Quality Problems Research Articles

The Relationship Between Poor Sleep and Memory Impairment Among Shift Nurses in China: A Cross-Sectional Study.

Many shift nurses experience poor sleep quality, and the effect on nurses' cognitive function remains unclear. The study aims to investigate sleep status and explore its association with cognitive function. This descriptive cross-sectional study was conducted in a tertiary care hospital in Fujian, China between March and September 2022. By convenience sampling method, 588 shift nurses participated in this study. The Pittsburgh Sleep Quality Index (PSQI) and Bergen Shift Work Sleep Questionnaire (BSWSQ) were used to assess sleep quality, while the Chinese version of Ascertain Dementia-8 (AD-8) was used to evaluate cognitive function. A PSQI score ≤7 is classified as good sleep quality, and a score >7 indicates poor sleep quality. An AD-8 score≥ 2 is considered indicative of memory impairment. Multivariate logistic regression analysis was conducted to explore the association between sleep status and memory impairment. A total of 310 (52.6%) participants presented poor sleep quality. Among them, 52.2% of participants had day shift-related sleep problems, 45.9% had evening shift-related sleep problems, 61.9% had night shift-related sleep problems, and 15.0% reported rest-day/vacation-related sleep problems. The prevalence of poor sleep quality and each shift-related sleep problem in the memory impairment group were higher than in the normalmemory group (P<0.05). Multivariate logistic regression analysis indicated that poor sleep quality (OR=2.073, 95% CI: 1.398~3.072), evening shift-related sleep problems (OR=1.707, 95% CI: 1.028~2.835), night shift-related sleep problems (OR=1.859, 95% CI:1.104~3.129), and rest-day/vacation-related sleep problems (OR=2.069, 95% CI:1.170~3.659) was significantly associated with memory impairment. This study highlights the prevalence of poor sleep quality among clinical nurses and identifies poor sleep quality and shift-related sleep problems (excluding day shift) as risk factors for memory impairment. Nurse managers should prioritize sleep quality and focus on cognitive function to enhance nurses' occupational health.

Mediterranean-DASH Diet Intervention for Neurodegenerative Delay Diet and Psychological Problems in Maintenance Hemodialysis Patients by the Structural Equation Modeling Analysis of Malnutrition and Inflammation Markers Approach

ObjectiveAlthough it is known that diet quality affects psychological problems in hemodialysis patients, there is need to explain the role of modifiable risk factors in this relationship. Therefore, the aim of this study was a) to investigate the relationship between diet quality and modifiable risk factors to depression and anxiety in end-stage renal disease (ESRD) patients receiving maintenance hemodialysis; b) to explore the mediating roles of modifiable factors in the relationship with diet quality to depression and anxiety. MethodsThis cross-sectional study included 216 patients with ESRD receiving maintenance hemodialysis treatment. We assessed participants' dietary records for Mediterranean-DASH Diet Intervention for Neurodegenerative Delay (MIND) quality, Global Leadership Initiative on Malnutrition (GLIM) criteria for malnutrition and Hospital Anxiety and Depression (HAD)-scale for psychological problems. Furthermore, biochemical findings and anthropometric measurements were performed to evaluate nutritional markers, metabolic risk factors and inflammation. We applied hierarchical regression analysis to estimate modifiable risk factors for depression and anxiety and Structural-Equation-Modeling analysis to determine the mediating role of modifiable risk factors between diet quality and psychological problems. ResultsDepression symptoms were observed in 59.2% (n=128) of the participants, whilst the rate was 35.1% (n=76) for anxiety. Depression and anxiety were found to be negatively correlated with MIND levels after covariate adjusting model, and the rates of explanation were found to be 16.2% and 12.2%, respectively. C-reactive protein (CRP), albumin levels and the presence of malnutrition were shown to be significant predictors of depression (ΔF=14.761 and ΔR2:0.071 for covariate-adjusted model). Albumin levels, CRP, hemodialysis duration, and malnutrition were found to be independent predictors of anxiety (ΔF=16.174 and ΔR2:0.077 for covariate-adjusted model). It was found that CRP and malnutrition partially mediated the association of MIND score with depression, and CRP mediated the association with anxiety. ConclusionIt was concluded that adherence to the MIND diet is associated with a better nutritional profile and reduced inflammation, which in turn may be linked to fewer psychological problems.These further studies are needed to validate and expand upon our findings.

