Reliable Applications Research Articles

Evaluation method of agricultural environmental geological system state based on optimized particle swarm optimization algorithm

The conventional evaluation methods for the state of agricultural environmental geological system mainly use the support vector regression (SVR) model to process the evaluation samples, which is vulnerable to the influence of the sensitive loss function, resulting in the high difference of the evaluation entropy. Therefore, a new evaluation method for the state of agricultural environmental geological system needs to be designed based on the optimized particle swarm optimization algorithm. That is to say, combining with the evolution process of regional agricultural environmental geology, the accurate state evaluation target is selected, the state evaluation system of agricultural environmental geology system is constructed, and the state evaluation model of agricultural environmental geology system is designed combined with the optimized particle swarm optimization algorithm, so as to complete the state evaluation of geological system. The results demonstrated the suggested methodology assesses the state of an agricultural environmental geological system. Key factors included soil texture (0.254), soil nutrient (0.118), and soil pH (0.256). It showed that the designed evaluation method of agricultural environmental geological system state based on optimized particle swarm optimization algorithm has good evaluation effect, reliability and certain application value, and has made certain contributions to the formulation of reasonable agricultural ecological protection scheme.

Capsizing due to friction-induced twist in the failure of stopper knots

We investigate the failure mechanism of stopper knots, with a particular focus on the figure-8 knot as a representative example. Stopper knots are widely used in climbing, sailing, racket stringing, and sewing to maintain tension in ropes, strings, or threads while preventing them from passing through an orifice. Combining high-precision model experiments and Finite Element Analyses, we systematically explore the influence of frictional interactions and their role in the build-up of mechanical twist. Our findings reveal that the failure of stopper knots via capsizing, which involves configurational alterations of the filament, is primarily due to friction-induced twisting when loading the knot against a restraining plate containing a clearance hole. Our study offers a comprehensive understanding of the mechanical behavior of stopper knots under diverse loading conditions, thereby providing crucial insights for their reliable application across various domains.

Strategy for establishing sensitive fluorescent sensor toward p-nitrophenol integrating magnetic molecularly imprinted materials and carbon dots

In this work, a sensitive fluorescent sensor toward p-nitrophenol (4-NP) integrating magnetic molecularly imprinted materials and carbon dots (CDs) was proposed. Magnetic material and CDs derived from K3 [Fe(CN)6] and glucose were simultaneously obtained through simple one-step hydrothermal process. Introducing of molecularly imprinted materials based magnetic solid phase extraction (MSPE) endowed the constructed fluorescent sensor with higher sensitivity and selectivity. The significant factors affecting the sensitivity of the sensor toward 4-NP were optimized. Good linearity was obtained between fluorescent intensity of CDs and different concentration of 4-NP from 0.08 to 62.5 μg L−1. The sensitivity of constructed sensor was very low with detection limit of 0.02 μg L−1. Reliable applicability was also proved by the well-pleasing recoveries of 94.2–97.8% with different spiked concentrations of 4-NP in real environmental waters.

Reliability of LoRaWAN Communications in Mining Environments: A Survey on Challenges and Design Requirements

While a robust and reliable communication network for monitoring the mining environment in a timely manner to take care of people, the planet Earth and profits is key, the mining environment is very challenging in terms of achieving reliable wireless transmission. This survey therefore investigates the reliability of LoRaWAN communication in the mining environment, identifying the challenges and design requirements. Bearing in mind that LoRaWAN is an IoT communication technology that has not yet been fully deployed in mining, the survey incorporates an investigation of LoRaWAN and other mining IoT communication technologies to determine their records of reliability, strengths and weaknesses and applications in mining. This aspect of the survey gives insight into the requirements of future mining IoT communication technologies and where LoRaWAN can be deployed in both underground and surface mining. Specific questions that the survey addresses are: (1) What is the record of reliability of LoRaWAN in mining environments? (2) What contributions have been made with regard to LoRa/LoRaWAN communication in general towards improving reliability? (3) What are the challenges and design requirements of LoRaWAN reliability in mining environments? (4) What research opportunities exist for achieving LoRaWAN communication in mining environments? In addition to recommending open research opportunities, the lessons learnt from the survey are also outlined.

Assessing women's and health professionals' views on developing a midwifery-led mobile health app intervention in pregnancy: A descriptive qualitative study.

