Reliability Analysis Techniques Research Articles

QuEChERS methodology and Gas Chromatography-mass spectrometry for detection of 345 pesticide residues in Egyptian poultry feed: a comprehensive study

Animal feed significantly influences the content and quality of animal products consumed by humans and plays a crucial role in the food chain. To simultaneously analyze 345 pesticide residues in poultry feed, a modified Quick, Easy, Cheap, Effective, Rugged, and Safe (QuEChERS) method was developed, utilizing gas chromatography-tandem mass spectrometry (GC-MS/MS). Validation studies were conducted with spiked pesticide standards, and commercial poultry feed samples were analyzed. The linearity of the QuEChERS method was evaluated, showing high coefficients of determination (R 2 ≥0.995) for all analytes. The method demonstrated excellent linearity (R 2 ≥0.995), recovery rates (86.8–109.2%), and precision (RSD ≤14.8%), meeting SANTE criteria. Limits of detection (LOD) ranged from 0.5 to 5 ng/kg and limits of quantification (LOQ) from 1 to 10 ng/kg, enhancing the method’s reliability. Analysis of the poultry feed revealed that 15 out of the 345 pesticide residues were detected in the samples, with concentrations ranging from 2.1 to 120 µg/kg. The detected pesticide categories included insecticides, fungicides, plant growth regulators, and herbicides, in that order. In conclusion, the QuEChERS method combined with the GC-MS/MS approach provides a reliable technique for the multi-residue analysis of pesticides in complex feed matrices.

RFOR-DQHFEM: Hybrid relaxed first-Order reliability and differential quadrature hierarchical finite element method for multi-physics reliability analysis of conical shells

In this current work, a hybrid reliability analysis and theoretical frequency technique are suggested for the reliability response of conical shells. Two levels of analyses are proposed as the main loop of the reliability method for finding the failure probability and the second level applied in the main loop for giving the performance function of frequency applied in conical shell structures with multi-physics vibration analysis. A dynamical adjusting procedure is proposed for computing the relaxed factor using the enough descent condition inside the reliability method. The superior convergence rate is considered for selecting the relaxed factor of the proposed first-order reliability method named RFORM. An elastic-electro-mechanical model based on the Higher-Order Shear Deformation Theory (HSDT) is extended for frequency analysis of conical shells. The innovative numerical procedure named Differential Quadrature Hierarchical Finite Element Method (DQHFEM) as a robust framework for giving the vibration behavior of studied mechanical structures is applied for solving motion equations. The developing DQHFEM and RFORM are applied for the laminated, nanocomposite, and piezoelectric conical shell structures with multi-source uncertainties. Increasing the volume percentage of nanoparticles from 0% to 10% significantly enhances the reliability index, with carbon nanoparticles showing a 132% increase, silica nanoparticles showing a 97% increase, and other nanoparticles showing an approximate 40% increase. Also, as moisture content increases from 0% to 30%, the reliability index for a thickness-to-large-radius ratio of 0.2 drops by about five times. Excessive moisture levels (above 20%) result in a negative reliability index, indicating a hazardous condition.

Enhanced colorimetric analysis substrate based on graphene oxide open-close actuator

Traditional colorimetric sensors often suffer from low color resolution and insufficient sensitivity. Therefore, for colorimetric detection to serve as a reliable quantitative analysis technique, an enhanced substrate with high sensitivity and repeatability is required. This strategy employs an interfacial network constructed between two-dimensional graphene oxide (GO) lamellar structures to create a substrate for colorimetric analysis. Under normal conditions, the newly synthesized GO inhibits oxidative activity due to van der Waals forces between the lamellar interfaces, resulting in the GO actuator being in the closed state. During Ag+ colorimetric analysis, the GO interlayer adsorption of silver nanoparticles (AgNPs) disrupts the interlayer van der Waals forces, releasing its oxidase activity and significantly enhancing Ag+ oxidation. The resulting actuator exhibits a faster driving speed (0.045 μM/s), excellent sensitivity, and stability. Additionally, the reducing properties of dopamine (DA) can be utilized to encapsulate the GO actuator with polydopamine, closing the GO actuator. Similarly, the reducing property of ascorbic acid (AA) can be utilized to restore the GO actuator to a closed state. These distinct reduction mechanisms allow for an effective visual distinction between AA and DA. With these advantages, the actuator can be developed into simple analytical tools (such as Ag+ colorimetric test strips and DA and AA test kits), demonstrating significant potential for practical application.