Call to Action: Quality and Simulation Professionals Should Collaborate.

Simulation is underutilized as a tool to improve healthcare quality and safety despite many examples of its effectiveness to identify and remedy quality and safety problems, improve teamwork, and improve various measures of quality and safety that are important to healthcare organizations, eg, patient safety indicators. We urge quality and safety and simulation professionals to collaborate with their counterparts in their organizations to employ simulation in ways that improve the quality and safety of care of their patients. These collaborations could begin through initiating conversations among the quality and safety and simulation professionals, perhaps using this article as a prompt for discussion, identifying one area in need of quality and safety improvement for which simulation can be helpful, and beginning that work.

Nanobubble-enabled foam fractionation to remove algogenic odorous micropollutants

Due to climate change and environmental pollution, natural lakes and reservoir water suffer increasingly serious algal blooms and associated water quality problems due to the presence of algal or algogenic organic matter (AOM) such as algal odour and toxins. Effective removal of these micropollutants, especially in the event of algal blooms, is critical to aesthetic values of water bodies, drinking water security and human health. The study investigated the removal efficiency of two common odorous compounds, trans-1,10-dimethyl-trans-9-decalol (geosmin) and 2-Methylisoborneol (2-MIB), using foam fractionation enabled by air nanobubbles with addition of two common cationic and anionic surfactants, sodium dodecyl sulfate (SDS) and cetyltrimethylammonium bromide (CTAB), to enhance foaming ability and stability. The results showed that the cationic surfactant (i.e., CTAB), a low pH, and high ionic strength significantly promoted the removal of geosmin and 2-MIB. For example, the removal tests using the synthetic water determined that the conditions of pH = 7, [CTAB] = 20 mg·L−1 and IS = 10 mM as NaCl resulted in both the highest geosmin removal rate of 91.81% and highest 2-MIB removal rate of 85.0%. The removal of two odorous compounds in real lake water was evaluated, which yielded removal rates of 83.2% for geosmin and 48.1% for 2-MIB, highlighting the minor inhibition from water matrixes on the removal performances. Compared to microbubbles, nanobubbles enabled greater surface areas of foam and higher removal efficiencies. The study provided new insights into the use of foam fractionation with air nanobubbles to enhance the removal of odorous compounds from impaired water and mitigate the negative environmental and health impacts of harmful algal blooms (HABs).

Reactive Power Compensation for Standalone Hybrid Power System Using Facts Devices

Reactive power compensation is essential in hybrid grid-connected systems because power electronic inverters utilized for supplying DC energy into the grid causes a reduction in the overall power factor of the power systems. Due to the ability to provide a reliable and efficient power supply to remote and off-grid areas, hybrid power systems are growing in popularity. It can provide greater opportunities for operational growth by combining economic and technology advancement. To meet the demand for electrical energy in both regular and critical situations, operators must be in charge of the power systems reliable and secure operation. The primary objective of the work is to control the reactive power flow in a grid-connected hybrid renewable energy system (PV-wind-battery). The power quality problems that these systems frequently experience include voltage sags, harmonics, and flicker. A FACTS device Unified Power Quality controller (UPQC) with a Genetic Algorithm based PI controller is suggested to handle these power quality issues. In order to improve the performance of the power system, the proposed optimization approaches are used to tune the UPQC in a multiline transmission system. The model was developed with the help of the MATLAB/Simulink work framework.