To explore women's and health professionals' views on the development of a midwifery-led mHealth app intervention in antenatal care and their demands for app functionality. Descriptive qualitative research was utilized. In total, 15 pregnant or postpartum women were interviewed via in-depth interviews and 10 health professionals including obstetricians, midwives and obstetric nurses were invited to participate in a focus group discussion (FGD). All interviews and the FGD were analysed using qualitative content analysis. Four key themes emerged from the data, including (1) limitations of current maternity care services; (2) potential benefits for mHealth app-based midwifery care; (3) possible challenges for providing midwifery care through mHealth apps and (4) suggestions and needs for developing a midwifery-led mHealth app. Participants agreed on the potential need of developing a midwifery-led mHealth app in antenatal care to increase access to midwifery care services and to meet women's diverse needs. Participants preferred to develop professional, reliable, full-featured and interactive mobile applications. The main functions of midwifery-led mHealth apps included personalized assessment and health education, self-monitoring and feedback, data sharing and interactive functions. Women mentioned that online communication and consultation with midwives could help them receive continuous support outside facilities. Health professionals expressed it would be of great convenience and timeliness to send personalized messages to women and to inform them of healthy lifestyles during pregnancy. The challenges included a shortage of human resources, medico-legal risks associated with mHealth and data security risks. This study explores the individual views and functional needs of target users and healthcare providers for developing a midwifery-led mHealth app in antenatal care, which will serve as a reference for future application development. Our study has important and practical implications for guiding the development of future midwifery-led mHealth app interventions. No patient or public contribution.

Pharyngeal Residue Scoring in Fiberoptic Endoscopic Evaluation of Swallowing: Reliability Comparison and Applicability Among Different Scales

Several scales to assess pharyngeal residue in Fiberoptic Endoscopic Evaluation of Swallowing (FEES) are currently available. The study aimed to compare the reliability and the applicability in real clinical practice among four rating scales: the Pooling Score (P-SCORE), the Boston Residue and Clearance Scale (BRACS), the Yale Pharyngeal Residue Severity Rating Scale (YPRSRS), and the Residue Ordinal Rating Scale (RORS). Twenty-five FEES videos were evaluated four times, once for each scale, by four speech and language pathologists. To test intra-rater reliability, the same raters re-assessed the videos two weeks apart. To test the applicability, raters recorded the time required to complete each assessment and the perceived difficulty/ease on a visual-analog scale (VAS). The intra-rater and the inter-rater reliability were calculated with Cohen’s weighted Kappa and the Fleiss weighted Kappa, respectively. Time and perceived difficulty/ease scores were compared. The intra-rater reliability analysis showed almost perfect agreement for YPRSRS (k = 0.91) and RORS (k = 0.83) and substantial agreement for P-SCORE (k = 0.76) and BRACS (k = 0.74). Pairwise comparison showed no significant differences among the scales. The inter-rater reliability for the YPRSRS (k = 0.78) was significantly higher than P-SCORE (k = 0.52, p < 0.001), BRACS (k = 0.56, p < 0.001), and RORS (k = 0.65, p = 0.005). The BRACS required the longest time (p < 0.001) and was perceived as the most difficult scale (p < 0.001). The RORS was perceived as the easiest scale (p < 0.05). In conclusion, the YPRSRS showed the highest reliability, while raters perceived the RORS as the easiest to score. These results will allow clinicians to consciously choose which scale to use in clinical practice.

Comparative Analysis of Time-Slotted Channel Hopping Schedule Optimization Using Priority-Based Customized Differential Evolution Algorithm in Heterogeneous IoT Networks.

The Time-Slotted Channel Hopping (TSCH) protocol is known for its suitability in highly reliable applications within industrial wireless sensor networks. One of the most significant challenges in TSCH is determining a schedule with a minimal slotframe size that can meet the required throughput for a heterogeneous network. We proposed a Priority-based Customized Differential Evolution (PCDE) algorithm based on the determination of a collision- and interference-free transmission graph. Our schedule can encompass sensors with different data rates in the given slotframe size. This study presents a comprehensive performance evaluation of our proposed algorithm and compares the results to the Traffic-Aware Scheduling Algorithm (TASA). Sufficient simulations were performed to evaluate different metrics such as the slotframe size, throughput, delay, time complexity, and Packet Delivery Ratio (PDR) to prove that our approach achieves a significant result compared with this method.

