Search Results (497)

Cancer metabolic reprogramming plays a critical role in tumor progression and therapeutic resistance, underscoring the need for advanced analytical strategies. Metabolomics, leveraging mass spectrometry and nuclear magnetic resonance (NMR) spectroscopy, offers a comprehensive and functional readout of tumor biochemistry. By enabling both targeted metabolite quantification and untargeted profiling, metabolomics captures the dynamic metabolic alterations associated with cancer. The integration of metabolomics with machine learning (ML) approaches further enhances the interpretation of these complex, high-dimensional datasets, providing powerful insights into cancer biology from biomarker discovery to therapeutic targeting. This review systematically examines the transformative role of ML in cancer metabolomics. We discuss how various ML methodologies—including supervised algorithms (e.g., Support Vector Machine, Random Forest), unsupervised techniques (e.g., Principal Component Analysis, t-SNE), and deep learning frameworks—are advancing cancer research. Specifically, we highlight three major applications of ML–metabolomics integration: (1) cancer subtyping, exemplified by the use of Similarity Network Fusion (SNF) and LASSO regression to classify triple-negative breast cancer into subtypes with distinct survival outcomes; (2) biomarker discovery, where Random Forest and Partial Least Squares Discriminant Analysis (PLS-DA) models have achieved >90% accuracy in detecting breast and colorectal cancers through biofluid metabolomics; and (3) prognostic modeling, demonstrated by the identification of race-specific metabolic signatures in breast cancer and the prediction of clinical outcomes in lung and ovarian cancers. Beyond these areas, we explore applications across prostate, thyroid, and pancreatic cancers, where ML-driven metabolomics is contributing to earlier detection, improved risk stratification, and personalized treatment planning. We also address critical challenges, including issues of data quality (e.g., batch effects, missing values), model interpretability, and barriers to clinical translation. Emerging solutions, such as explainable artificial intelligence (XAI) approaches and standardized multi-omics integration pipelines, are discussed as pathways to overcome these hurdles. By synthesizing recent advances, this review illustrates how ML-enhanced metabolomics bridges the gap between fundamental cancer metabolism research and clinical application, offering new avenues for precision oncology through improved diagnosis, prognosis, and tailored therapeutic strategies. Full article

This study develops a novel transverse steel damper (TSD) to enhance the seismic performance of tall-pier girder bridges, featuring superior lateral strength and energy dissipation capacity. The TSD’s design and arrangement are presented, with its hysteretic behavior simulated in ABAQUS. Key parameters (yield strength: 3000 kN; initial gap: 100 mm; post-yield stiffness ratio: 15%) are optimized through seismic analysis under near-fault ground motions, incorporating pulse characteristic investigations. The optimized TSD effectively reduces bearing displacements and results in smaller pier top displacements and internal forces compared to the bridge with fixed bearings. Due to the higher-order mode effects, there is no direct correlation between top displacements and bottom internal forces. As pier height decreases, the S-shaped shear force and bending moment envelopes gradually become linear, reflecting the reduced influence of these modes. Medium- to long-period pulse-like motions amplify seismic responses due to resonance (pulse period ≈ fundamental period) or susceptibility to large low-frequency spectral values. Higher-order mode effects on bending moments and shear forces intensify under prominent high-frequency components. However, the main velocity pulse typically masks the influence of high-order modes by the overwhelming seismic responses due to large spectral values at medium to long periods. Full article

HPMC, regulating slurry properties, is widely used in cement-based materials. Research on the application of HPMC in gangue slurry is still in its early stages. Moreover, the interactive effects of various factors on gangue slurry performance have not been thoroughly investigated. The work examined the effects of slurry concentration (X₁), maximum gangue particle size (X₂), and HPMC dosage (X₃) on slurry performance using response surface methodology (RSM). The microstructure of the slurry was characterized via scanning electron microscopy (SEM) and polarized light microscopy (PLM), while low-field nuclear magnetic resonance (LF-NMR) was employed to analyze water distribution. Additionally, industrial field tests were conducted. The results are presented below. (1) X₁ and X₃ exhibited a negative correlation with layering degree and slump flow, while X₂ showed a positive correlation. Slurry concentration had the greatest impact on slurry performance, followed by maximum particle size and HPMC dosage. HPMC significantly improved slurry stability, imposing the minimum negative influence on fluidity. Interaction terms X₁X₂ and X₁X₃ significantly affected layering degree and slump flow, while X₂X₃ significantly affected layering degree instead of slump flow. (2) Derived from the RSM, the statistical models for layering degree and slump flow define the optimal slurry mix proportions. The gangue gradation index ranged from 0.40 to 0.428, with different gradations requiring specific slurry concentration and HPMC dosages. (3) HPMC promoted the formation of a 3D floc network structure of fine particles through adsorption-bridging effects. The spatial supporting effect of the floc network inhibited the sedimentation of coarse particles, which enhanced the stability of the slurry. Meanwhile, HPMC only converted a small amount of free water into floc water, which had a minimal impact on fluidity. HPMC addition achieved the synergistic optimization of slurry stability and fluidity. (4) Field industrial trials confirmed that HPMC-optimized gangue slurry demonstrated significant improvements in both stability and flowability. The optimized slurry achieved blockage-free pipeline transportation, with a maximum spreading radius exceeding 60 m in the goaf and a maximum single-borehole backfilling volume of 2200 m³. Full article

