Reliability Analysis Method Research Articles

Rapid pre-analysis derivatization-enabled high-throughput immunoassay for nitrite detection in foods

Nitrite is a well-known food additive and water pollutant that has garnered extensive attention due to both of its adverse and beneficial effects on human health, calling for rapid and reliable analytical methods. However, the simple and diminutive structure of nitrite has impeded the development of screening immunoassay for this substance. In this work, based on the rapid and selective derivatization of nitrite into benzotriazole (BTA) by o-phenylenediamine, two haptens targeting the structure of BTA were designed and synthesized, and a specific monoclonal antibody against BTA was generated. Using this antibody, an indirect competitive enzyme-linked immunosorbent assay (ELISA), involving a rapid pre-analysis derivatization, was developed for the detection of nitrite in foods. The limits of detection in drinking water, pickle, and ham sausage were 2.0 μg/L, 0.05 mg/kg, and 0.05 mg/kg, respectively. The recoveries ranged from 82.7% to 114.6%, and the results of this ELISA correlated well with those from the standard spectrophotometry method. This pioneering immunoassay for nitrite holds great promise for routine monitoring, ensuring food safety and consumer health.

Could Flow Cytometry Provide New Prognostic Markers in Colorectal Cancer?

Background/Objectives: Colorectal cancer (CRC) is still accompanied by significant mortality, which poses the necessity of novel markers to predict treatment success and patient survival. This study aims to evaluate the prognostic and survival impact of flowytometry (FC) in CRC patients. Methods: In this prospective study, 106 surgically resectable CRC patients were included. Tissue specimens from tumor and normal mucosa were collected and analyzed by FC. DNA and tumor index were calculated. In a subgroup of 46 patients, the CD26 expression on tumor cells was estimated. These parameters were compared with patients' tumor characteristics as stage, histology data, responsiveness to treatment, metastasis/recurrence, and, finally, patients' survival to identify possible new biomarkers. Results: The overall survival and the disease-specific survival in our study group was 76% and 72%, respectively, during the 7-year follow up period. Diploid tumors had better median survival than the aneuploid ones. The DNA index had significant correlation to the tumor index and response to neoadjuvant treatment. Similarly, the tumor index was also significantly related to the response to neoadjuvant treatment. Patients with a higher tumor index had worst survival rates. Surprisingly, CD26 levels were not associated with any of the parameters examined and were negatively related to tumor stage and differentiation. Conclusions: FC is a rapid and reliable method of cell analysis. In CRC, it has been used for prognostic and diagnostic purposes. In this study, we have shown that DNA and tumor index could become predictive biomarkers of tumor response to neoadjuvant treatment and survival of resectable CRC patients.

A multi-objective Roadside Units deployment strategy based on reliable coverage analysis in Internet of Vehicles

The deployment of Roadside Units (RSUs) in the Cellular-Vehicle to Everything enabled Internet of Vehicles is crucial for the transition from individual intelligence of vehicles to collective intelligence of vehicle-road collaboration. In this paper, we focus on improving the adaptability of RSU deployment to real scenarios, and optimizing deployment costs and vehicle-oriented service performance. The RSU deployment problem is modeled as a Multi-objective Optimization Problem (MOP), with a cost model integrating the purchase and installation costs, and a service-oriented Quality of Service (QoS) model adopting the total time the RSUs cover the vehicles as the evaluation metric. Specifically, we propose an RSU reliable coverage analysis method based on Packet Delivery Ratio model to estimate the coverage distances in different scenarios, which will be used in QoS calculation. Then, an evolutionary RSU deployment algorithm is designed to solve the MOP. The performance of the proposed method is simulated and discussed in real road network and dynamic scenarios. The results prove that our method outperforms the baseline method in terms of significant cost reduction and total coverage time improvement.