Hydrogen peroxide priming promotes salinity tolerance in plants—A comprehensive review

AbstractSalinity stress is a growing concern for agriculture, as soil salinization is increasing worldwide and seriously affects global agricultural production and food security. How to minimize the adverse effects of salinity stress and meet the food needs of the growing human population becomes an urgent problem. Conventional techniques in agriculture and recently modern plant science have limitations; therefore, alternative technologies such as hydrogen peroxide (H2O2) priming have emerged as promising solutions. The findings highlight that H2O2 priming enhances antioxidant defenses, regulating the balance between reactive oxygen species production and scavenging. Furthermore, H2O2 priming promotes osmotic adjustment by stimulating the accumulation of osmoprotectants, maintaining cellular water status under salinity conditions. The review also discusses how H2O2 priming modulates gene expression and signaling processes, causing stress‐responsive genes and transcription factors activation. Moreover, H2O2 priming helps to preserve chloroplast integrity and photosynthetic efficiency, mitigating the detrimental impacts of salt stress on plant development and productivity. Overall, the collective knowledge gathered here, covering various aspects of the H2O2 priming phenomenon, may facilitate the design and conduct of future research on plant tolerance to salinity or other abiotic stresses. However, future research needs to focus on elucidating how priming can be applied on a large scale to diverse plants, understanding biochemical and molecular mechanisms of H2O2 priming for precise and reliable applications of this approach, and figuring out the potential benefits of in vitro priming (H2O2) technology in plant science.

Generative Artificial Intelligence in Patient Education: ChatGPT Takes on Hypertension Questions.

Introduction Uncontrolled hypertension significantly contributes to the development and deterioration of various medical conditions, such as myocardial infarction, chronic kidney disease, and cerebrovascular events. Despite being the most common preventable risk factor for all-cause mortality, only a fractionof affected individuals maintain their blood pressure in the desired range. In recent times, there has been a growing reliance on online platforms for medical information. While providing a convenient source of information, differentiating reliable from unreliable information can be daunting for the layperson, and false information can potentially hinder timely diagnosis and management of medical conditions. The surge in accessibility of generative artificial intelligence (GeAI) technology has led to increased use in obtaining health-related information. This has sparked debates among healthcare providers about the potential for misuse and misinformation while recognizing the role of GeAI in improving health literacy. This study aims to investigate the accuracy of AI-generated information specifically related to hypertension. Additionally, it seeks to explore the reproducibility of information provided by GeAI. Method A nonhuman-subject qualitative study was devised to evaluate the accuracy of information provided by ChatGPT regarding hypertension and its secondary complications. Frequently asked questions on hypertension were compiled by three study staff, internal medicine residents at an ACGME-accredited program, and then reviewed by a physician experienced in treating hypertension, resulting in a final set of 100 questions. Each question was posed to ChatGPT three times, once by each study staff, and the majority response was then assessed against the recommended guidelines. A board-certified internal medicine physician with over eight years of experience further reviewed the responses and categorized them into two classes based on their clinical appropriateness: appropriate (in line with clinical recommendations) and inappropriate (containing errors). Descriptive statistical analysis was employed to assess ChatGPT responses for accuracy and reproducibility. Result Initially, a pool of 130 questions was gathered, of which a final set of 100 questions was selected for the purpose of this study. When assessed against acceptable standard responses, ChatGPT responses were found to be appropriate in 92.5% of cases and inappropriate in 7.5%. Furthermore, ChatGPT had a reproducibility score of 93%, meaning that it could consistently reproduce answers that conveyed similar meanings across multiple runs. Conclusion ChatGPT showcased commendable accuracy in addressing commonly asked questions about hypertension. These results underscore the potential of GeAI in providing valuable information to patients. However, continued research and refinement are essential to evaluate further the reliability and broader applicability of ChatGPT within the medical field.

Improvement of PbSn Solder Reliability with Ge Microalloying-Induced Optimization of Intermetallic Compounds Growth.

