Characteristics Of Reliability Research Articles

Ru nanosheets with specific peroxidase activity for biosensor applications

Nanozymes displaying peroxidase-like activity are helpful for various biomedical applications. In this work, uniform triangular Ru nanosheets (NSs) prepared by a facile hydrothermal approach are first found to possess high performance and specific peroxidase (POD)-like activity which catalyzes the oxidation of colorless 3,3′,5,5′-tetramethylbenzidine (TMB) with H2O2 to blue oxidized TMB (oxTMB). On basis of their outstanding POD activity, we successfully developed two highly sensitive colorimetric methods for detections of ethanol (0.02–0.88 mM, R2 = 0.9909) and uric acid (UA) (3.91–500 μM, R2 = 0.9938) in serum under the optimal conditions. The detection limits were 0.011 mM and 3.05 μM for ethanol and UA, respectively. These Ru NSs-based biosensors possess prominent characteristics of simple operation, sensitivity, reliability and high selectivity, enabling them suitable for on-demand analysis.

Species-Selective Detection of Volatile Organic Compounds by Ionic Liquid-Based Electrolyte Using Electrochemical Methods.

The detection of volatile organic compounds (VOCs) is an important topic for environmental safety and public health. However, the current commercial VOC detectors suffer from cross-sensitivity and low reproducibility. In this work, we present species-selective detection for VOCs using an electrochemical cell based on ionic liquid (IL) electrolytes with features of high selectivity and reliability. The voltammograms measured with the IL-based electrolyte absorbing different VOCs exhibited species-selective features that were extracted and classified by linear discriminant analysis (LDA). The detection system could identify as many as four types of VOCs, including methanol, ethanol, acetone, formaldehyde, and additional water. A mixture of methanol and formaldehyde was detected as well. The sample required for the VOCs classification system was 50 μL, or 1.164 mmol, on average. The response time for each VOC measurement is as fast as 24 s. The volume of VOCs such as formaldehyde in solution could also be quantified by LDA and electrochemical impedance spectroscopy techniques, respectively. The system showed a tunable detection range for 1.6 and 16% (w/v) CH2O solution by adjusting the composition of the electrolyte. The limit of detection was as low as 1 μL. For the 1.6% CH2O solution, the linearity calibration range was determined to be from 5.30 to 53.00 μmol with a limit of detection at 0.53 μmol. The mechanisms for VOCs determination and quantification are also thoroughly discussed. It is expected that this work could provide a new insight into the concept of electrochemical detection of VOCs with machine learning analysis and be applied to both VOCs gas monitoring and fluid detection.

A Survey on In-Vehicle Time-Sensitive Networking

With the continued evolution of autonomous driving, the communication demand for in-vehicle networks has dramatically increased, and the traditional in-vehicle network solutions have become ineligible for the needs of new in-vehicle applications. Time-Sensitive Network(TSN) with the characteristics of determinism, low latency, high reliability, large bandwidth, and open industry standards, has been considered as the most promising technology for the next-generation in-vehicle networks(IVN). This paper summarizes the current state and methodologies of in-vehicle TSN research through the following five aspects: QoS(Quality of Service) strategy, reliability, clock synchronization, network planning, and network management. The corresponding research priorities and technical challenges are also addressed respectively.

PyNMME: A python toolkit to retrieve, calibrate and verify seasonal precipitation forecasts

The North American Multi-Model Ensemble (NMME) experiment assembles valuable ensemble forecasts from more than ten global climate models (GCMs). Focusing on environmental applications of NMME forecasts, this paper develops the pyNMME, a Python-based toolkit to implement data retrieval, forecast calibration and forecast verification. Specifically, the toolkit is composed of three modules that streamline the processes of retrieving the NMME data for locations and seasons of interest from high-dimensional datasets, calibrating raw GCM forecasts by the linear scaling, quantile mapping and Bernoulli-Gamma-Gaussian models and verifying predictive performances by twenty verification metrics and six types of diagnostic plots. The results show that the characteristics of bias, reliability and skill of raw forecasts vary across the globe and that the three calibration models in the pyNMME effectively improve raw forecasts to different extents. Overall, the pyNMME can serve as a useful tool to exploit valuable NMME precipitation forecasts for environmental modelling and management.

Fire Detection System and Spurious (False) Fire Warning Of the Aircraft - An Overview

New fire detection technologies are under evaluation for better aircraft safety and risk managements. The goal is to reduce the false alarm rate and to improve safety and reliability features of an aircraft. In this paper, an overview of different types of fire detection system of aircraft is given. Technology currently used for fire detection and protection are discussed. Also analysis and the probable causes of false fire warning of the aircraft are listed along with its future scope of fire protection system for aircraft are discussed.