A robust method for quantifying 42 phenolic compounds by RP-HPLC/DAD: Columns performance and characterization of Brazilian Citrus peels

Reliable analytical methods are the basis for the elucidation of phenolic compounds in foods. This study aimed to optimize and validate a method for determining 42 phenolics using reverse-phase (RP) high-performance liquid chromatography (HPLC) coupled to diode-array-detector-DAD. The performance of two RP columns was evaluated. The 150x4.6 mm 3-μm column showed superior separation quality, whereas 35 of the 42 phenolics showed a separation resolution ≥1.5. The method's linearity, precision (coefficient variation< 3.09%), recovery (87.5-103.2%), specificity, limits of detection (0.04-0.25 mg/L), and quantification (0.06-0.25 mg/L) had acceptable ranges. Thirty phenolics were quantified in Citrus peels, mainly flavanones, flavanols, flavonols, and phenolic acids, highlighting the high values of hesperidin (535-35070 mg/kg) and naringin (26-36466 mg/kg). Lemon peels named 'Lisboa,' 'Thaiti,' 'Thaiti-2000', and 'Thaiti-2001' presented the main phenolics associated with antioxidant capacity. The presented method was robust for determining 42 phenolic compounds, offering a new approach for bioactive compound quantification in food matrices.

Synthesis and application of magnetic graphene oxide covalent organic framework composite for extraction of anabolic androgenic steroids and β-agonists in animal urine and tissue samples

In this work, a magnetic graphene oxide covalent organic framework composite was synthesized and utilized as an effective sorbent for magnetic solid phase extraction. A comprehensive investigation was conducted to optimize the extraction conditions, including sample pH, sample volume, adsorbent amount, adsorption time, choice of elution solution, elution volume, elution time, and ionic strength. Subsequently, the developed extraction method was combined with liquid chromatography-tandem mass spectrometry for the detection of 7 anabolic androgenic steroids and 4 β-agonists. Under the optimized condition, the limits of detection for the 7 anabolic androgenic steroids and 4 β-agonists ranged from 0.5 to 2 ng L−1, with relative standard deviations varying from 4.18 % to 12.4 % at a concentration of 0.025 μg L−1. Calibration curves were established using the peak area of analytes at concentrations spanning 5–500 ng L−1, resulting in correlation coefficients within the range of 0.9949–0.9989. The enrichment factors achieved through magnetic solid phase extraction were found to be 91.4–97.3. The proposed method was successfully applied for the determination of target analytes in hog urine, bovine urine, beef, mutton, pig liver and pork powder samples. Ractopamine and clenbuterol were identified in the pork powder (quality control sample) at concentrations of 21.0 μg kg−1 and 42.6 μg kg−1, respectively. The proposed method is a sensitive, reliable, and convenient analytical method for anabolic androgenic steroids and β-agonists monitoring in animal urine and tissues.

Development of Safe Blood Transfusion Self‐Efficacy Scale for nurses: Validity and reliability study

AimThe aim of the study is to develop a scale to measure nurses' self-efficacy levels regarding safe blood transfusion practice. MethodsThis study, applied in methodological design, was conducted in a public university hospital in Istanbul between March-April 2021. The sample included 372 nurses. A draft form consisting of 75 items was prepared. Content validity, construct validity, distinctiveness, internal consistency reliability, two-half test reliability, test-retest, and item analysis methods were used to determine the psychometric properties of the scale. ResultsThe exploratory factor analysis showed that the scale had a four-factor structure that explained 71.36 % of the total variance. The factor loads of 49 items were found to vary between 0.50 and 0.92. The item-total correlations were found to be between 0.55 and 0.92. The Cronbach's alpha value for the whole scale was 0.96. ConclusionThe results of the analysis show the items constituting Safe Blood and Blood Products Transfusion Self-Efficacy Scale have validity and reliability criteria that can measure the nurses' self-efficacy levels regarding safe blood transfusion practice.

