Search Results (101)

Background: The rapid advancement of generative artificial intelligence is transforming music composition from an exclusively human-centric activity into a hybrid human–algorithmic domain. Despite technological progress and growing commercial integration, consumer acceptance of AI-generated music remains empirically underexplored. Methods: This study formulates and empirically evaluates a multidimensional theoretical model integrating nine frameworks—including UTAUT2, parasocial interaction theory, anthropomorphism theory, authenticity theory, and innovation resistance theory—through a quantitative cross-sectional survey of 466 young adults aged 17–28. Confirmatory factor analysis and multiple regression analysis (with robust standard errors) were employed. Results: The model explained 63.6% of the variance in behavioral intention (R² = 0.636). Five constructs emerged as significant predictors: hedonic motivation (β = 0.136, p = 0.017), parasocial relationships (β = 0.121, p = 0.002), social influence (β = 0.126, p = 0.002), performance expectancy (β = 0.102, p = 0.019), and innovation resistance (β = −0.089, p = 0.029). Authenticity concerns, ethical AI concerns, anthropomorphic perceptions, and technological substitution fears were non-significant in the multivariate model. Conclusions: Young consumers’ acceptance of AI-generated music is primarily driven by experiential, social, and relational factors rather than ethico-cultural concerns. These findings have substantive implications for creative industries navigating algorithmic cultural production. Full article

Entrepreneurship education in film and the creative industries refers to a set of pedagogical approaches, curricula, and institutional frameworks designed to foster entrepreneurial mindsets, competencies, and practices among students and professionals operating within the cultural and creative industries (CCIs). Going well beyond conventional business training, entrepreneurship education in this context encourages learners to identify opportunities for value creation—cultural, social, and economic—to develop sustainable modes of creative practice, and to engage critically with the markets, institutions, and communities that constitute the contemporary creative economy. Within film studies and adjacent disciplines such as media production, design, music, and the visual arts, entrepreneurship education plays an increasingly prominent role in preparing graduates for careers characterised by self-employment, project-based work, portfolio careers, and the continuous negotiation of artistic autonomy with the imperatives of professional sustainability. This entry aims to compile and organise existing knowledge on entrepreneurship education as it applies to the CCIs, with particular attention to the film and audiovisual sector, drawing on academic literature, European policy frameworks, and empirical industry evidence. The entry uses a narrative literature review approach, synthesising scholarly works from the fields of education, cultural economics, and creative industry research alongside institutional documentation and policy instruments, in order to provide a systematic and accessible account of the current state of knowledge in this area. Full article

Outdoor music festivals (OMFs) increasingly operate as a form of temporary land use that activates urban stock land, yet how such land-use reconfigurations unfold across uneven urban–rural geographies remains insufficiently examined. Taking China’s OMFs from 2013 to 2024 as a case, this study applies the Geodetector model within a spatial justice framework to analyze fifteen indicators organized along the distributional, procedural, and recognition dimensions. The results show a pronounced “market-sinking” trend accompanied by westward expansion, and the seasonal clustering gradually moderated. The three dimensions jointly shape OMFs’ diffusion through distinguishable pathways, with the procedural dimension exhibiting the highest explanatory power through institutional steering and industrial coordination, followed by the recognition dimension through demographic foundations and digital visibility, and the distributional dimension through material and infrastructural accessibility; interaction detection further indicates that their joint presence produces amplified effects. These mechanisms align with international land-use and territorial-governance studies, while reflecting the state-led coordination distinctive to China. The findings point to an emerging form of collaborative co-creation in which governmental, market, and community actors jointly shape the spatial production of cultural events, extending the classical core–periphery account and informing debates on the territorial governance of event spaces in non-metropolitan regions. Full article

We propose a Dual-Channel assisted Music Emotion Perception and Visualization (DC-MEPV) framework designed for ambient lighting in intelligent vehicle cockpits, addressing the increasing demand for advanced human–machine interaction in the automotive industry. This framework consists of three main components: the Multi-Scale Feature Extraction Block (MSFEB), the Global Sequence Modeling Block (GSMB), and the Emotional Color Visualization Algorithm (ECV-Algo). The MSFEB extracts valence and arousal (V-A) features from dual channels at multiple temporal scales, with each channel employing a hybrid neural network architecture to capture multi-scale emotional representations. The GSMB integrates positional encoding, bidirectional long short-term memory (BiLSTM) networks, and multi-head self-attention mechanisms to dynamically model global emotional sequences. The ECV algorithm utilizes personalized emotion–color association rules to achieve expressive emotion-driven lighting visualization based on a continuous mapping from emotion space to color space. We conducted comprehensive comparison and ablation experiments to evaluate the model’s emotion perception performance, and designed three metrics to evaluate the quality of the generated visualizations. The model outperformed other networks in both comparative and ablation experiments. Additionally, the generated lights demonstrated strong performance in terms of CIEDE2000 variation rates, unique color ratios, and joint histogram entropy. DC-MEPV achieved excellent performance in emotion perception and visualizations on the DEAM and PMEmo datasets. Full article

