Verification Of Solutions Research Articles

Efficient Zero-Knowledge Proofs for Set Membership in Blockchain-Based Sensor Networks: A Novel OR-Aggregation Approach

Blockchain-based sensor networks offer promising solutions for secure and transparent data management in IoT ecosystems. However, efficient set membership proofs remain a critical challenge, particularly in resource-constrained environments. This paper introduces a novel OR-aggregation approach (where “OR” refers to proving that an element equals at least one member of a set without revealing which one) for zero-knowledge set membership proofs, tailored specifically for blockchain-based sensor networks. We provide a comprehensive theoretical foundation, detailed protocol specification, and rigorous security analysis. Our implementation incorporates optimization techniques for resource-constrained devices and strategies for integration with prominent blockchain platforms. Extensive experimental evaluation demonstrates the superiority of our approach over existing methods, particularly for large-scale deployments. Results show significant improvements in proof size, generation time, and verification efficiency. The proposed OR-aggregation technique offers a scalable and privacy-preserving solution for set membership verification in blockchain-based IoT applications, addressing key limitations of current approaches. Our work contributes to the advancement of efficient and secure data management in large-scale sensor networks, paving the way for wider adoption of blockchain technology in IoT ecosystems.

Enhancing consumer trust in financial services: the role of technological security innovations

The financial services industry increasingly relies on technological advancements to maintain and enhance consumer trust. Consumer trust is a critical component influencing customer loyalty, financial inclusion, and overall economic stability. This paper explores the impact of technological security innovations on consumer trust in financial services. Fundamental innovations such as blockchain, advanced encryption, biometric authentication, artificial intelligence, and cloud security play significant roles in addressing security challenges. Blockchain technology provides a decentralized, tamper-proof method for recording transactions, enhancing transparency and reducing fraud risks. Advanced encryption techniques ensure the confidentiality and integrity of financial data, which is critical for protecting sensitive information. Biometric authentication methods offer secure and user-friendly solutions for identity verification, reducing reliance on traditional passwords and enhancing security. Artificial intelligence and machine learning improve fraud detection capabilities by providing real-time monitoring and response. Cloud security solutions offer scalable protection for financial data, ensuring compliance with regulatory standards. Through empirical analysis and case studies, this study demonstrates that these technologies significantly enhance consumer confidence by providing greater transparency, security, and reliability in financial transactions. The findings offer valuable insights for policymakers, financial institutions, and technology developers aiming to foster a more resilient and trust-based financial ecosystem. This research contributes to the existing literature by providing evidence of the direct impact of technological security innovations on consumer trust. Keywords: Consumer Trust, Financial Services, Technological Innovations, Blockchain, Encryption, Biometric Authentication, Artificial Intelligence, Cloud Security.

Analysis of students' computational thinking skills in solving algebraic problems in cultural contexts

This research aims to find out about students' computational thinking abilities in solving algebraic problems in cultural contests. This research is qualitative research carried out at Yogyakarta Middle School. The participants involved were 31 students in the even semester of grade 7. The data sources in this research were the results of students' computational thinking tests, interviews with students who were selected based on their communication skills and the results of test answers. Data instrument testing uses validity and reliability tests with data validity using triangulation techniques. Data analysis in this study used the pullulation technique between student test results and student interview results, then data reduction, data categorization, power display and conclusion drawing were carried out. The results of this research are that students in the high category can complete all indicators of computational thinking and problem-solving abilities. Students in the medium category can only complete two indicators of computational thinking and problem-solving abilities. Students in the low category have not been able to complete all indicators of computational thinking and problem-solving abilities. The study reveals a significant variance in the computational thinking abilities among junior high school students in Piyungan Development class VIII when solving algebra problems within a cultural context. High-achieving students demonstrated proficiency across all indicators of computational thinking, effectively navigating through various stages from abstraction to solution verification. In contrast, students categorized as moderate showed partial success, managing only a subset of the indicators, while struggling with the more complex stages of problem decomposition and implementation. Meanwhile, students classified in the low category were unable to achieve any indicators of computational thinking and problem-solving skills. This indicates a clear need for targeted educational interventions to enhance computational thinking among students, particularly those who are struggling, to ensure all students can develop the necessary skills to solve problems effectively.