Optimal Determination and Dynamic Control Analysis of the Graded and Staged Drought Limit Water Level of Typical Plateau Lakes

The technical research on determining the drought limit water level can be used as an important basis for starting the emergency response of drought resistance in the basin and guiding the drought resistance scheduling of water conservancy projects. When the concept of drought limit water level was first proposed, the main research object was reservoirs, and the method for determining the lake drought limit water level was not established. Referring to the calculation method of reservoir drought limit water level, the drought limit water level is used as a single warning indicator throughout the year, which lacks graded and staged standards, and also lacks rationality and effectiveness in practical application. Therefore, this article has improved the concept of lake drought limit water level (flow). Under different degrees of drought and water use patterns during the drought period, combined with the characteristics of lake water inflow, considering the factors such as ecology, water supply, and demand, lake inflow, evapotranspiration loss, a graded and staged standard of lake drought limit water level has been developed. For different types of lakes, a general method for determining the lake’s graded and staged drought limit water level has been established. The SCSSA-Elman neural network is used to construct the medium and long-term water inflow prediction model for lakes, and the calculation results of this model are used for the warning and dynamic control analysis of the lake drought limit water level. The application of this method has the characteristics of strong applicability and high reliability. Finally, the determination method and dynamic control method of the lake’s graded and staged drought limit water level have been successfully applied at Dianchi Lake in Yunnan.

A residual connection enabled deep neural network model for optic disk and optic cup segmentation for glaucoma diagnosis.

Glaucoma diagnosis at an early stage is vital for the timely initiation of its treatment for and preventing possible vision loss. For glaucoma diagnosis, an accurate estimation of the cup-to-disk ratio (CDR) is required. The current automatic CDR computation techniques attribute lower accuracy and higher complexity, which are important considerations for diagnostics system design to be used for such critical diagnoses. The current methods involve a deeper deep learning model, comprising a large number of parameters, which results in higher system complexity and training/testing time. To address these challenges, this paper proposes a Residual Connection (non-identity)-based Deep Neural Network (RC-DNN), which is based on non-identity residual connectivity for joint optic disk (OD) and optic cup (OC) detection. The proposed model is emboldened by efficient residual connectivity, which is beneficial in several ways. First, the model is efficient and can perform simultaneous segmentation of the OC and OD. Second, the efficient residual information flow permeates the vanishing gradient problem which results in faster converges of the model. Third, feature inspiration empowers the network to perform the segmentation with only a few network layers. We performed a comprehensive performance evaluation of the developed model based on its training in RIM-ONE and DRISHTIGS databases. For OC segmentation, for the images (test set) from {DRISHTI-GS and RIM-ONE} datasets, our proposed model achieves the dice coefficient, Jaccard coefficient, sensitivity, specificity, and balanced accuracy of {92.62, 86.52}, {86.87, 77.54}, {94.21, 95.36}, {99.83, 99.639}, and {94.2, 98.9}, respectively. These experimental results indicate that the developed model provides significant performance enhancement for joint OC and OD segmentation. Additionally, the reduced computational complexity based on reduced model parameters and higher segmentation accuracy provides the additional features of efficacy, robustness, and reliability of the developed model. These attributes of the developed model advocate for its deployment of population-scale glaucoma screening programs.

Miniature Temperature-Independent Curvature Sensor Based on a Phase-Shifted Long-Period Fiber Grating Using Deep Tapering

A miniature temperature-independent curvature sensor was proposed and prepared by using a phase-shifted long-period fiber grating (LPG) fabricated from deeply tapered conventional single-mode fibers (SMFs). As a typical example, a sensor made from a phase-shifted deeply tapered long-period grating (PS-DTLPG) with a length of 3.4 mm and a maximum curvature sensitivity of 12.05 nm/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> was successfully demonstrated. The proposed curvature sensor is of the temperature-independence and has features of high sensitivity, high reliability, miniature size and low cost, which could have potential applications in structural health monitoring, especially for the medical industry and shape reconstruction.