Ecological and toxicological screening of the safety of drinking water for livestock enterprises of Odesa region

Our scientific and industrial research aimed to determine tap water's safety for watering productive animals (cows, goats, sheep) in three different livestock enterprises of the Odesa region. By using the microbiological method - ecological-toxicological express biotesting (with the help of the preparation of test organisms of the dry culture of ciliates Colpoda steinii), it was determined that among 106 samples of tap water for watering animals, an average of 65.62 received a rating of “general weak toxicity”; 67.01 and 64.88 % of the samples, respectively, for each farm (P &lt; 0.001, which highlighted the problem of the unsatisfactory quality of drinking tap water for dairy cattle. A trend was established to increase the indicator of weak toxicity of drinking water in enterprises by +0.22–4.33 % during the year in each farm (P &lt; 0.001). Determination of the comprehensive indicator of the quality of tap water for watering animals (farm No. 3) by express analysis of samples by the TDS - metric method showed that the level of mineralization of untreated drinking water exceeded recommended norms in 27.65 and 31.17 times (in 2023 and 2024, respectively, (P &lt; 0.001), and samples of water purified on the “ECOSOFT-6500” line were within the recommended norms of high-quality drinking water: on average 35.63–33.59 mg/l, respectively (P &lt; 0.001). The analysis of experimental comparative data and literature sources showed the need for further studies on the quality and safety of water for watering productive animals to optimize the management of safe water supply and obtain organic livestock products.

청소년의 수면의 질이 주의집중 문제와 자아존중감을 통해 학업무기력에 미치는 영향

The purpose of this study is to examine the indirect effects of adolescents’ sleep quality in academic helplessness through attention problems and self-esteem. For this purpose, data from 2,265 first year middle school students from the ‘Korean Children and Youth Panel Survey 2018’ were used. The collected data were analyzed using SPSS 27.0 and AMOS 23.0. As a result, the adolescents' sleep quality was positively related to self-esteem and negatively related to attention problems and academic helplessness. Furthermore, sleep quality directly predicted attention problems, and attention problems directly predicted academic helplessness and indirectly through self-esteem. Finally, the indirect effects of sleep quality on academic helplessness through attention problems and self-esteem were significant. This research confirms that adolescent academic helplessness should be addressed not only through sleep quality and attention problems, but also through the psychological issue of self-esteem. These results are significant in that they provide a basis for educational and counseling programs to alleviate adolescent academic helplessness in South Korean culture, where academic performance is a major task of adolescence.

The Role of the Pareto Principle in Quality Management within Industry 4.0: A Comprehensive Bibliometric Analysis

Quality is a fundamental aspect of the management of almost every area of a company, directly influencing the efficiency of its processes. The increased importance of quality, as well as advances in industrialisation methods and computerisation, has stimulated the development of a new generation of technologies, leading to the fourth industrial revolution known as Industry 4.0. In addition to new technologies, the Pareto principle plays an important role in quality management, enabling the identification of the key factors that influence quality, emphasising that 20% of causes are responsible for 80% of quality problems. Its importance has been highlighted by the American Society for Quality (ASQ), which recognises the Pareto principle as one of the seven basic tools used in the area of process improvement. The purpose of this study is to identify the impact of the Pareto principle on quality management on the basis of a bibliometric analysis carried out to characterise the evolutionary aspects of the development of key terms specific to the area under consideration. The analysis focuses on a sample of 8002 items, and the data collected come from the extensive Scopus database of scientific articles, covering the years 1990-2024. VOSviewer software was used as a tool. On the basis of the empirical data, the exponential growth of the number of publications was confirmed in terms of the relationship between the Pareto principle and quality management. On the basis of the resulting visualisation maps, five areas (clusters) were identified and characterised in terms of keyword coincidence, and their evolutionary natures were described. This publication can serve as a basis for further considerations in the impact layer of the Pareto principle in terms of quality management.

