Search Results (4,413)

The quantitative polymerase chain reaction (PCR) standard curve is the central analytical tool for validating qPCR assays and can also be used to estimate target concentrations in test samples. This review explains how qPCR standard curves are constructed, validated, and analyzed for different purposes. We first examine an idealized standard curve generated using an exceptionally high number of replicates, far exceeding typical routine use. This approach clearly illustrates fundamental qPCR characteristics and provides an educational framework for defining and estimating PCR efficiency, limit of detection, and limit of quantification. Furthermore, we demonstrate that, in theory, variation in threshold crossing points across replicates can be used to estimate the number of target molecules in a sample. This method, which we term variance PCR, could complement digital PCR and potentially extend the dynamic range of absolute quantification. We also analyze a representative standard curve as typically processed in routine qPCR workflows. This includes validating its dynamic range, assessing the impact of outliers, estimating PCR efficiency and precision, and finally applying the curve to determine the concentration of test samples. Full article

In this work, a comprehensive first-principles investigation of the electronic, magnetic, and optical properties of pristine and Fe-, Co-, and Ni-doped MoS₂ monolayers is presented within the framework of density functional theory. Substitutional transition-metal doping at the Mo site is shown to induce spin-polarized impurity states within the pristine band gap, leading to significant modifications of the electronic structure, including metallic, semimetallic, or half-metallic behavior depending on the dopant species. The calculated spin-resolved band structures and projected density of states reveal a strong hybridization between the dopant $3d$ orbitals and the Mo-$4d$/S-$3p$ states, giving rise to sizable magnetic moments and dopant-dependent exchange splitting. When spin–orbit coupling is included, the combined effect of exchange interactions and relativistic effects leads to an effective valley splitting at the K and $K^{\prime }$ points, whose magnitude and sign depend sensitively on the chemical nature of the dopant. Optical properties are analyzed within a linear-response framework, showing pronounced dopant-induced modifications of the optical spectra. While the pristine monolayer exhibits well-defined excitonic features, transition-metal substitution introduces low-energy optical transitions associated with impurity-related states. Consequently, the exciton binding energies estimated from the difference between the electronic and optical gaps are interpreted as effective measures of dopant-induced perturbations to optical transitions, rather than as quantitative many-body excitonic binding energies in the strict sense. These results provide microscopic insight into the interplay between magnetism, spin–orbit coupling, and optical response in doped MoS₂ monolayers, highlighting the potential of transition-metal substitution as a route to engineer spin- and valley-dependent phenomena in two-dimensional materials. Full article

Scientists estimate that humanity has exceeded seven of nine planetary boundaries, threatening the entire planet with potentially catastrophic consequences for all species. We therefore have a moral imperative for future generations and other species to return to the safe side of those boundaries. Threats to these boundaries take the form of social dilemmas, defined as situations in which individuals acting in their own interest undermine collective welfare, which can only be solved through cooperation. Western economic theory has conditioned us to believe that humans are inherently selfish. This assumption has led economists, scientists, and policymakers to increasingly pursue market-based solutions to conservation approaches, which have yielded limited success. In contrast, this article argues that humans are inherently cooperative. We employ Multi-Level Selection Theory (MLS) to depict the evolutionary advantages of cooperation and to define morality as putting the group ahead of the individual. We examine two examples of MLS in action: Territories of Life (TOL) and Ubuntu. The paper provides guidance for pathways of Ecozoic governance, planning, and restoration. Applied in a Western context in Burlington, Vermont, the philosophies hold true, showing that social norms and group identity already shape ecological behavior in Burlington residents’ lawn care practices. Ultimately, providing an alternative economic model built on these ethical foundations, we introduce the Neighbor’s Goodwill that reframes social dilemmas in a game theory context. The Neighbor’s Goodwill demonstrates how loyalty, reciprocity, and social belonging alter payoff structures. This research is founded on the fact that humans are inherently social and tend to make decisions in the interest of the whole group over their own. Full article

Virtual power plants (VPPs) aggregate flexible resources, such as distributed photovoltaics (PV), energy storage, and flexible loads, to provide substantial reserve capacity for grid operation. However, the combined effects of renewable energy output uncertainty, load forecast errors, and user-bounded rationality responses lead to significant errors in traditional deterministic VPP reserve assessment methods, severely affecting the balance between system supply and demand. To address this challenge, this paper proposes a credible reserve assessment method that accounts for user-bounded rationality. First, thermodynamic models with on–off constraints for air conditioning loads, energy feasible region, and power constraint models for electric vehicles (EVs) and energy storage systems (ESSs), as well as PV forecast error models are established to characterize physical reserve boundaries. Second, prospect theory is introduced to describe user-bounded rationality and a logit-based response probability model is developed. Monte Carlo sampling and kernel density estimation are employed to derive credible reserve sets under different confidence levels, achieving a probabilistic quantification of VPP reserve capacity distribution. Case studies demonstrate that the proposed method accurately characterizes the probabilistic distribution characteristics of VPP reserve provision under multiple uncertainties, providing comprehensive and reliable assessment information for power dispatching agencies. Full article

