Standard Assumptions Research Articles

Effectiveness Analysis of the Non-Standard Reinforcement of Lattice Tower Legs Using the Component-Based Finite Element Method

This paper presents an analysis of the effectiveness of the existing reinforcement of steel lattice tower legs made of L-sections by expanding to closely spaced built-up members. Due to the significant differences between the standard assumptions and the existing reinforcement, numerical analyses based on the component-based finite element method (CBFEM) were used to estimate the capacity of the existing structure’s tower legs. Geometrically and materially nonlinear stress analysis and linear buckling analysis were performed. The obtained results (stress distribution maps, buckling forms, and corresponding critical forces) were used to modify the geometric parameters of the section of the analyzed tower legs in order to adapt the standard formulas in the calculation procedure. In the analyzed case, distance of the connections between the branches exceeded that indicated in EN 1993-1-1:2005 for the condition concerning the possibility of ignoring the deformation susceptibility in the calculation process. However, it did not result in the separate operation of each branch of the section. Thus, in the case of the analyzed reinforcement, it is possible to neglect the form susceptibility when calculating the buckling resistance of the element. The buckling capacity of the reinforced legs of the tower and the compression capacity of the section of the analyzed structure were calculated according to the method that took into account the results of the numerical analyses. These values are about 35–48% and 30–39% higher, respectively, than the capacity of the unreinforced angle calculated according to EN 1993-1-1:2005 and EN 1993-1-8:2006 standards. Thus, it may be possible to avoid costly and labor-intensive retrofitting of the existing reinforcement to meet the standard requirements. A key issue, and one that is particularly important in light of the lack of standard guidelines aimed at designing reinforcements for telecommunications structures, seems to be the performance of full-scale experimental tests.

Metabolic predictors and risk stratification of post-thyroidectomy hypocalcemia: A retrospective analysis and predictive machinery model

Introduction Post-thyroidectomy hypocalcemia remains a significant surgical complication with varying reported incidence rates. While traditional focus has centered on surgical factors, the role of metabolic predictors in calcium homeostasis disturbances remains incompletely understood. Methods We conducted a single-center retrospective analysis of 1,760 thyroidectomy patients based on data obtained from King Abdullah International Medical Research Center (KAIMRC), Saudi Arabia, evaluating metabolic predictors and long-term calcium homeostasis patterns. Preoperative assessment included thyroid function tests, glycemic control, vitamin D status, and anthropometric measurements. We have followed the TRIPOD + AI statement which provide updated guidance for reporting clinical prediction models that use regression or machine learning methods, We developed a risk stratification model using multivariate logistic regression and implemented mixed-effects modeling for longitudinal analysis. Recovery patterns were evaluated through standardized follow-up at immediate postoperative, one-month, three-month, six-month, and one-year timepoints. Results Post-thyroidectomy hypocalcemia occurred in 585 patients (33.2%, 95% CI: 31.0-35.4%). Preoperative thyroid stimulating hormone (TSH) levels appeared to be the strongest predictor (OR 2.24, 95% CI 1.71-2.93, p<0.0001), followed by T3 (OR 1.34, 95% CI 1.19-1.51) and glycated hemoglobin (Hemoglobin A1C), (OR 1.18, 95% CI 1.07-1.30). Our risk stratification system, including TSH, vitamin D, and immediate postoperative calcium, achieved superior discrimination (AUC=0.921) and effectively categorized patients into low (6.5%), intermediate (14.6%), and high-risk (83.2%) groups. Recovery patterns revealed a non-linear trajectory, with an unexpected decline at six months (79.71%) before maximal improvement at one year (88.61%). Conclusions Our findings highlight and demonstrate the significant role of metabolic factors in post-thyroidectomy hypocalcemia risk and challenge the current standard recovery assumptions, suggesting the need for extended monitoring protocols and comprehensive metabolic assessment in perioperative management.

Testing exchangeability in the batch mode with e-values and Markov alternatives

The topic of this paper is testing the assumption of exchangeability, which is the standard assumption in mainstream machine learning. The common approaches are online testing by betting (such as conformal testing) and the older batch testing using p-values (as in classical hypothesis testing). The approach of this paper is intermediate in that we are interested in batch testing by betting; as a result, p-values are replaced by e-values. As a first step in this direction, this paper concentrates on the Markov model as alternative. The null hypothesis of exchangeability is formalized as a Kolmogorov-type compression model, and the Bayes mixture of the Markov model w.r. to the uniform prior is taken as simple alternative hypothesis. Using e-values instead of p-values leads to a computationally efficient testing procedure. Two appendixes discuss connections with the algorithmic theory of randomness; in particular, the test proposed in this paper can be interpreted as a poor man’s version of Kolmogorov’s deficiency of randomness.