Climatic Impact on the Air Pollutant Concentrations Emitted from Gas Stations in Fallujah, Al-Anbar, Western Iraq

The research investigates the influence of climatic elements like temperature, relative humidity, rainfall, evaporation, dust storms, wind direction, and speed on the air pollutant concentrations at gas stations in Fallujah city while examining their impact on humans and the environment. This was achieved by measuring concentrations of CO, NO2, and CH4, suspended particles (TSP, PM10), and Cu, and Pb. It was found that the impact increased in July compared to January, because of the influence of the measured climatic elements. Rising temperatures increase air pollutants' concentrations in the atmosphere, while winds spread them from emission sites to distant locations. Moreover, summer temperatures significantly impacted air pollutant concentrations at the study stations, particularly at Al-Rehab gas station, with the highest recorded temperatures of 48.4°C. Some gas stations also exceeded local environmental limits for TSP, PM10, CO, NO2, and Pb. The study revealed that summer temperatures with wind speeds of 2.1 m/s resulted in the highest air pollutants values, while winter temperatures with wind speeds of 1.9 m/s decreased these values. Higher temperatures increase chemical reactions and temperature inversions, leading to increased pollution concentrations and exacerbating air quality problems in the study area. Strong winds disperse pollutants, reducing concentration in the study area, while low wind speed increases concentrations and stagnation, resulting in poor air quality in the studied sites. The importance of the current study lies in evaluating pollutants in the Fallujah city, determining their causes, the extent of the impact, and contribution of natural and human sources in causing changes in air pollutant concentrations; caused by emission from the source. Moreover, the current study showed its impact on human’s health, and trying to find solutions to limit these effects or reduce their levels to ensure the safety of the environment and health.

Hybrid Deep Neural Network Approaches for Power Quality Analysis in Electric Arc Furnaces

In this research, we investigate the power quality of the grid where an Electric Arc Furnace (EAF) with a very high load operates. An Electric Arc Furnace (EAF) is a highly nonlinear load that uses very high and variable currents, causing major power quality issues such as voltage sags, flickers, and harmonic distortions. These disturbances produce electrical grid instability, affect the operation of other equipment, and require strong mitigation measures to reduce their impact. To investigate these issues, data are collected from the Point of Common Coupling where the Electric Arc Furnace is fed. The following three main factors are identified for evaluating power quality: apparent power, active and reactive power, and distorted power. Along with these powers, Total Harmonic Distortion, an important indicator of power quality, is calculated. These data are collected during the full process of producing a complete steel batch. To create a Deep Neural Network that can model and forecast power quality parameters, a network is developed using LSTM layers, Convolutional Layers, and GRU Layers, all of which demonstrate good prediction performance. The results of the prediction models are examined, as well as the primary metrics characterizing the prediction, using the following: MAE, RMSE, R-squared, and sMAPE. Predicting active and reactive power and Total Harmonic Distortion (THD) proves useful for anticipating power quality problems in an Electric Arc Furnace (EAF). By reducing the EAF’s impact on the power system, accurate predictions will anticipate and minimize disturbances, optimize energy consumption, and improve grid stability. This research’s principal scientific contribution is the development of a hybrid deep neural network that integrates Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) layers. This deep neural network was designed to predict power quality metrics, including active power, reactive power, distortion power, and Total Harmonic Distortion (THD). The proposed methodology indicates an important step in improving the accuracy of power quality forecasting for Electric Arc Furnaces (EAFs). The hybrid model’s ability for analyzing both time-series data and complex nonlinear patterns improves its predictive accuracy compared to traditional methods.