Seismic reliability analysis using Subset Simulation enhanced with an explorative adaptive conditional sampling algorithm

Reliability analysis of structures under earthquake loading represents a significant engineering challenge. This is due to the required and rather numerically involving non-linear dynamic analysis, the large computational burden when targeting small failure probabilities, and the synthetic earthquake model representation that may contain thousands of random variables. Subset Simulation is an efficient reliability analysis technique that can handle the challenge of a high-dimensional space with a reduced number of structural analysis calls compared to crude Monte Carlo Simulation. In this contribution, firstly, we investigate the conditions for which Subset Simulation performs efficiently. Thereafter we propose an enhancement to the existing Subset Simulation schemes that shows significant potentials for enhancing the strategy for the starting of the Markov Chain Monte Carlo simulations whenever a new level is reached in the Subset Simulation. Finally, the information gathered from the simulations is investigated to verify that Subset Simulation provides meaningful results from a physical point of view.

Adaptation of trend analysis over a small-scale region for assessing climate change parameters

ABSTRACT Several natural and anthropogenic activities mainly escalate the demand for water and food and develop implications for their availability. Hence, it is necessary to analyse the natural and man-made changes for managing resources precisely. This study focuses on assessing the trends of climatic factors such as rainfall and temperature using the Mann–Kendall (MK) test and innovative trend analysis (ITA) for the agricultural cropping seasons. The findings revealed that a statistically insignificant increasing trend in rainfall is observed for all three cropping seasons (i.e.) Kuruvai/Samba (June–September), Thaladi/Navarai (October–January), and Sornavari (February–May), while the mean temperature shows a significantly increasing trend for all three cropping seasons. The results obtained using the MK test are compared with those of the ITA test to identify the reliable trend technique for climatic factor analysis of small regions. To manage the impacts of climate change and develop sustainable conditions in agriculture, this micro-level study assists the decision makers to prefer suitable trend analysis techniques and helps in identifying the climate change adaptation strategies for small regions. Also, this study is helpful for rural farmers to increase their adaptability conditions with regard to the climate change impacts as they are the frontline victims of climate change.

Analisis Faktor Penyebab Keterlambatan Proyek “XYZ”

A project tends to experience delays if planning and control are not carried out properly. Various things can happen in a construction project that can cause an increase in processing time so that project completion is delayed. The purpose of this research is to identify and classify the factors that cause delays in the "XYZ" project. This data processing uses the help of the SPSS (Statistical Product and Service Solution) computer program version 23.00. Data analysis techniques using validity, reliability and factor analysis techniques. The results of this study obtained 3 new factors from the extraction results of factor analysis, namely: Lack of Expertise and Slow Material Mobilization (Xb1), Lack of Planning and Inadequate Quality of Workforce (Xb2), Changes in Scope and Work Documents (Xb3).

Estimating the impact of label design on reducing the risk of medication errors by applying HEART in drug administration

Medication errors are one of the biggest problems in healthcare. The medicines’ poor labelling design (i.e. look-alike labels) is a well-recognised risk for potential confusion, wrong administration, and patient damage. Human factors and ergonomics (HFE) encourages the human-centred design of system elements, which might reduce medication errors and improve people’s well-being and system performance.ObjectiveThe aim of the present study is twofold: (i) to use a human reliability analysis technique to evaluate a medication administration task within a simulated scenario of a neonatal intensive care unit (NICU) and (ii) to estimate the impact of a human-centred design (HCD) label in medication administration compared to a look-alike (LA) label.MethodThis paper used a modified version of the human error assessment and reduction technique (HEART) to analyse a medication administration task in a simulated NICU scenario. The modified technique involved expert nurses quantifying the likelihood of unreliability of a task and rating the conditions, including medicine labels, which most affect the successful completion of the task.ResultsFindings suggest that error producing conditions (EPCs), such as a shortage of time available for error detection and correction, no independent checking of output, and distractions, might increase human error probability (HEP) in administering medications. Results also showed that the assessed HEP and the relative percentage of contribution to unreliability reduced by more than 40% when the HCD label was evaluated compared to the LA label.ConclusionIncluding labelling design based on HFE might help increase human reliability when administering medications under critical conditions.

