Efficient Implementation Research Articles

A Systematic Review of Fast, Scalable, and Efficient Hardware Implementations of Elliptic Curve Cryptography for Blockchain

Blockchain technology entered the enterprise domain under the name of permissioned blockchains and hybrid or verifiable database systems, as they provide a distributed solution that allows multiple distrusting parties to share common information. One drawback of these systems is the overhead added by the cryptographic functions which impacts the throughput in terms of transactions per second and increases the latency of transaction processing. Many of the cryptographic functions and protocols used in blockchains are based on Elliptic Curve Cryptography (ECC). Unfortunately, ECC operations such as modulo inverse or scalar point multiplication have considerable latency which causes the slowdown of the entire system. In such situations, reconfigurable computing architectures, such as FPGAs, can be used to offload these tasks to overcome the performance loss. This survey analyzes the current state-of-the-art designs and implementations of ECC from a hardware perspective. We use a PRISMA-based approach to filter recent publications and to reduce their number from over 16,000 to only 43 highly relevant designs. In the end, we show that very few designs are able to fulfill all three properties of high performance, scalability, and efficiency.

RhD alloimmunization – diagnostics, therapy and prophylaxis

Introduction: The success in preventing and treating Rh D alloimmunization is a major accomplishment in modern obstetrics. The widespread use of Rh D immune globulin has led to a decline in red cell alloimmunization. Despite evidence of its effectiveness, many cases of Rh D alloimmunization persist due to non-compliance with established protocols. Implementing routine anti-D prophylaxis (RAADP) during the third trimester is well-established but requires more careful. Aim of study: The aim of the study is to summarize the available knowledge about alloimmunization in pregnancy and methods of prophylaxis. The definition, patomechanism, methods of treatment and prophylaxis were summarized and described. Materials and methods: The literature available in PubMed database was reviewed using following keywords: “alloimmunization in pregnancy”, “serological conflict”, “fetal anemia”, “HDFN”, “prophylaxis of alloimmunization”. Conclusion: Advancements in diagnosing, treating, and preventing hemolytic disease of the fetus and newborn are notable. Quick, non-invasive diagnostics have facilitated more efficient treatment implementation. Prevention methods include specific and nonspecific prophylaxis, which can start during or after pregnancy. Numerous guidelines and studies exist regarding immunoglobulin use in pregnancy. However, medical staff education on managing hemolytic disease during pregnancy is crucial.

Preparation of Double-Networked Slow-Expanding Nanomicrospheres and Evaluation of Drive Modulation Performance

Aiming at the problem of excessive swelling of conventional microspheres for oilfield use, a novel amphiphilic polymerizable crosslinker (AE) was synthesized by quaternary ammonium modification of an unstable crosslinker (AE) using acrylamide, 2-acrylamido-2-methylpropanesulfonic acid as the monomers, N,N′-methylene bisacrylamide as the stabilizing crosslinker, ammonium peroxysulfate and sodium bisulfite as the initiator, and water as the solvent by using a reversed microemulsion method. Double-networked nanomicrospheres were prepared. The preparation conditions of the microspheres were optimized by the surface response method, focusing on the effects of the initiator addition and reaction temperature, and total crosslinker addition on the formation of nanomicrospheres. The samples were characterized by FTIR, TGA, laser particle sizer, and SEM to evaluate the retarded expansion performance and the modulation drive performance. The results showed that the optimal conditions for the preparation of microspheres were m(oil phase):m(water phase) = 3:2, stirring speed of 550 r/min, total crosslinking agent dosage of 0.6% (based on the total mass of monomers, hereinafter the same), initiator dosage of 0.30%, reaction temperature of 45 °C, and reaction time of 4 h. Compared with the conventional polymer microsphere PAM, PAE was slow-expanded for 45 d at 60 °C, and the expansion multiplier was about 16 times, with slow-expansion characteristics; the blocking rate of PAE reached 98.3%, the oil repulsion rate was 73.11%, and the increase in the recovery rate could be up to 11.23%. In this paper, a new type of nanomicrosphere material is investigated to realize the efficient implementation of oil field conditioning and driving.

