Explore the vast range of titles available.

Insertion of malicious circuits commonly known as Hardware Trojans into an original integrated circuit (IC) design to alter the functionality has been a major concern in recent years. As a result, over the years multiple techniques have been suggested by researchers to combat these malicious threats. Hard to test nets in any logic circuit are the most vulnerable to insertion of Hardware Trojans. Testability analysis is the process of identification of these hard to test nets in a logic circuit. Testability analysis is achieved through the testability metrics namely controllability and observability. Testability metrics can be used as a yardstick in devising efficient Hardware Trojan detection methods. The crux of this study is a novel method for identification of susceptible nets that are prone to Hardware Trojan insertions in a logic circuit. The study also presents a comprehensive analysis of the impact on testability parameters as a result of Hardware Trojans in the identified susceptible nets. The method utilises the testability parameters of nets to define threshold values for isolating susceptible nets in a design. The study details out the impact of the number of trigger inputs as well as the distribution of trigger nets on the testability metrics of digital circuits.

General quality characteristics can reflect the performance and quality of equipment and have great guiding significance for the design, production, trade, and use of equipment. Maintainability, testability, and supportability (MTS) are all related to equipment faults and maintenance support and have strong relevance between them. Solving the problem of the isolation of the three characteristics evaluation requires to reduce the duplication of work and improve the evaluation efficiency, comprehensively assessment methods based on model and test are proposed by analysing the correlation of the MTS parameters. In the case study, parameters of different characteristics are obtained instead of one single characteristic, which shows the effectiveness of the proposed methods compared with the traditional sequential assessment method.

A bstract Sterile neutrinos with masses at the keV scale and mixing to the active neutrinos offer an elegant explanation of the observed dark matter (DM) density. However, the very same mixing inevitably leads to radiative photon emission and the non-observation of such peaked X -ray lines rules out this minimal sterile neutrino DM hypothesis. We show that in the context of the Standard Model effective field theory with sterile neutrinos ( ν SMEFT), higher dimensional operators can produce sterile neutrino DM in a broad range of parameter space. In particular, ν SMEFT interactions can open the large mixing parameter space due to their destructive interference, through operator mixing or matching, in the X -ray emission. We also find that, even in the zero mixing limit, the DM density can always be explained by ν SMEFT operators. The testability of the studied ν SMEFT operators in searches for electric dipole moments, neutrinoless double beta decay, and pion decay measurements is discussed.

Understanding statistical inference under possibly nonsparse high-dimensional models has gained much interest recently. For a given component of the regression coefficient, we show that the difficulty of the problem depends on the sparsity of the corresponding row of the precision matrix of the covariates, not the sparsity of the regression coefficients. We develop new concepts of uniform and essentially uniform nontestability that allow the study of limitations of tests across a broad set of alternatives. Uniform nontestability identifies a collection of alternatives such that the power of any test, against any alternative in the group, is asymptotically at most equal to the nominal size. Implications of the new constructions include new minimax testability results that, in sharp contrast to the current results, do not depend on the sparsity of the regression parameters. We identify new tradeoffs between testability and feature correlation. In particular, we show that, in models with weak feature correlations, minimax lower bound can be attained by a test whose power has the √n rate, regardless of the size of the model sparsity.

Recently, Zhang et al. [Eur. Phys. J. C 84, 381, https://doi.org/10.1140/epjc/s10052-024-12723-8 (2024)] constrained the Yukawa correction in the general free scalar–tensor gravity by borrowing the measured values of the parametrized post-Newtonian (PPN) parameters γ - 1 = ( 2.1 ± 2.3 ) × 10 - 5 and β - 1 = ( - 4.1 ± 7.8 ) × 10 - 5 in the Solar System tests. They firstly defined two PPN parameters γ ( r ) and β ( r ) in the gravity, which depend on the radial distance r and include the Yukawa-type parameters. Then, by comparing between the experiments’ results of two PPN parameters and their γ ( r ) and β ( r ) , the authors claimed that they gave the tightest bound on the gravity by the Cassini tracking experiment. However, we find their approach is not rigorous. In this paper, corresponding astronomical experiments have been physically modelled by considering the lightlike and the timelike geodesics in the general free scalar–tensor gravity. Contrary to the wrong results in Zhang et al.’s work, it is shown that the Cassini tracking experiment is insensitive to the general free scaler–tensor gravity. Furthermore, we also find that the time delay and the light deflection are all independent of the gravity. Due to an additional Yukawa-type advance in the periastron shift, we derive very much improved bounds on the Yukawa-type parameters of this gravity.