A cellular dynamic mathematical model of a conveyor dryer as an object of automatic control

In the paper, based on the analysis of the technological process of fruit drying in a conveyor-type dryer, the major regime parameters, control effects of the object, control variables and disturbing factors are determined. Considering the equations of the kinetics of heat and mass transfer of the dried product, control diagrams, certain standard assumptions and initial conditions, a system of ordinary differential equations for the convective drying of fruit in a conveyor-type dryer has been developed. In the system of equations of heat and mass transfer of the process, the drying coefficient in the first and second periods and the heat transfer coefficient were determined due to identification. The solution of the problem of determining the above parameters is reduced to solving the problem of minimising the criterion for assessing the degree of the mean square deviation of the difference between the solutions of the system of experimental and heat-mass transfer differential equations of the plum drying process. During the drying of blue plums, we have obtained the following values ​​of unknown parameters: drying rate coefficient in the first period – K1=0.8309∙10-4 kg∙(m2∙s∙oC)-1; drying rate coefficient in the second period–K2=0.3413∙10-4 kg∙(m2∙s∙oC)-1 and heat transfer coefficients: α2∙ϕ2=0.0794 kW∙(kg∙oC)-1, α3∙F3=0.0245 kW∙(kg∙oC)-1 and critical moisture content of blue plums wCR=71%. The energy reduction spent on the drying process is 4%. The “cellular” dynamic characteristics of the relative humidity and temperature of the drying agent during blue plum drying, obtained as a result of the experimental and theoretical study of the conveyor drying unit, allow us to judge the 96% - 98% adequacy of the developed dynamic mathematical model. The research found that the dynamic characteristics of the residual moisture content of blue plums at the dryer outlet were 11%.

Deliberation during online bargaining reveals strategic information

A standard assumption in game theory is that decision-makers have preplanned strategies telling them what actions to take for every contingency. In contrast, nonstrategic decisions often involve an on-the-spot comparison process, with longer response times (RT) for choices between more similarly appealing options. If strategic decisions also exhibit these patterns, then RT might betray private information and alter game theory predictions. Here, we examined bargaining behavior to determine whether RT reveals private information in strategic settings. Using preexisting and experimental data from eBay, we show that both buyers and sellers take hours longer to accept bad offers and to reject good offers. We find nearly identical patterns in the two datasets, indicating a causal effect of offer size on RT. However, this relationship is half as strong for rejections as for acceptances, reducing the amount of useful private information revealed by the sellers. Counter to our predictions, buyers are discouraged by slow rejections-they are less likely to counteroffer to slow sellers. We also show that a drift-diffusion model (DDM), traditionally limited to decisions on the order of seconds, can account for decisions on the order of hours, sometimes days. The DDM reveals that more experienced sellers are less cautious and more inclined to accept offers. In summary, strategic decisions are inconsistent with preplanned strategies. This underscores the need for game theory to incorporate RT as a strategic variable and broadens the applicability of the DDM to slow decisions.

Imprecision plateaus in quantum steering

We study tests of quantum steering in which the trusted party does not have perfect control of their measurements. We report on steering inequalities that remain unaffected when introducing up to a threshold magnitude of measurement imprecision. This phenomenon, which we call an imprecision plateau, thus permits a departure from the standard assumption of idealized measurements without incurring any cost to the detection power of steering experiments. We provide an explanation for why imprecision plateaus are possible, a simple criterion for their existence, and tools for analyzing their properties. We also demonstrate that these plateaus have natural applications when the assumption of perfect measurements is relaxed: They allow for maintaining both the noise robustness and loss robustness of standard steering tests and the performance rate of idealized one-sided device-independent random number generators. Published by the American Physical Society 2025

The Quality of Government Financial Reports : Factor’s That Can Affect It

In this study, the standard of the Bandung Regency Health Office's financial reports will be examined in relation pertaining to internal control framework of the government, human resource competency and utilization of official accounting standards. The research approach used is a quantitative one that combines a descriptive and verifying approach. Applying the non-probability sampling method with the Slovin formula, sixty-four individuals from the Bandung district health office were chosen as samples for this study. Descriptive statistics use reliability and validity tests. Regression analysis using multiple linear, determination coefficients, correlation coefficients for product moments and statistical testing for verification uses standard assumptions. A variety of data analysis methods are used like as simultaneous and partial testing (f-test and t-tests). Data is processed using SPSS software. Internal Control System of the Government, Competency in Human Resource and the Implementation of Government Accounting Standards do not affect the quality of financial reports per data from a partial research. However, There isn't any relationship between the quality of financial reports and the Internal Control System of the Government, Competency in Human Resource and the Implementation of Government Accounting Standards according to contemporaneous research findings.

