Search Results (213)

The upscaling of turbines in the offshore wind industry has been unprecedented, as compared to 5–6 MW rated turbines 10 years ago. A typical 20–26 MW rated turbine in modern commercial applications (MingYang MySE 18.X-20 MW installed in 2025 and 26 MW prototype by Dongfang Electric tested in 2025) has been demonstrated. This scaling has been made possible by increasing rotor diameters (>250 m) and hub heights (>150–180 m) to achieve capacity factors of up to 55–65%, annual energy generation of more than 80 GWh/turbine, and significant decreases in levelised cost of energy (LCOE) to current values of up to 63–65 USD 2023/MWh globally averaged in 2023 (with minor variability in 2024 due to market changes and new regional areas). The paper analyses turbine upscaling over three levels of hierarchy, including turbine scale—rated capacity and physical aspect, project scale—multi-gigawatts of farms, and market scale—the global pipeline > 1500 GW level, and combines techno-economic evaluation, structural evaluation of loads, and infrastructure needs assessment. The upscaling has the advantage of reducing the number of turbines dramatically (e.g., 500 to 67 turbines in a 1 GW farm, as turbine size is increased to 15 MW) and balancing-of-plant (BoP) CAPEX (turbine-to-turbine foundations and cables) by some 20 to 30 percent per unit of capacity, and serial production learning rates of between 15 and 18% per doubling of capacity. But the problems that come with the increase in ultra-large designs are nonlinear increments in mass and load (i.e., blade-root and tower-bending moments), logistical constraints (blades > 120 m, nacelle up to 800–1000 tonnes demanding special vessels and ports), supply-chain issues (rare-earth materials, vessel shortages increase day rates by 30–50%), and technology limitations (aeroelastic compounded by numerical differences between reference 5 MW, 10 MW, and 15 MW models), it becomes evident that there is a significant increase in deflections of the tower and blades and platform surge/pitch responses with continued increases in power levels, but without a correspondingly mature infrastructure. The regional differences (mature ports of Europe vs. U.S. Jones Act restrictions vs. scale-up of vessels/manufacturing in China) lead to the necessity of optimisation depending on the context. The analysis concludes that, to the extent of mature markets with adapted logistics, continuous upscaling is an effective business strategy and can result in 5 to 12 percent further reductions in LCOE, but beyond that point, gains become marginal or even negative, as risks and costs increase. The competitiveness of the future depends on multi-scale/multi-market-based approaches—modular-based families of turbines, programmatic standardisation, vibration control innovations, and industry coordination towards supply-chain alignment and standards. Its major strength is that it transcends mere size–cost relationships and shows how nonlinear structural processes, aero-hydro-servo-elastic interactions, and bottlenecks in logistical systems are becoming more determinant of the efficiency of ultra-large turbines. The study demonstrates that upscaling turbines has LCOE benefits through the support of associated improvements in installation facility, supply-chain preparedness, and structural vibration control potential, based on the comparisons of quantitative loads, techno-economic scaling trends, and regional market differentiation. Full article

Spatial efficiency drives the adoption of integrated station–bridge structures in maglev transit, yet the rigid coupling between track and station poses inherent challenges to vibration serviceability. This study isolates the impact of support constraints, specifically contrasting rigid connections with pinned supports, on the dynamic performance of a five-story maglev station. Using a unified, high-fidelity 3D coupled model that incorporates electromagnetic suspension nonlinearity, we evaluated structural responses under train speeds of 60–120 km/h. Simulations identify a critical operational threshold: while the waiting hall remains compliant with standard comfort criteria (DIN 4150-3), the platform floor exceeds the 1.5% g acceleration limit during dual-track operations at speeds ≥ 100 km/h. Beyond standard safety checks, the main scientific innovation of this study is revealing the mechanical transmission paths of structure-borne vibrations at the track-frame interface. The results demonstrate that rigid connections create full mechanical coupling, directly passing train-induced bending moments into the station frame. Conversely, pinned supports release the rotational degrees of freedom, which physically cuts off the primary energy transmission route. By explaining this structural decoupling mechanism, this work moves beyond a specific engineering case study to provide a fundamental theoretical framework for vibration control in complex maglev hubs. Full article

