Powerful Analytical Method Research Articles

A Power Analysis Method for Self-Interference Signal Components in Full-Duplex Transceivers Under Constant/Nonconstant Modulus Signal Stimulation

The existence of multiple self-interference (SI) signal components, particularly the nonlinear ones, seriously constrains the performance of self-interference cancellation (SIC) methods. To decrease the complexity of SIC methods in full-duplex devices, this article proposes a power analysis method for SI signal components in a full-duplex transceiver. The proposed method comprises a separate analysis algorithm and a system-level power model. Initially, the algorithm is conducted to obtain the spectrum of the linear and nonlinear components in the power amplifier (PA) output signal. Once the linear-to-nonlinear power ratio (LNPR) has been obtained, a system-level power model is constructed by taking both the transmitter noise and analog-to-digital converter (ADC) quantization noise into account. The proposed power model allows for the allocation of SIC method performance in multiple domains during the design of full-duplex transceivers at the top level, thereby reducing the overall system complexity. The simulation results demonstrate that in a full-duplex transceiver with only antenna isolation, the power of the SI signal component is susceptible to alterations due to the operating waveform and transmission power. Finally, the accuracy of the power analysis method is verified through measurement and Simulink.

Continuous fiber printing of a modular heater for ion mobility spectrometry

Ion mobility spectrometry (IMS) is a powerful analytical method for separating and detecting gaseous substances like volatile organic compounds (VOCs). It is highly versatile, used both as a standalone technique and in combination with chromatographic methods for pre-separation or mass spectrometry for complex matrix analysis. This study explores the use of an advanced 3D printing technology to print a heater for an IMS-device.A continuous fiber printer, commonly used to enhance the mechanical properties of printed objects, was used to create a customized heating element by embedding a copper wire within the polymer matrix during the printing process. Cyclic olefin copolymer (COC) was chosen as the matrix material due to its analytical properties, such as low off-gassing and higher thermal stability, compared to the more commonly used polylactic acid (PLA).The 3D printed heater was incorporated into a drift tube ion mobility spectrometer, and its performance was evaluated by analyzing the separation of various ketones. The results show that the 3D printed heater effectively controlled the temperature within the drift tube, improving the resolving power of overlapping peaks.This study shows the potential of 3D printing technology to improve fabrication and functionality of IMS devices. The flexibility in design and material selection afforded by 3D printing not only enhances the analytical performance of IMS but also allows for customization and rapid prototyping of other analytical instruments or laboratory devices.

A general approach for testing independence in Hilbert spaces

We generalize the projection correlation idea for testing independence of random vectors which is known as a powerful method in multivariate analysis. A universal Hilbert space approach makes the new testing procedures useful in various cases and ensures the applicability to high or even infinite dimensional data. We prove that the new tests keep the significance level under the null hypothesis of independence exactly and can detect any alternative of dependence in the limit, in particular in settings where the dimensions of the observations is infinite or tend to infinity simultaneously with the sample size. Simulations demonstrate that the generalization does not impair the good performance of the approach and confirm our theoretical findings. Furthermore, we describe the implementation of the new approach and present a real data example for illustration.

Correction Method for Power Analysis of Digital Integrated Circuits Based on Standard Cells

Correction Method for Power Analysis of Digital Integrated Circuits Based on Standard Cells

Sample Size Planning in the Design of Two-Level Randomized Cost-Effectiveness Trials

This study introduces recent advances in statistical power analysis methods and tools for designing and analyzing randomized cost-effectiveness trials (RCETs) to evaluate the causal effects and costs of social work interventions. The article focuses on two-level designs, where, for example, students are nested within schools, with interventions applied either at the school level (cluster design) or student level (multisite design). We explore three statistical modeling strategies—random-effects, constant-effects, and fixed-effects models—to assess the cost-effectiveness of interventions, and we develop corresponding power analysis methods and tools. Power is influenced by effect size, sample sizes, and design parameters. We developed a user-friendly tool, PowerUp!-CEA, to aid researchers in planning RCETs. When designing RCETs, it is crucial to consider cost variance, its nested effects, and the covariance between effectiveness and cost data, as neglecting these factors may lead to underestimated power.

