Search Results (11,048)

In recent years, text-to-music models have been the biggest breakthrough in automatic music generation. While they are unquestionably a showcase of technological progress, it is not clear yet how they can be realistically integrated into the artistic practice of musicians and music practitioners. This paper aims to address this question via Prompt Audio Generation User Research Investigation (PAGURI), a user experience study where we leverage recent text-to-music developments to study how musicians and practitioners interact with these systems, evaluating their satisfaction levels. We developed an online tool through which users can generate music samples and/or apply recently proposed personalization techniques based on fine-tuning to allow the text-to-music model to generate sounds closer to their needs and preferences. Using semi-structured interviews, we analyzed different aspects related to how participants interacted with the proposed tool to understand the current effectiveness and limitations of text-to-music models in enhancing users’ creativity. Our research centers on user experiences to uncover insights that can guide the future development of TTM models and their role in AI-driven music creation. Additionally, they offered insightful perspectives on potential system improvements and their integration into their music practices. The results obtained through the study reveal the pros and cons of the use of TTMs for creative endeavors. Participants recognized the system’s creative potential and appreciated the usefulness of its personalization features. However, they also identified several challenges that must be addressed before TTMs are ready for real-world music creation, particularly issues of prompt ambiguity, limited controllability, and integration into existing workflows. Full article

Background/Objectives: The management of immature teeth with wide apical foramina presents significant challenges in endodontic treatment due to difficulties in achieving a hermetic seal. The aim of this study was to evaluate void formation in apical plugs created using three calcium silicate-based materials—Biodentine, NuSmile NeoPUTTY, and Well-Root PT—applied with the help of manual, ultrasonic, or rotary file condensation (XP-endo Shaper) in 3D-printed immature teeth. Methods: Micro-computed tomography analysis was used to assess the internal, external, and total void percentage of material volume. The statistical analysis was performed using two-way ANOVA and the post hoc Bonferroni test. Statistical significance was set at p < 0.05. Results: The materials and techniques used individually do not significantly influence the formation of internal voids, but their combination does (F(4, 99) = 2.717, p = 0.034). Both factors and their interaction are significant for external voids (F(4, 99) = 4.169, p = 0.004), and all have a notable effect on total void percentages (F(4, 99) = 3.456, p = 0.012). No significant differences were observed in internal voids across the groups (p > 0.05), ranging from 0.635% to 1.078%. External voids varied significantly, with Well-Root PT and ultrasonic condensation showing the highest values with a significant difference (p < 0.05), while NeoPUTTY and Biodentine with XP-endo Shaper exhibited the lowest. Total voids remained below 4%, with no significant differences among manual condensation groups. Neither material type nor application technique consistently influenced void formation, except for Well-Root PT with ultrasonic condensation. Conclusions: These findings suggest that modern bioceramic materials and application techniques produce comparable, low-void apical plugs, with XP-endo Shaper showing promise for minimizing external voids. The interaction between material and application technique plays a crucial role during the creation of apical plugs. Full article

Preformed porous media (PPM) technology has emerged as a transformative approach to enhance heat and mass transfer in vacuum freeze-drying (VFD) of agricultural and food products. This review systematically analyzes recent advances in PPM research, with particular focus on spray freeze-drying (SFD) as the dominant technique for precision pore architecture control. Empirical studies confirm PPM’s efficacy: drying time reductions of 20–50% versus conventional VFD while improving product quality (e.g., 15% higher ginsenoside retention in ginseng, 90% enzyme activity preservation). Key innovations include gradient porous structures and multi-technology coupling strategies that fundamentally alter transfer mechanisms through: resistance mitigation via interconnected macropores (50–500 μm, 40–90% porosity), pseudo-convection effects enabling 30% faster vapor removal, and radiation enhancement boosting absorption by 40–60% and penetration depth 2–3 times. While inherent VFD limitations (e.g., low thermal conductivity) persist, we identify PPM-specific bottlenecks: precision regulation of pore structures (<5% size deviation), scalable fabrication of gradient architectures, synergy mechanisms in multi-field coupling (e.g., microwave-PPM interactions). The most promising advancements include 3D-printed gradient pores for customized transfer paths, intelligent monitoring-feedback systems, and multiscale modeling bridging pore-scale physics to macroscale kinetics. This review provides both a critical assessment of current progress and a forward-looking perspective to guide future research and industrial adoption of PPM-enhanced VFD. Full article