A Novel NR-DA-Based ANN for SHEPWM in Cascaded Multilevel Inverters for Renewable Energy Applications

Harmonics is the major power quality problem caused by nonlinear devices, leading to malfunction or operational halt of the system. Low-order harmonics increase vibration and heat generation in motors. Therefore, controlling harmonics in the output waveform is the prime target for industrial applications to avoid economic loss. Selective harmonic elimination pulse width modulation (SHEPWM) is one of the techniques used for eliminating or minimizing selected harmonics in the output voltage waveform. This paper utilizes the Newton-Raphson method and Dragonfly Algorithms to calculate optimum switching angles for a Cascaded H-bridge Multilevel inverter (CHBMLI). The algorithms use non-linear equations to calculate the Switching Angles of MLI. The Dragonfly algorithm requires several iterations to reach an optimum solution. For complex problems, this algorithm becomes computationally expensive and time-consuming. A lookup table addresses the limitation by offline training an Artificial Neural Network (ANN) to generate the optimum switching angle for a given modulation index. Neural Fitting Tool in MATLAB software is used to train the ANN model. The simulation is performed using MATLAB SIMULINK software for both 5-level and 7-level CHBMLI configuration. The Dragonfly algorithm-based ANN achieves THD 8.84% when the modulation index (M) equals 0.8 for a 7-level inverter and THD 14.91% for a 5-level inverter and effectively minimizes third and fifth-order harmonics.

The problem of quality of qualified professional teacher training in Turkey

The education system of Ukraine is at the stage of European integration changes and internal modernization processes. The characteristic trends of this period are the realization of one's own national experience, its comparison with European and world traditions, as well as the need to analyze and highlight the most effective progressive ideas and advanced concepts that exist in the world space, the possibility of their adaptation to the needs of national education. Therefore, institutions of higher education must train qualified teachers, creative, possessing a high level of pedagogical skills, realizing their own autonomy and capable of developing original solutions. The novelty of the problematics of the studied topic is that the problem of the quality of professional training of a qualified teacher in Turkey has not yet found a comprehensive reflection and systematic generalization in the Ukrainian scientific and pedagogical discourse. Therefore, the goal of the study was to formulate the problem of the quality of professional training of a qualified teacher in Turkey. To implement the goals and objectives of scientific research, the authors relied on the following materials and methods of scientific research: 1) theoretical – analysis of scientific databases, electronic repositories of institutions of higher education, scientific institutions and libraries where dissertation abstracts, scientific publications and other scientific works are stored; systematization and generalization of research results; 2) empirical – conversations, correspondence with Turkish scientists and university professors; pedagogical supervision. The authors formulated the problem of the quality of training of a qualified teacher, which is a widely discussed problem for many years in the world and in Turkey itself, which proves the need to rethink the definition and boundaries of the teaching profession. On the basis of the generalization and systematization of the obtained results, the authors formulated the concept of "qualified teacher" – this is a novice teacher who has successfully completed training on an accredited educational program of initial teacher training. Scientific and practical recommendations on the use of constructive ideas of the Turkish experience to improve the quality of teacher training in the system of continuous pedagogical education of Ukraine are substantiated

Sliding mode control based dynamic voltage restorer for voltage sag compensation

The reliability and efficiency of electrical power systems are critical for modern industries, which increasingly rely on electric machines and devices. However, power electronic converters add non-linear demands to the electric power distribution systems (EPDS), that can cause power quality (PQ) problems, which could harm electric devices. As a result, EPDS are pointed for the most effective and economical techniques to enhance PQ. Therefore, Dynamic Voltage Restorer (DVR) is employed to maintain voltage stability in modern EPDS. This study focuses on the application of Sliding Mode Control (SMC) in DVRs to protect against voltage dips, enhancing power system sustainability. It details the construction, operation, and control methods of DVRs, comparing the conventional PI-controller with SMC. The arctic puffin optimizer is utilized to get the optimum gains of the PI regulator, and at the same time, it is used to specify the optimal sliding surface required for SMC. A distribution system contains DVR is suggested and implemented utilizing Simulink/MATLAB. The simulation results of the DVR employing SMC and the conventional PI-controller demonstrate a marked improvement in voltage regulation and power quality. Specifically, the SMC-based DVR exhibited a reduction in voltage sag duration and magnitude by approximately 30 % compared to the PI-controller. Additionally, the total harmonic distortion (THD) in the load voltage was reduced by 25 %, indicating a significant enhancement in power quality. These improvements of the enhanced performance of the SMC-based DVR supports the seamless integration of renewable energy sources, which often introduce variability into the power grid.