Millimeter-wave and sub-millimeter-wave techniques are widely used in non-destructive testing of multilayered materials due to their ability to penetrate non-conductive media and resolve dielectric stratifications. However, conventional thickness estimation methods suffer from an inherent resolution limit dictated by the available frequency bandwidth. In this paper, a MUSIC-based approach is proposed to achieve super-resolution localization of echoes in the reflective response of the structure under test. The method exploits the sparsity of the reflective response, similarly to compressive sensing approaches, while providing improved reconstruction accuracy. Moreover, the proposed strategy enables the retrieval of dielectric permittivities and layer thicknesses without resorting to complex nonlinear fitting procedures. Finally, the method operates on magnitude-only data, with phase information recovered through an interferometric holographic technique, making the proposed framework well-suited for cost-effective industrial applications. Full article

With the continuous development of the construction industry, work pressure faced by construction workers has been increasing, leading to a growing prominence of job burnout that adversely affects workers’ physical and mental health as well as work efficiency. Constantly exposed to high-pressure environments, construction workers are prone to symptoms such as emotional exhaustion, depersonalization, and diminished personal accomplishment, which in turn impair their work performance and quality of life. However, existing literature has largely overlooked the potential role of leisure activities such as music in alleviating job burnout. Although music is widely recognized as an effective tool for emotional regulation, its application and impact among construction workers remain underexplored. Based on a sample of 1086 construction workers (71.09% male, 48.99% aged 36–45), this study examines four dimensions of music engagement, including Time Commitment (TC), Economic Spending (ES), Emotional Investment (EI), and Personal Participation (PP), and investigates how these dimensions, through the mediating role of leisure crafting (LC), negatively influence job burnout (JB) among this population. This study employed covariance-based structural equation modeling (CB-SEM) with a sample of 1086 construction workers (71.09% male, 48.99% aged 36–45) to examine how four dimensions of music engagement, namely Time Commitment (TC), Economic Spending (ES), Emotional Investment (EI), and Personal Participation (PP), influence job burnout through leisure crafting. The results show that: (1) time commitment, economic spending, emotional investment and personal participation all have a negative influence on job burnout; and (2) leisure crafting mediates the effect of music engagement by construction workers on job burnout. This study emphasizes the necessity of incorporating mental health interventions into high-pressure work environments, providing guidance for companies to develop more flexible and effective employee care and welfare policies. This research therefore holds significant theoretical and practical value, as it promotes sustainable development in the construction industry, improves workers’ well-being and enhances the design of related work environments. Full article

The incorporation of digital music into the reform of education has become one of the primary methods to improve educational outcomes, increase creativity, and innovate the practices in the classroom. This combination, together with the close industry–academia cooperation, presents the possibilities to integrate educational strategies in accordance with the technological and creative demands of the contemporary world. Nevertheless, uncertainty, reluctance, symmetry, and subjectivity in expert ratings are significant problems to cope with when considering multi-criteria decision-making (MCDM). To resolve them, this paper suggests a Picture Fuzzy Faire Un Choix Adequat (PF-FUCA) decision support model, where fifteen options will be rated by seven criteria, depending on the contribution of four professional decision-makers. These findings indicate that the PF-FUCA framework is effective and superior to the current PF-MCDM models, as illustrated by sensitivity and comparison analysis. The identified best strategies based on the framework are blockchain-based music copyright education and integrated music–STEM platforms that, when combined, offer a viable policy instrument to policymakers, educators, and industry stakeholders. Full article

A new type of household/therapeutic air freshener is proposed, in which beneficial components are released from compositions containing plant raw materials through induction heating under an alternating-frequency electromagnetic field. The composition is heated by metal elements (e.g., pieces of metal foil) included in its composition. The advantage of the proposed air freshener is the possibility of using a wide range of plant raw materials without prior additional processing. Another advantage of this air freshener is the possibility of controlling it via a smartphone, which is in line with the concept of a ‘smart home.’ The proposed approach bypasses labour-intensive production of simple oils, reduces the product cost, and expands the number of plants (helpful for small-scale cultivation in countries such as Kazakhstan). At the same time, however, it remains possible to use traditional compositions based on industrially produced aromatic oils. Specific examples of the implementation of the proposed method are presented. Prospects include programme-controlled emission of odours from polymer matrices, Internet of Things applications, implementation of sound–visual effects (colour–aroma–music), and even integration with neuromorphic materials. Full article