Local Lorentz Invariance is one of the fundamental postulates of General Relativity, making its experimental verification of paramount importance. Given that various frontier theoretical models predict potential symmetry breaking, the Standard Model Extension framework has been established to systematically study such phenomena. Within the Standard Model Extension gravitational sector, the high-order Lorentz-violating terms with mass dimension $d=6$ exhibit a rapid signal decay with distance, providing a distinct detection advantage in short-range gravity experiments. This work is dedicated to optimizing the testing schemes for $d=6$ Lorentz-violating coefficients. Based on a high-precision torsion balance platform, we propose a novel scheme featuring a comb-stripe design. The improvements are twofold: first, the spatial orientation of the experimental apparatus is optimized to leverage the modulation effects of the Earth’s rotation, thereby enhancing the capability to distinguish and constrain different violation parameters; second, the test and source masses are reconfigured into specifically designed stripe patterns to significantly amplify the fringe-field signals sensitive to Lorentz-violating effects. This paper systematically elaborates on the theoretical foundation and design principles of the new scheme. By performing a detailed comparison of the constraint potentials of various stripe configurations, the five-stripe geometry is identified as the optimal experimental configuration. This study provides a new experimental methodology for exploring physics beyond the Standard Model at higher levels of precision. Full article

Moving beyond traditional sociodemographic models, this study investigates the psychometric drivers of lexical change. Using Swiss German as a case study, we compare historical data from the Sprachatlas der deutschen Schweiz (1939–1958) with a recent large-scale app-based survey (N = 1013) to quantify trajectories over the past century. We identify four distinct mechanisms: exogenous convergence (Schmetterling), endo-normative leveling (Rande), endogenous innovation and divergence (schlittschuhlaufen), and diachronic persistence (Stäge). For the locally rooted speakers in our dataset, structural analysis indicates that traditional variables carry less weight than expected. While age remains the primary vertical predictor, psychological factors outperform traditional variables (e.g., gender, social networks) in this environment of ubiquitous exposure. Multivariate models demonstrate that lexical choices are strongly influenced by individual disposition: traits such as agreeableness accelerate the adoption of supraregional forms, whereas a strong local identity functions as a “brake” against standardization. Ultimately, while macro-factors create the pressure for change, individual micro-factors determine whether it takes hold. A speaker’s attitude acts as a “filter” and their personality as a “gate,” deciding whether they accept or resist new forms. These findings challenge purely structural accounts, suggesting that for these locally rooter speakers, even without high physical mobility, lexical change is shaped by a psychometric architecture. Full article

In ground-based short-wave infrared (SWIR) astronomical observations, temperature drift in the detector readout circuit often introduces nonlinear, spatially non-uniform stripe noise together with Gaussian noise, making weak stellar targets easily submerged and difficult to detect. To address this challenge, we propose a structurally guided weighted low-rank denoising method for infrared star images. Going beyond traditional spatial filtering and standard low-rank decomposition, the proposed method integrates physical priors with mathematical optimization into a unified framework. First, the point spread function (PSF) characteristics of stellar targets are used to construct a hierarchical structural filter, which is further transformed into adaptive prior weights. This design preserves weak-target energy while suppressing noise during iterative optimization. Second, by exploiting the global spatial correlation of the image, residual stripes and the background are jointly modeled as a low-rank component for effective separation. Finally, Bilateral Random Projection (BRP) is introduced to accelerate the weighted soft-thresholding iterations. Experiments on real ground-based observation data, together with ablation studies and sensitivity analyses, demonstrate that the proposed method effectively suppresses structured stripe interference while preserving weak stellar targets under low-SNR conditions. In addition, the acceleration module further improves computational efficiency, making the framework more suitable for practical real-time processing. Full article

Radio-frequency (RF) impairments such as thermal noise, phase noise, and nonlinear distortion are inherently coupled in practical multiple-input multiple-output (MIMO) transceivers, yet most existing mitigation techniques treat them independently or rely on correlation-based black-box learning models. These approaches often fail to generalize under varying operating conditions because they do not capture the underlying causal relationships among hardware impairments. This paper proposes a causal representation learning framework that jointly models and mitigates coupled RF impairments by learning disentangled latent variables aligned with their physical causal structure. A causal variational autoencoder with a structured physics-informed prior and causal regularization is developed to recover impairment-specific representations and enable targeted compensation under diverse channel conditions. The framework is evaluated in a controlled MIMO simulation environment to systematically analyze impairment interactions and mitigation performance. Experimental results show that the proposed method significantly outperforms both classical receivers and conventional learning-based approaches. In particular, the framework achieves an average BER reduction of approximately 57% compared with the classical model-based receiver and about 30% relative to correlation-based deep learning models, while also outperforming recent variational autoencoder-based MIMO detectors in robustness under unseen operating conditions. The output signal-to-noise ratio improves by up to 2.2 dB across the evaluated SNR range. Furthermore, latent representation analysis shows a substantial reduction in cross-covariance, with the disentanglement score decreasing from above 0.48 in standard variational models to approximately 0.12 using the proposed causal approach. Under unseen combinations of SNR and impairment severity, the proposed model achieves the lowest BER degradation and a robustness score of 0.86, confirming improved generalization beyond the training distribution. These results demonstrate that causal representation learning provides a principled and effective solution for modeling and mitigating coupled RF impairments in MIMO communication systems. Full article