Enhancement of Si3N4 ceramics via evolution of grain interface between ZrO2 and Si3N4 under pressureless sintering

AbstractSilicon nitride (Si3N4) as a structural ceramic material, its strength, and toughness are the decisive characteristics of damage tolerance and reliability. In this work, Si3N4 ceramics is enhanced via evolution of grain interface between Si3N4 and ZrO2, which bending strength and fracture toughness reached 982.8 MPa and 9.81 MPa·m1/2, respectively. The interface evolution of the ZrO2 and Si3N4 grains were investigated via both first‐principles molecular dynamics simulation and experiment. The Si3N4/ZrO2 interface structure was observed by high‐resolution transmission electron microscopy, include: (i) glass phase film, (ii) Zr3N4 film. First‐principles molecular dynamics simulation reported here provide an atomic‐level description of the formation mechanisms of the interface structure. The Si‐O bond and Zr‐N bond was formed to provide the Si3N4/ZrO2 interface bond. Moreover, the embedded‐like composite structure between Si3N4 and ZrO2 was formed during the sintering process, which can deflect cracks and result in an increase in the fracture energy. Due to the strength difference of Si3N4 and ZrO2, cracks tend to propagate through the grains of ZrO2 and deflects when it encounters the rod‐like Si3N4 grains. In the same time, the ZrO2 plays a role with respect to refinement of the β‐Si3N4 grain size and decrease of the glass phase content.

Studying the relationship of reliability and success in the educational and professional activities of college students

The work is devoted to the analysis of the features of reliability and effectiveness of educational and professional activities of college students, their interrelations, identified on the basis of empirical methods and mathematical and statistical analysis. Reliability as a parameter of activity efficiency is defined by the author as an important integral characteristic of the subject. The author of the article points out the importance of the formation and development of this characteristic in the subject at the stage of mastering the future profession – during college. The typologisation of students according to the criteria – efficiency and stability in the conditions of the educational environment of the college – is proposed. On the basis of this typologisation, correlation relationships are presented and described, which allow identifying the specifics of the manifestation of reliability as a parameter of the effectiveness of educational and professional activities for 4 samples of students differentially. The article describes the results of correlation analysis between reliability and procedural, effective parameters of educational and professional activity of college students. To assess the effectiveness of the educational and professional activities of college students, taking into account the selected criteria, the method of expert assessments was carried out during the study. The presented results are characterised by practical significance, since they allow organising psychological support programmes for college students at the initial stages of their professionalisation.

Research progress and maturity assessment of continuous manufacturing of traditional Chinese medicine

The pharmaceutical manufacturing model is gradually changing from intermittent manufacturing to continuous manufacturing and intelligent manufacturing. This paper briefly reviewed the supervision and research progress in continuous pharmaceutical manufacturing in China and abroad and described the definition and advantages of continuous pharmaceutical manufacturing. The continuous manufacturing of traditional Chinese medicine(TCM) at the current stage was summarized in the following three terms: the enhancement of the continuity of intermittent manufacturing operations, the integration of continuous equipment to improve physical continuity between units, and the application of advanced process control strategies to improve process continuity. To achieve continuous manufacturing of TCM, the corresponding key technologies, such as material property characterization, process modeling and simulation, process analysis technology, and system integration, were analyzed from the process and equipment, respectively. It was proposed that the continuous manufacturing equipment system should have the characteristics of high speed, high response, and high reliability, &quot;three high(H~3)&quot; for short. Considering the characteristics and current situation of TCM manufacturing, based on the two dimensions of product quality control and production efficiency, a maturity assessment model for continuous manufacturing of TCM, consisting of operation continuity, equipment continuity, process continuity, and quality control continuity, was proposed to provide references for the application of continuous manufacturing technology for TCM. The implementation of continuous manufacturing or the application of key continuous manufacturing technologies in TCM can help to systematically integrate advanced pharmaceutical technology elements and promote the uniformity of TCM quality and the improvement of production efficiency.

Investigating the impact of cyber security risks and reliability scenarios under the influence of IoT on the Smart Grid environment

The modern infrastructure of the smart grid minimises the power losses and maintain the electric power flow closer to nominal values. It enables the bidirectional flow of energy between consumer end and generation end along with a communication flow. Due to increasing number of smart grid equipment, it is important to investigate security protection and appropriate recovery measures for the smart grid application. Cyber security measures are important to consider for improving the reliability and intelligence features of the smart grid. This paper investigated the security measurements and different standards to counter the future security threats to the smart grid power systems. False data injection and risk analysis is carried out along with an internet of thing (IoT) based security solution to increase the reliability of data flow and communication on the smart grid. The investigations are performed by creating a power system model on a MATALB/Simulink environment.