The multi-functional integrated RF system (MIRFS) is a crucial component of aircraft onboard systems. In the testability design process, traditional methods cannot effectively deal with the inevitable differences between system designs and usage requirements. By considering the MIRFS’s full lifecycle characteristics, a new testability allocation method based on multi-criteria decision-making (MCDM) is proposed in this paper. Firstly, the testability framework was constructed and more than 100 indicators were given, which included both different system-level and inter-system indicators. Secondly, to manage parameter diversity and calculate complexity, the basic 12 testability indicators were optimized through the Analytic Hierarchy Process and Technique for Order Preference by Similarity to Ideal Solution (AHP-TOPSIS) method. Thirdly, the detailed testability parameters were obtained by using the Decision-Making Trial and Evaluation Laboratory and Analytic Network Process (DEMATEL-ANP) to reduce the subjectivity and uncertainty. Finally, an example was utilized, and the results show that the MCDM method is significantly better than traditional methods in terms of accuracy and effectiveness, which will provide a more scientific basis for the MIRFS testability design process.

A bstract If any heavy neutral leptons are discovered in accelerator-based experiments, key questions will involve their possible connection to neutrino masses or leptogenesis. Working in a renormalisable extension of the Standard Model by three right-handed neutrinos, we address the question of how much information about the fundamental model parameters can be obtained by measuring the branching ratios in the decays of the heavy neutral leptons into individual SM generations. We find that, provided that these branching ratios could be measured with arbitrary precision and assuming kinematically distinguishable right-handed neutrinos, they can be sufficient to pin down all 18 parameters of the model when supplemented with light neutrino oscillation data. When considering a finite statistical uncertainty comparable to that which can be achieved by future lepton colliders like FCC-ee or CEPC in the mass range of tens of GeV, some parameter degeneracies remain, but measurements would still provide powerful consistency checks of the model. In the sub-GeV range a good sensitivity to individual model parameters can be expected for SHiP and potentially for DUNE. This shows the potential of these experiments to not only discover heavy neutral leptons, but play an important role in understanding their role in particle physics and cosmology.

In fact, the relationship between fault and test is uncertain, and the traditional testability model cannot deal with uncertain information. Bayesian network model with uncertain information reasoning ability and self-learning ability has become a research hotspot. Aiming at the difficulty and high cost of Bayesian network testability model, a automatic modeling method based on Bayesian network was proposed. This method uses the circuit functional structure relationship characterized by EDA to provide the required conditions for the K2 algorithm, and verifies the results of the structure and parameter learning. Through this method, the Bayesian network model can continuously update the model structure and parameters by combining expert knowledge, historical experience and later use data, making the model more and more accurate. After simulation and comparison, the algorithm can effectively build a model and reduce the workload of related personnel.

Model-based testing (MBT) is a method that supports the design and execution of test cases by models that specify the intended behaviors of a system under test. While systematic literature reviews on MBT in general exist, the state of the art on modeling and testing performance requirements has seen much less attention. Therefore, we conducted a systematic mapping study on model-based performance testing. Then, we studied natural language software requirements specifications in order to understand which and how performance requirements are typically specified. Since none of the identified MBT techniques supported a major benefit of modeling, namely identifying faults in requirements specifications, we developed the Performance Requirements verificatiOn and Test EnvironmentS generaTion approach (PRO-TEST). Finally, we evaluated PRO-TEST on 149 requirements specifications. We found and analyzed 57 primary studies from the systematic mapping study and extracted 50 performance requirements models. However, those models don’t achieve the goals of MBT, which are validating requirements, ensuring their testability, and generating the minimum required test cases. We analyzed 77 Software Requirements Specification (SRS) documents, extracted 149 performance requirements from those SRS, and illustrate that with PRO-TEST we can model performance requirements, find issues in those requirements and detect missing ones. We detected three not-quantifiable requirements, 43 not-quantified requirements, and 180 underspecified parameters in the 149 modeled performance requirements. Furthermore, we generated 96 test environments from those models. By modeling performance requirements with PRO-TEST, we can identify issues in the requirements related to their ambiguity, measurability, and completeness. Additionally, it allows to generate parameters for test environments.

The paper focuses on fault diagnosis in nonlinear analog circuits, a topic that has been explored in the literature for over 50 years but remains unresolved due to its complex nature. It reviews various aspects of nonlinear circuit diagnosis, including methods for assessing testability, selecting diagnostic tests, and conducting diagnostic processes through simulation before and after test methods. The paper also discusses the use of artificial intelligence tools in this context. Given the specific characteristics of nonlinear circuits used as DC-DC converters and the wealth of the existing literature—including review papers on the subject—this issue is addressed only briefly. The main aim of the paper is to identify research challenges in fault diagnosis for a general class of nonlinear circuits. To illustrate and discuss these challenges, the paper provides examples, including ambiguity in diagnostic equation solutions, multiple equilibrium points, and the effects of self-heating. These examples can serve as simple benchmarks for new proposals to advance comprehensive diagnosis methods for nonlinear circuits.