This article proposes a new type of analytic function called ${\mathbb{M}}_{tan}$ and introduces a new geometric structure that blends exponential and trigonometric properties. In addition, it obtains exact bounds for all second- and third-order Hankel determinants and establishes extremal results for the Fekete–Szegö and Zalcman functionals. Moreover, it discusses the validity of the Krushkal inequality. Furthermore, it applies the developed methodology to improve the contrast and quality of color images and demonstrates that the proposed enhancement filters yield notable improvements in contrast and quality compared to other filters, based on the PSNR, SSIM, MSE, RMSE, PCC, and MAE metrics. This article demonstrates its dual nature, namely advances in geometric function theory and practical advantages in digital image processing. Full article

This paper presents a comprehensive framework for the identification, state estimation, and robust control of a bistable Duffing–Holmes oscillator, validated through an experimental setup. First, to address parametric uncertainty, a Recursive Least Squares Method (RLSM) with a forgetting factor is applied to a filtered model representation, enabling accurate parameter convergence from noisy measurements. Subsequently, a Nonlinear Integral Extended State Observer (NIESO) is designed to reconstruct unmeasured states and estimate total disturbances. A key theoretical contribution is the derivation of explicit gain conditions that guarantee the observer’s stability, overcoming limitations of previous designs. For trajectory tracking, an observer-based backstepping controller is synthesized. Crucially, to bridge the gap between theory and practice, a drift-free integration scheme is implemented to generate feasible position commands for the shake table, preventing actuator saturation. Experimental results confirm the framework’s effectiveness, achieving a 3.7-fold reduction in RMS tracking error compared to open-loop operation, with the tracking error rapidly converging to a small neighborhood within approximately $0.2$ $\mathrm{s}$. Furthermore, the closed-loop system demonstrates superior energy efficiency, requiring significantly lower actuator voltage to sustain stable interwell oscillations. Full article

The paper presents four solutions to the voltage source inverter (VSI) control system with existing delays in the measurement channels and the middle switching frequency (25,600 Hz): Single-Input Single-Output Coefficient Diagram Method (SISO-CDM), Multi-Input Multi-Output Passivity-Based Control (MISO-PBC), Multi-Input Multi-Output One-Sample-Ahead Preview Controller (MISO-OSAP), and MISO-OSAP with Luenberger Observer (MISO-OSAP-LO). The theory, including adjustments to controller gains or to the coefficients of the characteristic equation of the closed-loop system, is presented. Simulations of the VSI operation with these control systems for the nonlinear load and the dynamic resistive load (per the requirements of the EN 62040-3 standard) are presented. The SISO-CDM and MISO-PBC are finally selected for experimental verification of the simulations. The results of the tests enable the selection of the control type for a particular VSI design based on its cost and an estimation of the advantages of the more expensive solution. The paper should help in engineering design according to the remarks in the paper. Full article

Precision medicine tailored to individual patient characteristics is crucial for improving long-term health outcomes. In survival analysis, a significant challenge for learning an optimal treatment regimen is to handle censoring, which is ubiquitous due to insufficient follow-up or other reasons. While there exist some ready-made methods under right censoring, learning an optimal treatment regimen with the more complicated interval censoring mechanism is still unexplored. To address this significant gap, this work proposes a novel semiparametric single-index modeling method, in which the interaction between the treatment and a single-index combination of covariates is linked through an unknown monotonic function. The proposed approach can capture complex, nonlinear treatment–covariate relationships while maintaining interpretability for clinical decision-making. Our estimation strategy employs sieve maximum likelihood, utilizing monotone splines to approximate the cumulative baseline hazard and B-splines for the unknown link function. To tackle the challenge of maximizing the complicated likelihood, we develop a stable and computationally efficient EM algorithm. The consistency and asymptotic distribution of the resultant estimators are established through the empirical process theory. Simulation studies demonstrate that the proposed approach performs well in finite samples. An application to a clinical trial data set on AIDS highlights the practical utility of the proposed method. Full article