Robust Semi‐Global Output Regulation of Uncertain Two‐Time‐Scale Systems With Input Saturation

ABSTRACTThis paper investigates the robust semi‐global output regulation of uncertain linear systems with input saturation. In this setup, one cannot apply off‐the‐shelf techniques to reject structural uncertainties due to numerical issues caused by the two time scales but also due to technical issues generated by the input saturation. To solve this problem, we combine internal model principle with low gain technique and singular perturbation theory. Explicitly, the regulator design is based on the computation of the solutions of a Sylvester equation, which can be ill‐conditioned in the two‐time scales setting. To overcome this, we propose an easily solvable equation, and the existence and uniqueness of its solution are guaranteed under some standard assumptions. Accordingly, an internal model‐based controller is designed such that the semi‐global robust output regulation problem can be solved. In order to cope with the input saturation, we impose that the ultimate upper bound of the infinity norm of the exosystem state is limited and satisfies certain constraints. Finally, three examples are provided to illustrate the effectiveness of the results.

White Noise and Its Misapplications: Impacts on Time Series Model Adequacy and Forecasting

This paper contributes significantly to time series analysis by discussing the empirical properties of white noise and their implications for model selection. This paper illustrates the ways in which the standard assumptions about white noise typically fail in practice, with a special emphasis on striking differences in sample ACF and PACF. Such findings prove particularly important when assessing model adequacy and discerning between residuals of different models, especially ARMA processes. This study addresses issues involving testing procedures, for instance, the Ljung–Box test, to select the correct time series model determined in the review. With the improvement in understanding the features of white noise, this work enhances the accuracy of modeling diagnostics toward real forecasting practice, which gives it applied value in time series analysis and signal processing.

An interior point method for nonlinear constrained derivative-free optimization

In this paper, we consider constrained optimization problems where both the objective and constraint functions are of the black-box type. Furthermore, we assume that the nonlinear inequality constraints are non-relaxable, i.e. the values of the objective function and constraints cannot be computed outside of the feasible region. This situation happens frequently in practice especially in the black-box setting where function values are typically computed by means of complex simulation programs which may fail to execute if the considered point is outside of the feasible region. For such problems, we propose a new derivative-free optimization method which is based on the use of a merit function that handles inequality constraints by means of a log-barrier approach and equality constraints by means of an exterior penalty approach. We prove the convergence of the proposed method to KKT stationary points of the problem under standard assumptions (we do not require any convexity assumption). Furthermore, we also carry out a preliminary numerical experience on standard test problems and comparison with state-of-the-art solvers showing the efficiency of the proposed method.

Stochastic homogenisation for functionals defined on asymptotically piecewise rigid functions

We study stochastic homogenisation of free-discontinuity surface functionals defined on piecewise rigid functions which arise in the study of fracture in brittle materials. In particular, under standard assumptions on the density, we show that there exists a Γ-limit almost surely and that it can be represented by a surface integral. In addition, the effective density can be characterised via a suitable asymptotic cell formula and is deterministic under an ergodicity assumption. We also show via Γ-convergence that the homogenised functional defined on piecewise rigid functions can be recovered from a Griffith-type model by passing to the limit of vanishing elastic deformations.

GENES: An Efficient Recursive zk-SNARK and Its Novel Application in Blockchain

The rapid development of blockchain has significantly promoted research on zero-knowledge proofs (ZKPs), especially zero-knowledge succinct noninteractive arguments of knowledge (zk-SNARK). As is well known, protocol proof and verification time, as well as proof size, are the main obstacles that restrict the implementation of ZKPs in practical applications, so they have become the main concerns of researchers in recent years. This work achieves a new recursive zk-SNARK called GENES, which does not have a trusted setup and is secure under the standard discrete logarithm assumption. GENES is designed from the form of the rank-1 constraint system (R1CS) satisfiability problem. Recursive proof composition is achieved by merging multiple R1CS instances, which transforms the verification of numerous proofs into the verification of a single proof. Moreover, multi-helpers amortize proof commitments in this study, significantly reducing the computational pressure and time cost of proof generation. Compared with previous work, GENES effectively improves the proof time and verification time, but at the cost of larger proof sizes. We provide a blockchain Layer-1 scaling solution leveraging GENES to demonstrate its practicality.