Human memory is inherently susceptible to errors, including the formation of false memories—instances where individuals mistakenly recall information they were never exposed to. While prior research has largely focused on neural activity associated with false memory, the structural brain correlates of this phenomenon remain relatively unexplored. This study bridges that gap by investigating gray matter structure as it relates to individual differences in false memory performance. Using publicly available magnetic resonance imaging datasets, we analyzed cortical thickness (CT) in neural regions implicated in memory processes. To assess false memory, we applied signal detection theory, which provides a robust framework for differentiating between true and false memory. Our findings reveal that increased CT in the parietal lobe and middle occipital gyrus correlates with greater susceptibility to false memories, highlighting its role in integrating and manipulating memory information. Conversely, CT in the middle frontal gyrus and occipital pole was associated with enhanced accuracy in memory recall, emphasizing its importance in perceptual processing and encoding true memories. These results provide novel insights into the structural basis of memory errors and offer a foundation for future investigations into the neural underpinnings of memory reliability. Full article

As high-speed electric multiple units (EMUs) advance in speed and complexity, quasi-static design methods may underestimate the fatigue risks associated with high-frequency dynamic excitations. This study quantifies the contribution of gear meshing-induced vibrations (2512 Hz) to fatigue damage in EMU gearbox housings, revealing resonance amplification of local stresses up to 1.8 MPa at 300 km/h operation. Through integrated field monitoring and bench testing, we demonstrated that gear meshing excites structural modes, generating sustained, very high-cycle stresses (>10⁸ cycles). Crucially, fatigue specimens were directly extracted from in-service gearbox housings—overcoming the limitations of standardized coupons—passing the very high-cycle fatigue (VHCF) test to derive S-N characteristics beyond 10⁸ cycles. Results show a continuous decline in fatigue strength (with no traditional fatigue limit) from 10⁸ to 10⁹ cycles. This work bridges the gap between static design standards (e.g., FKM) and actual dynamic environments, proving that accumulated damage from low-amplitude gear-meshing stresses (3.62 × 10¹¹ cycles over a 12 million km lifespan) contributes to a 16% material utilization ratio. The findings emphasize that even low-magnitude gear-meshing stresses can significantly influence gearbox fatigue life due to their ultra-high frequency, warranting design consideration beyond current standards. Full article

Urban green spaces (UGSs) and forests play a vital role in shaping sustainable and livable cities, offering not only ecological benefits but also spaces that are essential for human well-being, social interactions, and everyday life. Understanding the landscape features that resonate most with public preferences is essential for enhancing the appeal, accessibility, and functionality of these environments. However, traditional approaches—such as surveys or single-data analyses—often lack the nuance needed to capture the complex and multisensory nature of human responses to green spaces. This study explores a cross-modal methodology that integrates natural language processing (NLP) and deep learning techniques to analyze text and image data collected from public reviews of 19 urban parks in Nanjing. By capturing both subjective emotional expressions and objective visual impressions, this study reveals a consistent public preference for natural landscapes, particularly those featuring evergreen trees, shrubs, and floral elements. Text-based data reflect users’ lived experiences and nuanced perceptions, while image data offers insights into visual appeal and spatial composition. By bridging human-centered insights with data-driven analysis, this research provides a robust framework for evaluating landscape preferences. It also underscores the importance of designing green spaces that are not only ecologically sound but also emotionally resonant and socially inclusive. The findings offer valuable guidance for the planning, design, and adaptive management of urban green infrastructure in ways that support healthier, more responsive, and smarter urban environments. Full article