Simultaneous determination of β-agonists in pork samples using liquid chromatography-tandem mass spectrometry

The misuse of β-agonists has been reported to be a potential risk to public health and is forbidden in many countries. The present study aimed to develop a simple, rapid, and reliable method for the multi-residue analysis of β-agonists including salbutamol, clenbuterol, and ractopamine in pork samples using liquid chromatography/tandem mass spectrometry (LC-MS/MS). In the proposed method, the pretreatment procedure was shortened to reduce time-consuming and chemical usage was minimized to decrease costconsuming. Based on the LC-MS/MS experiments, linearities obtained ranged from 0.1–2 µg/kg for clenbuterol and 1–20 µg/kg for salbutamol and ractopamine, with a correlation coefficient of R2 value of &gt;0.998. The average recoveries of clenbuterol, salbutamol, and ractopamine were 105.3%, 101.2%, and 99.4%, respectively. The limits of quantification for clenbuterol, salbutamol, and ractopamine were 0.10, 0.54, and 0.63 µg/kg, respectively. These results suggested that this proposed method can be used for rapid and sensitive detection of β-agonists in pork with a simple treatment of samples and minimized solvent usage.

A Reliability-Based Design Approach for the Flexural Resistance of Compression Yielded Fibre-Reinforced Polymer (FRP)-Reinforced Concrete Beams

Fibre-reinforced polymer (FRP) reinforcement has been employed as an alternative to conventional steel reinforcement in concrete structures, which is attributed to its excellent strength and corrosion resistance. However, one drawback is that FRP reinforcements are brittle and affect the ductility of concrete structures. One of the recent effective techniques proposed to overcome ductility issues is the compression yielding (CY) concept. The CY mechanism allows the structure to fail differently than the conventional FRP-reinforced concrete structure. Thus, the existing design recommendations as per the current codes for FRP-reinforced concrete structures are not appropriate. Hence, reliability studies are crucial for the development of a functional CY beam design in order to emphasise the structure’s lifetime performance and to guarantee safety requirements. In this study, a reliability-based design approach is developed for a compression-yielded FRP-reinforced concrete beam (CY beam) using load and resistance factor design (LRFD). Firstly, the flexural failure modes of CY beams are discussed. The uncertainties involved in the development of the probabilistic model for the CY beam are defined. A case study is consequently conducted for a CY beam with random variables that are associated with the statistical characteristics of material properties and load. The reliability analysis method employed in this research is the Hasofer–Lind method. The results suggest the importance of choosing appropriate design variables and stochastic parameters for CY blocks that contribute to a higher level of reliability. The reliability index and resistance factors of a CY beam are then evaluated using the Monte Carlo Simulation computational method. The reliability index value of 3.336 is obtained from the simulation, which indicates that the CY beam demonstrates ductile behaviour. The results not only demonstrate ductile behaviour but also contribute to a possible reduction in material costs and a substantial safety margin. When compared to conventional FRP-reinforced concrete beams, for different load ratios, CY beams showed higher resistance and better reliability levels.

Literature Review on Analytical Method Development and Validation of Alfuzosin Hydrochloride

Alfuzosin hydrochloride, an alpha-adrenergic blocker, is extensively used in the treatment of benign prostatic hyperplasia. Robust and reliable analytical methods are indispensable for quantifying alfuzosin hydrochloride in various matrices, ensuring the quality, efficacy, and safety of the drug. Recent advancements in analytical method development and validation for alfuzosin hydrochloride encompass a spectrum of techniques, including UV spectrometry, RP-HPLC, HPTLC, UPLC, and voltammetry. Optimization of chromatographic conditions, such as mobile phase composition, column selection, and detection wavelength, is crucial for achieving optimal separation and quantification of the drug. Method validation parameters, including accuracy, precision, linearity, and robustness, are assessed in accordance with ICH guidelines. These analytical methods find application in the determination of alfuzosin hydrochloride in bulk drugs, pharmaceutical formulations, and biological matrices. Stability-indicating methods are developed to evaluate drug stability under various stress conditions. The current state of analytical method development and validation for alfuzosin hydrochloride facilitates the selection of suitable analytical techniques for routine quality control and research purposes