Periodic and Axial Perturbations of Chaotic Solitons in the Realm of Complex Structured Quintic Swift-Hohenberg Equation

This research work employs a powerful analytical method known as the Riccati Modified Extended Simple Equation Method (RMESEM) to investigate and analyse chaotic soliton solutions of the (1 + 1)-dimensional Complex Quintic Swift–Hohenberg Equation (CQSHE). This model serves to describe complex dissipative systems that produce patterns. We have found that there exist numerous chaotic soliton solutions with periodic and axial perturbations to the intended CQSHE, provided that the coefficients are constrained by certain conditions. Furthermore, by applying a sophisticated transformation, the provided transformative approach RMESEM transforms CQSHE into a set of Nonlinear Ordinary Differential Equations (NODEs). The resulting set of NODEs is then transformed into an algebraic system of equations by incorporating the extended Riccati NODE to assume a series form solution. The soliton solutions to this system of equations can be found as periodic, hyperbolic, exponential, rational-hyperbolic, and rational families of functions. A variety of 3D and contour visuals are also provided to graphically illustrate the axially and periodically perturbed dynamics of these chaotic soliton solutions and the formation of fractals. Our findings are noteworthy because they shed light on the chaotic nature of the framework we are examining, enabling us to better understand the dynamics that underlie it.

Higher Order Structure Differences Among Insulin Crystalline Drugs Revealed by 2D heteronuclear NMR.

During therapeutic protein development, two-dimensional (2D) heteronuclear NMR spectra can be a powerful analytical method for measuring protein higher order structure (HOS) in solution since the spectra exhibit much higher resolution than homonuclear 1H spectra. However, 2D NMR capabilities for characterizing protein HOS in crystalline states remain to be assessed, given the low 13C natural abundance and intrinsically broader lines in solid-state NMR (SSNMR). Herein, high-resolution heteronuclear correlation (HETCOR) SSNMR was utilized to directly measure intact crystal drug products of insulin human, insulin analogs of insulin lispro and insulin aspart. The fingerprint regions in 2D 1H-13C HETCOR spectra were identified, which distinguished the insulin crystals in their primary structure, HOS heterogeneity and dynamics, as well as the manufacturing processes. The HOS heterogeneity in insulin analogs is consistent with their therapeutic effect of rapid action; while insulin human crystals showed more structural homogeneity, consistent with their slower pharmacokinetics (PK)peak time than insulin analogs. Therefore, heteronuclear NMR could be broadly applicable to study protein drug dosage forms from liquid to solid, yielding improved molecular level structure data for assessing drug HOS in biosimilar drug development.

Probabilistic Power and Energy Balance Risk Scheduling Method Based on Distributed Robust Optimization

The volatility and uncertainty associated with the high proportion of wind and PV output in the new power system significantly impact the power and energy balance, making it challenging to accurately assess the risks related to renewable energy abandonment and supply guarantee. Therefore, a probabilistic power and energy balance risk analysis method based on distributed robust optimization is proposed. Firstly, the affine factor and the flexible ramp reserve capacity of thermal power are combined to establish a probabilistic index, which serves to characterize the risk associated with the power and energy balance. Drawing upon the principles of the conditional value at risk theory, the risk indexes of the load shedding power and curtailment power under a certain confidence probability are proposed. Secondly, the probability distribution fuzzy sets of uncertain variables are constructed using the distributionally robust method to measure the Wasserstein distance between different probability distributions. Finally, aiming at minimizing the operation cost of thermal power, the risk cost of power abandonment, and the risk cost of load shedding, a distributed robust optimal scheduling model based on a flexible ramp reserve of thermal power is established.

Conducting power analysis for meta-analysis with dependent effect sizes: Common guidelines and an introduction to the POMADE R package.

Sample size and statistical power are important factors to consider when planning a research synthesis. Power analysis methods have been developed for fixed effect or random effects models, but until recently these methods were limited to simple data structures with a single, independent effect per study. Recent work has provided power approximation formulas for meta-analyses involving studies with multiple, dependent effect size estimates, which are common in syntheses of social science research. Prior work focused on developing and validating the approximations but did not address the practice challenges encountered in applying them for purposes of planning a synthesis involving dependent effect sizes. We aim to facilitate the application of these recent developments by providing practical guidance on how to conduct power analysis for planning a meta-analysis of dependent effect sizes and by introducing a new R package, POMADE, designed for this purpose. We present a comprehensive overview of resources for finding information about the study design features and model parameters needed to conduct power analysis, along with detailed worked examples using the POMADE package. For presenting power analysis findings, we emphasize graphical tools that can depict power under a range of plausible assumptions and introduce a novel plot, the traffic light power plot, for conveying the degree of certainty in one's assumptions.

Exposure range matters: considering non-linear associations in the meta-analysis of environmental pollutant exposure using examples of per- and polyfluoroalkyl substances and birth outcomes.