An Omitted Variable Bias Framework for Sensitivity Analysis of Instrumental Variables

Abstract We develop an omitted variable bias framework for sensitivity analysis of instrumental variable estimates that naturally handles multiple side-effects (violations of the exclusion restriction assumption) and confounders (violations of the ignorability of the instrument assumption) of the instrument, exploits expert knowledge to bound sensitivity parameters, and can be easily implemented with standard software. Specifically, we introduce sensitivity statistics for routine reporting, such as (extreme) robustness values for instrumental variables, describing the minimum strength that omitted variables need to have to change the conclusions of a study. Next we provide visual displays that fully characterize the sensitivity of point estimates and confidence intervals to violations of the standard instrumental variable assumptions. Finally, we offer formal bounds on the worst possible bias under the assumption that the maximum explanatory power of omitted variables is no stronger than a multiple of the explanatory power of observed variables. Conveniently, many pivotal conclusions regarding the sensitivity of the instrumental variable estimate (e.g. tests against the null hypothesis of zero causal effect) can be reached simply through separate sensitivity analyses of the effect of the instrument on the treatment (the first stage) and the effect of the instrument on the outcome (the reduced form). We apply our methods in a running example that uses proximity to college as an instrumental variable to estimate the returns to schooling.

Nanosecond Molecular Motion in pHP1α Liquid-Liquid Phase Separation Captured by Solid-State NMR.

The relationship among protein structure, function, and dynamics is fundamental to biological activity, particularly in more complex biomolecular systems. Solid-state and solution-state NMR techniques offer powerful means to probe these dynamics across various time scales. However, standard assumptions about molecular motion are often challenged in phase-separated systems like phosphorylated heterochromatin protein 1 alpha (pHP1α), which exhibit both solid- and solution-like characteristics. This study investigates the nanosecond molecular motions in pHP1α liquid-liquid phase separation (LLPS) using relaxation in hetNOE-filtered HSQC signals. By systematically analyzing motions captured by hetNOE-filtered HSQC and conventional HSQC, we characterize the global dynamics site-specifically in pHP1α LLPS. Our findings reveal ∼15 ns motion in the pHP1α LLPS system, suggesting the coexistence of different dynamic phases, and support previous observations on its role in chromatin organization. This work contributes to the expanding literature on phase-separated biomolecular behavior, with implications for understanding the molecular basis of chromatin compaction and genomic stability.

Contribution of Weak Bases to Isoxaflutole Hydrolysis: Implications for Hydrolysis in Laboratory and Natural Waters.

The hydrolysis rates of many organic chemicals are accelerated under alkaline conditions by the presence of hydroxide (HO-), which is typically assumed to be the predominant species contributing to base-catalyzed hydrolysis in both natural waters and laboratory buffers used in standard protocols. In this study, we demonstrated that weak bases (e.g., carbonates and phosphates) are able to catalyze the hydrolysis of the proherbicide isoxaflutole, an emerging contaminant of concern in water resources, under conditions relevant to both natural waters and laboratory protocols. Second-order rate constants for isoxaflutole hydrolysis catalyzed by individual bases were correlated with the pKa of the conjugate acid of the base using the Brønsted relationship. Using these values, we determined that buffer effects accounted for 43-99% of previously reported rate constants for base-catalyzed isoxaflutole hydrolysis misattributed to HO-, resulting in hydrolysis rates predicted in natural waters overestimated by 5- to 30-fold from pH 7.0 to 8.5. In comparison, our corrected rate constants accurately predicted isoxaflutole hydrolysis measured in natural waters within a factor of 5. While isoxaflutole hydrolysis in surface water remains predominantly catalyzed by HO- following standard assumptions, carbonate species (particularly bicarbonate) are predicted to drive the reaction in as many as 83% of groundwater systems.