PENGARUH HEDONIC CONSUMPTION, LINGKUNGAN TOKO DAN POTONGAN HARGA TERHADAP IMPULSE BUYING DENGAN EMOSI POSITIF SEBAGAI VARIABEL INTERVENING PADA D’CHICKEN SAMBAL BAKAR SITUBONDO

The culinary business is one of the businesses booming and has quite a large development potential. This is illustrated by the number of culinary entrepreneurs who are successful in making profits in their businesses. The aim of this research is to analyze and test the influence of Hedonic Consumption, Store Environment, and Price Discounts on Impulse Buying with Positive Emotions as an Intervening Variable. The population in this research is consumers of D'Chicken Sambal Bakar Situbondo. The sampling technique in this research is simple random sampling. Data analysis and hypothesis testing in this research used the advanced Partial Least Square - Structural Equation Model (PLS-SEM), a robust and reliable method for complex data analysis. The results of the direct influence hypothesis test using the Smart PLS 3.0 application reveal significant insights. Hedonic consumption has a significant positive effect on positive emotions. The store environment, while not significant, has a positive effect on positive emotions. Price discounts have a significant positive effect on positive emotions. Hedonic consumption also has a significant positive effect on impulse buying. The store environment, while not significant, has a positive effect on impulse buying. Price cuts have a significant positive effect on impulse buying. Positive emotions, while not significant, have a positive effect on impulse buying. The indirect influence hypothesis test results show that Hedonic consumption, while not significant, has a positive effect on impulse buying through positive emotions. The store environment, while not significant, has a positive effect on impulse buying through positive emotions. Price discounts, while not significant, have a positive effect on impulse buying through positive emotions.

Estimating the frequency of traffic overloading on road bridges

Load limits, which appear to be routinely exceeded by trucks, occasionally result in road bridge failures. Therefore, predicting failures is crucial for safeguarding road safety. Past studies have largely focused on forecasting bridge failure event probability using the reliability analysis method, whilst occasionally accounting for vehicular overloading effects. Only recently, a study has investigated design traffic overloading event frequency using generalised linear regression models (GLRMs), including a power component and negative binomial regressions (NBRs). However, as far as the authors know, artificial neural network models (ANNMs) have never been applied to this field. This paper is an attempt to fill in these gaps. First a frequency-based metric of traffic overloading was adopted as a driver of failure probability. Second, two alternative ‘frequency’ models were specified, calibrated, and validated. The former was based on a GLRM, the latter on ANNMs. Then, these models were compared using regression plots (RPs), measures of errors (MoEs) and the ratio between the number of observed vs predicted design load overcoming events to evaluate their performance. The models analysed more than 2 million weigh-in-motion (WIM) data records from a pilot station on a bridge on a heavily used ring road in Brescia (Italy). Results showed that ANNMs outperformed GLRMs. ANNMs have a higher correlation coefficient (between predicted and target frequencies), lower MoEs, and a closer-to-unity ratio (between predicted and target frequencies). These findings may increase prediction accuracy of design traffic overloading events and give road authorities more effective traffic management to protect bridges from load hazards.

Determination of volatile organic compounds (VOCs) in indoor work environments by solid phase microextraction-gas chromatography-mass spectrometry

Volatile organic compounds (VOCs) are continuously emitted into the atmosphere from natural and anthropogenic sources and rapidly spread from the atmosphere to different environments. A large group of VOCs has been included in the class of air pollutants; therefore, their determination and monitoring using reliable and sensitive analytical methods represents a key aspect of health risk assessment. In this work, an untargeted approach is proposed for the evaluation of the exposure to volatile organic compounds of workers in an engine manufacturing plant by GC–MS measurements, coupled with solid-phase microextraction (SPME). The analytical procedure was optimized in terms of SPME fiber, adsorption time, desorption time, and temperature gradient of the chromatographic run. For the microextraction of VOCs, the SPME fibers were exposed to the air in two different zones of the manufacturing factory, i.e., in the mixing painting chamber and the engine painting area. Moreover, the sampling was carried out with the painting system active and running (system on) and with the painting system switched off (system off). Overall, 212 compounds were identified, but only 17 were always present in both zones (mixing painting chamber and engine painting area), regardless of system conditions (on or off). Finally, a semi-quantitative evaluation was performed considering the peak area value of the potentially most toxic compounds by multivariate data analyses.