Realizing the Calculation of a Fully Normalized Associated Legendre Function Based on an FPGA

A large number of fully normalized associated Legendre function (fnALF) calculations are required to compute Earth’s gravity field elements using ultra high-order gravity field coefficient models. In the surveying and mapping industry, researchers typically rely on CPU-based systems for these calculations, which leads to limitations in execution speed and power efficiency. Although modern CPUs improve instruction execution efficiency through instruction-level parallelism, the constraints of a shared memory architecture impose further limitations on the execution speed and power efficiency. This results in exponential increases in computation time as demand rises alongside high power consumption. In this article, we present a new computational implementation of an fnALF based on the ZYNQ platform. We design a task-parallel “pipeline” architecture which converts the original serial logic into a more efficient hardware implementation, and we utilize a redundant calculation layer to handle repetitive coefficient computations separately. The experimental results demonstrate that our system achieved accurate and rapid calculations. Under the only one geocentric residual latitude condition, we measured the computation times for spherical harmonic coefficient degrees of 360, 720, and 1080 to be 0.155922 s, 0.520950 s, and 1.401609 s, respectively. In the case of the multiple geocentric residual latitudes condition, our design generally yielded efficiency gains of over three times those of MATLAB R2020b implementation. Additionally, our calculated results were used to determine the geoid height in the field with an error of less than ±0.1m, confirming the reliability of our computations.

The economics of translating a biosimilar from lab to market in India.

This study aims to establish a cost basis for biologics manufacturers and policymakers by quantifying the price and time required to bring a biosimilar from the lab to market. For efficient implementation of a cost-based policy, especially for life-saving medicines like biosimilars, it is imperative to establish a benchmark for the cost involved in biosimilar development. In this holistic and multiple-case study, stage-wise cost estimates of biosimilar development were obtained for microbial and mammalian systems. The investigation of six biopharmaceutical companies based in India concluded that biosimilar development through the microbial system costs ∼18 million USD and ∼21 million USD for the mammalian system. Additionally, 45-50 million USD is required as a one-time capital investment. Further, US/EU authorization can cost ∼25 million USD per product. Clinical studies are the most expensive and account for 60%-70% of total development cost. The presented information can serve as a basis for implementing cost-based pricing in countries like India and reimbursement policies for biosimilars under Medicare Part B in the United States.

A Novel RNS Hardware Architecture of FRM Filter Banks for Digital Hearing Aids

This paper presents a novel hardware architecture for Frequency Response Masking (FRM) Filter Bank based on Residue Number System (RNS) arithmetic for Digital Hearing Aids (DHA). FRM is a computationally efficient approach for filter bank design that separates different frequencies based on the Audiograms of a patient. However, filter banks are critical power-hungry elements that require high computational resources. Therefore, the proposed architecture aims to provide an efficient implementation of FRM filter banks, which can improve the value and affordability of DHAs. The proposed architecture employs FIR filters in direct form, linear phase, and poly phase using RNS arithmetic. This approach enables the performance of arithmetic operations in parallel and independently. The concepts are further extended for the efficient implementation of FRM filter bank. The results of the proposed architecture show that direct implementation reduces the delay when the number of taps increases in the FIR filter. However, to reduce computational resources and optimize the area requirement, a Folded RNS architecture is also proposed. The Folded RNS architecture offers hardware savings of 23–27% for a 40-tap FIR filter and 29–31% for a 60-tap FIR filter. Additionally, it improves speed by 13–15% for a 40-tap FIR filter and 16–19% for a 60-tap folded RNS FIR filter. The implementation of FRM filter bank using the folded FIR filter shows the same trend in hardware saving and speed improvement as in the case of FIR filters. These results demonstrate that the proposed hardware architecture has the potential to improve the efficiency and affordability of DHAs while enhancing the listening comfort, which can enhance the quality of life for patients with hearing impairments.

Cluster perturbation theory. X. A parallel implementation of Lagrangian perturbation series for the coupled cluster singles and doubles ground-state energy through fifth order.

We describe an efficient implementation of cluster perturbation and Møller-Plesset Lagrangian energy series through the fifth order that targets the coupled cluster singles and doubles energy utilizing the resolution of the identity approximation. We illustrate the computational performance of the implementation by performing ground state energy calculations on systems with up to 1200 basis functions using a single node and by comparison to conventional coupled cluster singles and doubles calculations. We further show that our hybrid message passing interface/open multiprocessing parallel implementation that also utilizes graphical processing units can be used to obtain fifth order energies on systems with almost 1200 basis functions with a 90min"time to solution" running on Frontier at Oak Ridge National Laboratory.