Background/Objectives: Healthcare professionals operate in complex and demanding environments characterized by high workloads, emotional strain, and organizational pressures that can undermine well-being. According to Self-Determination Theory, the fulfillment of core psychological needs (autonomy, competence, and relatedness) leads to increased job satisfaction, a key indicator of occupational well-being. Additionally, leadership plays a central role in shaping needs-fulfilling environments. Drawing on Leader–Member Exchange Theory (LMX), which emphasizes that high-quality leader-follower relationships foster greater discretion, provide learning opportunities, and build constructive team interactions, this study aimed to examine whether supportive leadership is associated with job satisfaction through the mediation of autonomy, team task cohesion, and perceived training opportunities. Methods: Data were collected from a local health authority in Northern Italy through an anonymous online survey, completed by 697 healthcare professionals, including 546 non-medical healthcare staff (primarily nurses) and 151 physicians. Structural equation modeling with a robust maximum likelihood estimator was employed to test the mediation model, including professional role as a covariate. Results: Higher LMX was positively and directly associated with job satisfaction, through the partial mediation of autonomy, team cohesion, and training opportunities, all positively associated with satisfaction. Team task cohesion showed the strongest associations with both LMX and satisfaction. Physicians reported slightly higher levels of autonomy, training opportunities, and job satisfaction than non-medical professionals. Conclusions: The findings suggest that supportive leadership contributes to healthcare professionals’ job satisfaction both directly and indirectly by contributing to core needs fulfillment. Interventions that strengthen relational quality, promote team cohesion, and enhance professional development may help sustain well-being and adaptive functioning in high-demand healthcare environments. Full article

Viscoelastic solutions are developed for the axial dynamic response of single piles in soil profiles that are inhomogeneous both vertically (with depth) and horizontally (with radial distance from the pile). While vertical soil inhomogeneity has been well explored, horizontal inhomogeneity has received limited research attention. In this work, the problem is treated in the realm of linear elastodynamic theory by employing a rigorous finite-element formulation specifically developed by the authors for the problem at hand. The effect of double soil inhomogeneity is investigated with reference to: (1) pile head stiffness; (2) pile-head radiation damping; (3) soil reaction along the pile; and (4) variation of the above with loading frequency. To this end, four different soil profiles are considered in conjunction with different levels of soil inhomogeneity, pile lengths, pile–soil stiffness contrasts, and boundary conditions at the pile tip. It is shown that the effect of inhomogeneity has unique features that cannot be captured by using a substitute homogeneous profile. Modeling an inhomogeneous soil as a homogeneous layer providing equal pile-head stiffness (to be referred in this work to as “stiffness-equivalent soil”) may grossly overestimate wave radiation, leading to dampened estimates of dynamic pile response. Simulations of two field experiments are reported, and implications of radiation damping in design are discussed. Full article

Pricing multi-peril agricultural insurance under compound climate hazards demands a framework that captures stochastic dependence among heterogeneous perils, accommodates non-stationary loss dynamics, and supports adaptive policy optimisation. We demonstrate that backward stochastic differential equations, combined with copula dependence, recurrent neural networks, and reinforcement learning, provide a unifying language for this task; the contribution lies in their principled integration. The dynamic premium is the unique adapted solution of a BSDE whose driver encodes compound-risk dependence through a Student-t copula, forward loss dynamics through a jump-diffusion process, and a green-finance adjustment through an optimal control variable. Within this framework we derive three progressive results by adapting standard BSDE theory to the compound-dependence and policy-control setting. First, existence and uniqueness hold under Lipschitz and square-integrability conditions. Second, a comparison theorem guarantees that a larger correlation matrix yields higher premiums; the degrees-of-freedom effect enters separately through the risk-loading magnitude. Third, the Euler discretisation converges at a rate of one half of the time-step size, with copula estimation, LSTM conditional expectation approximation, and Q-learning HJB solution as sequential components. Applied to eleven Zhejiang cities (2014–2023, $N \times T=110$), in this illustrative application the framework reduces premium variance by 43.5 percent (bootstrap 95% CI: $[38.2\%,48.7\%]$) while maintaining actuarial adequacy with a mean loss ratio of 0.678, though the modest sample size warrants caution in generalising these findings. Each component contributes statistically significant improvements confirmed by the Friedman test at the 0.1 percent significance level. Full article

Amidst the polycrisis of 2018–2024, Asia-Pacific trade flows exhibited a structural resilience that contrasts with traditional theoretical predictions of severe trade contraction under high uncertainty. This study investigates these resilience dynamics using a structural gravity model estimated via the Poisson Pseudo Maximum Likelihood (PPML) approach. The analysis utilizes a balanced panel of 14 key regional economies (N = 4914), explicitly disaggregated into geographic (ASEAN-6 vs. non-ASEAN) and global value chain (high vs. low GVC intensity) subgroups to capture heterogeneous responses. The empirical results confirm that economic policy uncertainty (EPU) acts as a significant trade friction (β = −3.371), consistent with the wait-to-invest mechanism of real options theory. However, this effect is heterogeneous and significantly mitigated by institutional frameworks. We identify a robust institutional shield effect, where participation in trade agreements effectively neutralizes the adverse transmission of policy shocks (interaction coefficient = 3.396). Furthermore, this study uncovers a structural break during periods of extreme geopolitical conflict, characterized by a convex U-shaped relationship between uncertainty and trade. This pattern provides macro-level evidence of a behavioral shift in regional supply chains from a just-in-time cost-efficiency optimization model to a just-in-case security maximization paradigm, consistent with precautionary inventory accumulation. These findings underscore the critical role of modern trade pacts as institutional credibility anchors and the necessity of adaptive strategies in navigating heightened macroeconomic volatility. Full article