A deep learning fusion model for accurate classification of brain tumours in Magnetic Resonance images

AbstractDetecting brain tumours is complex due to the natural variation in their location, shape, and intensity in images. While having accurate detection and segmentation of brain tumours would be beneficial, current methods still need to solve this problem despite the numerous available approaches. Precise analysis of Magnetic Resonance Imaging (MRI) is crucial for detecting, segmenting, and classifying brain tumours in medical diagnostics. Magnetic Resonance Imaging is a vital component in medical diagnosis, and it requires precise, efficient, careful, efficient, and reliable image analysis techniques. The authors developed a Deep Learning (DL) fusion model to classify brain tumours reliably. Deep Learning models require large amounts of training data to achieve good results, so the researchers utilised data augmentation techniques to increase the dataset size for training models. VGG16, ResNet50, and convolutional deep belief networks networks extracted deep features from MRI images. Softmax was used as the classifier, and the training set was supplemented with intentionally created MRI images of brain tumours in addition to the genuine ones. The features of two DL models were combined in the proposed model to generate a fusion model, which significantly increased classification accuracy. An openly accessible dataset from the internet was used to test the model's performance, and the experimental results showed that the proposed fusion model achieved a classification accuracy of 98.98%. Finally, the results were compared with existing methods, and the proposed model outperformed them significantly.

Differential Glycoform Analysis of MUC1 Derived from Biological Specimens Using an Antibody-Overlay Lectin Microarray.

The association between altered glycosylation of MUC1 and various disease events has sparked significant interest. However, analytical technologies to investigate the disease-related glycoforms of endogenous MUC1 in blood and tissue specimens are limited. Therefore, we devised a reliable technique for differential analysis of endogenous MUC1 glycoforms based on an antibody-assisted lectin microarray. Its highly sensitive detection aids in analyzing soluble MUC1 from relatively small amounts of serum via a simple enrichment process. Micro-/macro-dissection of the MUC1-positive region is combined with glycoform analysis of the membrane-tethered MUC1. Thus, we have optimized the protocol for sample qualification using immunohistochemistry, sample pretreatment for tissue sections, protein extraction, purification via immunoprecipitation, and the antibody-overlay lectin microarray, which are sequentially essential for differential glycoform analysis of endogenous MUC1.

Clinical application of single nucleotide polymorphism microarray analysis in pregnancy loss in Northwest China.

Background: Spontaneous abortion is the most common complication of early pregnancy. In this study, we aim to investigate the clinical application value of genetic diagnosis using single nucleotide polymorphism (SNP) microarray analysis on the products of conception and to characterize the types of genetic abnormalities and their prevalence in pregnancy loss in Northwest China. Methods: Over 48months, we selected 652 products of conception, which included chorionic villi, fetal tissues, germ cell samples, amniotic fluid samples, cord blood samples, and a cardiac blood sample. We analyzed the distribution of chromosomal abnormalities leading to fetal arrest or abortion using SNP array. The patients were then categorized divided into groups based on maternal age, gestational age, number of miscarriages, and maternal ethnic background. The incidences of various chromosomal abnormalities in each group were compared. Results: Of the 652 cases, 314 (48.16%) exhibited chromosomal abnormalities. These included 286 cases with numerical chromosomal abnormalities, 24 cases with copy number variation, and four cases with loss of heterozygosity. Among them, there were 203 trisomy cases, 55 monosomy cases, and 28 polyploidy cases. In the subgroup analysis, significant differences were found in the frequency of numerical chromosomal abnormalities and copy number variation between the advanced and younger maternal age group as well as between the early and late abortion groups. Furthermore, we identified significant differences in the frequency of numerical chromosomal abnormalities between the first spontaneous abortion and recurrent miscarriage groups. However, there were no significant differences in the frequency of numerical chromosomal abnormalities between the Han and Uighur groups. Conclusion: Our research highlights chromosomal abnormalities as the primary cause of spontaneous abortion, with a higher incidence in early pregnancy and among women of advanced age. The use of SNP array analysis emerges as an effective and reliable technique for chromosome analysis in aborted fetuses. This method offers a comprehensive and dependable genetic investigation into the etiology of miscarriage, establishing itself as a valuable routine selection for genetic analysis in cases of natural abortions.

