Effect Analysis Model Research Articles

Fuzzy failure mode and effect analysis model for operational supply chain risks assessment: an application in canned tuna manufacturer in Thailand

Fuzzy failure mode and effect analysis model for operational supply chain risks assessment: an application in canned tuna manufacturer in Thailand

Image Sentiment Analysis via Active Sample Refinement and Cluster Correlation Mining

Training an effective image sentiment analysis model using high-quality samples and the implicit cross-modal semantics among heterogeneous features is still challenging. To address this problem, we propose an active sample refinement (ASR) strategy to obtain sufficient high-quality images with definite sentiment semantics. We mine the cluster correlation among the heterogeneous SENet features. Discriminative cross-modal semantics is generated to train an effective but robust image classifier. Ensemble learning is employed to further boost performance. Our method outperforms other competitive baselines, demonstrating its effectiveness and robustness. Meanwhile, the ASR strategy is a useful supplement to the current data augmentation method.

The Outcomes of XEN Gel Stent Implantation: A Systematic Review and Meta-Analysis

PurposeXEN gel stents are used for the treatment of open-angle glaucoma (OAG), including primary and secondary glaucoma that are uncontrolled by previous medical therapy and cases with previous failed surgery. Our aim was to systematically review of the clinical data of currently published ab-interno XEN gel stents with an emphasis on intraocular pressure (IOP), antiglaucoma medication outcomes, and safety profiles.MethodsWe analyzed all of the publications (MEDLINE, EMBASE, Cochrane Library) on the ab-interno XEN gel stent to evaluate the reduction in IOP and antiglaucoma medications following the procedure. The primary outcomes measured for the meta-analysis were reduction in IOP and anti-glaucoma medications. The secondary outcome were adverse events. For each study, we used a random effects analysis model to calculate the mean difference and 95% confidence intervals for the continuous results (reduction in IOP and antiglaucoma medications) using the inverse variance statistical method.ResultsFive hundred twenty-seven articles were checked and 56 studies were found to be relevant with a total of 4,410 eyes. There was a significant reduction in IOP as well as in the number of medications required in patients treated with ab-interno XEN implant either alone or combined with cataract surgery. This new treatment for various types of glaucoma reduced the IOP by 35% to a final average close to 15 mmHg. This reduction was accompanied by a decrease in the number of antiglaucoma medications in all the studies, approximately 2 classes of medication at the price of more needlings. The overall complete success rate was 21.0–70.8% after 2 years using strict criteria originally designed to record success rate in filtration surgery. The incidence of complications vision-threatening was low at <1%.ConclusionsXEN gel stent was effective and safe for primary and secondary OAG. Further studies should be performed to investigate the impact of ethnicity on the success and failure rate after XEN implantation.

City association pattern discovery: A flow perspective by using cultural semantic similarity of place name

The study of cultural similarity between cities is of great significance for understanding the association of cities. However, there are still the following challenges in measuring and expressing cultural similarities: finding reliable data source to reveal urban cultural features and designing effective analysis and visualization model for cultural similarity. As a cultural symbol, place name can effectively reflect urban cultural characteristics. Therefore, this paper constructs a cultural eigenvector based on the semantics of place names. A measurement method is proposed to identify the cultural similarity between different cities and to explore and express similar patterns. In the form of OD flow, a cultural semantic similarity flow (CSSF) is defined to formally express cultural relations between cities. The cultural semantic similarity flow contains three patterns, namely one-to-one patterns, one-to-many patterns, and one-to-all patterns. These patterns explain the form of cultural similarity among cities from different perspectives, as follows: (a) One-to-one patterns may well represent the potential path of cultural diffusion; (b) one-to-many patterns reveal the regional cultural center to some extent; (c) one-to-all patterns show the spatial differentiation of culture on global scale. This general analysis and visualization method aim to accurately quantitatively analyze cultural similarities and their forms.