Parameterized covering in semi-ladder-free hypergraphs

In this article, we study the parameterized complexity of the Set Cover problem restricted to semi-ladder-free hypergraphs, a class defined by Fabianski et al. [Proceedings of STACS 2019]. We observe that two algorithms introduced by Langerman and Morin [Discrete & Computational Geometry 2005] in the context of geometric covering problems can be adapted to this setting, yielding simple FPT and kernelization algorithms for Set Cover in semi-ladder-free hypergraphs. We complement our algorithmic results with a compression lower bound for the problem, which proves the tightness of our kernelization under standard complexity-theoretic assumptions.

Robust Access Control for Secure IoT Outsourcing with Leakage Resilience.

The Internet of Things (IoT) has revolutionized various industries by enabling seamless connectivity and data exchange among devices. However, the security and privacy of outsourced IoT data remain critical challenges, especially given the resource constraints of IoT devices. This paper proposes a robust and leakage-resilient access control scheme based on Attribute-Based Encryption (ABE) with partial decryption outsourcing. The proposed scheme minimizes computational overhead on IoT devices by offloading intensive decryption tasks to the cloud, while ensuring resilience against master secret key leakage, side-channel attacks, and other common security threats. Comprehensive security analysis demonstrates the scheme's robustness under standard cryptographic assumptions, and performance evaluations show significant improvements in decryption efficiency, scalability, and computational performance compared to existing solutions. The proposed scheme offers a scalable, efficient, and secure access control framework, making it highly suitable for real-world IoT deployments across domains such as smart healthcare, industrial IoT, and smart cities.

A solution to the pervasive problem of response bias in self-reports

Self-reports are used ubiquitously to probe people's thoughts, feelings, and behaviors and inform medical decisions, enterprise operations, and government policy and legislation. Despite their pervasive use, self-report measures such as Likert scales have a profound problem: Standard analytic approaches do not control for the confounding effects of idiosyncratic response biases. Here, we present a model-based solution to this problem. Our model disentangles response bias from latent constructs of interest to obtain less biased scores of the latent states of respondents. Inspired by Thurstonian approaches in the psychophysics literature, the model requires nothing further than standard Likert scale design assumptions. The model uses a data-driven approach to control for response biases, without the need to prespecify bias types or response strategies. We demonstrate the model's ability to uncover more precise estimates of latent state associations, outperforming bias-affected standard scoring techniques, and garner insights into previously undetected codependencies between certain latent states and particular forms of response bias. The model is thus a tool which outperforms standard scoring methods and generates insights into, and controls for, the potentially confounding effects of response bias on self-report Likert scale data.

Wind–Wave Misalignment in Irish Waters and Its Impact on Floating Offshore Wind Turbines

This study examined the impact of wind–wave misalignment on floating offshore wind turbines (FOWTs) in Irish waters, analysing average weather and extreme events, including hurricane conditions. Using the ERA5 reanalysis dataset validated against Irish Marine Data Buoy Observation Network measurements, the results showed a satisfactory accuracy with an average wind speed error of −0.54 m/s and a strong correlation coefficient of 0.92. Wind–wave misalignment was found to be inversely correlated with wind speed (correlation coefficient: −0.41), with minimum misalignment occurring approximately seven hours after a change in wind direction. The study revealed that misalignment could exceed 30∘ during hurricanes, contradicting standard assumptions of alignment under extreme conditions. The investigation highlighted that in western coastal areas, average misalignment could reach 57.95∘, while sheltered Irish Sea regions experienced lower values, such as 23.06∘. Numerical simulations confirmed that these misalignment events amplified side-to-side turbine deflections significantly. This research underscores the need to incorporate misalignment effects into industry testing standards and suggests that current methodologies may underestimate fatigue loads by up to 50%. This work emphasizes improved design and testing protocols for FOWTs in complex marine environments and highlights the suitability of ERA5 for climate analysis in Ireland.

A simple proximal algorithm based on the golden ratio for equilibrium problem on Hadamard manifolds

In this paper, we propose a simple proximal method which is combined with the inertial and golden ratio technique as extrapolation methods for approximating a solution to the equilibrium problem. The proposed method is self-adaptive such that its execution does not depend on the knowledge of the Lipschitz condition of the associated pseudomonotone cost operator. Under mild and standard assumptions on the control parameters, we showed that the sequence of iterates generated by this method converges to a solution of the problem. Assuming that the cost operator is strongly pseudomonotone, we prove that the sequence of iterates converge R-linearly to a solution of the EP. To show the numerical efficiency of this proposed method, we displayed some numerical experiments.