Halifax Harbour, a major seaport in Nova Scotia that is approximately 100 km southeast of the Bay of Fundy, comprises a deep inner region called Bedford Basin, connected to the adjacent ocean by a shallow channel called The Narrows. A study of sea level and currents reveals the presence of episodic oscillations in The Narrows, with a period of approximately 2 h. The oscillation strength varies from day to day and, to some extent, through the seasons. The median amplitude of the associated sea level variation is $18\%$ that of the de-tided signal, rising to $32\%$ at the 95-th percentile. Values this large may be of concern for the transit of deep-draft vessels through shallow parts of the harbour and for the clearance of tall vessels under the two bridges that span The Narrows. Another concerning issue is the matter of oscillations being superimposed on storm surges. In addition to such direct effects of sea level variation, shear associated with the oscillations may increase the turbulent mixing in the region, affecting the overall state of this estuarine system. We explore the nature of the oscillations as a first step towards the improvement of prediction schemes for sea level and currents in the region. This involves an analysis of the oscillations in the context of seiche and Helmholtz resonance theories and the use of a 2D numerical model to handle realistic bathymetric conditions and other complications that the simpler theories cannot address. We conclude that the predictions of Helmholtz resonance theory are in reasonable agreement with both the observations and the predictions of the numerical model. Full article

This paper introduces an enhanced bi-directional full-bridge resonant converter designed for Vehicle-to-Grid (V2G) systems. A key innovation lies in the incorporation of an auxiliary LC resonant circuit connected via a tertiary transformer winding. This circuit dynamically modifies the magnetizing inductance based on operating frequency, enabling soft-switching across all primary switches, specifically, Zero-Voltage Switching (ZVS) at turn-on and near Zero-Current Switching (ZCS) at turn-off across the entire load spectrum. Additionally, the converter supports both Constant Current (CC) and Constant Voltage (CV) charging modes at distinct, fixed operating frequencies, thus avoiding wide frequency variations. A 3.3 kW prototype developed for onboard electric vehicle charging applications demonstrates the effectiveness of the proposed topology. Experimental results confirm high efficiency in both charging and discharging operations, achieving up to 98.13% efficiency in charge mode and 98% in discharge mode. Full article

Background/Objectives: International guidelines recommend the combined use of the General Movement Assessment (GMA), Hammersmith Infant Neurological Examination (HINE), and magnetic resonance imaging (MRI) to support early and accurate diagnosis of cerebral palsy (CP). However, their implementation remains inconsistent. This study aimed to map their reported global use and identify associated enablers and barriers. Methods: A scoping review was conducted following JBI and PRISMA-ScR guidelines. Systematic searches were performed in PubMed, Cochrane, PEDro, ProQuest, Web of Science, and Scopus. Eligible studies were charted and thematically analyzed, focusing on tools use and implementation factors at individual, organizational, and system levels. Results: Fourteen articles (seven surveys, seven implementation studies) from seven countries met the inclusion criteria. While awareness of GMA, HINE, and MRI was generally high, routine clinical use was limited—particularly outside structured implementation initiatives. Major barriers emerged at the system level (e.g., limited training access, time constraints, lack of standardized referral pathways) and social level (e.g., unclear leadership and coordination). Conclusions: The limited integration of GMA, HINE, and MRI into routine practice reflects a persistent “know–do” gap in early CP detection. Since implementation is shaped by the dynamic interplay of capability, opportunity, and motivation, bridging this gap demands sustained and equitable action—by addressing system-wide barriers, supporting professional development, and embedding early detection within national care pathways. Full article

Multi-channel LED drivers are crucial for high-power lighting applications. Maintaining a constant average forward current is essential for stable LED luminous intensity, necessitating drivers capable of consistent current delivery across wide operating ranges. Meanwhile, achieving precise current sharing among channels without incurring high costs and system complexity is a significant challenge. Leveraging the constant-current characteristics of the LCL-T network, this paper presents a multi-channel DC/DC LED driver comprising a full-bridge inverter, a transformer, and a passive resonant rectifier. The driver generates a high-frequency AC bus with series-connected diode rectifiers, a structure that guarantees excellent current sharing among all output channels using only a single control loop. Fully considering the impact of higher harmonics, this paper derives an exact solution for the output current. A step-by-step parameter design methodology ensures soft switching and enhanced switch utilization. Finally, experimental verification was conducted using a prototype with five channels and 200 W, confirming the correctness and accuracy of the theoretical analysis. The experimental results showed that within a wide input voltage range of 380 V to 420 V, the driver was able to provide a stable current of 700 mA to each channel, and the system could achieve a peak efficiency of up to 94.4%. Full article

In the context of the rapid digital transformation of the catering industry, the design of ordering interfaces—key hubs of human–computer interaction—has become critical to user service quality and brand competitiveness, especially in terms of usability, visual appeal, and emotional resonance. Based on a human–computer interaction design framework, this study proposes a dual-path optimization model integrating user demand mapping and conflict resolution to synergize explicit need translation with innovative design problem solving. The model employs KE to capture implicit user needs, applies AHP to construct a weighted design element system, and uses QFD to establish a matrix linking user needs with technical attributes. To address contradictions among design elements, TRIZ is introduced to resolve conflicts between functional redundancy and interaction efficiency. Additionally, generative AI tools such as MidJourney are incorporated to accelerate concept generation and improve innovation. Based on user evaluations and controlled experiments, the optimized design demonstrates measurable improvements in task efficiency and visual appeal. Overall, the dual-path approach effectively bridges the gap between vague user needs and concrete design solutions, achieving a balanced integration of functionality, aesthetics, and interactivity. The proposed model overcomes the limitations of experience-driven design by offering a systematic methodology encompassing demand analysis, technological transformation, conflict resolution, and intelligent generation, with practical value for enhancing the user experience of digital service touchpoints in the catering sector. Full article