Meta-analysis is a powerful analytic method for summarizing effect estimates across studies. However, conventional meta-analysis often assumes a linear exposure-outcome relationship and does not account for variability over the exposure ranges. In this work, we first used simulation techniques to illustrate that the linear-based meta-analytical approach may result in oversimplistic effect estimation based on three plausible non-linear exposure-outcome curves (S-shape, inverted U-shape, and M-shape). We showed that subgroup meta-analysis that stratifies on exposure levels can investigate non-linearity and identify the consistency of effect magnitudes in these simulated examples. Next, we examined the heterogeneity of effect estimates across exposure ranges in two published linear-based meta-analyses of prenatal exposure to per- and polyfluoroalkyl substances (PFAS) on changes in mean birth weight or risk of preterm birth. The re-analysis found some varying effect sizes and potential heterogeneity when restricting to different PFAS exposure ranges, but findings were sensitive to the cut-off choices used to rank the exposure levels. Finally, we discussed methodological challenges and recommendations for detecting and interpreting potential non-linear associations in meta-analysis. Using meta-analysis without accounting for exposure range could contribute to literature inconsistency for exposure-induced health effects and impede evidence-based policymaking. Therefore, investigating result heterogeneity by exposure range is recommended.

Identification of body coloration related genes in ricefield eel (Monopterus albus) using GWAS

Body color is one of the most diverse external morphological characteristics of fish. It not only affects the function of fish predation and individual communication but also has important economic value. The ricefield eel (Monopterus albus) is a popular freshwater aquaculture fish species in China. Few studies have suggested some candidate genes involved in the formation of body color in this fish; however, further genetic evidences are required. Genome-wide association analysis (GWAS) is a powerful analytical method for identifying relationships between target traits and genetic markers or candidate genes in natural populations. In this study, two populations of ricefield eel with different body coloration were used to study the genetic factors that regulate the formation of body color. Through GWAS, 67 SNPs, 463 genes and 5 signaling pathways were found to be associated with body color. These results suggest that a complex molecular regulatory mechanism regulates ricefield eel body coloration. Our findings lay the foundation for subsequent genome selection breeding of ricefield eel.

A dual-modality sensing probe of fluorescent and colorimetric for detection of cobalt ion based on silver nanoparticles functionalized rhodamine 6G derivatives

The combination of fluorescent probe and colorimetric technique has become one of the most powerful analytical methods due to the advantages of visualization, minimal measurement errors and high sensitivity. Hence, a novel dual-modality sensing probe with both colorimetric and fluorescent capabilities was developed for detecting cobalt ions (Co2+) based on homocysteine mediated silver nanoparticles and rhodamine 6G derivatives probe (AgNPs-Hcy-Rh6G2). The fluorescence of the AgNPs-Hcy-Rh6G2 probe turned on due to the opening of the Rh6G2 spirolactam ring in the presence of Co2+ by a catalytic hydrolysis. The fluorescent intensity of probe is proportional to Co2+ concentration in the range of 0.10–50 μM with a detection limit of 0.05 μM (S/N = 3). More fascinatingly, the color of AgNPs-Hcy-Rh6G2 probe changed from colorless to pink with increasing Co2+ concentration, which allowing colorimetric determination of Co2+. The absorbance of AgNPs-Hcy-Rh6G2 probe is proportional to Co2+ concentration in the range from 0.10 to 25 μM with a detection limit of 0.04 μM (S/N = 3). This colorimetric and fluorescent dual-modal method exhibited good selectivity, and reproducibility and stability, holding great potential for real samples analysis in environmental and drug field.

Powerful numerical method for analysis of electromagnetic scattering from multiple 3D coated targets buried under rough surface

ABSTRACT In this work, an efficient numerical method is proposed for three-dimensional (3D) electromagnetic scattering from multiple coated targets buried under a two-dimensional dielectric rough surface. In the proposed method, the interior region of each coated target is regarded as an independent subregion. The field within each coated target is modeled by 3D vector finite element method (FEM). The multiple scattering interactions between the buried targets and between targets and rough surface are considered by the electric field integral equation and the Poggio–Miller–Chang–Harrington–Wu integral equations, respectively. The fields inside and outside the targets are coupled together via boundary conditions on the boundary of each target. To improve the calculation efficiency and reduce the memory requirement, the fast multipole technique is used in our method. The presented method is numerically validated by comparing it with method of moments and conventional FEM-boundary integral method (BIM). The results indicate that, compared with traditional FEM-BIM, our method has significant advantages in computational efficiency and memory requirements.