Solution Verification of Anomalous Waves in Nonideal Gases

Solution Verification of Anomalous Waves in Nonideal Gases

Monitoring the Spatial Distribution of Cover Crops and Tillage Practices Using Machine Learning and Environmental Drivers across Eastern South Dakota

The adoption of conservation agriculture methods, such as conservation tillage and cover cropping, is a viable alternative to conventional farming practices for improving soil health and reducing soil carbon losses. Despite their significance in mitigating climate change, there are very few studies that have assessed the overall spatial distribution of cover crops and tillage practices based on the farm’s pedoclimatic and topographic characteristics. Hence, the primary objective of this study was to use multiple satellite-derived indices and environmental drivers to infer the level of tillage intensity and identify the presence of cover crops in eastern South Dakota (SD). We used a machine learning classifier trained with in situ field samples and environmental drivers acquired from different remote sensing datasets for 2022 and 2023 to map the conservation agriculture practices. Our classification accuracies (>80%) indicate that the employed satellite spectral indices and environmental variables could successfully detect the presence of cover crops and the tillage intensity in the study region. Our analysis revealed that 4% of the corn (Zea mays) and soybean (Glycine max) fields in eastern SD had a cover crop during either the fall of 2022 or the spring of 2023. We also found that environmental factors, specifically seasonal precipitation, growing degree days, and surface texture, significantly impacted the use of conservation practices. The methods developed through this research may provide a viable means for tracking and documenting farmers’ agricultural management techniques. Our study contributes to developing a measurement, reporting, and verification (MRV) solution that could help used to monitor various climate-smart agricultural practices.

Certifying Accuracy, Privacy, and Robustness of ML-Based Malware Detection

Recent advances in artificial intelligence (AI) are radically changing how systems and applications are designed and developed. In this context, new requirements and regulations emerge, such as the AI Act, placing increasing focus on strict non-functional requirements, such as privacy and robustness, and how they are verified. Certification is considered the most suitable solution for non-functional verification of modern distributed systems, and is increasingly pushed forward in the verification of AI-based applications. In this paper, we present a novel dynamic malware detector driven by the requirements in the AI Act, which goes beyond standard support for high accuracy, and also considers privacy and robustness. Privacy aims to limit the need of malware detectors to examine the entire system in depth requiring administrator-level permissions; robustness refers to the ability to cope with malware mounting evasion attacks to escape detection. We then propose a certification scheme to evaluate non-functional properties of malware detectors, which is used to comparatively evaluate our malware detector and two representative deep-learning solutions in literature.

Heuristic solution to the problem of diffraction of a TE-polarized electromagnetic field on a half-plane with generalized two-sided impedance boundary conditions

Employing the method of fundamental components, heuristic formulas are constructed for solving the problem of diffraction on a half-plane with non-ideal boundary conditions. The problem of diffraction of a TE-polarized wave on a half-plane with generalized two-sided impedance boundary conditions was chosen as a verification solution. The accuracy was increased by two types of adjustment functions. The effectiveness of the applied technique is proven by quantification of accuracy. This approach represents a new way to obtain high-accuracy heuristic formulas and allows one to create solvers that simultaneously have both high accuracy and high performance. The main result is the development and description of adjustment methods for increasing the accuracy of heuristic formulas.

