity. Full article

Research Problem and Gap: Live streaming sales rely heavily on streamers, with both genuine and AI-generated virtual streamers gaining popularity. However, these streamer types possess contrasting capabilities. Genuine streamers are superior at building trust and reducing product valuation uncertainty but have limited reach, while virtual streamers excel at broad audience engagement but are less effective at mitigating uncertainty, often leading to higher product return rates. This trade-off creates a critical strategic gap; that is, brand firms lack clear guidance on whether to invest in genuine or virtual streamers or adopt a hybrid approach for their live channels. Objective and Methods: This study addresses this gap by developing a theoretical analytical model to determine a monopolistic brand firm’s optimal streamer strategy among three options: using only a genuine streamer, only a virtual streamer, or a combination of the two (hybrid approach). The researchers model consumer utility, factoring in uncertainty and the streamers’ differential impact on reach, to derive optimal decisions on pricing and streamer selection. Results and Findings: The analysis yields several key findings with direct managerial implications. First, while a hybrid strategy leverages the complementary strengths of both streamer types, its success depends on employing high-quality streamers; in other words, this strategy does not justify settling for inferior talent of either type. Second, employing a virtual streamer requires a moderate price reduction to compensate for higher consumer uncertainty and prevent high profit-eroding return rates. Third, a pure strategy (only genuine or only virtual) is optimal only when that streamer type has a significant cost advantage. Otherwise, the hybrid strategy tends to be the most profitable. Moreover, higher product return costs directly diminish the viability of virtual streamers, making a genuine or hybrid strategy more attractive for products with expensive return processes. Conclusions: The results provide a clear framework for brand firms—that is, the choice of streamer is a strategic decision intertwined with pricing and product return costs. Firms should pursue a hybrid strategy not as a compromise but as a premium approach, use targeted pricing to mitigate the risk of virtual streamers, and avoid virtual options altogether for products with high return costs. Full article

We investigated the impact of incorporating quantitative reasoning for deeper sense-making in a Quantum Interactive Learning Tutorial (QuILT) on students’ conceptual performance using a framework emphasizing integration of conceptual and quantitative aspects of quantum optics. In this investigation, we compared two versions of the QuILT that were developed and validated to help students learn various aspects of quantum optics using a Mach Zehnder Interferometer with single photons and polarizers. One version of the QuILT is entirely conceptual while the other version integrates quantitative and conceptual reasoning (hybrid version). Performance on conceptual questions of upper-level undergraduate and graduate students who engaged with the hybrid QuILT was compared with that of those who utilized the conceptual QuILT emphasizing the same concepts. Both versions of the QuILT focus on the same concepts, use a scaffolded approach to learning, and take advantage of research on students’ difficulties in learning these challenging concepts as well as a cognitive task analysis from an expert perspective as a guide. The hybrid and conceptual QuILTs were used in courses for upper-level undergraduates or first-year physics graduate students in several consecutive years at the same university. The same conceptual pre-test and post-test were administered after traditional lecture-based instruction in relevant concepts and after student engaged with the QuILT, respectively. We find that the post-test performance of physics graduate students who utilized the hybrid QuILT on conceptual questions, on average, was better than those who utilized the conceptual QuILT. For undergraduates, the results showed differences for different classes. One possible interpretation of these findings that is consistent with our framework is that integrating conceptual and quantitative aspects of physics in research-based tools and pedagogies should be commensurate with students’ prior knowledge of physics and mathematics involved so that students do not experience cognitive overload while engaging with such learning tools and have appropriate opportunities for metacognition, deeper sense-making, and knowledge organization. In the undergraduate course in which many students did not derive added benefit from the integration of conceptual and quantitative aspects, their pre-test performance suggests that the traditional lecture-based instruction may not have sufficiently provided a “first coat” to help students avoid cognitive overload when engaging with the hybrid QuILT. These findings suggest that different groups of students can benefit from a research-based learning tool that integrates conceptual and quantitative aspects if cognitive overload while learning is prevented either due to students’ high mathematical facility or due to their reasonable conceptual facility before engaging with the learning tool. Full article

Hybrid marine energy platforms that integrate wave energy converters (WECs) and hydrokinetic turbines (HKTs) offer potential to improve energy yield and system stability in marine environments. This study identifies a compatible WEC–HKT integrated system concept through a structured concept selection framework based on multi-criteria decision analysis (MCDA). The framework follows a two-stage process: individual technology assessment using eight criteria (efficiency, TRL, self-starting capability, structural simplicity, integration feasibility, environmental adaptability, installation complexity, and indicative cost) and pairing evaluation using five integration-focused criteria (structural compatibility, PTO feasibility, mooring synergy, co-location feasibility, and control compatibility). Criterion weights were assigned through a four-level importance framework based on expert judgment from 11 specialists, with unequal weights for the individual evaluation and equal weights for the integration stage. Four WEC types (oscillating water column, point absorber, overtopping wave energy converter, and oscillating wave surge converter) and four HKT types (Darrieus, Gorlov, Savonius, and hybrid Savonius–Darrieus rotor) are assessed using literature-derived scoring and weighted ranking. The results show that the oscillating water column achieved the highest weighted score among the WECs with 4.05, slightly ahead of the point absorber, which scored 3.85. For the HKTs, the Savonius rotor led with a score of 4.05, surpassing the hybrid Savonius–Darrieus rotor, which obtained 3.50, by 0.55 points. In the pairing stage, the OWC–Savonius configuration achieved the highest integration score of 4.2, surpassing the PA–Savonius combination, which scored 3.4, by 0.8 points. This combination demonstrates favorable structural layout, PTO independence, and mooring simplicity, making it the most promising option for early-stage hybrid platform development. Full article