Triple decomposition and sparse representation for noisy pressure-sensitive paint data

Triple decomposition is a powerful analytical method for a deep understanding of the flow structure by extracting the mean value, organized coherent motion, and stochastic part from a fluctuating quantity. Here, we perform the triple decomposition of the spatial two-dimensional data, especially pressure-sensitive paint (PSP) data, since the PSP method is widely used to measure the pressure distribution on a surface in wind tunnel testing. However, the PSP data measuring near atmospheric pressure contain significant noise, and this makes it difficult to conduct the decomposition. To construct phase-averaged data representing an organized coherent motion, we propose a relatively simple method based on a multi-dimensional scaling plot of the cosine similarity between each PSP datum. Then, the stochastic part is extracted by selecting phase-averaged data with an appropriate phase angle based on the similarity between the measurement and phase-averaged data, and the PSP data are successfully decomposed. Moreover, we consider sparse optimal sensor positions, in which the data are effectively represented, based on the stochastic part as a data-driven approach. The optimal sensor positions are determined as a combinatorial optimization problem and estimated using Fujitsu computing as a service digital annealer. We reconstruct the pressure distribution from the pressure data at the optimal sensor positions using the mean value, organized coherent motion, and stochastic part obtained from the triple decomposition. The root mean square error between the pressure measured by a pressure transducer and the reconstructed pressure obtained by the proposed method is small, even when the number of modes and sensor points is small. The application of PSP measurement is expected to expand further, and the framework for calculating triple decomposition and sparse representation based on the decomposition will be useful for detailed flow analysis.

Analysis and authentication of avocado oil by low-field benchtop NMR spectroscopy and chemometrics.

Avocado oil is a nutritious, edible oil produced from avocado fruit. It has high commercial value and is increasing in popularity, thus powerful analytical methods are needed to ensure its quality and authenticity. Recent advancements in low-field (LF) NMR spectroscopy allow for collection of high-quality data despite the use of low magnetic fields produced by non-superconductive magnets. Combined with chemometrics, LF NMR opens new opportunities in food analysis using targeted and untargeted approaches. Here, it was used to determine poly-, mono-, and saturated fatty acids in avocado oil. Although direct signal integration of LF NMR spectra was able to determine certain classes of fatty acids, it had several challenges arising from signal overlapping. Thus, we used partial least square regression and developed models with good prediction performance for fatty acid composition, with residual prediction deviation ranging 3.46-5.53 and root mean squared error of prediction CV ranging 0.46-2.48. In addition, LF NMR, combined with unsupervised and supervised methods, enabled the differentiation of avocado oil from other oils, namely, olive oil, soybean oil, canola oil, high oleic (OL) safflower oil, and high OL sunflower oil. This study showed that LF NMR can be used as an efficient alternative for the compositional analysis and authentication of avocado oil. PRACTICAL APPLICATION: Here, we describe the application of LF-NMR for fatty acid analysis and avocado oil authentication. LF-NMR can be an efficient tool for targeted and untargeted analysis, thus becoming an attractive option for companies, regulatory agencies, and quality control laboratories. This tool is especially important for organizations and entities seeking economic, user-friendly, and sustainable analysis solutions.

In-depth Correlation Power Analysis Attacks on a Hardware Implementation of CRYSTALS-Dilithium

During the standardisation process of post-quantum cryptography, NIST encourages research on side-channel analysis for candidate schemes. As the recommended lattice signature scheme, CRYSTALS-Dilithium, when implemented on hardware, has seen limited research on side-channel analysis, and current attacks are incomplete or requires a substantial quantity of traces. Therefore, we conducted a more complete analysis to investigate the leakage of an FPGA implementation of CRYSTALS-Dilithium using the Correlation Power Analysis (CPA) method, where with a minimum of 70,000 traces partial private key coefficients can be recovered. Furthermore, we optimise the attack by extracting Point-of-Interests using known information due to parallelism (named CPA-PoI) and by iteratively utilising parallel leakages (named CPA-ITR). Our experimental results show that CPA-PoI reduces the number of traces by up to 16.67%, CPA-ITR by up to 25%, and both increase the number of recovered key coefficients by up to 55.17% and 93.10% using the same number of traces. They outperfom the CPA method. As a result, it suggests that the FPGA implementation of CRYSTALS-Dilithium is more vulnerable than thought before to side-channel analysis.