Effect of mineral dissolution on fault slip behavior during geological carbon storage

Geological carbon dioxide storage is one of the most effective technologies to reduce greenhouse gas emissions to the atmosphere. The mineral dissolution is inevitable during the long-term storage process, while the chemical effect on fault slip behavior remains poorly understood. In this paper, we present a hydro-chemical–mechanical coupled fault reactivation study using the unified pipe-interface element method (UP-IEM). The impact of chemical dissolution on stress redistribution is considered. The accuracy of the numerical method to simulate reactive solute transport and fault frictional slip are confirmed by analytical solution and numerical verification. Two different fault tectonic scenarios, of which fault locating in the caprock and fault crosscutting the reservoir and caprock, are designed to investigate the fault slip behavior induced by mineral dissolution. The effect of dissolved CO2 solute concentration, fault aperture, reservoir permeability and chemical reaction rate are discussed. Results show that fault locating in the caprock is easier to be activated. The chemical reaction has a more significant effect on fault slip behavior than fluid pressure during the long-term low-velocity CO2 leakage. The increase of CO2 solute concentration and reservoir reaction rate cause an obvious growth on fault slip displacement. The decrease of fault initial aperture results in the fault slip velocity slower and the slipping area smaller. The high-permeability reservoir keeps the fault more stable than low-permeability reservoir under the influence of mineral dissolution.

Dynamic verification of an optimisation algorithm for power dispatch of integrated energy systems

The urgent need to achieve net-zero carbon emissions by 2050 has led to a growing focus on innovative approaches to producing, storing, and consuming energy. Integrated energy systems (IES) have emerged as a promising solution, capitalising on synergies between energy networks and enhancing efficiency. Such a holistic approach enables the integration of renewable energy sources and flexibility provision from one energy network to another, reducing emissions while facilitating strategies for operational optimisation of energy systems. However, emphasis has been mostly made on steady-state methodologies, with a dynamic verification of the optimal solutions not given sufficient attention. To contribute towards bridging this research gap, a methodology to verify the outcomes of an optimisation algorithm is presented in this paper. The methodology has been applied to assess the operation of a civic building in the UK dedicated to health services. This has been done making use of real energy demand data. Optimisation is aimed at improving power dispatch of the energy system by minimising operational costs and carbon emissions. To quantify potential discrepancies in power flows and operational costs obtained from the optimisation, a dynamic model of the IES that better captures real-world system operation is employed. By incorporating slow transients of thermal systems, control loops, and non-linearity of components in the dynamic model, often overlooked in traditional optimisation modules, the methodology provides a more accurate assessment of energy consumption and operational costs. The effectiveness of the methodology is assessed through model-in-the-loop co-simulations between MATLAB/Simulink and Apros alongside a series of scenarios. Results indicate significant discrepancies in power flows and operational costs between the optimisation and the dynamic model. These findings illustrate potential limitations of conventional operational optimisation modules in addressing real-world complexities, emphasising the significance of dynamic verification methods for informed energy management and decision-planning.

Brief Notes on the European Geographical Indication Law: Among Sustainability Implications and Artificial Intelligence Applications

Purpose: Geographical indications (GIs) within the European Union are – as is known – legally protected designations highlighting unique qualities of products tied to their place of origin; accordingly, GIs can promote sustainable agricultural practices, preserve traditional knowledge and contribute to rural development, but, notwithstanding, issues in verifying and authenticating GI products persist. Within this framework, this paper investigates the intersection of EU geographical indication law with sustainability goals and potential applications of artificial intelligence (AI) in streamlining GI compliance and enhancing consumer trust. Study design/methodology/approach: The behind research employs a mixed-methods approach; in fact, it includes a systematic review of relevant EU legislation and policy documents alongside qualitative case studies exploring the use of AI-based technologies (e.g., precision agriculture, terroir monitoring, traceability systems…) in GI value chains. Findings: The analysis confirms the wanted complex interplay between GI law and sustainability; in this way, while GIs can be powerful tools for promoting environmentally responsible production, existing frameworks may not fully capture all dimensions of sustainability; in addition, case studies demonstrate the promise of AI in improving product traceability, combating fraud and supporting informed consumer choices regarding GI products. Originality/value: Therefore, the paper argues for a more holistic approach to GI law that integrates sustainability metrics beyond geographic origin, suggesting that responsible AI adoption presents the potential to significantly strengthen GI systems: policy recommendations should include incentivising the development of ethical AI solutions for GI verification, promoting data sharing along supply chains, and raising consumer awareness of the sustainability benefits associated with GI products.