Saca-FI: A microarchitecture-level fault injection framework for reliability analysis of systolic array based CNN accelerator

As convolutional neural network CNN accelerators are being adopted in emerging safety-critical areas, their reliability becomes prominent. The systolic array is widely used as the major processing structure in CNN accelerators, so its reliability features need to be comprehensively explored. In this work, we develop a microarchitecture-level fault injection framework, saca-FI, to analyze the reliability of systolic array based CNN accelerators. It is able to investigate various vulnerability sources (e.g., single-bit flips, multi-bit flips, stuck-at faults, multiple faults) and at different levels (e.g., model layer and storage locations). Based on saca-FI, we evaluate the resilience of several commonly used networks, and observe several important architecture-level reliability characteristics. We then propose two opportunistic protections to demonstrate the usage of saca-FI in designing energy-efficient error protection mechanisms.

A metal-based microencapsulated phase change material (MEPCM) with high thermal reliability and its performance regulation

An ideal microencapsulated phase change material (MEPCM) should have the characteristics of high thermal reliability, low supercooling degree and high thermal conductivity. Herein, we report a novel metal-based MEPCM with high thermal reliability based on “double-layer coating, sacrificial inner layer” method, solving the problem of MEPCM cracking caused by thermal expansion. Furthermore, synergistic regulation of supercooling and thermal conductivity was studied by loading nanoparticles into shell. The results showed that the supercooling degree was reduced by 81.8% when the nano-Co loading was 0.8%, which achieved the best supercooling suppression effect so far, and the properties kept stable after 200 thermal cycles. Furthermore, thermal performance of MEPCM loaded/unloaded with 0.8% Co was compared. The results demonstrated that the MEPCM loaded with 0.8% Co showed a 12.6% increase in thermal conductivity and a faster thermal response rate. This paper provides a novel strategy for the preparation and performance regulation of metal-based MEPCM.

Optimization Design of Filament Wound Composite Pressure Vessel Based on OpenSees

Composite pressure vessels have the characteristics of light weight, corrosion resistance, good sealing, and high reliability, and have been widely used in military and civilian applications. With the widespread use of composite pressure vessels today, determining how to conduct scientific research and correct analysis becomes very important. With the continuous development of computer technology and the continuous improvement of finite element algorithms, the numerical calculation method has become an important method for studying composite pressure vessels. In this paper, the finite element analysis of filament wound composite pressure vessels is carried out by means of ANSYS and OpenSees. The semi-analytical method is used to optimize the design with the help of the Python tool. The parametric language is used to design different fiber layer schemes. The optimal fiber layer scheme is obtained by Nelder-Mead optimization function optimization. The optimal angle and thickness obtained after multiple iterations are 9.96875° and 0.03325 m, respectively.

METHODOLOGICAL AND TECHNOLOGICAL BASICS OF RESEARCH OF SOLID FUEL ROCKET ENGINES OF ATTACK MEANS

The article presents methodical and technological approaches to the study of performance and confirming the main characteristics of solid fuel rocket engines (SFRI), which are part of various means of destruction, as well as the main directions of organization and features of the relevant fire bench tests.&#x0D; SFRI is one of the most important components of modern means of destruction, which largely determine their tactical and technical characteristics and operational safety. By their technical condition, we mean a set of properties that change during operation and are characterized by the parameters (signs) defined by technical documentation.&#x0D; A structural-functional reliability scheme is developed to assess the reliability of SFRI, which allows taking into account not only the structural elements, but also phenomena that can lead to failures, including those caused by connections between elements. According to the results of bench fire tests of SFRI that are conducted taking into account the structural-functional reliability scheme, the following information is obtained: internal ballistic, mass, energy and traction characteristics; performance of structural elements and nodes; characteristics of operational reliability and control bodies. At the end of the tests, the remaining thickness of the heat shield, the integrity of the SFRI body and the condition of the exit nozzle are also evaluated. The probability of failure-free operation is used as the main indicator of reliability. As the main indicator of reliability, SFRI uses the probability of failure-free operation for a given time of its operation. The reliability indicators of individual components are determined in accordance with its structural and functional reliability scheme.&#x0D; Fire stand tests of SFRI are carried out on a special stand, which includes original technological equipment developed and manufactured at the State Enterprise “Kyiv State Design Bureau “Luch”, the main of which are: equipment for remote launch of marching and starting АЗСД 7Х2.700.017; equipment for remote measurement and registration of operating parameters АИР 7X2.700.016; slipway С–40T–1 E240.125.00.000.0 with sensors and a sensor for registering internal gas pressure.&#x0D; The implementation of the main provisions of the methodological and technological basis of SFRI research made it possible to make scientifically based decisions regarding the possibility and expediency of their further exploitation, as well as the need to introduce a number of restrictions, including decommissioning.