Mental calculation is key to the development of number sense and flexibility in thinking, but in practice, it is often neglected in favour of written algorithms. The aim of this study was to examine the relationship between the success in mental calculation and the number of strategies used, as well as to explore differences between age groups and genders. The study included 233 participants from various age groups, and data were collected through a mental calculation test and individual interviews regarding the strategies employed. The study follows quantitative, cross-sectional, correlational-comparative design, and the data was analyzed using key statistical techniques including the Kolmogorov–Smirnov test, Pearson’s correlation analysis, linear regression, one-way ANOVA with Bonferroni post hoc correction, and two-way ANOVA to examine main effects and interactions. The results showed a statistically significant positive correlation between the number of strategies and success in mental calculation. Differences between age groups were marginally significant; it was found that upper primary and secondary school students used a greater number of strategies. Additionally, boys, on average, applied more strategies than girls. In conclusion, the variety of mental calculation strategies positively correlates with accuracy in mental calculation, and teaching a greater number of strategies may contribute to the development of flexibility and confidence in mathematical thinking. It is recommended that greater emphasis is placed on the development of mental strategies within formal education. Full article

Pipelines play a vital role in material transportation within industrial settings. This review synthesizes detection technologies for early-stage small gas leaks from pipelines in the industrial sector, with a focus on acoustic-based methods, optical gas imaging (OGI), and multimodal fusion approaches. It encompasses detection principles, inherent challenges, mitigation strategies, and the state of the art (SOTA). Small leaks refer to low flow leakage originating from defects with apertures at millimeter or submillimeter scales, posing significant detection difficulties. Acoustic detection leverages the acoustic wave signals generated by gas leaks for non-contact monitoring, offering advantages such as rapid response and broad coverage. However, its susceptibility to environmental noise interference often triggers false alarms. This limitation can be mitigated through time-frequency analysis, multi-sensor fusion, and deep-learning algorithms—effectively enhancing leak signals, suppressing background noise, and thereby improving the system’s detection robustness and accuracy. OGI utilizes infrared imaging technology to visualize leakage gas and is applicable to the detection of various polar gases. Its primary limitations include low image resolution, low contrast, and interference from complex backgrounds. Mitigation techniques involve background subtraction, optical flow estimation, fully convolutional neural networks (FCNNs), and vision transformers (ViTs), which enhance image contrast and extract multi-scale features to boost detection precision. Multimodal fusion technology integrates data from diverse sensors, such as acoustic and optical devices. Key challenges lie in achieving spatiotemporal synchronization across multiple sensors and effectively fusing heterogeneous data streams. Current methodologies primarily utilize decision-level fusion and feature-level fusion techniques. Decision-level fusion offers high flexibility and ease of implementation but lacks inter-feature interaction; it is less effective than feature-level fusion when correlations exist between heterogeneous features. Feature-level fusion amalgamates data from different modalities during the feature extraction phase, generating a unified cross-modal representation that effectively resolves inter-modal heterogeneity. In conclusion, we posit that multimodal fusion holds significant potential for further enhancing detection accuracy beyond the capabilities of existing single-modality technologies and is poised to become a major focus of future research in this domain. Full article

Two-factor authentication (2FA) has become widely adopted as an efficient and secure way of validating someone’s identity online. Two-factor authentication is difficult in virtual reality (VR) because users are usually wearing a head-mounted display (HMD) which does not allow them to see their real-world surroundings. We present NRXR-ID, a technique to implement two-factor authentication while using extended reality systems and smartphones. The proposed method allows users to complete an authentication challenge using their smartphones without removing their HMD. We performed a user study in which we explored four types of challenges for users, including a novel checkers-style challenge. Users responded to these challenges under three different configurations, including a technique that uses a smartphone to support gaze-based selection without the use of a VR controller. A 4 × 3 within-subjects design allowed us to study all of the proposed variations. We collected performance metrics along with user experience questionnaires containing subjective impressions from thirty participants. Results suggest that the checkers-style visual matching challenge was the most preferred option, followed by the challenge involving entering a digital PIN submitted via the smartphone. Participants were fastest at solving the digital PIN challenge, with an average of 12.35 ± 5 s, followed by the Checkers challenge with 13.85 ± 5.29 s, then the CAPTCHA-style challenge with 14.36 ± 7.5 s, whereas the alphanumeric password took almost twice as long, averaging 32.71 ± 16.44 s. The checkers-style challenge performed consistently across all conditions with no significant differences (p = 0.185), making it robust to different implementation choices. Full article

