Indistinguishability obfuscation, introduced by [Barak et. al. Crypto’2001], aims to compile programs into unintelligible ones while preserving functionality. It is a fascinating and powerful object that has been shown to enable a host of new cryptographic goals and beyond. However, constructions of indistinguishability obfuscation have remained elusive, with all other proposals relying on heuristics or newly conjectured hardness assumptions. In this work, we show how to construct indistinguishability obfuscation from subexponential hardness of four well-founded assumptions. We prove: Suppose there exists any set of constants \(\tau \in (0,\infty), \delta \in (0,1), \epsilon \in (0,1)\) such that the sub-exponential security of the following assumptions hold: — the Learning With Errors ( \(\mathsf {LWE}\) ) assumption with subexponential modulus-to-noise ratio \(2^{k^\epsilon }\) and noises of magnitude polynomial in k , where k is the dimension of the \(\mathsf {LWE}\) secret, — the Learning Parity with Noise ( \(\mathsf {LPN}\) ) assumption over general prime fields \(\mathbb {Z}_p\) with polynomially many \(\mathsf {LPN}\) samples and error rate \(1/\ell ^\delta\) , where \(\ell\) is the dimension of the \(\mathsf {LPN}\) secret, — the existence of a Boolean Pseudo-Random Generator ( \(\mathsf {PRG}\) ) in \(\mathsf {NC}^0\) with stretch \(n^{1+\tau }\) , where n is the length of the \(\mathsf {PRG}\) seed, — the Decision Linear ( \(\mathsf {DLIN}\) ) assumption on symmetric bilinear groups of prime order. Then, (subexponentially secure) indistinguishability obfuscation for all polynomial-size circuits exists. Furthermore, assuming only polynomial security of the aforementioned assumptions, there exists collusion resistant public-key functional encryption for all polynomial-size circuits.
Supercapacitors are a family of energy storage devices that have attracted significant attention because of their high power density, quick charge and discharge times, and extended cycle life. For the development of high-performance supercapacitors, electrodes with high specific capacitance values along with good structural stability are essential. In this context, we report the synthesis of a surface oxygen-modified CoAlFe LDH@Fe3O4 heterostructure using a simple hydrothermal route followed by post modification using hydrogen peroxide. High crystallinity and phase purity were confirmed by structural studies employing X-ray diffraction (XRD), and functional groups essential to electrochemical performance were revealed by Fourier-transform infrared spectroscopy (FTIR). XRD and FTIR results confirmed the formation of the heterostructure with the presence of magnetic Fe3O4. In addition, the surface oxygen coordination was found to be significantly improved. Excellent retention, low internal resistance, and better capacitive behavior were demonstrated by electrochemical studies. For the electrochemical evaluation of the heterostructure, a 1 M KOH electrolyte was used. A remarkable specific capacitance value of 1709.6 F g-1 at a current density of 1 A g-1 was achieved. In addition, the heterostructure exhibited excellent durability with 89.39% capacity retention after 10 000 consecutive charge-discharge cycles. A low charge transfer resistance value was confirmed using electrochemical impedance spectroscopy. A scalable and efficient method for creating high-performance supercapacitor electrodes is presented in this study. Additionally, the relationship between surface chemistry and structural order in affecting electrochemical performance is thoroughly examined, providing a fresh perspective on the future of innovative energy materials.
Nanotechnology is evolving as ground breaking tool in agriculture that presents promising applications for improved plant growth and productivity. This study explores the influence of soil applied copper sulfide nanoparticles (CuS NPs) at 8 Kg/ha on the growth, biochemical profile, and oil quality parameters of Glycine max under field environment in one season. CuS NPs significantly improved the agronomic parameters i.e., root length, shoot length, number of branches per plant, dry weight per plant, pods per plant, seeds per pod, and oil content percentage as compared to control plants. Antioxidative enzymes (superoxide dismutase and peroxidase) and non-enzymatic antioxidants (total phenolic contents and total flavonoid contents) significantly increased to reduce the oxidative stress induced by NPs. Biochemical investigation also showed that CuS NPs significantly improved protein content, DPPH free radical scavenging activity, total antioxidant capacity, and total reducing power in shoots. Atomic absorption spectroscopy demonstrated the highest concentration of Cu in soil, followed by accumulation in root> shoot> seed of G. max. Oil quality analysis via FT-IR, GC-MS, and NMR indicated minor dissimilarities in oil properties extracted from G. max, without any significant variation in the overall fatty acid profile of treated plants. The findings demonstrate the potential of CuS NPs as an effective soil amendment for promoting plant growth and biochemical properties, and better plant productivity without compromising the oil quality.
In today’s visually saturated media landscape, television genres have evolved significantly, embracing hybrid, dynamic, and aesthetically complex patterns. This evolution has transformed traditional genre classification systems, which rely on manual tagging and fixed metadata structures, making them inefficient and often inconsistent. As storytelling formats blend elements from documentaries, fiction, and cinematic production, the lines between genres blur, posing significant challenges for conventional categorization techniques. Addressing this complexity requires intelligent, automated systems capable of detecting visual patterns and semantic cues. In response to these demands, this study investigates the application of advanced vision transformer-based deep learning techniques to classify television content by genre with greater accuracy and adaptability, focusing on the pyramid vision transformer (PVT) architecture. PVT incorporates hierarchical feature extraction with multiscale attention mechanisms, allowing it to capture intricate spatial and semantic details in visual media, to achieve high classification accuracy while significantly reducing computational complexity. Its progressive spatial reduction attention (PSRA) reduces token complexity while retaining context, making PVT more efficient than flat ViTs. By uniting multiscale feature learning with global attention, PVT effectively models hybrid genres where subtle differences in lighting, actor placement, or scene composition are critical. Additionally, explainability through Grad-CAM enhances transparency and practical applicability. Empirical analysis is based on an available dataset comprising 4,475 images across four major TV genres, ensuring a broad representation of content styles. The PVT model’s performance was evaluated against two benchmarks: a conventional convolutional neural network (CNN) and a transfer learning approach using NASNetMobile. The results show that PVT achieves a classification accuracy of 97
The simulation of the observed properties of type I X-ray bursts, also known as superbursts, poses challenges once Cooper pair neutrino emission from the crust of the neutron star are included. Further, additional heating of the accumulating fuel layer is required. The emission of gamma-rays caused by electron captures to excited states in astrophysical environments is a major source of heat loss competing with that carried away by weak-interaction neutrinos. gamma-heating significantly affect the presupernova evolution of massive stars and the calculation of the thermal structure in the crust and core of superbursts. This energy deposition enhances entropy production and promotes convection at this stage of stellar evolution. Effective gamma-heating rates reduce the ignition depth of superbursts. A recent investigation ranked the leading electron capturing nuclei as the cause for significant changes in the lepton-to-baryon fraction ( ) of the stellar matter after silicon core burning. We investigate gamma-heating rates from the excited states of the top 100 electron capture and positron decay nuclei identified in recently published ranking lists. Each nucleus was analyzed using four different empirical pairing gaps and three distinct sets of nuclear deformation parameters to assess the effect of gamma-heating rates. We report our calculations for the temperature range of 1-10 GK and density range of - g/cm . The calculated gamma-heating rates changes up to a factor 26 (16) with changing deformation values (pairing gaps). Our findings may contribute to more realistic simulations of post-silicon burning phases of massive stars and superbursting neutron stars.