Near-optimal quantum kernel principal component analysis

Abstract Kernel principal component analysis (kernel PCA) is a nonlinear dimensionality reduction technique that employs kernel functions to map data into a high-dimensional feature space, thereby extending the applicability of linear PCA to nonlinear data and facilitating the extraction of informative principal components. However, kernel PCA necessitates the manipulation of large-scale matrices, leading to high computational complexity and posing challenges for efficient implementation in big data environments. Quantum computing has recently been integrated with kernel methods in machine learning, enabling effective analysis of input data within intractable feature spaces. Although existing quantum kernel PCA proposals promise exponential speedups, they impose stringent requirements on quantum hardware that are challenging to fulfill. In this work, we propose a quantum algorithm for kernel PCA by establishing a connection between quantum kernel methods and block encoding, thereby diagonalizing the centralized kernel matrix on a quantum computer. The query complexity is logarithmic with respect to the size of the data vector, $D$, and linear with respect to the size of the dataset. An exponential speedup could be achieved when the dataset consists of a few high-dimensional vectors, wherein the dataset size is polynomial in $\log(D)$, with $D$ being significantly large. In contrast to existing work, our algorithm enhances the efficiency of quantum kernel PCA and reduces the requirements for quantum hardware. Furthermore, we have also demonstrated that the algorithm based on block encoding matches the lower bound of query complexity, indicating that our algorithm is nearly optimal. Our research has laid down new pathways for developing quantum machine learning algorithms aimed at addressing tangible real-world problems and demonstrating quantum advantages within machine learning.

SAT solving for variants of first-order subsumption

AbstractAutomated reasoners, such as SAT/SMT solvers and first-order provers, are becoming the backbones of rigorous systems engineering, being used for example in applications of system verification, program synthesis, and cybersecurity. Automation in these domains crucially depends on the efficiency of the underlying reasoners towards finding proofs and/or counterexamples of the task to be enforced. In order to gain efficiency, automated reasoners use dedicated proof rules to keep proof search tractable. To this end, (variants of) subsumption is one of the most important proof rules used by automated reasoners, ranging from SAT solvers to first-order theorem provers and beyond. It is common that millions of subsumption checks are performed during proof search, necessitating efficient implementations. However, in contrast to propositional subsumption as used by SAT solvers and implemented using sophisticated polynomial algorithms, first-order subsumption in first-order theorem provers involves NP-complete search queries, turning the efficient use of first-order subsumption into a huge practical burden. In this paper we argue that the integration of a dedicated SAT solver opens up new venues for efficient implementations of first-order subsumption and related rules. We show that, by using a flexible learning approach to choose between various SAT encodings of subsumption variants, we greatly improve the scalability of first-order theorem proving. Our experimental results demonstrate that, by using a tailored SAT solver within first-order reasoning, we gain a large speedup in solving state-of-the-art benchmarks.

Scalable Experimental Bounds for Entangled Quantum State Fidelities

Estimating the state preparation fidelity of highly entangled states on noisy intermediate-scale quantum (NISQ) devices is important for benchmarking and application considerations. Unfortunately, exact fidelity measurements quickly become prohibitively expensive, as they scale exponentially as O (3 N ) for N -qubit states, using full state tomography with measurements in all Pauli bases combinations. However, Somma et al. established that the complexity could be drastically reduced when looking at fidelity lower bounds for states that exhibit symmetries, such as Dicke States and GHZ States. These bounds must still be tight enough for larger states to provide reasonable estimations on NISQ devices. For the first time and more than 15 years after the theoretical introduction, we report meaningful lower bounds for the state preparation fidelity of all Dicke States up to N = 10 and all GHZ states up to N = 20 on Quantinuum H1 ion-trap systems using efficient implementations of recently proposed scalable circuits for these states. Our achieved lower bounds match or exceed previously reported exact fidelities on superconducting systems for much smaller states. Furthermore, we provide evidence that for large Dicke States \(\vert {{D_{N}^{N}{/2}}\rangle } \) , we may resort to a GHZ-based approximate state preparation to achieve better fidelity. This work provides a path forward to benchmarking entanglement as NISQ devices improve in size and quality.