Decision making methods used for improving power quality to meet the sustainable energy targets

A higher quality energy demand in line with sustainable goals has led researchers to new studies. The concept of power quality has become very popular in this direction in the last decade. Decision making concept, which is another important and popular concept, has become an indispensable tool in solving difficult decision making problems in many areas. The main starting point of this study was to unearth the crucial studies on “decision making for a better power quality,” which is at the intersection of these two important issues. In this direction, the deficiency in the literature was noticed, the related studies were examined, and the methodological features and prominent aspects of the studies in different categories were examined. The active use of many decision-making methods in the search for solutions to different power quality problems has been demonstrated. The conclusion that decision-making mechanisms offer effective solutions in solving power quality problems has been more clearly emphasized in this study. This review furnishes researchers with crucial insights into current trends in power quality and multi-criteria decision-making research. It also delineates potential avenues for additional research in this increasingly popular field.

Problems of the Service Quality in Georgia

In the article, the essence and importance of service quality problems in various sectors of Georgia are discussed. Selected and characterized factors that are the main provoking circumstances of the problem. The research results are evaluated, the ways of improvement and improvement of service quality are described in detail, critical conclusions and important recommendations are presented. Keywords: Problems of the service, service Quality.

Timely Quality Problem Resolution in Peer-Production Systems: The Impact of Bots, Policy Citations, and Contributor Experience

Online peer-production systems create value by enabling people to participate in the production of a common good such as an open encyclopedia by building freely on each other’s work. Fixing quality problems in peer production in a timely manner is critical because millions of people rely on peer-produced content for learning and decision making. The longer low-quality content remains in place, the more it can harm the reputation of a peer-production system and diminish the capability of the system to maintain its contributor base. We study different mechanism affecting the timeliness of quality problem resolution in Wikipedia and find that the speedy resolution of quality problems depends on the successful integration of software robots (bots) and the careful calibration of policy citations to the different levels of experience among contributors. Most control mechanisms found in firm-based production do not apply to peer production, and instead, quality control in peer production must leverage the strengths of different contributors and harness the benefits of technological support and adaptive policy frameworks to improve productivity and achieve high-quality outcomes.

Terahertz Spectroscopy in the Field of Food Safety: A Review

with the continuous improvement of people's living standards, food quality and safety problem has caused the wide attention of the whole society, the traditional food quality detection method has certain limitations, terahertz spectral technology (THZ) is a new spectral detection technology, its strong perspective, efficient spectral resolution ability, special fingerprint and nondestructive, in the field of food quality and safety detection has important application value. Due to the high efficiency of food quality and the need for accurate nondestructive testing, automated nondestructive testing for food quality and safety has aroused great interest from the food sector. Faster, accurate, reliable and simplified techniques are needed to classify good quality. With their unique low-energy, penetrating, rapid and non-destructive techniques are widely used as a non-destructive and cost-effective method in the food sector. This paper summarizes the principles and instruments of terahertz spectrum, and introduces the research progress of terahertz spectral detection in food, analyzes the problems in the field of food quality and safety detection, and discusses the future development trend of terahertz spectral technology.

How Local Pollution and Transboundary Air Pollution Impact Air Quality in Jakarta?