Developing CBTC system safety requirement hierarchy through STPA methodology

Safety-critical systems, such as the railway signal system, are subject to potentially high costs from failures, including loss of life and property damage. The use of new technology, including communication-based train control (CBTC) systems with software and computers, has changed the types of accidents that occur. Software-related issues and dysfunctional interactions between system components controlled by the software are increasingly the cause of incidents. Developing a “safe” safety-critical system requires accurate and complete safety requirements, which are the foundation of system development. Traditional hazard analysis techniques are insufficient for identifying the causes of accidents in modern railway signaling systems. Systems-Theoretic Process Analysis (STPA) is a powerful new hazard analysis method designed to address these limitations. Building upon this foundation, a hierarchical approach to safety requirement development has been further developed. This approach combines STPA analysis with a hierarchical modeling approach to establish traceability links from safety requirements to specific architectures, refine and allocate system-level safety requirements to relevant subsystems, and abstract safety requirements at higher hierarchical levels to enable easy changes to lower-level implementations. This paper employs the aforementioned methodology within the context of the CBTC system, thereby enhancing risk management and hazard analysis, enabling early insights, and facilitating the generation of safety requirements of CBTC System.

Counting the truxillines-11 or more, the question is now.

Truxillines are a group of tropane alkaloids present in coca leaves that are formed by photochemical dimerization of cinnamoylcocaine(s). Proportion of different truxilline forms present in cocaine serves as its geographical, manufacture, and storage "fingerprint"; thus, the quantitative determination of truxilline content represents one of the powerful methods of analysis and characterization of cocaine samples. Contrary to the statements repeatedly presented in the literature, namely, that there exist exactly 11 truxillines and that every single truxilline is diastereomer of any other, here we show that,in fact, a total of 15 truxillines exist, which can be divided in two structurally isomeric groups-five mutually diastereomeric truxillates and 10 mutually diastereomeric truxinates.

Structural elucidation of complex polyesters polyols from bio-lubricant using off-line liquid chromatography x supercritical fluid chromatography coupled with Orbitrap mass spectrometry

Synthetic complex esters and polyol esters are incorporated as partially bio-based and biodegradable alternatives to petroleum base oils in lubricant formulations, to provide specific properties or performance and to help reducing their carbon footprint in certain cases. A sample can contain over 400 molecules of high chemical similarity including numerous isomers. To resolve such complexity, a separation technique with large peak capacity coupled to high-resolution mass spectrometry (HRMS) is essential. In this study, comprehensive off-line LCxSFC hyphenated with an Orbitrap analyzer was used for the structural elucidation of a synthetic bio-lubricant composed of a polyol reacted with fatty acids of varying length or with repetitive units of polyesters of ricinoleic acid. Retention in the LC first dimension was mostly due to the degree of oligomerization of ricinoleic acid within the polyester and to the chain length of the fatty acid. The SFC second dimension highlighted the esterification degree of the polyalcohol and the number and positions of fatty acids double bonds. The combination of both dimensions permitted the separation of isomers. The coupling of SFC with Orbitrap analyzer allowed an accurate assignment of molecular formulas. Finally, the fragmentation in the ionization source confirmed the attributed structures. By introducing a clear distribution of the chemical structures in the retention space, LCxSFC-HRMS provided a powerful analytical method for the comprehensive molecular characterization of the complex polyester polyols sample.

Comprehensive O-Glycan Analysis by Porous Graphitized Carbon Nanoliquid Chromatography-Mass Spectrometry.

The diverse and unpredictable structures of O-GalNAc-type protein glycosylation present a challenge for its structural and functional characterization in a biological system. Porous graphitized carbon (PGC) liquid chromatography (LC) coupled to mass spectrometry (MS) has become one of the most powerful methods for the global analysis of glycans in complex biological samples, mainly due to the extensive chromatographic separation of (isomeric) glycan structures and the information delivered by collision induced fragmentation in negative mode MS for structural elucidation. However, current PGC-based methodologies fail to detect the smaller glycan species consisting of one or two monosaccharides, such as the Tn (single GalNAc) antigen, which is broadly implicated in cancer biology. This limitation is caused by the loss of small saccharides during sample preparation and LC. Here, we improved the conventional PGC nano-LC-MS/MS-based strategy for O-glycan analysis, enabling the detection of truncated O-glycan species and improving isomer separation. This was achieved by the implementation of 2.7 μm PGC particles in both the trap and analytical LC columns, which provided an enhanced binding capacity and isomer separation for O-glycans. Furthermore, a novel mixed-mode PGC-boronic acid-solid phase extraction during sample preparation was established to purify a broad range of glycans in an unbiased manner, including the previously missed mono- and disaccharides. Taken together, the optimized PGC nano-LC-MS/MS platform presents a powerful component of the toolbox for comprehensive O-glycan characterization.