Study of the Reliability for the Leakage Mitigation Methods using FinFETs

Introduction: In the very large-scale integration (VLSI) industry, scaling plays an important role in providing compact size and high-speed digital circuits. The major drawbacks faced by logic circuits are power dissipation and process, voltage, and temperature (PVT) variations. In the VLSI industry, the prediction of variability tolerance capability is mandatory to know the future performance of the circuits. The impact of PVT variation is large in nanoscale logic circuits and it has the power to alter the output characteristics of any logic circuit. The reasons that cause PVT variations are manufacturing defects, environmental conditions, and mishandling issues. Aims and Objective: This paper aims to discuss the process variations and briefly describe the previous work related to variability and various factors involved in PVT simulations. It also provides the idea of Monte-Carlo simulation in the Cadence Virtuoso tool. Methods: In this paper, the impact of PVT variations on different fin-shaped field effect transistor (FinFET) circuits was evaluated using the Cadence Virtuoso tool. Monte-Carlo simulation was performed on various leakage reduction techniques for the domino logic with the help of a multi-gate predictive technology model (PTM) FinFET at a 16nm technology node. Results: The footer-less domino logic (FLDL) circuit is designed and simulated using different leakage reduction techniques for reliability analysis. Conclusion: Cascaded leakage control transistor (CLCT) approach shows 81.75%, 67.83%, and 51.25% less statistical mean value for power dissipation as compared to conventional, on-off logic (ONOFIC), and alternative ONOFIC approaches in the case of FLDL OR2 logic circuit, respectively.

Bitcoin in Conventional Markets: A Study on Blockchain-Induced Reliability, Investment Slopes, Financial and Accounting Aspects

Based on traditional market theory, this study aims to investigate whether conventional market investment slopes affect the unconventional Bitcoin market, considering both normal conditions and crises. This study examines three main characteristics of the economy-intensive blockchain system, namely reliability, investment slopes, financial and accounting aspects that ultimately determine the confidence in the choice to invest in cryptocurrency. The analysis focuses on the study of the Bitcoin (BTC) investment slopes during January 2014–April 2023, considering the specifics of blockchain technology and the inferences of ethics, reliability and real-world data on investment Tassets in the context of conventional regulated markets. Using an econometric model that incorporates reliability analysis techniques, factorial comparisons and multinomial regression using economic crisis periods as a dummy variable, this study reveals important findings for practical and academic purposes. The results of this study show that the investment slopes of Bitcoin (BTC) are mostly predictable for downward trends, when statistically significant correlations with the investment slopes of conventional stock markets are observable. The moderate or high increase in performance slopes pose several challenges for predictive analysis, as they are influenced by other factors than conventional regulated market performance inferences. The results of this study are of intense interest to researchers and investors alike, as they demonstrate that investment slopes analysis sheds light on the intricacies of investment decisions, allowing a comprehensive assessment of both conventional markets and Bitcoin transactions.

Validation of Sience, Technology, Engineering, and Matemathic Based Diagnostik Assessment on Natural Resource Material for Phase B Elementary School Students using Rasch Model

One of the ways to design learning the implementation of the independent curriculum is assessment diagnostic. This study aims to develop STEM-based questions to measure students' critical thinking skills. The questions are used for the initial assessment of science subject material "Natural Resources" phase B elementary school. The type of research used is research and development (RD) with the ADDIE development model, consisting of the analysis, design, developmen, implementation, and evaluation stages. The school students in the city of Semarang with a total of 258 students consisting of 126 male students and 132 female students, using convenience sampling. The items' validation and reliability analysis techniques used the Aiken index and the RASCH model. The results showed that all items had a high validity category based on the following aspects: Outfit items, measure, test difficulty level, person and item reliability. There are several phase bias items, where students in phase C benefit more than students in phase B. The average respon ability is above the averange item difficulty level. The conclusion is that the items developed can be used as initial assessments in science subjects

FindVehicle and VehicleFinder: a NER dataset for natural language-based vehicle retrieval and a keyword-based cross-modal vehicle retrieval system