Early traffic accident risk prediction in complex road environments poses significant challenges due to the heterogeneous nature and incomplete semantic alignment of multimodal data. To address this, we propose a novel Early Accident Risk Cross-modal Collaborative Perception Mechanism Network (EAR-CCPM-Net) that integrates hierarchical fusion modules and cross-modal attention mechanisms to enable semantic interaction between visual, motion, and textual modalities. The model is trained and evaluated on the newly constructed CAP-DATA dataset, incorporating advanced preprocessing techniques such as bilateral filtering and a rigorous MINI-Train-Test sampling protocol. Experimental results show that EAR-CCPM-Net achieves an AUC of 0.853, AP of 0.758, and improves the Time-to-Accident (TTA_0.5) from 3.927 s to 4.225 s, significantly outperforming baseline methods. These findings demonstrate that EAR-CCPM-Net effectively enhances early-stage semantic perception and prediction accuracy, providing an interpretable solution for real-world traffic risk anticipation. Full article

In this perspective article, we introduce Ecocebo as a novel concept describing the modulatory effects of physical environments, whether natural or built, on drug effect. Positioned as a spatial component of the placebo effect, Ecocebo is grounded in evidence-based design principles and proposes that environmental features such as natural light, greenery, spatial geometry, and calming esthetics can significantly influence sensory, emotional, and cognitive processes. These environmental factors may enhance or modify pharmacological responses, especially for analgesics, anxiolytics, and antidepressants. We highlighted how exposure to restorative spaces can reduce pain perception, stress, and the need for medication, paralleling findings in placebo research where contextual and sensory cues influence brain regions linked to emotion and pain regulation. We propose virtual reality (VR) as the most suitable methodological tool to study Ecocebo in controlled and ecologically valid settings. VR allows for the precise manipulation of spatial features and real-time monitoring of physiological and psychological responses. We also propose integrating VR with neuromodulation techniques to investigate brain–environment–drug interactions. Finally, we addressed key methodological challenges such as defining control conditions and standardizing the measurement of presence. This perspective opens new directions for the integration of non-pharmacological and pharmacological interventions and personalized therapeutic environments to optimize clinical outcomes. Full article

This study investigated the molecular mechanisms by which phthalates induce depression, utilizing network toxicology and molecular docking techniques. By integrating the TargetNet, GeneCards, and PharmMapper databases, 658 potential target genes of phthalates were identified. Additionally, 5433 depression-related targets were retrieved from the GeneCards and OMIM databases. Comparative analysis revealed 360 common targets implicated in both phthalate action and depression. A Protein-Protein Interaction (PPI) network was constructed using the STRING database. Subsequently, the CytoHubba plugin (employing the MCC algorithm) within Cytoscape was used to screen the network, identifying the top 20 hub genes. These core genes include AKT1, CASP3, TNF, TP53, BCL2, and IL6, among others. Validation on the GEO dataset (GSE23848) revealed that the expression of multiple core genes was significantly upregulated in patients with depression (p < 0.05). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses indicated that phthalates mainly regulate biological processes such as extracellular stimulus response, lipopolysaccharide metabolism, and chemical synaptic transmission. Depression is mediated by the AGE-RAGE signaling pathway (a complication of diabetes), lipids and atherosclerosis, Endocrine resistance, and the PI3K-Akt signaling pathway. Molecular docking confirmed that phthalates have strong binding activity with key targets (CASP3, TNF, TP53, BCL2, IL6). These findings present a novel paradigm for evaluating the health risks posed by environmental pollutants. Full article