We review recent experimental progress in charmed baryon physics achieved by the Belle and Belle II experiments, with an emphasis on measurements reported since 2022. Using large $e^{+}{e}^{-}$ data samples collected at or near the ${\rm Y}\left(4S\right)$ resonance, Belle and Belle II have delivered a series of precision results on hadronic weak decays of anti-triplet charmed baryons, providing critical inputs for testing flavor-symmetry approaches and dynamical models. We summarize new and improved branching fraction determinations for ${\Xi }_c^0$, ${\Xi }_c^{+}$, and ${\Lambda }_c^{+}$ decays, including channels with neutral hadrons in the final state and the first measurements of several singly Cabibbo-suppressed modes. We also highlight the first determination of the decay asymmetry parameter in ${\Xi }_c^0\to {\Xi }^0{\pi }^0$. In addition, we review the first Belle II measurements of $CP$ asymmetries in three-body singly Cabibbo-suppressed decays of ${\Xi }_c^{+}$ and ${\Lambda }_c^{+}$, and we discuss their implications for U-spin sum rules and searches for physics beyond the Standard Model. Finally, we look forward to exploiting the Belle II data set to perform more stringent tests of decay dynamics. Full article

The construction industry faces persistent challenges in productivity, efficiency, and sustainability. Digital twin (DT) technology has emerged as a promising pathway for lifecycle optimisation, yet its construction adoption remains limited. Key barriers include fragmentation across project phases, weak data continuity at handover, and conceptual ambiguity between DT and Building Information Modelling (BIM). This systematic literature review analyses 160 peer-reviewed studies (2018–2026) selected from 463 Scopus records using a PRISMA-guided process and inter-rater reliability testing (Cohen’s κ = 0.83). The review clarifies that DTs extend beyond BIM in three ways: they enable bidirectional, automated physical-digital data exchange; integrate heterogeneous real-time sources such as IoT sensors and operational systems; and maintain lifecycle continuity from design through to end-of-life. Select advanced implementations report notable performance gains. These include rework and logistics reductions of up to 80%, cost savings of approximately 5%, schedule acceleration of around two months, energy reductions of 15–30%, and maintenance cost reductions of 10–25%. These figures reflect case-level outcomes from high-performing pilots and should not be read as typical industry benchmarks. Broader adoption remains constrained by interoperability gaps, data quality challenges, digital maturity deficits, misaligned stakeholder incentives, and paper-based regulatory environments. DTs represent a socio-technical transformation, not a standalone technology upgrade. Realising their potential requires coordinated progress in standards development, governance frameworks, collaborative delivery models, and workforce capability. Future research should focus on scalable interoperability, longitudinal lifecycle value validation, human-centred adoption strategies, and sustainability assessment methods to support evidence-based diffusion of DTs in the built environment. Full article

The electroweak phase transition serves as a crucial portal to explore physics beyond the Standard Model, with profound implications for gravitational waves, baryogenesis, dark matter, and vacuum stability. We review the computational workflow for analyzing cosmological phase transitions, which includes constructing the finite-temperature effective potential, identifying possible phases, tracing transition history, calculating transition rates, milestone temperatures, and thermal parameters, as well as the numerical tools developed for each step. We compare the functionalities, strategies, and applicable scopes of these tools, aiming to provide a practical guide that helps researchers select the most appropriate computational resources for their studies. Full article