Deploying Large Language Models (LLMs) in edge environments faces two major challenges: (i) the conflict between limited device resources and high computational demands, and (ii) the dynamic impact of user mobility on model partitioning and task offloading decisions. To address these challenges, this paper proposes the Dynamic Adaptive Partitioning and Offloading (DAPO) framework, an intelligent solution for multi-user, multi-edge Mobile Edge Intelligence (MEI) systems. DAPO employs a Deep Deterministic Policy Gradient (DDPG) algorithm to jointly optimize the model partition point and the task offloading destination. By mapping continuous policy outputs onto valid discrete actions, DAPO efficiently addresses the high-dimensional hybrid action space and dynamically adapts to user mobility. Through extensive simulations, we demonstrate that DAPO outperforms baseline strategies and mainstream RL methods, achieving up to 27% lower latency and 18% lower energy consumption compared to PPO and A2C, while maintaining fast convergence and scalability in dynamic mobile environments. Full article

Unimodal brain–computer interfaces (BCIs) often suffer from inherent limitations due to the characteristic of using single modalities. While hybrid BCIs combining electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) offer complementary advantages, effectively integrating their spatiotemporal features remains a challenge due to inherent signal asynchrony. This study aims to develop a novel deep fusion network to achieve synergistic integration of EEG and fNIRS signals for improved classification performance across different tasks. We propose a novel Temporal-Spatial Fusion Network (TSFNet), which consists of two key sublayers: the EEG-fNIRS-guided Fusion (EFGF) layer and the Cross-Attention-based Feature Enhancement (CAFÉ) layer. The EFGF layer extracts temporal features from EEG and spatial features from fNIRS to generate a hybrid attention map, which is utilized to achieve more effective and complementary integration of spatiotemporal information. The CAFÉ layer enables bidirectional interaction between fNIRS and fusion features via a cross-attention mechanism, which enhances the fusion features and selectively filters informative fNIRS representations. Through the two sublayers, TSFNet achieves deep fusion of multimodal features. Finally, TSFNet is evaluated on motor imagery (MI), mental arithmetic (MA), and word generation (WG) classification tasks. Experimental results demonstrate that TSFNet achieves superior classification performance, with average accuracies of 70.18% for MI, 86.26% for MA, and 81.13% for WG, outperforming existing state-of-the-art multimodal algorithms. These findings suggest that TSFNet provides an effective solution for spatiotemporal feature fusion in hybrid BCIs, with potential applications in real-world BCI systems. Full article

We study unconventional multipole moments arising from noncollinear magnetic structures within an augmented framework encompassing electric, magnetic, magnetic toroidal, and electric toroidal multipoles. Employing a tight-binding model for an s-p hybridized orbital system, we analyze two spiral magnetic textures and classify the resulting multipoles according to magnetic point group symmetry. Different spiral wave types, such as cycloidal and proper-screw forms, activate distinct multipole components, with odd-parity multipoles emerging from local s-p parity mixing induced by magnetically driven inversion-symmetry breaking. Calculated multipole structure factors reveal finite-q peaks originating from higher-order magnetic-dipole-scattering processes and their characteristic couplings between Fourier components of the magnetic dipole texture. Our results demonstrate that magnetic ordering can generate parity-mixed states without intrinsic structural inversion asymmetry, offering new pathways to realize cross-correlation phenomena in functional magnetic materials. Full article

This study investigates the effect of incorporating an electromagnetic harvester inside the bluff body of a 2-DoF hybrid harvester in comparison to a standalone piezoelectric harvester for various external loads. The harvester is excited through a vortex-induced vibration owing to the resultant wake vortices created behind the bluff body. The coupled dynamics of the two harvester components are modeled, and numerical simulations are conducted to evaluate the system’s performance under varying electrical loads. Numerical results show that at high, optimum electrical load, the standalone piezoelectric harvester outperforms the hybrid harvester. Nevertheless, for small electrical loads, the results show that the hybrid harvester outperforms the standalone PZT harvester by up to 18% in peak power output, while reducing the bandwidth by approximately 10% compared to the standalone piezoelectric harvester. Optimal spring stiffness values were identified, with the hybrid harvester achieving its maximum output power at a spring stiffness of 83.56 N/m. These findings underscore the need for careful design considerations, as the hybrid harvester may not achieve enhanced power output and bandwidth under higher electrical loads. Full article