Browse Articles
Discover research articles across all indexed journals
Static scheduling method for aircraft flat-tail assembly production based on improved bi-level genetic algorithm
Abstract Aircraft flat-tail assembly is a complex process that involves multiple assembly processes, multiple parallel frames, and multi-configuration mixed flow assembly, and its assembly processes exhibit extended processing times (typically measured in days) combined with temporal fluctuations arising from human factors, leading to a certain degree of uncertainty in single-process durations, thereby presenting a complicated flexible flow-shop scheduling problem (FFSP), which is a typical NP-hard problem. Despite its significance, the research on FFSP in aircraft flat-tail assembly production scheduling is limited. This study proposes an improved bi-level genetic algorithm to address the two sub-problems of flat-tail assembly production scheduling: frame assignment and assembly task sequencing. The objective is to minimize the maximum delay penalty cost. A two-stage coding scheme is introduced for frame assignment and task sequencing, respectively. To mitigate genetic algorithms’ convergence to local optima and enhance positive feedback, we implement a variable neighborhood search mechanism combined with elite retention. The efficacy of the improved bi-level genetic algorithm is evaluated through experiments and case studies in enterprises, indicating a significant impact on the assembly production scheduling of flat-tail, with potential applications to similar large and complex equipment. Overall, this study contributes to FFSP research in aircraft flat-tail assembly production scheduling by offering a novel solution approach to effectively address the sub-problems of frame assignment and assembly task sequencing.
Bilateral ankle dorsiflexion force control impairments in older adults
Age-related impairments in ankle dorsiflexion force modulation are associated with gait and balance control deficits and greater fall risk in older adults. This study aimed to investigate age-related changes in bilateral ankle dorsiflexion force control capabilities compared with those for younger adults. The study enrolled 25 older and 25 younger adults. They performed bilateral ankle dorsiflexion force control at 10% and 40% of maximum voluntary contraction (MVC), for vision and no-vision conditions, respectively. Bilateral force control performances were evaluated by calculating force accuracy, variability, and complexity. To estimate bilateral force coordination between feet, vector coding and uncontrolled manifold variables were quantified. Additional correlation analyses were performed to determine potential relationships between age and force control variables in older adults. Older adults demonstrated significantly lower force accuracy with greater overshooting at 10% of MVC than those for younger adults. At 10% and 40% of MVC, older adults significantly showed more variable and less complex force outputs, and these patterns appeared in both vision and no-vision conditions. Moreover, older adults revealed significantly less anti-phase force coordination patterns and lower bilateral motor synergies with increased bad variability than younger adults. The correlation analyses found that lower complexity of bilateral forces was significantly related to increased age. These findings suggest that aging may impair sensorimotor control capabilities in the lower extremities. Considering the importance of ankle dorsiflexion for executing many activities of daily living, future studies may focus on developing training programs for advancing bilateral ankle dorsiflexion force control capabilities.
Research on visual comfort of color matching in space station experiment module
Robotic fingers can tell objects apart by touch
Assessment of utilization of automated systems and laboratory information management systems in clinical microbiology laboratories in Thailand
Introduction Clinical microbiology laboratories are essential for diagnosing and monitoring antimicrobial resistance (AMR). Here, we assessed the systems involved in generating, managing and analyzing blood culture data in these laboratories in an upper-middle-income country. Methods From October 2023 to February 2024, we conducted a survey on the utilization of automated systems and laboratory information management systems (LIMS) for blood culture specimens in 2022 across 127 clinical microbiology laboratories (one each from 127 public referral hospitals) in Thailand. We categorized automated systems for blood culture processing into three steps: incubation, bacterial identification, and antimicrobial susceptibility testing (AST). Results Of the 81 laboratories that completed the questionnaires, the median hospital bed count was 450 (range, 150-1,387), and the median number of blood culture bottles processed was 17,351 (range, 2,900-80,330). All laboratories (100%) had an automated blood culture incubation system. Three-quarters of the laboratories (75%, n = 61) had at least one automated system for both bacterial identification and AST, about a quarter (22%, n = 18) had no automated systems for either step, and two laboratories (3%) outsourced both steps. The systems varied and were associated with the hospital level. Many laboratories utilized both automated systems and conventional methods for bacterial identification (n = 54) and AST (n = 61). For daily data management, 71 laboratories (88%) used commercial microbiology LIMS, three (4%) WHONET, three (4%) an in-house database software and four (5%) did not use any software. Many laboratories manually entered data of incubation (73%, n = 59), bacterial identification (27%, n = 22) and AST results (25%, n = 20) from their automated systems into their commercial microbiology LIMS. The most common barrier to data analysis was ‘lack of time’, followed by ‘lack of staff with statistical skills’ and ‘difficulty in using analytical software’. Conclusion In Thailand, various automated systems for blood culture and LIMS are utilized. However, barriers to data management and analysis are common. These challenges are likely present in other upper-middle-income countries. We propose that guidance and technical support for automated systems, LIMS and data analysis are needed.