The effect of GLP-1 receptor agonists in pre-clinical rodent models of Parkinson’s disease: A systematic review and meta-analysis

BackgroundParkinson’s Disease (PD) is a progressive neurodegenerative condition associated with significant morbidity. Currently, there are limited pharmacological options and none of the therapies available are disease-modifying. This systematic review and meta-analysis considers a novel drug class through the research question – in pre-clinical rodent models of PD, is GLP-1 receptor agonist therapy neuroprotective when compared to vehicle controls? MethodsA literature search was conducted to locate relevant pre-clinical studies. Two separate outcomes were considered. The primary outcome was indicators of dopaminergic neurotransmission. The secondary outcome was indicators of motor symptoms. Untreated PD models were compared to PD-models treated with GLP-1 receptor agonists. The final meta-analysis was conducted using the Cochrane RevMan software and represented continuous data using the inverse variance statistical method and random effects analysis model. The final study statistic was represented as an SMD value with a p-value < 0.05 considered statistically significant. Study heterogeneity and publication bias was assessed using I2 values and funnel plots respectively. ResultsEleven studies fit the inclusion criteria and were included in the final analysis. For the primary outcome (n = 128), there was a statistically significant relative improvement of dopaminergic neurotransmission (SMD 1.71, 95% CI = 0.74–2.68, p = 0.0005, I2 = 76%). For the secondary outcome (n = 280), there was a statistically significant improvement in motor outcomes (SMD 2.11, 95% CI = 1.14–3.09, p < 0.0001, I2 = 89%). ConclusionsGLP-1 receptor agonist therapy is neuroprotective in pre-clinical models of PD. This study provides the clinical foundation and research support for the design of rigorous clinical trials to further investigate these results in human PD populations.

Evaluation of approaches for accommodating interactions and non-linear terms in multiple imputation of incomplete three-level data.

Three-level data structures arising from repeated measures on individuals clustered within larger units are common in health research studies. Missing data are prominent in such studies and are often handled via multiple imputation (MI). Although several MI approaches can be used to account for the three-level structure, including adaptations to single- and two-level approaches, when the substantive analysis model includes interactions or quadratic effects, these too need to be accommodated in the imputation model. In such analyses, substantive model compatible (SMC) MI has shown great promise in the context of single-level data. Although there have been recent developments in multilevel SMC MI, to date only one approach that explicitly handles incomplete three-level data is available. Alternatively, researchers can use pragmatic adaptations to single- and two-level MI approaches, or two-level SMC-MI approaches. We describe the available approaches and evaluate them via simulations in the context of three three-level random effects analysis models involving an interaction between the incomplete time-varying exposure and time, an interaction between the time-varying exposure and an incomplete time-fixed confounder, or a quadratic effect of the exposure. Results showed that all approaches considered performed well in terms of bias and precision when the target analysis involved an interaction with time, but the three-level SMC MI approach performed best when the target analysis involved an interaction between the time-varying exposure and an incomplete time-fixed confounder, or a quadratic effect of the exposure. We illustrate the methods using data from the Childhood to Adolescence Transition Study.

Data Envelopment Analysis and Analytics Software for Optimizing Building Energy Efficiency

This research was motivated by the need to identify the most effective Data Envelopment Analysis (DEA) model and associated data analytics software for measuring, comparing, and optimizing building energy efficiency. By analyzing literature sources, the authors identified several gaps in the existing DEA approaches that were resolved in this research. In particular, the authors introduced energy efficiency indices like energy consumption per square foot and per occupant as a part of DEA models’ outputs. They also utilized inverse and min-max normalized output variables to resolve the issue of undesirable outputs in the DEA models. The evaluation of these models was done by utilizing various data analytics software including Python, R, Matlab, and Excel. The authors identified that the CCR DEA model with inverse output variables provided the most reliable energy efficiency scores, and the Python’s PyDEA package produces the most consistent efficiency scores while running the CCR model.