Employee Safety in the Conditions of Industry 4.0

Abstract The safety of employees during the implementation of work processes is one of the important conditions for the efficient implementation of production tasks. The situation does not change with the use of automation and robotization. On the contrary, it may be a source of hazards in new areas, requiring specific actions to limit the possibility of accidents occurring or to limit their consequences. These actions should be taken in a manner appropriate to the nature of the non-conformities. The study, based on the analysis of literature and a case study conducted in a production organization, showed the most important factors that may disturb the efficient implementation of production tasks. The study presents the key conditions for the development and operation of Industry 4.0, in accordance with the recommendations of the European Union and guidelines indicated in the scientific literature. The obtained results are addressed to the information of people responsible for the development of I4.0, taking into account “the safety aspects”, in industrial enterprises where the implementation of tasks requires cooperation between humans and machines. Statistical analysis was not included in the study. The author attempts to identify relationships that justify further research conducted to establish the existing correlations regarding supporting organizational development professionals, and in particular helping them identify potential problems in order to provide their organization with an advantage in the global competitive environment.

Precise control of neural activity using dynamically optimized electrical stimulation.

Neural implants have the potential to restore lost sensory function by electrically evoking the complex naturalistic activity patterns of neural populations. However, it can be difficult to predict and control evoked neural responses to simultaneous multi-electrode stimulation due to nonlinearity of the responses. We present a solution to this problem and demonstrate its utility in the context of a bidirectional retinal implant for restoring vision. A dynamically optimized stimulation approach encodes incoming visual stimuli into a rapid, greedily chosen, temporally dithered and spatially multiplexed sequence of simple stimulation patterns. Stimuli are selected to optimize the reconstruction of the visual stimulus from the evoked responses. Temporal dithering exploits the slow time scales of downstream neural processing, and spatial multiplexing exploits the independence of responses generated by distant electrodes. The approach was evaluated using an experimental laboratory prototype of a retinal implant: large-scale, high-resolution multi-electrode stimulation and recording of macaque and rat retinal ganglion cells ex vivo. The dynamically optimized stimulation approach substantially enhanced performance compared to existing approaches based on static mapping between visual stimulus intensity and current amplitude. The modular framework enabled parallel extensions to naturalistic viewing conditions, incorporation of perceptual similarity measures, and efficient implementation for an implantable device. A direct closed-loop test of the approach supported its potential use in vision restoration.

Capabilities toward adoption of outcome-based contracts

PurposeManufacturing enterprises have started to offer the “outcome” derived from machines with the help of outcome-based contracts (OBCs). Offering OBCs leads to benefits such as increased revenues, stronger customer relationships and sustainability. However, implementing OBCs requires critical capabilities. Existing literature has focused on identifying these necessary capabilities, but the prioritization and interrelationships among them remain unexplored. This study aims to address this gap.Design/methodology/approachOur study employs a hybrid analytical hierarchy process and interpretative structural modeling approach to prioritize and map interrelationships among OBC-related capabilities within small and medium-sized enterprises (SMEs).FindingsThe findings highlight the importance of digitalization capabilities such as data privacy and security, remote monitoring, and data analytics; and organizational and governance capabilities, including quantifying, controlling, and monitoring risks, teamwork, and leadership, are highlighted.Research limitations/implicationsWe quantitatively prioritize OBC capabilities and establish their level-wise structural interrelationships, which will facilitate a more effective and efficient implementation of OBCs. Due to the emergent nature of OBCs, our study could identify just one SME case company meeting our selection criteria.Originality/valueExisting OBC literature focusses on the design of OBCs in large companies. Similarly, earlier capability-related OBC literature is oriented toward identifying the OBC capabilities to perform specific functions. However, in the current study, we propose a systematic decision-making approach that comprehensively prioritizes and identifies the interrelationships among the capabilities necessary to provide OBCs, thus complementing the existing scientific literature on OBCs. In addition, we focus on SMEs, that have specific limitations and characteristics.

Acceleration Of Recombinant Viral Sequences Search By 3SEQ Algorithm Via Adding Support Of Multi-Threaded Calculations And Considering Sample Collection Dates