Deformation of rectangular orthotropic laminate resting on thin elastic foundation with nonlinear change of displacements over thickness under edge loading

Deformation of rectangular orthotropic laminate resting on a thin elastic orthotropic foundation with fully fixed bottom plane is studied in this paper. The laminate is subjected to an in-plane uniaxial load uniformly distributed over one of its edges. Models of joint deformation of the laminate and foundation are created based on the equations of plane elasticity for an orthotropic body. The applicability of such equations is justified by the uniformity of edge loading. A laminate model allows for the transverse shear strain, whereas a model created for the foundation considers a nonlinear character of variation of the transverse and axial displacements. A governing system of differential equationsmodelling the joined deformation of the laminate and foundation is derived using static and kinematic contact conditions. The corresponding boundary value problem is solved using the sweep method. Based on this solution, effects of laminate and foundation thicknesses, and elasticity characteristics on the stress–strain state of the jointed structure are investigated. The model created and the method of its analysis enable the strong oscillating behaviour of stresses and strains to be studied. The results obtained can be utilized for verification of solutions of similar problems found by other numerical methods, including finite-element analyses.

Building Models for a Scientific Approach to Problem Solving in Production Systems

The paper presents a model of scientific approach to problem solving. The model is based on a gradual approach, preparation, resolution, verification and improvement of solutions. Each of the steps in the process of solving a problem or task requires thorough consideration and analysis after which the question "satisfied?". The affirmative answer points to the next, while the negative requires further analysis or a return to one of the previous steps. The presented approach can be successfully used to solve problems and tasks in economic systems and in scientific research in general. The presented model confirmed its simplicity and practicality in concrete scientific research.

MmSign: mmWave-based Few-Shot Online Handwritten Signature Verification

Handwritten signature verification has become one of the most important document authentication methods that are widely used in the financial, legal, and administrative sectors. Compared with offline methods based on static signature images, online handwritten signature verification methods are more reliable because of the temporary dynamic information (e.g., signing velocity, writing force, stroke order) that alleviates the risk of being forged. However, most existing online handwritten signature verification solutions are reliant on specific signing devices (e.g., customized pens or writing pads) and require extensive data collection during the registration phase, resulting in poor adaptability and applicability for new users. In this article, we propose mmSign, a millimeter wave (mmWave)–based online handwritten signature verification system, which enables accurate sensing of the user’s hand movements when signing through the superior sensing capability of mmWave. mmSign extracts the time-velocity feature maps from the captured mmWave signals by the carefully designed signal processing algorithms and then exploits a transformer-based verification model for signature verification. In addition, a novel meta-learning strategy with proposed task generation and data augmentation methods is introduced in mmSign to teach the verification model to learn effectively with limited samples, allowing our model to quickly adapt to new users. Extensive experiments show that mmSign is a robust, efficient, and secure handwritten signature verification system, achieving 84.07%, 87.31%, 91.12%, and 96.54% verification accuracy when 1, 3, 5, and 10 labeled signatures are available, respectively, while being resistant to common forgery attacks.

Bioconvective flow of thermally radiative Casson nanofluid with aerobic microorganisms over a stretching surface

A mathematical model is proposed to analyze the flow characteristics of a thermally radiative, chemically reacting Casson nanofluid incorporating aerobic microorganisms over a stretching sheet. Nonlinear dimensionless ordinary differential equations are formulated through suitable similarity transformations. The resulting coupled system of nonlinear ordinary differential equations is numerically solved using the finite difference method with the bvp4c routine in MATLAB. The verification of the numerical solution’s accuracy is conducted by comparing it with previously established results under specific conditions pertinent to the present investigation. Skin friction coefficients, local Nusselt number, and Sherwood number are computed for various parameters and their implications for engineering applications are discussed. The investigation also explores the impact of different dimensionless parameters on velocity, temperature, mass concentration, motile microorganism density, and oxygen concentration profiles for both Casson and Newtonian nanofluids, offering a comprehensive analysis. The study highlights that suspending non-Newtonian nanofluids with fast-moving aerobic microorganisms and nanoparticles results in enhanced heat transfer rates, thus presenting potential benefits for practical applications such as the manufacturing of biofilms.