To reduce the production cost of ultra-high performance concrete (UHPC), this study incorporated polyacrylonitrile (PAN) fibers and coarse aggregates (CA) to develop a novel UHPC with both excellent performance and reduced cost. A two-stage mortar-concrete design approach was employed to optimize the UHPC mix proportion. First, the mortar matrix was preliminarily optimized based on particle packing theory, and its strength development mechanism was analyzed. Subsequently, response surface methodology was applied to systematically investigate the effects of PAN fiber content, CA content, and superplasticizer (SP) dosage on the slump flow, compressive strength, flexural strength, indirect tensile strength, freeze–thaw resistance, and dynamic mechanical properties of UHPC. The entropy weight method was then adopted to determine the optimal mix proportion, followed by cost estimation. The results indicate that the optimal mortar matrix composition consists of 61.4% cement, 15% silica fume, and 23.6% fly ash, achieving a flow spread of 246 mm, a compressive strength of 117.2 MPa, and a flexural strength of 24.9 MPa. When the PAN fiber content, CA content, and SP dosage were 0.5%, 20%, and 3.8%, respectively, the prepared PAN-CA UHPC(PCUHPC) exhibited the best overall performance. Compared with conventional UHPC, the material cost was reduced by 81.7%, and the compressive strength-normalized cost decreased by 75.4%. The UHPC developed in this study, characterized by outstanding performance and significant cost advantages, provides a feasible solution and theoretical support for broader engineering applications. Full article

This paper investigates the optimization of maintenance strategies to improve the reliability of equipment at the Kapshagay Hydropower Plant (HPP), located in Kazakhstan. Operational data for the period 2020–2025 were analyzed to evaluate the effectiveness of existing maintenance systems. The analysis showed that the failure frequency of the main equipment averaged 3.8–4.2 events per year, while annual unplanned downtime reached 80–100 h, resulting in electricity generation losses of 2.5–3.2%. In addition, total maintenance costs were approximately 150 million KZT per year, with about 40% related to unplanned repairs. A reliability-centered maintenance model was developed using mathematical modeling and simulation tools such as Python 3.11 and SMath Solver 0.99.7920. The study integrates reliability theory, exponential failure modeling, and statistical performance analysis based on operational data from the Kapshagay HPP. Simulation-based validation was performed to compare baseline and optimized maintenance strategies under real operating conditions. After implementing the proposed model, equipment failure probability decreased by 15%, failure rate decreased by 28%, the mean time between failures increased from 120 days to 165 days, and repair duration decreased from 6 days to 4 days. Additionally, failure probability decreased from 0.10 to 0.07, while annual downtime decreased from 6.2 days to 4.1 days. Electricity generation losses decreased by approximately 18–22 GWh per year, while the annual economic benefit was estimated at 320–480 million KZTn. The results demonstrate that reliability-centered maintenance can increase equipment reliability by 20–30%, reduce maintenance costs by 10–12%, and improve electricity generation efficiency by 1.8–2.4%. The obtained results have practical significance for improving the technical and economic performance of hydropower plants. Full article

This paper mainly considers the fractional parameter identification algorithms of the bilinear-in-parameter autoregressive (AR-BIP) system. The data filtering technique is introduced to improve the parameter estimation accuracy of the AR-BIP system, which involves using a filter to filter the data of the identification model. The filtering-based hierarchical fractional least mean square algorithm (F-HFLMS) and the filtering-based multi-innovation hierarchical fractional least mean square algorithm (F-MHFLMS) are proposed for effective and accurate parameter estimation of the AR-BIP system. Using the multi-innovation theory and expanding the scalar innovation into the innovation vector, the F-MHFLMS could take full advantage of the input and output data information of the system. The performance of the F-MHFLMS algorithm is compared with the F-HFLMS strategy for the AR-BIP system using the values of the mean square error (MSE) and the average predicted output error. The effectiveness and accuracy of F-HFLMS and F-MHFLMS algorithms are demonstrated under the numerical experimentation based on different noise variances, fractional orders and innovation lengths. Compared with the F-HFLMS algorithm, the F-MHFLMS algorithm can acquire more accurate and robust parameter estimation. Full article