The article presents an efficient multithreaded implementation of the modern 3SEQ algorithm for detecting recombinant genetic sequences, tested on viral genomes. The work was carried out within the framework of the project to create a domestic (Russian) web-platform (bioprojects.iis.nsk.su) for solving a wide range of problems related to data analysis in the field of bioinformatics, virology and epidemiology. A recombinant viral genome emerges when two different variants of virus genomes of the same species exchange their parts, which is possible in case of infection with both variants simultaneously. The emergence of recombinants is rare but important events in the context of virus evolution research. One of the most promising among the existing algorithms for searching for recombinants is 3SEQ, but the author's version works only in single-threaded mode. We implemented this algorithm with support for multithreaded computing and taking into account the dates of sample collection, which provided a significant increase in the computing speed. The developed software was used to search for recombinants in the samples of influenza A H1N1 (only PB2 segments from 2174 genomes were analyzed), Dengue fever (726 genomes), Ebola virus (865 genomes) and in two samples of SARS-CoV-2 coronavirus (776 and 2132 genomes). No recombinants were found for influenza A H1N1 (PB2 segment) and the first dataset on SARS-CoV-2 (variant from Russia), which is in agreement with the analysis of the same data by the RDP algorithm. For the second SARS-CoV-2 dataset (variants from the Siberian Federal District), the only recombinant present in the dataset was correctly found. 725 recombinants were found in Dengue fever viruses, with a recombination region length in the range from 50 to 1000 nucleotides. In Ebola viruses, the length of the recombination region was shorter – in 572 recombinants it was in the range of 50 to 100 nucleotides, and in 249 genomes – was less than 50 nucleotides.

CunuSHT: GPU Accelerated Spherical Harmonic Transforms on Arbitrary Pixelizations

Abstract We present cunuSHT https://github.com/Sebastian-Belkner/cunuSHT, a general-purpose Python package that wraps a highly efficient CUDA implementation of the nonuniform spin-0 spherical harmonic transform. The method is applicable to arbitrary pixelization schemes, including schemes constructed from equally-spaced iso-latitude rings as well as completely nonuniform ones. The algorithm has an asymptotic scaling of $\mathcal {O}{(\ell _{\rm max}^3)}$ for maximum multipole ℓmax and can be made to achieve machine precision accuracy, considering band-limited transforms for which $N\approx \ell _{\rm max}^2$ (where N is the number of pixels in the map). While cunuSHT is developed for applications in cosmology in mind, it is applicable to various other interpolation problems on the sphere. We outperform the fastest available CPU algorithm at problem sizes ℓmax ∼ 4 · 102 and larger. The speed-up increases with the problem size and reaches a factor of up to 5 for problems with a nonuniform pixelization and ℓmax > 4 · 103 when comparing a single modern GPU to a modern 32-core CPU. This performance is achieved by utilizing the double Fourier sphere method in combination with the nonuniform fast Fourier transform and by avoiding transfers between the host and device. For scenarios without GPU availability, cunuSHT wraps existing CPU libraries. cunuSHT is publicly available and includes tests, documentation, and demonstrations.

Compact Digital Immunoassay Platform Integrating ELISA with a Lateral Flow Strip

Background/Objectives: On-site diagnosis of infection in their early stages requires assays with high sensitivities that are compact and easy to operate out of the laboratory and hospital environments. However, current assay technologies fall short of these requirements and require highly skilled technicians to set up, operate, and interpret the results. Methods: To address these challenges, we developed and evaluated a Point-of-Care-Testing (PoCT) immunoassay platform called the D-strip. The D-strip platform combines the capabilities of a digital enzyme-linked immunoassay (ELISA) with a lateral flow assay (LFA). The D-strip sample flow cell is composed of the same components found in conventional LFAs, and its high sensitivity is due to its efficient implementation of ELISA. The fully integrated platform is simple and requires minimal user intervention to operate. Results: The D-strip exhibited a sample-to-result time of 15 min with a limit of detection (LOD) of 1.7 × 103 copies/mL for severe acute respiratory syndrome coronavirus 2 (SARS-2-CoV) antigen. The LOD of the D-strip is 488-fold higher than that for conventional LFAs and is comparable to a clinical laboratory test. Conclusions: The D-strip is a compact and highly sensitive immunoassay platform with a strong potential for application as a confirmatory assay outside the clinical laboratory.

Distinguishing Structures from Textures by Patch‐based Contrasts around Pixels for High‐quality and Efficient Texture filtering

AbstractIt is still challenging with existing methods to distinguish structures from texture details, and so preventing texture filtering. Considering that the textures on both sides of a structural edge always differ much from each other in appearances, we determine whether a pixel is on a structure edge by exploiting the appearance contrast between patches around the pixel, and further propose an efficient implementation method. We demonstrate that our proposed method is more effective than existing methods to distinguish structures from texture details, and our required patches for texture measurement can be smaller than the used patches in existing methods by at least half. Thus, we can improve texture filtering on both quality and efficiency, as shown by the experimental results, e.g., we can handle the textured images with a resolution of 800 × 600 pixels in real‐time. (The code is available at https://github.com/hefengxiyulu/MLPC)