Jakarta’s rapid population growth and industrialization have severely impacted air quality, with limited monitoring stations and increasingly complex pollutant sources influenced by meteorological factors. Source apportionment studies are critical for addressing urban air quality issues because knowing the source of pollution is an important step in overcoming urban air quality problems. This study uses the Geographically Weighted Regression (GWR) and Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) models to analyze the distribution and sources of NO2, SO2, and CO in Jakarta and surrounding areas. Data includes spatial information from road networks, industries, and the energy sector during the wet (December) and dry (June) months of 2023. GWR results show that network density significantly contributes to pollutant concentrations, particularly during the dry month, while industrial and energy sectors display seasonal variations. The HYSPLIT model indicates that in December, air masses carrying pollutants mainly originate from the northwest and north, and in June, from the southeast and southwest. These patterns underscore the regional influence of transboundary air pollution from industrial and energy plants. The combined GWR and HYSPLIT findings provide a comprehensive understanding of air pollution dynamics and the critical role of meteorological factors in pollutant distribution.

An improved Coati Optimization Algorithm with multiple strategies for engineering design optimization problems

Aiming at the problems of insufficient ability of artificial COA in the late optimization search period, loss of population diversity, easy to fall into local extreme value, resulting in slow convergence and lack of exploration ability; In this paper, an improved COA algorithm based on chaotic sequence, nonlinear inertia weight, adaptive T-distribution variation strategy and alert updating strategy is proposed to enhance the performance of COA (shorted as TNTWCOA). The algorithm introduces chaotic sequence mechanism to initialize the position. The position distribution of the initial solution is more uniform, the high quality initial solution is generated, the population richness is increased, and the problem of poor quality and uneven initial solution of the Coati Optimization Algorithm is solved. In exploration phase, the nonlinear inertial weight factor is introduced to coordinate the local optimization ability and global search ability of the algorithm. In the exploitation phase, adaptive T-distribution variation is introduced to increase the diversity of individual population under low fitness value and improve the ability of the algorithm to jump out of the local optimal value. At the same time, the alert update mechanism is proposed to improve the alert ability of COA algorithm, so that it can search within the optional range. When Coati is aware of the danger, Coati on the edge of the population will quickly move to the safe area to obtain a better position, while Coati in the middle of the population will randomly move to get closer to other Coatis. IEEE CEC2017 with 29 classic test functions were used to evaluate the convergence speed, convergence accuracy and other indicators of TNTWCOA algorithm. Meanwhile, TNTWCOA was used to verify 4 engineering design optimization problems, such as pressure vessel optimization design and welding beam design. The results of IEEE CEC2017 and engineering design Optimization problems are compared with Improved Coati Optimization Algorithm (ICOA), Coati Optimization Algorithm (COA), Golden Jackal Optimization Algorithm (GJO), Osprey Optimization Algorithm (OOA), Sand Cat Swarm Optimization Algorithm (SCSO), Subtraction-Average-Based Optimizer (SABO). The experimental results show that the improved TNTWCOA algorithm significantly improves the convergence speed and optimization accuracy, and has good robustness. Three‑bar truss design problem, The Gear Train Design Problem, Speed reducer design problem shows a strong solution advantage. The superior optimization ability and engineering practicability of TNTWCOA algorithm are verified.

A method for quality fault diagnosis based on Bayesian network D-cut set

Abstract Quality issues are important factors restricting the development of hydrogen fuel cells. Quality problems in hydrogen fuel cells may stem from various stages, including technology, preparation processes, equipment used, usage environment, and personnel operation. Moreover, different problems are coupled with each other, resulting in the characteristics of one cause with multiple effects and multiple causes with one effect in the quality issues of hydrogen fuel cells. However, there are often similarities between these qualities in high-level information, such as mechanism, object, and behaviour, which are reflected in the similarity of network models in Bayesian diagnostic networks. Therefore, this article discusses how to reuse the Bayesian diagnosis network of previous quality problem cases to construct a Bayesian diagnosis network for hydrogen fuel cell quality problem diagnosis, thereby effectively improving the diagnosis efficiency of hydrogen fuel cell quality problems.