Kaleb Mercado, Claire Chang

We ask whether lightweight, explainable methods can model musical mood and support mood-aware retrieval without raw-audio pipelines or listener ratings. Using the Coimbra MIR 4Q dataset annotated under Russell’s circumplex (900 clips), we merged musically interpretable features that summarize tempo, timbre, rhythm, and dynamics, alongside compact tag encodings. After standardizing feature blocks, we applied principal component analysis (PCA) to obtain a low-dimensional embedding; loadings suggested that PCA1 tracked dynamics and meter steadiness, and PCA2 tracked rhythmic variability. Without using labels to fit PCA, the two-dimensional map aligned with the four circumplex quadrants. Model selection used scree and reconstruction-error curves, which indicated diminishing returns after about 6–8 components. Treating quadrants as clusters for evaluation yielded strong separation (silhouette 0.609, Davies–Bouldin 0.483, Calinski–Harabasz 3661.9). A cosine-similarity recommender retrieved nearest neighbors that were musically and emotionally coherent, with an option to emphasize items near quadrant boundaries to surface blended emotions. Because the approach remains in a tabular feature space, it is transparent, fast, and easy to tune through feature weights and tag contributions. The results demonstrate a practical path to mood-aware recommendations using explainable techniques and publicly available features.

10.69831/beeebb291f

Ryan Mehra, Anshoo Mehra

Large-language-model (LLM) “hallucinations” are usually condemned as reliability faults because they generate confident yet false statements [1]. Emerging research, however, finds that such confabulations mirror divergent thinking and can seed novel hypotheses [2, 3]. This study is conducted by an independent investigators with no physical laboratory but unlimited API access to OpenAI models(4o, 4o-mini, 4.1, 4.1-mini)—tests whether deliberately elicited hallucinations can accelerate medical innovation. We target three translational aims: (i) epistemological creativity for medicine, where speculative errors inspire fresh research questions; (ii) generative biomedical design, exemplified by hallucinated protein and drug candidates later validated in vitro [4]; and (iii) speculative clinical engineering, where imaginative missteps suggest prototypes such as infection resistant catheters [5]. A controlled prompt-engineering experiment compares a truth-constrained baseline to a hallucination-promoting condition across the four OpenAI models. Crucially, all outputs are scored for novelty and prospective clinical utility by an autonomous LLM-based “judge” system, adapted from recent self-evaluation frameworks [6], instead of human experts. The LLM judge reports that hallucination-friendly prompts yield 2–3× more ideas rated simultaneously novel and potentially useful, albeit with increased low-quality noise. These findings illustrate a cost-effective workflow in which consumer-accessible LLMs act both as idea generator and evaluator, expanding the biomedical creative search space while automated convergence techniques preserve epistemic rigor—reframing hallucination from flaw to feature in at-home medical R&D.

10.69831/e59eafc04e

Shivani Shivu Singh, Shivu Singh

The accelerating expansion of the universe remains one of the most profound challenges in modern cosmology. The standard ΛCDM model attributes this to a cosmological constant (Λ), yet persistent discrepancies — such as the Hubble tension and S₈ tension — suggest the need for alternative frameworks. This study proposes the Shivanic Force (SF), a dynamic repulsive effect arising from large-scale tension gradients in spacetime, generated by the asymmetric clustering of matter and expansion of cosmic voids. I introduce a modified Friedmann equation incorporating SF, and test its predictions against observational data from SDSS DR16 eBOSS LRG galaxies and Pantheon supernovae. The model increases the expansion rate at intermediate redshifts, improving consistency with local H₀ measurements and alleviating the S₈ tension by extending cosmic structure growth time. This work presents SF as a physically motivated, late-time phenomenon capable of addressing key cosmological tensions.

10.69831/6c4f3399fd

Akshaj Dewan, Lewi Bayssa, Sait Babanazarov, Scott Robinson

Traditional plaster casts are heavy, uncomfortable, and bad for the environment. This study set out to create a better alternative by combining 3D printing with mycelium, a biodegradable fungal material. We asked whether a 3D-printed cast reinforced with mycelium could outperform a standard plaster cast in strength and stiffness. Our team designed a custom-fit cast with a breathable Voronoi pattern, scanned a human arm using a phone app, and printed the cast with a hollow space for the mycelium. After growing and drying the mycelium inside the cast, we ran compression tests and found that the new design held 32.8% more weight and had 25.3% higher stiffness than plaster. These results suggest that a mycelium-reinforced 3D cast isn’t just more sustainable, it’s also stronger and more supportive. With further testing and refinement, this method could lead to more comfortable, eco-friendly, and effective orthopedic care.

10.69831/e6351d6573