APPLICATION OF MATHEMATICAL SOFTWARE FOR THE IMPROVEMENT OF TEACHING-LEARNING TO THE TEACHERS OF MATHEMATICS OF THE ACCALAUREATE OF THE SCHOOL YEAR 2021-2022, IN THE FISCAL COLLEGE OF THE CANTON CHAMBO

In this research work, the objective was to apply the Mathematica software for the improvement of teaching-learning to high school mathematics teachers, in the fiscal college of the Gambo Canton, in the elective year 2021 - 2022. The use of ICT in the area of mathematics is unusual due to the lack of training for teachers for this reason this work helps teaching-learning through the Mathematica software. This research had a qualitative approach, its scope is descriptive and its design is not experimental, but documentary. The population was made up of 2 teachers of the mathematics area of the baccalaureate and 159 students who were duly enrolled officially in the various courses 1 BGU, parallel A, B, C, D and E, 2 BGU, parallel B in the school year 2021 - 2022 in the fiscal college of the Canton Chambo, the technique used was the evaluations and the instrument was the questionnaire, These instruments met the characteristics of validity and reliability for the collection of the information required in the research. The results obtained were through a didactic strategy, after the second evolution the academic performance of the students had a considerable increase with respect to the first evaluation and also increased the number of students who obtained grades greater than 8.99 points in the second evaluation. It is concluded that: with the application of didactic training using Mathematica, the Baccalaureate Teachers improved the teaching-learning of Mathematics and the students with the trainings improved their academic performance in learning Mathematics.

The Importance of Digital Signature in Sustainable Businesses: A Scale Development Study

Today, the interest and the needs of all sectors for digital signatures are increasing day by day. The next approvals in businesses cannot take place without the previous approvals, which follow one after the other, such as a chain of transactions. For this reason, the approval (that is, the signature of a relevant unit) should be carried out as soon as a document is received and immediately forwarded to the next unit. Digital signatures are needed to carry out business transactions quickly and reliably. The use of digital platforms and systems, which are indispensable parts of digital transformation, can be achieved primarily with awareness and understanding of their importance. However, a digital signature awareness scale was not found in the literature. Therefore, in this study, a new scale was developed which can be used to determine the digital signature awareness of people and it is hoped to eliminate this deficiency in the current literature. The developed scale consists of two dimensions: awareness and benefits. The study was designed as descriptive research and 567 participants voluntarily participated in the study. Factor analysis and descriptive analysis methods were used to analyze the collected data. In line with the statistical analysis results, it has been observed that the developed scale provides validity and reliability features and is qualified to be used in scientific research to determine the awareness of people about digital signatures. With the digital signature scale, which is thought to fill the gap in the literature, it is hoped that the digital signatures awareness of people in developing countries, and in particular in Cyprus will be determined, and it is also hoped that people will be encouraged to use digital signatures after necessary training.

Motivational Intervention to Modify Knowledge about Periodontal Disease Prevention in Pregnant Women

BACKGROUND: Currently, periodontal disease during pregnancy is associated with gestational diabetes, low birth weight, and pre-term birth. AIM: The aim of this study was to determine the difference in the level of knowledge about periodontal disease prevention, before and after applying a motivational educational intervention in pregnant women at a primary health-care center. METHODS: Quasi-experimental study in 180 pregnant women from a health center, during the period September to December 2022. The data collection technique was a questionnaire survey with satisfactory characteristics of validity and reliability. An educational intervention with a motivational approach was developed, which was validated in a pre-liminary pilot test. The Wilcoxon statistical test was used to compare knowledge before and after the intervention, and the Chi-square test to assess the association between variables. RESULTS: The age and educational level that pre-dominated were 21–30 years and complete secondary school, respectively. A significant difference was found between the level of knowledge before and after in pregnant women (p &lt; 0.001). There was no association between the level of knowledge about periodontal disease prevention with age and educational level in pregnant women at the health center, before and after a motivational intervention (p &gt; 0.05). CONCLUSION: The motivational educational intervention managed to significantly improve the level of knowledge in pregnant women about the prevention of periodontal disease; however, it was not associated with age and educational level.

On Importance of Sensitivity Analysis on an Example of a k-out-of-n System

Reliability and sensitivity issues are very close and important problems in any technical system. The system’s sensitivity is understood as the dependence of its behavior on changes in some internal parameters. To perform sensitivity analysis, a general procedure based on a theoretical and numerical study is proposed and applied to a repairable k-out-of-n model. The results show the asymptotic insensitivity of the non-stationary and stationary characteristics of the system reliability to the shape of the repair-time distribution, as well as to the value of its coefficient of variation at a fixed mean. The proposed methodology can be useful to researchers and engineers at the designing stage of real systems, as well as applied to other stochastic reliability models.