Natural language (NL) based vehicle retrieval is a task aiming to retrieve a vehicle that is most consistent with a given NL query from among all candidate vehicles. Because NL query can be easily obtained, such a task has a promising prospect in building an interactive intelligent traffic system (ITS). Current solutions mainly focus on extracting both text and image features and mapping them to the same latent space to compare the similarity. However, existing methods usually use dependency analysis or semantic role-labelling techniques to find keywords related to vehicle attributes. These techniques may require a lot of pre-processing and post-processing work, and also suffer from extracting the wrong keyword when the NL query is complex. To tackle these problems and simplify, we borrow the idea from named entity recognition (NER) and construct FindVehicle, a NER dataset in the traffic domain. It has 42.3k labelled NL descriptions of vehicle tracks, containing information such as the location, orientation, type and colour of the vehicle. FindVehicle also adopts both overlapping entities and fine-grained entities to meet further requirements. To verify its effectiveness, we propose a baseline NL-based vehicle retrieval model called VehicleFinder. Our experiment shows that by using text encoders pre-trained by FindVehicle, VehicleFinder achieves 87.7% precision and 89.4% recall when retrieving a target vehicle by text command on our homemade dataset based on UA-DETRAC [1]. From loading the command into VehicleFinder to identifying whether the target vehicle is consistent with the command, the time cost is 279.35 ms on one ARM v8.2 CPU and 93.72 ms on one RTX A4000 GPU, which is much faster than the Transformer-based system. The dataset is open-source via the link https://github.com/GuanRunwei/FindVehicle, and the implementation can be found via the link https://github.com/GuanRunwei/VehicleFinder-CTIM.

Dried blood spot-based host genome analysis technique targeting pathological associations with hepatitisB: Development and clinical application in the Cambodian population.

Reports of patients with hepatitisB have highlighted associations between polymorphisms in the human leukocyte antigen (HLA)-DPB1, CXCL13, and CXCR5 genes and disease pathology. Owing to its potential to contribute to the development of new diagnostic and therapeutic methods, we aimed to establish a reliable host genome analysis technique that can be used in countries with inadequate infrastructure. We compared multiple commercially available kits for dried blood spot (DBS)-based sample collection to develop a basic DBS-based host genome analysis technique. We then collected blood samples from Cambodian patients with hepatitis B and performed single-nucleotide polymorphism genotyping and HLA allele typing by the DBS system. We were able to perform single-nucleotide polymorphism genotyping and HLA allele typing with host DNA samples obtained using a combination of a HemaSpot™ filter paper-based device and a SMITEST® EX-R&D DNA extraction kit. The accuracy of genotyping using samples obtained by this method was not inferior to one using samples obtained by venipuncture. In the Cambodian population, significant associations of HLA-DPB1*04:01 with protection against chronic hepatitisB virus (HBV) infection, and HLA-DPB1*05:01 and HLA-DPB1*13:01 with susceptibility to chronic HBV infection were identified. Based on the DBS system, we clarified the associations of HLA-DPB1 alleles with chronic HBV infection in the Cambodian population for the first time. Because the DBS is a low-cost, durable, transportable, and easy-to-handle modality, genetic analysis based on the DBS system is a feasible strategy for obtaining a deeper understanding of HBV epidemiology, especially in middle- or low-income countries.

The Relationship between Job Stress and Employee Job Satisfaction : A Multivariate Analisis Approach in an Organizational Context

Significant interest and study have been focused on the connection between job stress and employee job satisfaction in organizational psychology. This study uses a multivariate analysis technique to examine the association between workplace stress and job satisfaction among Gen Z workers in Bandung, Indonesia. A cross-sectional survey of Gen Z employees from various firms in Bandung is used as part of the study's quantitative research design. Data will be gathered using a standardized questionnaire that evaluates job stress, job satisfaction, and potential moderating factors within the organizational context. The collected data will be examined using descriptive statistics, reliability analysis, and multivariate analytic techniques such multiple regression analysis, mediation analysis, and moderation analysis. The research's results and conclusions will advance our understanding of organizational management and employee well-being while shedding light on the relationship between Gen Z workers' job satisfaction and workplace stress. The study's ramifications will be examined, along with suggestions for how businesses may better control employee stress and boost job happiness among Bandung's Gen Z workforce.