PbSn solders are used in semiconductor devices for aerospace or military purposes with high levels of reliability requirements. Microalloying has been widely adopted to improve the reliability for Pb-free solders, but its application in PbSn solders is scarce. In this article, the optimization of PbSn solder reliability with Ge microalloying was investigated using both experimental and calculation methods. Intermetallic compounds (IMC) growth and morphologies evolution during reliability tests were considered to be the main factors of device failure. Through first-principle calculation, the growth mechanism of interfacial Ni3Sn4 was discussed, including the formation of vacancies, the Ni-vacancies exchange diffusion and the dominant Ni diffusion along the [1 0 0] direction. The doping of Ge in the cell increased the exchange energy barrier and thus inhibited the IMC development and coarsening trend. In three reliability tests, only 0.013 wt% Ge microalloying in Pb60Sn40 was able to reduce IMC thickness by an increment of 22.6~38.7%. The proposed Ge microalloying method in traditional PbSn solder could yield a prospective candidate for highly reliable applications.

The Upper Quarter Y-Balance Test: A Scoping Review of Reference Values, Reliability, Determinants, and Practical Application

The aim of this scoping review was to comprehensively examine the Upper Quarter Y-Balance Test (UQYBT), including reference values, reliability, determinants, and its practical application. By including studies irrespective of publication date, participant demographics, or research purpose, provided they were in English and incorporated UQYBT as a primary or secondary variable, an extensive dataset was collected. The focus was on limb-length-normalized results to establish standardized reference metrics. Reliability studies highlighted the UQYBT’s consistency across sessions and raters. The application of UQYBT to assess injury risk is questionable. Studies assessing its relationship with sports performance have also yielded varying results. UQYBT seems to be sensitive to various exercise based-interventions, as shown by several clinical trials. This review furnishes practitioners and clinicians with valuable insights for the application of UQYBT in sports and healthcare settings.

Rational Design of Antifungal Peptides Based on the γ-Core Motif of a Neosartorya (Aspergillus) fischeri Antifungal Protein to Improve Structural Integrity, Efficacy, and Spectrum.

Antifungal peptides offer promising alternative compounds for the treatment of fungal infections, for which new antifungal compounds are urgently needed. Constant and broad antifungal spectra of these peptides play essential roles in their reliable therapeutic application. It has been observed that rationally designed peptides using the evolutionarily conserved γ-core region (GXC-X3-9-C) of an antifungal protein from Neosartorya (Aspergillus) fischeri highly inhibit the growth of fungi. The cysteines in these peptides have free sulfhydryl groups, which allow cyclization and dimerization under oxidative conditions, thereby impairing antifungal efficacy. To overcome this problem, one or two cysteine residues were substituted by serines or S-tert-butyl was applied as a cysteine-protecting group. Furthermore, structural integrity and antifungal efficacy investigations before and after oxidative exposure revealed that substituting both cysteines with serines and S-tert-butylation helped maintain the structural integrity. However, it slightly decreased the antifungal efficacy against a yeast, Candida albicans. Interestingly, S-tert-butylation maintained the efficacy and could extend the antifungal activity to a mold, Aspergillus fumigatus. Usually, cyclization and dimerization did not influence the antifungal efficacy of most peptides. Additionally, hemolysis tests and Galleria mellonella toxicity model experiments indicated that none of the applied modifications made the peptides harmful to animals.

Bayesian Estimation of the Odd Lindley Exponentiated Exponential Distribution : Applications in-Reliability

In this work, we investigate the estimation of the unknown parameters and the reliability characteristics ofthe Odd Lindley Exponentiated Exponential distribution. The Bayes estimators and corresponding risks are derived usingvarious loss functions with complete data and a gamma prior distribution. A simulation study was carried out to calculate allthe results. We used Pitman’s closeness criterion and the integrated mean squared error to compare the performance of theBayesian and maximum likelihood estimators. Finally, we illustrate our techniques by analysing a real-life data set.