High-calcium fly ash (HCFA), produced from the lignite combustion, has emerged as a global concern due to its fine particle size and adverse environmental impacts. This study presents the characteristics of dispersed microspheres from HCFA obtained using modern techniques, such as XRD, SEM-EDS, ⁵⁷Fe Mössbauer spectroscopy, DSC-TG, particle size analysis, and magnetic measurements. It is found that an increase in microsphere size is likely due to the growth of the silicate glass-like phase, while the magnetic crystalline phase content remains stable. According to the ⁵⁷Fe Mössbauer spectroscopy, there are two substituted Ca-based ferrites—CaFe₂O₄ and Ca₂Fe₂O₅ with a quite different magnetic behavior. Besides, the magnetic ordering temperature of the brownmillerite (Ca₂Fe₂O₅) phase increases with the average diameter of the microspheres. FORC analysis reveals enhanced magnetic interactions as microsphere size increases, indicating an elevation in the concentration of magnetic microparticles, primarily on the microsphere surface, as supported by electron microscopy data. The discovered the magnetic crystallographic phases distribution on the microsphere’s surface claims the accessibility for further enrichment of the magnetically active particles and the possible application of fly ashes as a cheap source for magnetic materials synthesis. Full article

Against the backdrop of accelerating global energy transition, developing high-performance energy-storage systems is crucial for achieving carbon neutrality. Traditional electrode materials are limited by a single densification storage mechanism and low conductivity, struggling to meet demands for high energy/power density and a long cycle life. Carbon/high-entropy alloy nanocomposites provide an innovative solution through multi-component synergistic effects and cross-scale structural design: the “cocktail effect” of high-entropy alloys confers excellent redox activity and structural stability, while the three-dimensional conductive network of the carbon skeleton enhances charge transfer efficiency. Together, they achieve synergistic enhancement via interfacial electron coupling, stress buffering, and dual storage mechanisms. This review systematically analyzes the charge storage/attenuation mechanisms and performance advantages of this composite material in diverse energy-storage devices (lithium-ion batteries, lithium-sulfur batteries, etc.), evaluates the characteristics and limitations of preparation techniques such as mechanical alloying and chemical vapor deposition, identifies five major challenges (including complex and costly synthesis, ambiguous interfacial interaction mechanisms, lagging theoretical research, performance-cost trade-offs, and slow industrialization processes), and prospectively proposes eight research directions (including multi-scale structural regulation and sustainable preparation technologies, etc.). Through interdisciplinary perspectives, this review aims to provide a theoretical foundation for deepening the understanding of carbon/high-entropy alloy composite energy-storage mechanisms and guiding industrial applications, thereby advancing breakthroughs in electrochemical energy-storage technology under the energy transition. Full article

The electronics industry has significantly contributed to the development of efficient heat dissipation systems. One widely used technique is pool boiling, a simple method requiring no moving parts or complex structures. It enables the removal of large amounts of heat at relatively low temperature differences. Enhancing pool boiling performance involves increasing the critical heat flux and the heat transfer coefficient, which defines how effectively a surface can transfer heat to a cooling fluid. This method is commonly applied in cooling electronic devices, digital circuits, and power systems. In this study, pool boiling at atmospheric pressure was investigated using copper surfaces. To validate the Rohsenow model used to estimate the maximum bubble departure diameter, a planimetric approach was applied. Measurements included average contact angle (CA), surface tension (σ), and droplet diameter for four working fluids: deionised water, ethanol, Novec-649, and FC-72. For each fluid, at least 15 measurements of CA and σ were conducted using the Young–Laplace model. This study provides a comprehensive analysis of the influence of contact angle and surface tension on nucleate boiling using four different fluids on copper surfaces. The novelty lies in combining high-precision experimental measurements with validation of the Rohsenow model, offering new insights into surface-fluid interactions critical for thermal system performance. Full article