Immutable Identity Validation Using Soul bound Token Abhishek Sharma,

The "Immutable Identity Validation System: A Blockchain and Soulbound Token Approach" paper introduces an innovative method for verifying digital identities. By combining blockchain technology and Soulbound Tokens (SBTs), it enhances security and privacy in identity verification processes. SBTs, designed as non-transferable and tamper-proof digital assets, play a crucial role in bolstering the security and reliability of the system. Their unique properties ensure that user privacy is prioritized while also facilitating swift verification through blockchain transparency. This approach addresses the growing demand for robust identity verification solutions, particularly in sectors like education and corporations, where fraud prevention and streamlined processes are critical. The methodology involves various steps, including SBT generation, blockchain integration, user control mechanisms, and stringent security measures. Both frontend and backend development, along with the integration of blockchain using tools like Ganache and decentralized data storage through IPFS, contribute to building a secure and user-friendly system. Thorough testing, documentation, and knowledge transfer are essential to ensuring the system's reliability, security, and compliance with legal standards. Ultimately, the goal is to establish a globally accepted framework for identity verification in today's interconnected digital landscape. Keywords:; privacy; security; Blockchain; Cryptography; Decentralization; Soul Bound Token (SBT), distributed ledger.

Uncertainty Benchmarks for Time-Dependent Transport Problems

Verification solutions for uncertainty quantification (UQ) are presented for time-dependent transport problems where , the scattering ratio, is uncertain. The method of polynomial chaos expansions is employed for quick and accurate calculation of the quantities of interest (QoIs), and uncollided solutions are used to treat part of the uncertainty calculation analytically. We find that approximately six moments in the polynomial expansion are required to represent the solutions to these problems accurately. Additionally, the results show that if the uncertainty interval spans c = 1, which means it is uncertain whether the system is multiplying or not, the confidence interval will grow in time. Finally, since the QoI is a strictly increasing function, the percentile values are known and can be used to verify the accuracy of the expansion. These results can be used to test UQ methods for time-dependent transport problems.

Signature Fraud Detection Using Deep Learning

The use of signatures for personal identification and verification is quite common. Signatures are validated for many documents such as Bank cheques and legal transactions. The necessity for effective automated solutions for signature verification has grown as signatures are now a prerequisite for both authorization and authentication in legal activities. Two images—the original signature and the test signature—are used as input in this process. To determine whether the signature is fake or not, the characteristics that were extracted are compared, and the difference in error values between them is examined. The growing digital landscape has increased the need for robust and efficient fraud detection systems. This project presents a unique approach to detecting signature fraud using deep learning techniques, specifically employing Siamese Neural Networks, implemented in Python. With a dataset comprising 2149 signature images, encompassing both genuine and fraudulent samples from Dutch users, our model demonstrates remarkable accuracy. The Siamese Neural Network architecture excels in signature verification tasks by learning to distinguish between genuine and fraudulent signatures through contrastive learning. The key achievement of this project is the exceptional accuracy levels attained during the training and validation phases. The model's training accuracy stands at an impressive 98.00%, while the validation accuracy reaches an astonishing 99.00%. These high accuracy rates are a testament to the effectiveness of Siamese Neural Networks in signature fraud detection. In a world where the security of digital signatures is paramount, this project showcases the power of deep learning and Siamese Neural Networks in safeguarding against fraudulent activities. The model's success in accurately distinguishing between authentic and forged signatures offers promising potential for enhancing security measures in various domains, including finance, legal, and document management.