PNO1 enhances ovarian cancer cell growth, invasion, and stemness via activating the AKT/Wnt/β-catenin pathway
Accuracy and repeatability of the COSMED® Q-NRG max mobile metabolic system
Purpose To investigate the accuracy and repeatability of the Q-NRG Max® metabolic system against a VacuMed metabolic simulator using a wide range of metabolic rates. Methods Sixteen metabolic rates (oxygen consumption 0.9–6 L/min), with different combinations of minute ventilation, oxygen consumption, and carbon dioxide production, were measured for 5 minutes, two times by a single Q-NRG Max® unit over the course of one week. Recordings were performed early in the morning, by the same trained technician, in a ventilated laboratory under the same atmospheric conditions. Accuracy was assessed by ordinary least products (OLP) regression analysis, Bland-Altman plots, intraclass correlation coefficients (ICC), mean percentage differences, technical errors (TE) and minimum detectable change (MDC) for all three variables. This analysis was performed using 10 metabolic rates (oxygen consumption 0.9–4 L/min) and 16 metabolic rates (oxygen consumption 0.9–6 L/min) to allow comparisons with previous research. Intra-device repeatability was performed by absolute percentage differences between measurements (MAPE), ICC, TE, and MDC for the same variables. Repeatability was investigated using 16 metabolic rates. Results High agreement and excellent ICCs (>0.998) were observed for all variables when considering both 10 and 16 metabolic rates. The mean percentage difference, TE and MDC were 0.87%–1.01%, 0.67%–1.07%, 1.55%–2.49%, respectively for the first 10 metabolic rates, and −0.39%–0.65%, 0.58%–1.63%, 1.35%–3.81%, respectively for the 16 metabolic rates. The intra-device repeatability results showed an excellent ICCs (=1.000), MAPE < 0.5%, TE < 1%, and MDC ≤ 2%. Conclusion The Q-NRG Max® is a valid and reliable mobile metabolic system for the measurement of ventilation, oxygen consumption, and carbon dioxide production. Measurements were below the 5% TE and MDC, and 2% MAPE recommended thresholds across a wide range of metabolic rates up to 6 L/min oxygen consumption.
Genome-wide associations of sweetpotato metabolites enhance genomic prediction and identify genes in metabolic and regulatory pathways
Correction: Potentials of invasive Bidens pilosa, Conyza bonariensis and Parthenium hysterophorus species based on germination patterns and growth traits
Brain handedness associations depend on how and when handedness is measured
Correction: Longitudinal mediation analysis of the factors associated with trajectories of posttraumatic stress disorder symptoms among postpartum women in Northwest Ethiopia: Application of the Karlson-Holm-Breen (KHB) method
Adherence to the planetary health diet reduces dietary costs by 21% supporting affordable healthy eating among older adults in Iran
The relationship between body image and nutritional behaviors in adult individuals
Objective Dissatisfaction with body image and maladaptive nutritional behaviors can have profound effects on psychological, social, and physical health and may pave the way for the development of eating disorders. However, research into this topic in the adult population is relatively limited. Therefore, this study aimed to examine various factors affecting dissatisfaction with body image and maladaptive nutritional behaviors in adults living in Türkiye and the relationship between these two concepts. Methods This descriptive study was conducted with 3,153 adult individuals who were ≥ 18 years old living in Türkiye. The data of the study, which was conducted as an online survey, were collected using the Descriptive Information Form, the Body Image Scale, and the Three-Factor Eating Questionnaire. Results Of the participants, 70.1% were women. The mean age was 28.02 ± 9.27 (Min.: 18, Max.: 74) years. The relationship between the mean scores on the total Body Image Scale and Uncontrolled Eating (r = -0.094, p < 0.000), Emotional Eating (r = -0.171, p < 0.001), and Susceptibility to Hunger (r = -0.108, p < 0.001) scores was negative. A statistically significant and positive relationship was detected between the mean scores on the total Body Image Scale and the Cognitive Restraint score (r = 0.089, p < 0.001). Statistically significant relationships were detected in the model adjusted for age and gender between the Body Image Scale and Emotional Eating scores (B = -1.085, p < 0.000), and Cognitive Restraint scores indicated positive relationships (B = 0.848, p < 0.001). Conclusion Body image satisfaction was found to be negatively associated with uncontrolled eating, emotional eating, and susceptibility to hunger. On the other hand, a positive relationship was found between body image satisfaction and cognitive restraint. These findings highlight the critical importance of body image satisfaction on eating behaviors and provide potential insight into prevention and intervention programs to improve body image to promote adaptive eating behaviors in the adult population.