Accounting for dependencies among performance shaping factors in SPAR-H using a regularized autoencoder and WINGS-AISM

The standardized plant analysis risk human reliability analysis (SPAR-H) method is widely used for human reliability analysis to adjust the nominal human error probability (HEP) by assigning different multipliers to the performance shaping factors (PSFs). Nevertheless, SPAR-H suffers from assuming PSFs to be independent without considering any overlaps and dependencies. Therefore, this study introduces a new systematic method to analyze the relationships among the PSFs in SPAR-H qualitatively and quantitatively to obtain more reasonable HEP estimation results. The proposed method comprises three primary aspects: 1) a regularized autoencoder for the denoising and feature extraction of expert evaluation results, 2) the weighted influence non-linear gauge system-based adversarial interpretive structure modeling (WINGS-AISM) method to analyze the relationships among the PSFs and construct their causal hierarchy, and 3) a new relative weighting system to modify the PSF multipliers based on this hierarchy. The results of experiments comparing the proposed method with conventional methods highlight that our method effectively reduces the double counting of overlapping PSFs in SPAR-H, providing more reasonable and accurate HEP estimation results.

A structural reliability analysis method under non-parameterized P-box based on double-loop deep learning models

A structural reliability analysis method under non-parameterized P-box based on double-loop deep learning models

Hybrid reliability analysis method for systems with random and non-parameterized p-boxes based on weight coefficients

This paper establishes a hybrid variable system failure probability optimization model based on sampling methods and weighting coefficients. By introducing auxiliary input variables, important sampling functions, and p-box, failure samples are mapped from the random variable space to the p-box variable space. The new weight coefficients are constructed, including important sampling weights and interval weights. Combining discretization methods and Monte Carlo simulation (MCS), the interval weights are transformed into variables, and constraints conforming to the p-box variable distribution are constructed. After calculating the weighting coefficients for all failure samples, the new failure probability optimization model is built. This model is independent of the performance functions and does not involve cyclic optimization, with computational complexity only related to the dimensions. Six cases are used for method comparison, validating that the new method exhibits higher efficiency and accuracy.

Matrix-assisted laser desorption/ionization mass spectrometry for the rapid and high throughput analysis of betaine and trigonelline in Lycium chinense Mill. and trigonelline in coffee

A rapid, simple, effective, and green method for the determination of betaine and trigonelline from Lycium chinense Mill. (LCM) and the quantification of the trigonelline in coffee was proposed and validated by matrix-assisted laser desorption ionization time-of-flight mass spectrometric (MALDI-TOF MS) detection. Due to without chromatographic separation, the method greatly shortened the detection time. The detection of betaine and trigonelline concentration showed good linearity in the range of 1–100 μg/mL and 0.01–100 μg/mL, with correlation coefficients r2 = 0.9962 and 0.9946, respectively. The good reproducibility and reliability of the method were demonstrated by excellent intraday and interday precisions with RSD <8.3%, and the recovery of betaine and trigonelline ranged from 92.2% to 116.0%. Analysis of LCM and coffee extracts (raw, light-roasted, and dark-roasted coffee beans) gave results in agreement with the literature. The method appeared as a fast and reliable alternative method for routine Lycium chinense and coffee analysis.