Design and analysis of a switch fault-tolerant multi-input single-output DC-DC converter for high reliability applications

The DC–DC converters have been used extensively in various industrial applications such as consumer electronics, aerospace, electric vehicles, and renewable energy systems. The reliability ensures that the converter continues to operate safely and efficiently despite the presence of faults. Enhancing the reliability of DC-DC converters is a challenging task, as power switches are the most fragile components that can be affected by faults in the system. Hence, to address these challenges and ensure the safety of the converter, it is vital to implement appropriate fast fault-diagnosis techniques and fault-tolerant strategies. Many new network topologies have been presented in literature, which lead to a shift from single input–single output to multiport converters. These converters are suitable for integrating different energy sources. However, the majority of them are operated using a time-sharing method, in which only one energy source is used at a time, and the others are inactive at any specified duty cycle. Therefore, the converter and input sources are underutilized in the conventional time-sharing approach. This paper proposes a new multi-input single-output (MISO) converter topology with fully integrated switch fault tolerance. It can perform multi-input buck, boost, and buck-boost operations. More importantly, the converter can operate uninterruptedly for single or multiple switch faults. Using simulation and experimental results, a 400 W prototype circuit is designed to analyze the converter's reliability and performance.

Reliability contract in hydrogen networks: Another step towards sustainable transportation

While hydrogen energy is increasingly incorporated into energy hubs supplying the fuel cell electric vehicles, the question arose as to whether the inflexible hydrogen networks can provide a reliable supply for sustainable transportation. This study proposes a reliability contract method to capture the economic benefits of differentiated reliability according to the preferences of energy hubs for hydrogen outages. The innovative method divides the hydrogen network into multiple zones to construct the premiums and reimbursements for the reliability contract. According to the key factors of uncertainty in the energy hub operation problem, risk management and net premium principle, this research identifies the effective application of the zone-based reliability contract to hydrogen networks by using a stochastic programming model. Simulations implemented on a typical structure of hydrogen network with 5.2 h outage duration per year shows that the zone division of the proposed reliability contract effectively reduce the outage to 3.1 h.

Theoretical exploration of a free-piston Stirling generator with a gas-compressing self-circulating heat exchanger

The free-piston Stirling generator (FPSG) holds great promise as a thermal-to-electrical conversion device for space applications. However, heat transfer remains a critical factor obstructing its scalability to high power levels. In this study, we propose a high-power FPSG with a gas-compressing self-circulating heat exchanger (GSHX) that utilizes high-pressure helium as the heat transfer fluid, enhancing the reliability of the heat exchanger. By incorporating a built-in gas-compressing device and check valves, the performance degradation caused by significant pressure fluctuations in the resonant self-circulating heat exchanger is effectively addressed. Through detailed simulations, we demonstrate the principle feasibility of the GSHX with a 4 m heat transfer loop, achieving an output electric power of 11 kW and a thermal-to-electric efficiency of 20.4%. Furthermore, the GSHX exhibits similar operating characteristics to traditional FPSGs, as we analyze the systems' stability under various operating conditions. Importantly, our findings reveal that the acoustic characteristics of the loop play a pivotal role in system performance, with operating parameters exhibiting periodic variations related to helium wavelength. By carefully optimizing system parameters, GSHX with a long loop length of 29 m can achieve comparable performance to systems with shorter loops. This study provides valuable insights into the operational characteristics and performance optimization of the GSHX, offering a comprehensive heat transfer solution for long-distance, high-efficiency, and highly reliable applications of high-power FPSGs in the space domain.

A New Sine-Based Probabilistic Approach: Theory and Monte Carlo Simulation with Reliability Application

Data modeling is a very crucial stage for decision making in applied sectors. Probability distributions are considered important tools for decision making. So far, numerous probability distributions have been developed and implemented. Most of these distributions are developed by introducing from one to eight additional parameters. Sometimes, the addition of new parameters leads to re-parameterization problems. To avoid such issues, we introduce a novel probabilistic approach. The proposed approach may be termed as a new weighted sine-G method. The beauty and key advantage of the new weighted sine-G method are that it has no additional parameters. Through using the new weighted sine-G method, a new weighted sine-Weibull distribution is introduced, which is a modification of the Weibull distribution. The estimators of the new model are also derived. Furthermore, a simulation study is carried out to evaluate the estimators of the new weighted sine-Weibull distribution. Finally, a practical application from the reliability sector is considered to evaluate the new weighted sine-Weibull distribution. Based on certain decision tools, it is observed that the proposed model is the best competing distribution for applying it in the reliability sector.