Research on Deployment Process Dynamics and Vibration for Replaceable Interface Mast

The mast of the rover is a device used to carry precision instruments such as cameras on the rover, and its performance directly affects the working quality of these devices. The replaceable interface mast can effectively solve the problems of a single structure and poor maintenance of ordinary masts, so we study the deployment process dynamics properties and vibration for replaceable interface mast. Firstly, we analyzed the spatial motion of the reconfigurable rigid body module by the absolute node coordinate method and the natural coordinate method, and established the dynamic equation of the interface, the reconfigurable module without external constraints. Then, we established a dynamic model of the mast system deployment process. We analyzed the dynamic behavior of the replaceable interface mast and studied and compared the differences in the deployment behavior of the replaceable interface mast under different system configurations, flexible interface geometric parameters, and different driving rules. Finally, we built a model of the mast system and experimentally analyzed the deployment process of the replaceable interface mast. Using numerical solution and experimental verification, we proved that the established dynamic model of the mast system can correctly analyze the deployment behavior dynamics of the replaceable interface mast, and the study can provide a reference for the design and behavior analysis of the mast system.

Single-Well Simulation for Horizontal Wells in Fractured Gas Condensate Reservoirs.

Fractured gas condensate reservoirs (FGCR) are a complex, special, and highly valuable type of gas reservoir, accounting for a significant proportion of gas reservoir development. In recent years, with the continuous advancement of horizontal well technology, it has become the main approach for the development of FGCR. The current model is unable to accurately represent the fluid distribution in the near-well area of horizontal wells due to the unique retrograde condensation phenomenon in GCR. Additionally, the presence of fractures complicates the solution of traditional analytical models. In response to this issue, this paper proposes a novel semianalytical model for horizontal wells in FGCR, which incorporates natural fractures, multiphase flow, and the influence of stress sensitivity on pressure response. A dual-porosity model is employed to simulate fractured reservoirs, and a four-region radial composite model is developed to characterize multiphase flow resulting from retrograde condensation in GCR. The pseudopressure transform, Pedrosa transform, Laplace transform, and Finite Cosine transform are utilized to address the nonlinear partial differential equation. A systematic verification of the semianalytical solution is confirmed through a comparison with the numerical solution from computer modeling group (CMG). We thoroughly explain the physical significance of the various features by identifying the 12 flow regimes of the typical curve. Furthermore, we offer a method for assessing the extent of retrograde condensation and the size of the retrograde condensate region based on the curve's characteristics. Finally, the pressure measurements recorded from the Bohai field are carried out to validate the accuracy of the proposed model. The results show that the predictions of the new model are in good agreement with the actual production data, demonstrating the proposed solution's applicability.

Absorption of collimated incident radiation in a semitransparent strongly scattering medium: Computational analysis and explanation of red algae blooming in snow

The study of directional radiation absorption in weakly absorbing and strongly scattering media is of interest for a variety of important applications, including laser hyperthermia of human tumors in a therapeutic window of transparency, determination of the optical and thermal properties of highly porous ceramics, and calculations of the deep solar heating of snow cover in polar regions and mountains. In such problems, multiple scattering of radiation can lead to a non-monotonic variation of the absorbed radiation power with distance from the illuminated surface of the medium and to the appearance of the absorption maximum at some depth below the surface. This effect has a known physical explanation and can be predicted using the suggested analytical solution based on the transport approximation for the scattering phase function and the two-flux approximation for the diffuse component of the radiation intensity. The range of illumination angles at which the absorption maximum inside an optically thick medium is observed is determined. The position and magnitude of this maximum are also obtained. In the present work, the verification of this approximate solution using reference numerical calculations is carried out for the first time. It is shown that the simple analytical model is acceptable for regular calculations. Computational results for pure snow illuminated by the Sun showed that red light gives the strongest absorption maximum at a small distance from the snow surface. This is a basis of the presented physical explanation for the intense photosynthesis of red microalgae responsible for the well-known ``snow blood'' phenomenon.