The Impacts of Material Uncertainty in Electro-Migration of SAC Solder Electronic Packaging by Monte Carlo-Based Stochastic Finite-Element Model

Electro-migration (EM) induced by electric current under the conditions of multi-physical fields in integrated circuits and electronic packaging is a crucial factor that affects reliability and safety of the entire system. The uncertainty quantification and propagation in the EM process are the challenging issues that deserve more attention. In this paper, the uncertainties in the related material parameters of Sn-Ag-Cu (SAC) solder and copper conductors are taken into consideration based on the stochastic finite element model. The corners and edges of the contact surface in SAC solder are the most dynamic and active places with the maximum concentration gradient. This reaches a satisfied agreement with the experimental results and parallel numerical investigations in the literatures. The extreme values of the concentration and its gradient in each time step are computed and recorded. The accuracy and convergence of results are confirmed by the comparison of different period durations and time step scales with discrete time points. Furthermore, the probability density distribution, mean and variance of the extreme values in different time steps are recorded and compared. Based on the huge database provided by the Monte Carlo based stochastic finite element model (MC-SFEM), the correlations between the material parameters and the concentration as well as the concentration gradients are analyzed. The proposed MC-SFEM is a feasible and effective model for the comprehensive analysis of EM with the potential in uncertainty and reliability analysis.

Effect of intra-partum Oxytocin on neonatal encephalopathy: a systematic review and meta-analysis

BackgroundOxytocin is widely used for induction and augmentation of labour, particularly in low- and middle-income countries (LMICs). In this systematic review and meta-analysis, we examined the effect of intra-partum Oxytocin use on neonatal encephalopathy.MethodsThe protocol for this study was registered with PROSPERO (ID: CRD42020165049). We searched Medline, Embase and Web of Science Core Collection databases for papers published between January 1970 and May 2021. We considered all studies involving term and near-term (≥36 weeks’ gestation) primigravidae and multiparous women. We included all randomised, quasi-randomised clinical trials, retrospective studies and non-randomised prospective studies reporting intra-partum Oxytocin administration for induction and/or augmentation of labour. Our primary outcome was neonatal encephalopathy. Risk of bias was assessed in non-randomised studies using the Risk Of Bias In Non-randomised Studies of Interventions (ROBINS-I) tool. The RoB 2.0 tool was used for randomised studies. A Mantel-Haenszel statistical method and random effects analysis model were used for meta-analysis. Odds ratios were used to determine effect measure and reported with 95% confidence intervals.ResultsWe included data from seven studies (6 Case-control studies, 1 cluster-randomised trial) of which 3 took place in high-income countries (HICs) and 4 in LMICs. The pooled data included a total of 24,208 women giving birth at or after 36 weeks; 7642 had intra-partum Oxytocin for induction and/or augmentation of labour, and 16,566 did not receive intra-partum Oxytocin. Oxytocin use was associated with an increased prevalence of neonatal encephalopathy (Odds Ratio 2.19, 95% CI 1.58 to 3.04; p < 0.00001).ConclusionsIntra-partum Oxytocin may increase the risk of neonatal encephalopathy. Future clinical trials of uterotonics should include neonatal encephalopathy as a key outcome.

The Saturation Effect of Body Mass Index on Bone Mineral Density for People Over 50 Years Old: A Cross-Sectional Study of the US Population.

Background: Previous studies had revealed that Body Mass Index (BMI) positively affected Bone Mineral Density (BMD). However, an excessively high BMI was detrimental to health, especially for the elderly. Moreover, it was elusive how much BMI was most beneficial for BMD in older adults to maintain.Objective: To investigate whether there was a BMI saturation effect value that existed to maintain optimal BMD.Methods: A cross-sectional study was conducted using the datasets of the National Health and Nutrition Examination Survey (NHANES) 2005–2006, 2007–2008, 2009–2010, 2013–2014, and 2017–2018. After adjusting for covariates, an analysis of the association between BMI and BMD in different femoral regions (Total femur, Femoral neck, Trochanter, Intertrochanter, and Ward's triangle) and lumbar spine regions (Total spine, L1, L2, L3, and L4) in the whole population was performed using the multivariate linear regression models, smoothing curve fitting, and saturation effects analysis models. Then, subgroup analyses were performed according to gender, age, and race.Results: A total of 10,910 participants (5,654 males and 5,256 females) over 50 years were enrolled in this population-based study. Multivariate linear regression analyses in the population older than 50 years showed that BMI was positively associated with femoral BMD and lumbar spine BMD (P < 0.001, respectively). Smoothing curve fitting showed that the relationship between BMI and BMD was not simply linear and that a saturation value existed. The saturation effect analysis showed that the BMI saturation value was 26.13 (kg/m2) in the total femur, 26.82 (kg/m2) in the total spine, and showed site-specificity in L1 (31.90 kg/m2) and L2 (30.89 kg/m2). The saturation values were consistent with the whole participants in males, while there was high variability in the females. BMI saturation values remained present in subgroup analyses by age and race, showing specificity in some age (60–70 years old) groups and in some races.Conclusions: Our study showed a saturation value association between BMI and BMD for people over 50 years old. Keeping the BMI in the slightly overweight value (around 26 kg/m2) might reduce other adverse effects while obtaining optimal BMD.