Impact of ICT Proficiency on Teachers' Work Performance in Distance Learning

This study investigated the impact of Information and Communication Technology Proficiency (ICT-P) on teachers' work performance (WP) within distance learning contexts. Employing a quantitative design, a cross-sectional survey was administered to assess the ICT-P and WP of teachers working in distance learning institutes; Virtual University and Allama Iqbal Open University. Using stratified sampling, data was collected from 237 teachers through two online self-administered questionnaires, involving ICT-P and WP. Both questionnaires consist of a 6-point semantic differential scale. The ICT-P was framed around the Joint Information Systems Committee (JISC) model of digital literacy, examining constructs such as the use of ICT-P based devices, digital applications, software, services, and digital proficiency. The WP construct, sourced from an extensive literature review, is divided into seven primary areas. Data was analysed using descriptive statistics, reliability analysis, correlation, and regression techniques. The research found an affirmative impact of ICT Proficiency on teachers’ work performance.

A reliability analysis technique for complex systems with retaining fuzzy information

AbstractThis paper proposed a method for the reliability analysis of systems characterized by fuzzy failure probabilities and intricate failure behaviors, while retaining the fuzzy information throughout the analysis process. Specifically, we introduced a combinational modeling method that integrates generalized stochastic Petri nets (GSPN) and dynamic fault trees (DFT) to capture the dynamic failure behaviors and address the limitations of DFT modeling. This combinational approach is capable of capturing more sophisticated failure behaviors, such as competing failures and global failure propagation patterns. Furthermore, we propose an Monte Carlo technique based on endpoint sampling to enable quantitative analysis of GSPN‐based composite models, which preserves the fuzzy fault information of the system. Finally, we demonstrate the effectiveness of the proposed method through an example of a flight control system.

Integration of fuzzy reliability analysis and consequence simulation to conduct risk assessment

Losses of containment within the natural gas network, located in most populated areas, could cause environmental damage, injuries, or even death. Accordingly, it is pivotal to adopt proper approaches to assess and mitigate the risk arising from potential losses. Within this context, it is required to exploit solid reliability and consequence analysis techniques. To this end, this paper presents a methodology established on the integration of a Fuzzy Bayesian Network and consequence simulation. The Bayesian Network is more flexible and realistic than classic approaches because it can consider conditional probabilities and prior information. Furthermore, Leaky Noisy-OR Gates are exploited to allow an easier filling of the Conditional Probability Tables. This task is performed through expert elicitation, adopting Intuitionistic Fuzzy Set Theory and Similarity Aggregation Method. Finally, the severity analysis is performed via a software, named Safeti, which provides an accurate evaluation of the consequences. To show the applicability of the framework, a pressure regulator of a Natural Gas Regulating and Metering Station is considered as case study. The proposed approach can assist asset managers in evaluating the risk arising from the operations, and, accordingly, it can guide them in making maintenance-related decisions to assure the safety of the operations.

One-step multiplex analysis of breast cancer exosomes using an electrochemical strategy assisted by gold nanoparticles.

Exosomes, as a non-invasive biomarker, perform an important role in breast cancer screening and prognosis monitoring. However, establishing a simple, sensitive, and reliable exosome analysis technique remains challenging. Herein, a one-step multiplex analysis electrochemical aptasensor based on a multi-probe recognition strategy was constructed to analyze breast cancer exosomes. HER2-positive breast cancer cell (SK-BR-3) exosomes were selected as the model targets and three aptamers including CD63, HER2 and EpCAM aptamers were used as the capture units. Methylene blue (MB) functionalized HER2 aptamer and ferrocene (Fc) functionalized EpCAM aptamer, which were modified on gold nanoparticles (Au NPs), i.e. MB-HER2-Au NPs and Fc-EpCAM-Au NPs, were used as signal units. When the mixture of target exosomes, MB-HER2-Au NPs and Fc-EpCAM-Au NPs were added on the CD63 aptamer modified gold electrode, two Au NPs modified by MB and Fc could be specifically captured on the electrode by the recognition of three aptamers with target exosomes. Then one-step multiplex analysis of exosomes was achieved by detecting two independent electrochemical signals. This strategy can not only distinguish breast cancer exosomes from other exosomes (including normal exosomes and other tumor exosomes) but also HER2-positive breast cancer exosomes and HER2-negative breast cancer exosomes. Besides, it had high sensitivity and can detect SK-BR-3 exosomes with a concentration as low as 3.4×103 particles mL-1. Crucially, this method can be applicable to the examination of exosomes in complicated samples, which is anticipated to afford assistance for the screening and prognosis of breast cancer.