Sami Labs (India)

The persistent H0 and growth tensions in ΛCDM cosmology motivate a re-examination of the expansion paradigm’s foundational assumptions. The Dead Universe Theory (DUT) proposes an alternative in which observable galaxy separation is not due to metric expansion but results from irreversible, entropy-driven deformation of a viscoelastic spacetime continuum undergoing global gravitational retraction. This work formalizes DUT within a relativistic continuum-mechanics framework. We derive the entropic deformation tensor Ξ_μν from a variational principle and incorporate it into the Einstein field equations as G_μν + Ξ_μν = 8πG T_μν, replacing the cosmological constant with a dynamical, entropy-sourced curvature. A key prediction follows from the asymptotic stability analysis of the resulting system: a unique, non-adjustable growth index γ = (√5 − 1)/2 ≈ 0.6180339887…, the signature of the theory’s Unique Vacuum Attractor. Consequently, a high-precision measurement of the growth index consistent with γ ≈ 0.55 at the <0.01 level would strongly falsify DUT. This formulation provides a falsifiable, thermodynamically grounded geometric description of cosmic dynamics, directly linking late-time acceleration-like phenomena to residual entropy gradients of a “dead” gravitational substrate. Numerical validation of the attractor is available in the mission-grade inference pipeline (repository and preprint DOI provided in the Data/Code Availability statement).

This review initiative had explored the applicability of the extract isolated from various plant parts of Sapindus (S.) species as the potential green corrosion inhibitor (GCI). Additionally, insights on topics like plant extracts in broad, concept of corrosion inhibition by the pool of biomolecules, use of (S.) species plant parts as the extract to retard corrosion of metals/alloys, composition of (S.) species extract, extraction techniques, etc were also covered. This article would form a platform to establish the present global status of (S.) species towards corrosion inhibition filed and related sectors (surfactant based). The favorable functional groups featuring in the biomolecules (having maximum hydrophilic part) along with the dominant inhibitor component (saponins) present in the (S.) extract had assisted the effective inhibition of corrosion in metals/alloys. This behavior of the extract (having the pool of biomolecules) can be attributed to various factors like superior adsorption, firm complexation and impermeable layer development over the surface of metal/alloy. This review initiative can promote the use of (S.) extract in commercial scale to retard the corrosion of various metals/alloys and hence can add financial angle for the cultivators.

People derive much purpose from their work, yet time spent on work is decreasing. Here, we ask if effortful leisure is a powerful source of meaning and purpose that could supplement the reduction in labor time. In five studies (N = 2569), we investigated the relationship between effort and meaning in leisure activities. In Study 1 (N = 1145), we found that participants rated effortful activities as more meaningful, although less enjoyable, suggesting a trade-off between eudaimonic and hedonic wellbeing. Studies 2a (N = 375), 2b (N = 389), and 3 (N = 400) provided causal evidence by comparing effortful (Sudoku puzzling) and non-effortful leisure (watching videos in Studies 2a and 2b; Click-to-Reveal game in Study 3). Effortful activities consistently felt more meaningful, though the effects plateaued at higher levels of effort. Finally, Study 4 (N = 260) used experience sampling to assess activities as they occurred in real life. Effortful leisure fostered meaning while maintaining enjoyment, whereas other activities tended to feel less enjoyable with increased effort. Across all studies, we found that effort promotes meaningful experiences, particularly in leisure contexts, where effort does not diminish enjoyment. Effortful leisure may offer a powerful opportunity to supplement or replace the once plentiful purpose we derived from our now diminishing time at work.

An on-chip hybrid photonic plasmonic cavity is proposed for channeling plasmon-enhanced polarized single photons into guided modes. We show that the device leads to a high Purcell factor ~ 6000, and degree of polarization ~99%.

Organizations engaged in development and shipment of software products must strike a balance between delivery of business requirements versus product standards. Implementation of product standards is crucial from development till realization phases of product development to derive maximum benefits it is essential that the product standards are incorporated during the initial stages of product development like design phase. In this paper an evaluation is carried out to highlight the significance of timely incorporation of product standards to ensure compliance. A methodology is proposed for incorporating product standards at various stages of the product life cycle. Later using the proposed model, a use case is illustrated to derive the pros and cons of incorporating product standards at various stages of product development.

Skull stripping is a crucial pre-processing step in medical imaging analysis and has been widely studied using various techniques. Deep learning networks have proven to be effective in many image segmentation tasks, including skull stripping. This study compares four popular deep learning networks, namely U-Net, SegNet, PSPNet, DeepLab, and a novel Vision Transformer for skull stripping. The performance of each network was evaluated on the skull stripping dataset, and the results were analyzed based on various metrics. We also propose a novel loss function to improve model performances. The new loss function uses a combination of shape-aware loss and regional losses to improve the performance of the vision transformer. The study found that Vision Transformer performed significantly better in terms of correctly classifying boundary pixels than non-boundary pixels than the state-of-the-art networks when trained on the new loss function metrics. Furthermore, these findings provide valuable insights into the performance of different deep-learning networks for skull stripping and can aid in the selection of an appropriate network.