The pods from Neltuma ruscifolia (vinal), an underutilized species, are rich in bioactive functional compounds. However, the extraction procedures to obtain the highest proportion of these compounds, considering sustainability aspects, have not been optimized. This study aimed to optimize and compare three affordable extraction methods—dynamic maceration (DME), ultrasound-assisted extraction (UE), and microwave-assisted extraction (ME)—to obtain enriched extracts. The effects of temperature, ethanol-to-water ratio in the solvent, extraction time, and frequency (for ME) were evaluated using a Box–Behnken design and response surface methodology to optimize total polyphenolic content (TPC), total flavonoids (TF), and antioxidant capacity (DPPH). Energy consumption and carbon footprints were also assessed, and phenolic compounds in the optimized extracts were identified by HPLC. The ethanol-to-water ratio emerged as the most influential factor, showing synergistic effects with both time and temperature, enabling optimal yields at intermediate ethanol concentrations. Gallic acid, rutin, and theobromine were found to be the most abundant components, followed by cinnamic, caffeic, and chlorogenic acids. Although UE exhibited the lowest energy consumption (0.64 ± 0.03 Wh/mg of TPC), the simple and easily implementable DME—optimized at 40 min, 50 °C, and 42% ethanol—proved to be the most efficient method, combining high extractive performance (TPC 1432 mg GAE/100 g Dw), reduced solvent use, and intermediate energy efficiency (1.84 Wh/mg of TPC). These findings highlight the potential of vinal as a natural source of bioactive ingredients obtained through simple and cost-effective techniques adaptable to small producers while underscoring the value of experimental design in optimizing sustainable extraction technologies and elucidating the interactions between key processing factors. Full article

Somatic embryogenesis (SE) is a key plant regeneration technique involving the reprogramming of somatic cells into embryogenic structures. This developmental transition is regulated by complex genetic and epigenetic mechanisms, including post-translational modifications such as SUMOylation—the covalent attachment of small ubiquitin-like modifier (SUMO) proteins to target proteins, influencing their function, stability, and interactions. While SUMOylation is known to regulate plant development and stress responses, its role in SE has remained unknown. In this study, we investigated the involvement of the SUMOylation pathway in SE induction in Medicago truncatula. Using BLASTp analysis with known SUMO pathway proteins from Arabidopsis thaliana and Glycine max, we identified 10 homologous genes in M. truncatula. Phylogenetic relationships, gene structure, and conserved motif analyses confirmed their evolutionary conservation and characteristic domains. Expression profiling revealed significant upregulation of SUMO pathway genes—including Mt SUMO2, Mt SAE1-2, Mt SCE1a-b, Mt MMS21, and Mt PIAL2—in embryogenic cell lines during early SE induction. Additionally, in silico prediction of SUMOylation sites and SUMO-interacting motifs (SIMs) in 12 key SE regulatory proteins indicated a broad potential for SUMO-mediated regulation. These findings suggest that SUMOylation may contribute to the acquisition of embryogenic competence during somatic cell reprogramming in plants. Full article

In the cybersecurity attack and defense space, the “attacker” and the “defender” form a dynamic and symmetrical adversarial pair. Their strategy iterations and capability evolutions have long been in a symmetrical game of mutual restraint. We will introduce modern Intrusion Detection Systems (IDSs) from the defender’s side to counter the techniques designed by the attacker (APT attack). One major challenge faced by IDS is to identify complex attack paths from a vast provenance graph. By constructing an attack behavior tracking graph, the interactions between system entities can be recorded, but the malicious activities of attackers are often hidden among a large number of normal system operations. Although traditional methods can identify attack behaviors, they only focus on the surface association relationships between entities and ignore the deep causal relationships, which limits the accuracy and interpretability of detection. Existing graph anomaly detection methods usually assign the same weight to all interactions, while we propose a Causal Autoencoder for Graph Explanation (CAGE) based on reinforcement learning. This method extracts feature representations from the traceability graph through a graph attention network(GAT), uses Q-learning to dynamically evaluate the causal importance of edges, and highlights key causal paths through a weight layering strategy. In the DARPA TC project, the experimental results conducted on the selected three datasets indicate that the precision of this method in the anomaly detection task remains above 97% on average, demonstrating excellent accuracy. Moreover, the recall values all exceed 99.5%, which fully proves its extremely low rate of missed detections. Full article