This article examines how K-Pop Demon Hunters (2025) portrays women’s agency and sorority while curating Korean cultural specificity within the context of global streaming. Adopting a Gender Media Studies approach, the study conducts a scene-indexed close reading of nine key sequences, applying a coding scheme (co-presence, agency, solidarity, body framing, choreography–camera, colour) and a cultural-codes matrix that classifies elements as retained, hybridised, or globalised. Findings show a consistent pattern: when two or more women protagonists appear together, agency and sorority co-occur; this is visible in the narrative arcs and through full-body staging, ensemble composition, and a persistent we/together rhetoric. Korean local specificity is divided by purpose: English-led song hooks extend transnational reach; retained social anchors (space, ritual, foodways, and folklore) preserve locality; and hybridised cues (stylised folklore; idol/traditional blends) manage cultural density without erasure. Authorship and industry context align with this encoding, combining a women centred creative core and Korean cast with on-screen emphasis on women’s friendship, repair, and shared agency. Two tensions remain: traditional attire in spectacle numbers, and the narrow body diversity in the idol-slim body ideal, inviting comparative and interpretative scrutiny. Overall, the case demonstrates how an animated musical can emphasise women’s empowerment and cultural specificity without reducing either to mere marketing tools. Full article

Traditional time-frequency diagnostics for high-speed bearings face an entrenched trade-off between resolution and real-time feasibility. We present a fast Dual-Phase Short-Time Root-MUSIC pipeline that exploits Hankel structure via FFT-accelerated Lanczos bidiagonalization and Sliding-window Singular Value Decomposition to deliver sub-Hz super-resolution under millisecond budgets. Validated on the Politecnico di Torino aerospace dataset (seven fault classes, three severities), fDSTrM detects 150 μm inner-race and rolling-element defects with 98% and 95% probability, respectively, at signal-to-noise ratio down to −3 dB (78% detection), while Short-Time Fourier Transform and Wavelet Packet Decomposition fail under identical settings. Against classical Root-MUSIC, the approach sustains approximately 200 times speedup with less than ${10}^{-11}$ relative frequency error in offline scaling, and achieves 1.85 milliseconds per 4096-sample frame on embedded-class hardware in streaming tests. Subspace order pre-estimation with adaptive correction preserves closely spaced components; Kalman tracking formalizes uncertainty and yields 95% confidence bands. The resulting early warning margin extends maintenance lead-time by 24–72 h under industrial interferences (Gaussian, impulsive, and Variable Frequency Drive harmonics), enabling field-deployable super-resolution previously constrained to offline analysis. Full article

Blade tip timing (BTT) is a core technique for investigating the blade vibration of large-scale centrifugal compressors, and identifying the parameters of blade asynchronous vibration is crucial for implementing blade condition monitoring based on the BTT technique. In this study, the multiple signal classification (MUSIC) algorithm and the estimating signal parameters via rotational invariance techniques (ESPRIT) algorithm were first applied separately to identify the asynchronous vibration parameters of centrifugal compressor blades, with their advantages and disadvantages discussed. Subsequently, based on the frequency distribution characteristics in the ESPRIT results, the concept of “frequency distribution rate” was proposed. Finally, the results of the MUSIC algorithm were weighted by the frequency distribution rate, and an improved MUSIC algorithm was proposed. This enhanced confidence in the real frequency in the MUSIC algorithm results. Compared with the strain gauge method, the maximum relative error of the improved algorithm is 0.23%. The improved MUSIC algorithm improves the accuracy of parameter identification for blade asynchronous vibration, which holds great significance for the industrial application of the BTT technique. Full article