Discrete half-logistic distributions with applications in reliability and risk analysis

In the statistical literature, several discrete distributions have been developed so far for modeling non-negative integer-valued phenomena, yet there is still room for new counting models that adequately capture the diversity of real data sets. Here, we first discuss a count distribution derived as a discrete analogue of the continuous half-logistic distribution, which is obtained by preserving the expression of its survival function at each non-negative integer support point. The proposed discrete distribution has a mode at zero and allows for over-dispersion; these two features make it suitable for modeling purposes in many fields (e.g., insurance and ecology), when these conditions are satisfied by the data. In order to widen its spectrum of applications, a discrete analogue is also presented of the type I generalized half-logistic distribution (obtained by adding a shape parameter to the simple one-parameter half-logistic), which allows us to model count data whose mode is not necessarily zero. For these new count distributions, the main statistical properties are outlined, and parameter estimation along with related issues is discussed. Their feasibility is proved on two real data sets taken from the literature, which have already been fitted by other well-established count distributions. Finally, a possible application is illustrated in the insurance field, related to the exact/approximate determination of the distribution of the total claims amount through the well-known Panjer’s recursive formula, within the framework of collective risk models.

Securing IIoT applications in 6G and beyond using adaptive ensemble learning and zero-touch multi-resource provisioning

The advent of Industry 5.0, driven by cutting-edge 6G technologies such as immersive cloud eXtended Reality (XR), Autonomous vehicles, holographic communication, and Digital Twin (DT), is set to trigger a substantial upswing in the deployment of the Industrial Internet of Things (IIoT) devices interconnected within networks. This expansion of IIoT devices will create a wider attack surface and increase the risk of data breaches, privacy violations, and system disruptions. Therefore, it is essential to design innovative mechanisms to ensure the reliability and security of these advanced 6G applications, as well as the IIoT devices that support them. In this context, we propose a novel Software-defined Networking (SDN)-based Ensemble Learning (EL) Framework for Secure IIoT applications in 6G and beyond, called AdaptSDN. The proposed framework leverages SDN technology to dynamically allocate network resources and deploy security measures on demand. It also leverages EL techniques to improve the intrusion detection system’s accuracy. By isolating IIoT devices into network slices, the framework limits the impact of attacks and reduces the potential for cascading failures. Digital twins are used for creating a virtual replica of the IIoT network, allowing for a real-time security threat detection. In particular, AdaptSDN includes three main modules: (1) A novel digital Twin (DT)-enabled data gathering and selection of informative features module to achieve two main objectives: reducing computational complexity and improving detection performance; (2) An SDN-based lightweight adaptive boosting module that uses an advanced boosting EL techniques to dynamically adjust weights and to effectively identify and respond to IIoT attacks in real-time; and (3) A zero-touch resources provisioning module that employs a non-cooperative game theory approach. This approach allows for automatically provisioning various resources in a network to efficiently mitigate network attacks; it enables SDN nodes to obtain the required virtual resources, i.e., storage, computing, and bandwidth, from the main orchestrator with respect to IIoT attack type. We carried out comprehensive experiments to assess the effectiveness of our proposed framework in detecting real-world IIoT attacks. The numerical results confirm that AdaptSDN has the potential to enable secure and reliable IIoT applications in 6G and beyond, meeting the stringent service requirements of the new emerging applications.

Reliability Evaluation of EB-PVD Thermal Barrier Coatings in High-Speed Rotation and Gas Thermal Shock

The uncertain service life of thermal barrier coatings (TBCs) imposes constraints on their secure application. In addressing this uncertainty, this study employs the Monte Carlo simulation method for reliability evaluation, quantifying the risk of TBC peeling. For reliability evaluation, the failure mode needs to be studied to determine failure criteria. The failure mode of high-speed rotating TBCs under gas thermal shock was studied by combining fluid dynamics simulations and experiments. Based on the main failure mode, the corresponding failure criterion was established using the energy release rate, and its limit state equation was derived. After considering the dispersion of parameters, the reliability of TBCs was quantitatively evaluated using failure probability and sensitivity analysis methods. The results show that the main mode is the fracture of the ceramic layer itself, exhibiting a distinctive top-down “step-like” thinning and peeling morphology. The centrifugal force emerges as the main driving force for this failure mode. The failure probability value on the top side of the blade is higher, signifying that coating failure is more likely at this location, aligning with the experimental findings. The key parameters influencing the reliability of TBCs are rotation speed, temperature, and the thermal expansion coefficient. This study offers a valuable strategy for the secure and reliable application of TBCs on aeroengine turbine blades.