An efficient system reliability analysis method for flap mechanism under random-interval hybrid uncertainties

An efficient system reliability analysis method for flap mechanism under random-interval hybrid uncertainties

Discriminating Craft and Industrial Beers by Untargeted Gas Chromatography-Mass Spectrometry (GC-MS) Metabolomics Combined with Multivariate Statistical Analysis

Developing robust analytical methodologies for the discrimination of craft and industrial beer presents a significant yet formidable challenge. The identification and differentiation of products necessitate the utilization of reliable analytical methods, which are indispensable for ensuring market stability and safeguarding consumer interests. In this study, representative samples with a significant market share were selected for headspace solid-phase microextraction coupled with gas chromatography-mass spectrometry (HS-SPME-GC-MS) analysis to compare the volatile substance characteristics of craft and industrial beers. The findings suggested that alcohols and esters constituted the primary volatiles. Craft beer exhibited a higher abundance of both types and concentrations compared to industrial beer. Subsequently, unsupervised and supervised chemometric techniques were employed for further analysis of the data matrix. An orthogonal partial least squares discriminant analysis (OPLS-DA) method was established to effectively discriminate between industrial and craft beers, enabling the identification of characteristic volatile markers. The untargeted GC-MS metabolomics coupled with multivariate statistical analysis holds promise for the authentication of craft and industrial beers.

A point mapping strategy-based sparse grid integration method for statistical moments estimation and structural reliability analysis

The high-order moment-based method is an attractive non-intrusive approach for structural reliability analysis, while it remains a challenge to strike a balance between accuracy, efficiency, and versatility in estimating statistical moments of performance functions. In this paper, a point mapping strategy-based sparse grid integration (SGI) method is proposed for statistical moment estimation and structural reliability analysis, with improved efficiency while ensuring the accuracy. Firstly, a point mapping strategy (PMS) is developed based on the adaptive high dimensional model representation (HDMR), by which the traditional SGI is reconstructed as a sum of multiple integrations over the sparse grid point sets with different effective dimensions. With this new form of SGI, the estimation of statistical moments only requires the evaluation of the performance function at a small number of necessary reference points, once the effects of some interaction terms are negligible. The validity and feasibility of the proposed PMS-based SGI method for statistical moments estimation are verified by several numerical examples. Then, the proposed method is applied to structural reliability analysis with the aid of an improved maximum entropy method. Two engineering examples are investigated, including the strength failure problem of tubular tower K-shape connection joints and the fatigue failure problem of a marine riser resulting from the vortex-induced vibration (VIV). The results indicate that the proposed method provides a better trade-off between accuracy and efficiency in evaluating the first four order statistical moments and structural failure probability compared to conventional methods.

Analysis method of point defects evolution in fused silica under multi-pulse nanosecond laser irradiation.

Although no optically visible damage is produced in the fused silica under laser irradiation below its laser-induced damage threshold (LIDT), defect proliferation may occur due to the evolution of its internal atomic structure. The escalation in defect content leads to heightened absorption, and resulting in the degradation of the optical performance of the optics. In recent decades, there have been a lot of experimental studies on laser-induced damage and laser conditioning, but there is still a great lack of in-depth understanding and theoretical analysis of the evolution process of point defects in fused silica. In this study, the emphasis is on the evolution of point defects and fatigue damage in fused silica under multi-pulse nanosecond laser irradiation. To address this, a point-defect evolution model is developed, and the coupled evolution law of temperature and defect during laser irradiation is derived by integrating it with a numerical model. The results demonstrate that the model effectively predicts the defect evolution of fused silica under laser irradiation and facilitates the prediction of fatigue damage. It is revealed that the rate of defect evolution in fused silica is more influenced by temperature than stress, and a temperature threshold can be used to judge the condition of damage occurrence. Furthermore, through an analysis of the effect of laser fluence on defect relaxation rate, a defect relaxation method employing variable laser fluence was proposed. This study provides a reliable theoretical analysis method for understanding the fatigue damage induced by multi-pulse laser irradiation in fused silica and offers a new perspective for the annealing treatment of point defects in fused silica.