This paper presents a hybrid fast charging strategy for static wireless power transfer (WPT) systems that synergistically combines pulsed current and multi-stage current (MCM) modes to enable rapid yet battery-health-conscious electric vehicle (EV) charging, thereby promoting sustainable transportation. The proposed approach employs a memetic algorithm (MA) to dynamically optimize the charging parameters, achieving an optimal balance between speed and battery longevity while maintaining 90.78% system efficiency at the SAE J2954-standard 85 kHz operating frequency. A neural-network-based state of charge (SOC) estimator provides accurate real-time monitoring, complemented by MA-tuned PI control for enhanced resonance stability and adaptive pulsed current–MCM profiles for the optimal energy transfer. Simulations and experimental validation demonstrate faster charging compared to that using the conventional constant current–constant voltage (CC-CV) methods while effectively preserving the battery’s state of health (SOH)—a critical advantage that reduces the environmental impact of frequent battery replacements and minimizes the carbon footprint associated with raw material extraction and battery manufacturing. By addressing both the technical challenges of high-power WPT systems and the ecological imperative of battery preservation, this research bridges the gap between fast charging requirements and sustainable EV adoption, offering a practical solution that aligns with global decarbonization goals through optimized resource utilization and an extended battery service life. Full article

Water’s unique physicochemical properties arise from its dynamic hydrogen-bonding network, yet the precise molecular threshold at which these cooperative behaviors emerge remains a key question. This study employed nuclear magnetic resonance (NMR) spectroscopy and density functional theory (DFT) calculations to investigate the evolution of hydrogen bonding strength in small water clusters, ranging from dimers to pentamers. The observed exponential increase in NMR chemical shift up to the pentamer reflects growing hydrogen bond cooperativity, identifying the (H₂O)₅ cluster as a critical structural and energetic threshold. At this size, the network achieves sufficient connectivity to support key bulk-like phenomena such as proton transfer and dielectric relaxation. These conclusions were corroborated by complementary FT-IR and electrochemical impedance spectroscopy (EIS) measurements of bulk water. Our results position the water pentamer as the molecular onset of emergent solvent behavior, effectively bridging the divide between discrete clusters and the macroscopic properties of liquid water. Full article

Train–bridge dynamic interaction analysis is critical for the dynamic design of bridges and the safety and comfort assessment of trains. This study introduces a train–bridge dynamic model of a straddle monorail train and a steel–concrete composite track beam to investigate the dynamic performance of the bridge and train. It explores the influence of track irregularities and passenger loads on the dynamic response of train–bridge systems at various traveling speeds. The numerical results indicate that there is no significant resonance between the straddle monorail train and the steel–concrete composite bridge. However, track irregularities and train speed significantly amplify the responses of the train and bridge, including displacement, acceleration, and impact coefficient. Additionally, increased passenger load leads to a substantial rise in the vertical displacement of the bridge while reducing the vibration of the train, thereby improving riding comfort. The findings of this study provide valuable scientific insights and have significant practical applications for the use of steel–concrete composite bridges in straddle monorail systems. Full article

This paper reexamines the enduring role of esoteric traditions, as articulated by Frances Yates, in shaping the intellectual landscape of the scientific revolution and their resonance in the digital age. Challenging the linear, progress-centered narratives of traditional historiographies, it explores how esoteric principles—symbolized by transformative motifs like the Philosopher’s Stone—provided a framework for early scientific inquiry by promoting hidden knowledge, experimentation, mathematics, and interdisciplinary synthesis. This paper argues that moments of accelerated scientific and technological development magnify the visibility of esoteric structures, demonstrating how the intellectual configurations of Renaissance learned circles persist in contemporary expert domains. In particular, artificial intelligence exemplifies the revival of esoteric modes of interpretation, as AI systems—much like their Renaissance predecessors—derive authority through the identification of unseen patterns and the extrapolation of hidden truths. By bridging Renaissance esotericism with the modern information revolution, this study highlights how such traditions are not mere relics of the past but dynamic paradigms shaping the present and future, potentially culminating in new forms of digital mysticism. This study affirms that the temporal gap during periods of rapid technological change between industrial practice and formal scientific treatises reinforces esoteric knowledge structures. Full article