The challenge of designing generative AI (GenAI) tutors that are both pedagogically sound and effective for guided inquiry remains significant. This paper introduces and evaluates a replicable design framework-termed a Pedagogy-to-Prompt Engineering Framework-that systematically translates established pedagogical models into structured AI interactions. We engineered a 5E-structured GenAI coach by integrating the 5E Learning Cycle as the instructional architecture and the 5S Prompting Principles to govern the AI’s dialogue. The coach was evaluated in a middle school chemistry context (N = 60) focusing on procedural skill acquisition for balancing chemical equations. A quasi-experimental study showed the GenAI group achieved significantly higher learning gains than a control group receiving traditional instruction (t(58) = 2.646, p = 0.011, Cohen’s d = 0.68). Crucially, a Johnson-Neyman analysis revealed that the coach was particularly beneficial for students with lower prior knowledge (pre-test scores < 39.39), effectively narrowing the achievement gap. Furthermore, Lag Sequential Analysis of the interaction logs confirmed that the student-AI dialogue successfully adhered to the intended 5E pedagogical sequence (e.g., Engage → Explore transition, z = 11.157). This study demonstrates that the proposed framework is a viable method for creating effective, scalable AI-driven learning environments. Beyond chemistry, this approach is readily adaptable to other STEM disciplines requiring guided inquiry, such as physics and mathematics. By validating a low-code, pedagogy-first methodology, this work offers a scalable blueprint for instructional designers to bridge the gap between generative AI capabilities and rigorous educational standards. Full article

Background: Although frailty has emerged as an important determinant of outcomes following cardiovascular surgery, the clinical significance of early postoperative physical frailty assessed during the acute recovery phase has not been investigated. Methods: We conducted a single-center retrospective observational study including patients who underwent cardiac or aortic surgery and completed a standardized physical function assessment within 10 days postoperatively. Physical frailty was defined using four objective indicators: Medical Research Council (MRC) sum score, gait speed, Timed Up and Go test, and five-times sit-to-stand test. Frailty was defined as the presence of ≥3 abnormal physical frailty indicators. Clinical outcomes included hospital length of stay (LOS) and postoperative medical complications. Negative binomial regression was used to evaluate factors associated with hospital LOS. Results: Among 441 patients included in the analysis, 308 (69.8%) were classified as frail. Frail patients were older and demonstrated significantly impaired physical performance across all frailty indicators (all p < 0.001). Frailty was associated with longer ICU stay and hospital LOS (both p < 0.001). In multivariable negative binomial regression, postoperative frailty was independently associated with prolonged hospital LOS (incidence rate ratio [IRR] 1.38, 95% CI 1.26–1.51; p < 0.001), after adjustment for age and timing of frailty assessment. Additional adjustment for surgical approach and surgical target did not improve model fit. Postoperative frailty was not significantly associated with the overall incidence of medical complications. Conclusions: Early postoperative physical frailty, assessed during the acute recovery phase, is independently associated with prolonged hospitalization after cardiac and aortic surgery. These findings suggest that early functional vulnerability captures clinically meaningful risk beyond surgical characteristics and may serve as a valuable target for postoperative risk stratification and rehabilitation planning. Full article

Additive manufacturing has emerged as a key enabling technology for in-space manufacturing, offering the potential to reduce logistics mass, enhance mission autonomy, and support long-duration exploration. The suppression of gravity-driven phenomena fundamentally alters melt pool dynamics, heat transfer, surface-tension-dominated flow, and defect formation, limiting the direct transferability of terrestrial AM process knowledge to space applications. This paper reviews the current understanding of metallic additive manufacturing process physics under reduced gravity, with emphasis on melt pool behavior, dimensional stability, and in situ monitoring constraints. Approaches for qualification and certification are critically examined, including the applicability of existing AM standards, the role of digital twins and model-based verification, and emerging strategies for space-based validation. Enabling technologies such as autonomous and AI-assisted fabrication, compact hardware architectures, and alternative energy sources are discussed in the context of reliable in-space operation. By synthesizing current developments and identifying key limitations and open challenges, the review provides a roadmap for advancing additive manufacturing toward operational readiness, supporting sustainable exploration, in-space infrastructure development, and long-duration human presence beyond low Earth orbit. Full article