The Model for Urban Stormwater Improvement Conceptualization (MUSIC) serves as a key hydrological tool for simulating urban stormwater runoff pollution and evaluating the treatment performance in Water-Sensitive Urban Designs like constructed wetlands (CWs). However, a significant limitation exists in MUSIC’s current inability to model heavy metal contaminants, even though they are commonly found in urban stormwater and pose significant environmental risks. This eventually affects the model’s utility during critical planning phases for urban developments. Thus, there is a need to address this limitation. Field investigations were conducted across established CWs in residential and industrial catchments throughout Greater Melbourne, Australia. Through systematic monitoring and calibration, an approach was developed to extend MUSIC’s predictive capabilities to include several prevalent heavy metals. The results indicate that the enhanced model can generate plausible estimates for targeted metals while differentiating catchment-specific pollutant generation and treatment patterns. This advancement enhances MUSIC’s functionality as a planning support tool, enabling the preliminary assessment of heavy metal dynamics alongside conventional pollutants during both design and operational stages. The findings underscore the value of incorporating metal-specific parameters into stormwater models, offering improved support for urban water management decisions and long-term water quality protection. Full article

Spruce wood is a natural polymeric material, consisting of cellulose, lignin, hemicelluloses and other secondary components, which gives it a unique chemical footprint and architecture. Varnishes are used in musical instruments to protect the wood against humidity variations, wood being a hygroscopic material, but also to protect the wood from dirt. The varnishes used both to protect the wood from resonance and to ensure a special aesthetic appearance are either polymeric varnishes (nitrocellulose, oil-based) or volatile solvents (spirit). In this study, the color changes, the surface morphology and the chemical spectrum produced by three types of varnishes, applied in 5, 10 and 15 layers, on resonance spruce plates were analyzed. The results revealed significant changes in the color parameters: the lightness decreased by approximately 17% after the first layer, by 50% after 5 layers, by 65% after 10 layers and by 70% after 15 layers. The color parameters are most influenced by the anatomical quality of spruce wood (annual ring width and earlywood/latewood ratio) in the case of oil-based varnishes and least influenced in the case of nitrocellulose varnishes. The chemical fingerprint was determined by FTIR spectrum analysis, which revealed that the most pronounced absorptions were the double band 2926–2858 cm⁻¹, corresponding to aliphatic methylene and methyl groups (asymmetric and symmetrical C-H stretch), and the bands at 1724 cm⁻¹ (oil-based varnish), 1722 cm⁻¹ (nitrocellulose varnish) and 1708 cm⁻¹ (spirit varnish), all assigned to non-conjugated carbonyl groups in either carboxylic acids, esters aldehydes or ketones. The novelty of the study lies in the comparative analysis of three types of varnishes used in the musical instrument industry, applied to samples of spruce resonance wood with different macroscopic characteristics in three different layer thicknesses. Full article

Artificial intelligence (AI) has emerged as a significant driving force in the development of technology and industry. It is also integrated with music as music AI in music generation and analysis. It originated from early algorithmic composition techniques in the mid-20th century. Recent advancements in machine learning and neural networks have enabled innovative music generation and exploration. This article surveys the development history and technical route of music AI, analyzes the current status and limitations of music artificial intelligence across various areas, including music generation and composition, rehabilitation and treatment, as well as education and learning. It reveals that music AI has become a promising creator in the field of music generation. The influence of music AI on the music industry and the challenges it encounters are explored. Additionally, an emotional music generation system driven by multimodal signals is proposed. Although music artificial intelligence technology still needs to be further improved, with the continuous breakthroughs in technology, it will have a more profound impact on all areas of music. Full article

Aggressive behaviour in pigs poses significant challenges to animal welfare, production efficiency, and economic performance in the pork industry. This review explores the multifaceted causes of pig aggression, focusing on genetic, environmental, and physiological factors. Aggression in pigs is categorized into social, maternal, fear-induced, play, and redirected aggression, with early-life hierarchies and environmental stressors playing critical roles. Physiological markers, such as elevated cortisol and reduced serotonin levels, are closely linked to aggressive behaviour, while dietary interventions, including tryptophan supplementation, have shown promise in mitigating aggression. Environmental factors, such as overcrowding, noise, and heat stress, exacerbate aggressive tendencies, whereas enrichment strategies, like music and improved housing conditions, can reduce stress and aggression. Genome-wide analyses have pinpointed specific polymorphisms in neurotransmitter genes (DRD2, SLC6A4, MAOA) and stress-response loci (RYR1) as significant predictors of porcine aggression. Advances in genomic technologies, including genome-wide association studies (GWASs) and transcriptomic analyses, have further elucidated the genetic and epigenetic underpinnings of aggressive behaviour. Practical application in breeding programmes remains challenging due to aggression polygenic nature and industry hesitancy toward genomic approaches. Future research should focus on integrating genetic markers into breeding programmes, developing multitrait selection indices, and exploring epigenetic modifications to improve animal welfare and production efficiency. By addressing these challenges, the pork industry can enhance both the well-being of pigs and the sustainability of production systems. Full article