Three decades of atrial fibrillation and flutter epidemiology and risk factors in Iran with a focus on the impact of COVID-19
Abstract Atrial fibrillation/flutter (AF/AFL) is one of the most common sustained heart rhythm disorders in clinical practice and a major public health concern. This study aimed to evaluate the disease burden of AF/AFL in Iran and analyze trends using the Global Burden of Disease (GBD) 2021 data, by age, sex, location, risk factor, and socio-demographic index (SDI), considering the impacts of COVID-19. Data on the prevalence, incidence, disability-adjusted life years (DALYs), deaths, and six attributable risk factors related to AF/AFL in Iran and its 31 provinces from 1990 to 2021 was collected from the GBD 2021 study. The International Classification of Disease (ICD) codes used were I48–48.9 for ICD-10 and 427.3 for ICD-9. The data was sourced from surveys, censuses, vital statistics, and other health-related records. In Iran, in 2021, the AF/AFL age-standardized incidence rate (ASIR) was 40.6 (30.0 to 54.4), the age-standardized prevalence rate (ASPR) was 425.4 (327.2 to 559.2), the age-standardized DALY rate was 72.4 (57.4 to 88.3), and the age-standardized death rate (ASDR) was 3.3 (2.5 to 3.8) per 100,000 population. Following COVID-19, there were significant decreases in age-standardized DALY and death rates over 2019–2021. By province, Fars had the highest AF/AFL ASIR and ASPR in 2021. In addition, East and West Azarbayejan had the highest age-standardized DALY rate and ASDR, respectively. The national incidence, prevalence, DALYs, and death rates of AF/AFL showed an overall increasing trend with age. Males experienced higher rates of incidence and prevalence compared to females. In contrast, females had higher rates of DALYs and deaths compared to males. The burden of AF/AFL increased with advancing age. The risk factor with the highest DALYs and deaths attributable to AF/AFL was high systolic blood pressure. Notably, no remarkable association was found between SDI and the burden of AF/AFL in Iran. The incidence and prevalence of AF/AFL in Iran have significantly increased, highlighting the critical need for cost-effective and nationwide interventions.
Elisabeth Vrba obituary: palaeontologist who solved a problem that vexed Darwin
Data-driven cultural background fusion for environmental art image classification: Technical support of the dual Kernel squeeze and excitation network
This study aims to explore a data-driven cultural background fusion method to improve the accuracy of environmental art image classification. A novel Dual Kernel Squeeze and Excitation Network (DKSE-Net) model is proposed for the complex cultural background and diverse visual representation in environmental art images. This model combines the advantages of adaptive adjustment of receptive fields using the Selective Kernel Network (SKNet) and the characteristics of enhancing channel features using the Squeeze and Excitation Network (SENet). Constructing a DKSE module can comprehensively extract the global and local features of the image. The DKSE module adopts various techniques such as dilated convolution, L2 regularization, Dropout, etc. in the multi-layer convolution process. Firstly, dilated convolution is introduced into the initial layer of the model to enhance the original art image’s feature capture ability. Secondly, the pointwise convolution is constrained by L2 regularization, thus enhancing the accuracy and stability of the convolution. Finally, the Dropout technology randomly discards the feature maps before and after global average pooling to prevent overfitting and improve the model’s generalization ability. On this basis, the Rectified Linear Unit activation function and depthwise convolution are introduced after the second layer convolution, and batch normalization is performed to improve the efficiency and robustness of feature extraction. The experimental results indicate that the proposed DKSE-Net model significantly outperforms traditional Convolutional Neural Networks (CNNs) and other existing state-of-the-art models in the task of environmental art image classification. Specifically, the DKSE-Net model achieves a classification accuracy of 92.7%, 3.5 percentage points higher than the comparative models. Moreover, when processing images with complex cultural backgrounds, DKSE-Net can effectively integrate different cultural features, achieving a higher classification accuracy and stability. This enhancement in performance provides an important reference for image classification research based on the fusion of cultural backgrounds and demonstrates the broad potential of deep learning technology in the environmental art field.
Fault detection of taper roller bearings using tunable Q-factor wavelet transform and fault classification using long–short-term memory network
Abstract Taper roller bearing is a widely used moving component in heavy industrial machinery. Hence, early detection and repair of even minor faults in taper roller bearing is a fault diagnosis and prognosis strategy followed by modern industries. Although many methods for this exist today, the penetration of artificial intelligence and big data analysis into modern industries opens up the possibility of developing better fault diagnosis methods. Such a fault diagnosis and fault classification strategy is going to be discussed in this article. For that, a Tunable Q-factor Wavelet Transform (TQWT) is employed for signal processing, and a Long–Short-Term Memory (LSTM) network is employed for fault classification in this work. It is clear from the experimental findings that the TQWT and LSTM combination can very efficiently and reliably diagnose the faults present in the bearings, and it can classify the types of faults with one hundred percent accuracy. Also, the superiority of the method proposed in this article is confirmed by the fact that it is able to produce better results when compared with the other four combinations of Variational Mode Decomposition (VMD) and Convolutional Neural Network (CNN).