The microscopic origin of the de Sitter entropy remains a central puzzle in quantum gravity that is related to the cosmological constant problem. Within the paradigm of $\mathcal{H}$olographic $\mathcal{N}$aturalness, we propose that this entropy is carried by a vast number of light, coherent degrees of freedom—called “hairons”—which emerge as the moduli of gravitational instantons on orbifolds. Starting from the Euclidean de Sitter instanton ($S^4$), we construct a new class of orbifold gravitational instantons, $S^4/{\mathbb{Z}}_N$, where N corresponds to the de Sitter entropy. We demonstrate that the dimension of the moduli space of these instantons scales linearly with N, and we identify these moduli with the hairon fields. A ${\mathbb{Z}}_N$ symmetry, derived from Wilson loops in the instanton background, ensures the distinguishability of these modes, leading to the correct entropy count. The hairons acquire a mass of the order of the Hubble scale and exhibit negligible mutual interactions, suggesting that the de Sitter vacuum is a coherent state, or Bose–Einstein condensate, of these fundamental excitations. Then, we present a novel framework which unifies neutrino mass generation with the cosmological constant through gravitational topology and holography. The small neutrino mass scale emerges naturally from first principles, without requiring new physics beyond the Standard Model and Gravity. The gravitational Chern–Simons structure and its anomaly with neutrinos force a topological Higgs mechanism, leading to neutrino condensation via $S^4/{\mathbb{Z}}_N$ gravitational instantons. The number of topological degrees of freedom $N\sim M_{\mathrm{P}}^2/\mathrm{\Lambda }\sim {10}^{120}$ provides both the holographic counting of the de Sitter entropy and a $1/N$information see-saw mechanism for neutrino masses. Our framework makes the following predictions: (i) a neutrino superfluid condensation forming Cooper pairs below meV energies, as a viable candidate for cold dark matter; (ii) a possible resolution of the strong CP problem through a QCD composite axion state; (iii) time-varying neutrino masses which track the evolution of dark energy; and (iv) several distinctive signatures in astroparticle physics, ultra-high-energy cosmic rays and high magnetic field experiments. Full article

Objectives: Field-based testing is commonly used to evaluate key physical qualities related to soccer performance. However, limited research has examined the degree of shared variance among measures of aerobic capacity, change of direction (COD), and explosive power in youth athletes. This study investigated the relationships between the 20 m shuttle run (20MSR), T-test (TT), and vertical countermovement jump (CMJ) to determine their unique and overlapping contributions to each other’s performance in competitive youth soccer players. Methods: Twenty-five competitive male youth soccer players (13.7 ± 0.8 years) completed standardized assessments of TT, CMJ, and 20MSR during pre-season evaluations. Pearson correlations and hierarchical multiple regression analyses were used to examine associations and independent variance explained among the performance measures. Results: Large, significant correlations were observed between TT and CMJ (r = −0.65, p < 0.001), TT and 20MSR (r = −0.59, p < 0.001), and CMJ and 20MSR (r = 0.53, p = 0.007). CMJ explained 42.3% of TT variance, whereas adding 20MSR did not significantly improve model fit (ΔR² = 0.087, p = 0.062). Across models, aerobic capacity did not contribute significant unique variance beyond neuromuscular performance. Conclusions: COD and lower-body power share a common physiological foundation in youth soccer athletes, while aerobic capacity represents a distinct performance domain. When field tests are administered under applied conditions typical of youth soccer environments, TT and CMJ demonstrate substantial shared variance, whereas 20MSR remains largely independent. Therefore, the findings support the continued use of multi-modal testing batteries in practice. Full article

Non-muscle-invasive bladder cancer (NMIBC) accounts for approximately 70% of newly diagnosed bladder cancer cases but exhibits significant clinical heterogeneity in treatment response and progression risk. While intravesical bacillus Calmette–GuérinCa (BCG) therapy remains the gold standard for high-risk disease, approximately 30–50% of patients experience BCG failure, creating a critical decision point between additional bladder-sparing therapy (BST) and early radical cystectomy (RC). Recent clinical data from the CISTO study suggest that, in appropriately selected patients, RC may be associated with higher 12-month recurrence-free survival while maintaining comparable cancer-specific survival and physical functioning. In this narrative review, we synthesize contemporary evidence on NMIBC genomic and transcriptomic subtypes, immune contexture, and clinicopathologic features associated with BCG response and progression risk, with emphasis on clinically oriented classification systems such as BCG Response Subtypes (BRS1–3) and UROMOL21. We highlight how tumor-intrinsic biology (e.g., EMT-associated programs), immune phenotypes (inflamed vs. immune-cold microenvironments), and genomic alterations may help refine risk stratification beyond traditional clinicopathologic models. To facilitate clinical integration, we propose a conceptual decisional framework that combines molecular subtype assignment, immune profiling, key pathologic risk factors, and patient considerations to generate probabilistic risk tiers that support selection among early RC, BST, and clinical trial strategies. Standardized multicenter cohorts and prospective evaluation are needed to validate integrated models and define their clinical utility for the precision timing of cystectomy in BCG-unresponsive NMIBC. Full article