Gas-Kinetic Unified Algorithm for Aerodynamics Covering Various Flow Regimes by Computable Modeling of Boltzmann Equation

The gas-kinetic unified algorithm (GKUA), to solve the modeling of the Boltzmann equation, has been developed to study the aerothermodynamics problems with the effects of wall activation energy covering various flow regimes. The unified velocity distribution function equation could be accordingly presented on the basis of the Boltzmann-Shakhov model. To remove the dependence of the distribution function on velocity space, the conservational discrete velocity ordinate method has been developed for hypervelocity flows. The gas-kinetic finite difference scheme is constructed to directly solve the discrete velocity distribution functions by the operator splitting technique. The discrete velocity numerical integration method with the Gauss-type weight function has been developed to evaluate the macroscopic flow variables. Specially, to model the real physical process between gas molecules and the surface, the Maxwell-type gas-surface interaction model has been presented by the MD (molecular dynamic) simulation to obtain the energy adaptability coefficient. The multi-processing domain decomposition strategy and parallel implementation of high parallel efficiency and expansibility designed for the gas-kinetic numerical method is presented with good load balance and data communication efficiency. To validate the accuracy and feasibility of the present algorithm, the supersonic flows past two-dimensional circular cylinder are simulated covering various flow regimes. The results are in good agreement with the related theoretical, DSMC (Direct Simulation Monte Carlo), N-S (Navier-Stokes), and experimental data. The hypersonic reentry flows with the effects of wall activation energy around the Tianzhou-5 cargo spacecraft are simulated by the present GKUA and the massive parallel strategy. It has been confirmed that the present algorithm from the gas-kinetic point of view probably provides a promising approach to resolve the hypersonic aerothermodynamic problems with the complete spectrum of flow regimes during the re-entry and disintegration of the large-scale spacecraft.

Exploring experimental tests concerning liquid hydrogen releases

In recent years, the adoption of liquid hydrogen (LH2) has increased significantly in industrial and transport applications, driven by its low carbon footprint, thereby aiding the fight against global warming. Additionally, its high volumetric energy density, compared to gaseous or compressed hydrogen, enhances hydrogen storage capabilities. However, safety remains a major concern due to its physical-chemical properties and inherent hazardous characteristics, especially in the event of spillage scenarios. Therefore, to better understand the consequences of LH2 releases onto or into water, large-scale experimental tests were conducted by Bundesanstalt für Materialforschung und -prüfung (BAM) within the Safe Hydrogen Fuel Handling and Use for Efficient Implementation (SH2IFT) project at the Test Site Technical Safety of BAM, comprising 75 single spill events at varied release rates and orientations. While the rapid phase transition (RPT) phenomenon was not observed, self-ignition of the hydrogen-air cloud occurred, accompanied by blast wave overpressure and heat radiation, without a discernible ignition source. These findings emphasize the need for further investigation into LH2 safety. Leveraging experimental data for real-world applications provides insights into safe LH2 infrastructure implementation, laying foundational knowledge for addressing safety challenges and advancing LH2 technology.

Low-carbon and high-efficiency nanosheet-enhanced CO2 huff-n-puff (HnP) for heavy oil recovery

CO2 huff-n-puff (CO2-HnP) process is a promising technique for enhanced heavy oil recovery. The huff stage of this process involves the formation of foamy oil flow, characterized by dispersed CO2 bubbles within heavy oil, which plays a pivotal role in both oil recovery and CO2 sequestration. Maintaining the stability of foamy oil is crucial for the efficient implementation of CO2-HnP processes. Herein, ultrathin plate-shaped nanosheets are employed as stabilizers for foamy oil. Carboxyl-alkyl composite Janus nanosheets (SANs) are synthesized using a bottom-up approach. Comprehensive characterizations illustrate that SANs, owing to their amphiphilic and Janus nature, efficiently adsorb at the CO2-oil interface, thereby modifying interfacial properties and enhancing foamy oil stability even at low concentration (0.005 wt%). The incorporation of SANs extends the duration of foamy oil effect, resulting in a substantial enhancement of oil recovery efficiency from 30.7% to 39.4%, and an increase in CO2 storage factor from 6.5% to 7.8%, compared to conventional CO2-HnP processes. Micromodel experiments and molecular dynamics simulations show that SANs strengthen foamy oil stability by increasing interfacial activity and reducing CO2 diffusion rate. Overall, the multifunctional capabilities of SANs in interfacial modification offer promising prospects for enhanced oil recovery and optimizing CCUS efficiency.