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The healthcare industry operates under strict rules yet experiences fast-paced changes which demand adaptable methods for handling ongoing transformations. The healthcare sector faces its most critical challenges from technological progress and regulatory changes and frequent health crises which need effective strategic planning solutions. The research investigates healthcare frameworks and methodologies and best practices to evaluate their effects on medical facility project management operations. The research unites academic discoveries with professional expertise to develop an extensive manual which helps healthcare personnel and project managers handle their challenging work responsibilities.

Abstract Saffron ( Crocus sativus L.), valued for its culinary and medicinal properties, is increasingly affected by viral infections that compromise its biochemical integrity and market quality. This study investigates the impact of cucumber mosaic virus (CMV; Cucumovirus CMV ) and turnip mosaic virus (TuMV; Potyvirus rapae ) on saffron’s antioxidant profile, flavonoid content, and physiological parameters. Sixteen traits were quantified, including enzymatic antioxidants (CAT, POD, PPO, PAL), non-enzymatic markers (Trolox, vitamin C, proline), total flavonoids and phenolics, gas exchange parameters (Ci, Cond, VpdL), and morphological indicators. Principal component analysis (PCA) revealed that antioxidant-related traits contributed strongly to the first principal component (F1), explaining 78.29% of the total variance. Viral infection led to elevated antioxidant enzyme activity and flavonoid accumulation, alongside reduced stomatal conductance and altered water vapor dynamics. These findings suggest that CMV and TuMV induce oxidative stress responses and metabolic shifts that may affect saffron’s nutritional and sensory quality. Integrative biochemical profiling offers a valuable approach for assessing viral impact and guiding crop management strategies.

Since the earliest days of civilization, creativity has served as the foundation of human progress. From ancient cave paintings and musical expressions to groundbreaking scientific revolutions and modern digital innovations, it is the creative spirit that empowers individuals to solve problems, express their identities, and adapt to an ever-evolving world. This research aimed at studying the relationship between Artificial Intelligence (Motivation to use AI and Dependence on AI) and perceived Creativity among students in India. A sample of 51 college students aged 18-25 years from various cities in India were selected for the study. Standardized self-report Questionnaires were used to assess students’ Motivation (Questionnaire for Use AI Motives; QUAIM), Dependence (Dependence on AI Scale; DIA) and Creative behavior (Kaufmann Domains of Creativity Scale; KDOCS). Descriptive statistics and Pearson’s product moment correlation coefficients were calculated using SPSS. Results indicated positive correlations between intrinsic interest in AI and – self/everyday and mechanical- forms of creativity. While utility value was positively correlated with everyday creativity. Conversely, Vulnerability to AI, showcasing dependence, was negatively correlated with Artistic and Performance creativity. While correlations do not speak for causal attributions, these results suggest that while the intrinsic motivation to engage with AI tool may foster or facilitate Creativity in certain aspects, overdependence on AI could negative impact expressive and performance-based creativity. This study also highlights the importance of fostering a balanced and interest focused approach to the integration of AI tools in the Education workspace.

Purpose This paper aims to analyze the thermo-mechanical interactions in living tissue. This study also aims to improve the understanding of heat transport dynamics and their coupled biomechanical responses during thermal therapies by incorporating higher order fractional time derivatives and Eringen’s nonlocal elasticity. Design/methodology/approach A novel bio-thermoelastic model is developed. This model uses a higher order fractional Moore–Gibson–Thompson (MGT) approach for heat propagation and Eringen’s nonlocal elasticity for the constitutive behavior of viscoelastic tissue with voids. Analytical solutions for displacements, temperature, void volume fractions and stresses have been established using normal mode analysis. Numerical simulations illustrate the effects of fractional-order parameters, nonlocality, viscoelasticity and temporal variations. Findings The fractional-order parameter significantly impacts thermophysical values, increasing the strength of each physical field. Nonlocal theory influences the size of biophysical quantities. The fractional MGT theory more accurately represents heat transport in tumor cells than the classical Pennes model. It eliminates non-physical results by predicting thermo-mechanical wave propagation within tissue over a finite spatial range. Research limitations/implications This study does not incorporate interstitial fluid flow, chemical messenger synthesis and diffusion, multi-scale human tissue modeling, or large deformation effects for cancerous cells. Practical implications The findings offer critical insights for thermal therapies such as hyperthermia and laser surgery, where precise thermal shock is essential for controlled destruction of pathological tissues while minimizing collateral damage. The parametric analysis can optimize key design parameters, enhancing the precision of hyperthermia protocols. Originality/value This work develops an analytical framework that integrates higher order fractional time derivatives with Eringen’s nonlocal elasticity and viscoelasticity while also considering porosity within bio-thermoelastic models. This approach provides a more comprehensive understanding of thermo-mechanical interactions in living tissue compared to conventional models.

Recognizing human emotions from electroencephalography (EEG) signals is crucial for applications like affective computing and healthcare, yet faces challenges due to the complex, non-stationary nature of EEG data and the inherent ordinal structure of many emotional states. This study introduces the Hierarchical Cross-Attention Transformer Network (HCAT-Net), a novel deep learning architecture specifically designed for improved ordinal emotion classification from 1D EEG time-series. HCAT-Net integrates 1D Residual Convolutional Neural Network blocks with a two-stage hierarchical Transformer encoder, enhanced by Rotary Positional Embeddings (RoPE), to effectively capture dependencies across multiple time scales. The study presents a cross-attention mechanism for improved context integration across Transformer stages and introduces a balanced ordinal loss combining cross-entropy with scaled MAE to preserve label order. Evaluated rigorously on a benchmark EEG emotion dataset, HCAT-Net achieves 99.8% test accuracy, demonstrating its effectiveness. Detailed architectural descriptions and formulations are provided, presenting HCAT-Net as a powerful new approach.

Abstract The interplay of coherence and decoherence is played out in a three-level quantum system, in which the third level is incoherently coupled to the second one which itself is in coherent interaction with the first level. The study is based on a stochastic scenario in which the coherent, unitary evolution of the system is randomly interrupted by a Poisson-driven pulse sequence. In the absence of an external pulse, the system undergoes coherent, unitary evolution restricted to the subspace spanned by the first level (level 1) and the second level (level 2). The application of a pulse induces transitions between the second and the third level (level 3), thereby introducing non-unitary effects that perturb the otherwise isolated two-level dynamics. The pulses are assumed to have infinitesimal duration, with strengths modeled as random variables that are uncorrelated across different pulses. A representative model for the stochastically-averaged transition (super)operator mimicking the dynamics induced by the application of pulses allows for an analytical derivation of the matrix elements of the averaged density operator, especially, the element pertaining to the first level. When the system is initially in level 1, we obtain in particular the temporal behavior of the stay-put probability, that is, the probability P 1 ( t ) that the system is still in level 1 at time t. As a function of time, the quantity P 1 ( t ) exhibits a coherence-to-decoherence crossover behavior. At short times t ≪ 1 / λ , where λ is the average frequency at which pulses are applied to the system, coherent dynamics dominate. Consequently, P 1 ( t ) displays pronounced Rabi-like oscillations. At long times t ≫ 1 / λ , decoherence effects prevail, leading to an exponential decay of the form P 1 ( t ) − 1 / 3 ∼ exp ⁡ ( − λ t ) . The results are relevant in the context of well-established Rabi oscillations, applicable to molecular beams and magnetic resonance experiments, as well as in more contemporary frameworks including quantum information processing, quantum resetting protocols, and the theory of quantum trajectories.