A likelihood‐based ORESTE method for failure mode and effect analysis (FMEA) based risk analysis problem under interval type‐2 fuzzy environment

AbstractThe failure mode and effect analysis (FMEA) model is an effective safety system analysis method to identify and analyze potential risks. Nevertheless, the traditional FMEA possesses deficiencies associating with questionable multiplication procedure, overlooking uncertainty and interaction relationships among risk factors. Besides, determining the risk priority considering the detailed interrelation among the failure modes is beyond the capability of current FMEA models, especially in the case of complex preference information. Thus, this paper proposed an extended FMEA by integrating ORESTE (organization, rangement et Synthèse de données relarionnelles, in French) method, Heronian Mean (HM) operator, and interval type‐2 fuzzy sets (IT2FSs). First, the IT2FSs are utilized to depict the complex risk preference information. Second, the HM operator, as an aggregated tool, is utilized to obtain collective evaluation information, which can fully reflect the overall correlations among individual risk assessment information. Then, an extended ORESTE method based on the likelihood of IT2FSs is utilized to obtain the priority ranking orders of failure modes. In this extended method, a new IT2FSs global preference score is constructed to determine the weak ranking. And, three new preference intensities based on the likelihood of IT2FSs are constructed to determine PIR (preference, indifference, and incomparability) structure which can show the detailed relationships between failure modes. Finally, an illustrative example of a nuclear reheat valve system is selected to verify its application and efficiency.

Intervention Effect of Nursing Intervention Based on Failure and Effect Analysis Model on the Quality and Stress State of Patients Undergoing General Anesthesia during Recovery Period

Intervention Effect of Nursing Intervention Based on Failure and Effect Analysis Model on the Quality and Stress State of Patients Undergoing General Anesthesia during Recovery Period

An Effective Thermal Model of Coated Particle Dispersed Fuel with High Packing Fraction

Accurate prediction of the temperature distribution in nuclear fuel is an important step for the design and screening of multi-coating particle dispersed nuclear fuel. This study aims to establish an effective thermal model and numerical solutions for macro scale heat transfer analysis by investigating the effective thermal properties of multi-coating particles and the dispersed fuel bulk. The effects of particle arrangement, sizes, clustering on temperature distribution inside nuclear fuel element was studied with the established effective model. This study helps clarify the micro- and macro- scale heat transfer mechanism of multi-coating particle dispersed fuel, and provides a guidance for the design, optimization and safety analysis of this type of fuel.

Case Study of Expected Loss Failure Mode and Effect Analysis Model Based on Maintenance Data

Failure mode and effect analysis (FMEA) is one of the most widely employed pre-evaluation techniques to avoid risks during the product design and manufacturing phases. Risk priority number (RPN), a risk assessment indicator used in FMEA, is widely used in the field due to its simple calculation process, but its limitations as an absolute risk assessment indicator have been pointed out. There has also been criticism of the unstructured nature and lack of systematicity in the FMEA procedures. This work proposes an expected loss-FMEA (EL-FMEA) model that organizes FMEA procedures and structures quantitative risk assessment metrics. In the EL-FMEA model, collectible maintenance record data is defined and based on this, the failure rate of components and systems and downtime and uptime of the system are calculated. Moreover, based on these calculated values, the expected economic loss is computed considering the failure detection time. It also provides an alternative coefficient to evaluate whether or not a detection system is installed to improve the expected loss of failure. Finally, a case study was conducted based on the maintenance record data, and the application procedure of the EL-FMEA model was presented in detail, and the practicality of this model was verified through the results.

Interval type-2 fuzzy inference-based failure mode and effect analysis model in a group decision-making setting

PurposeThis paper proposes an integrated IT2F-FMEA model under a group decision-making setting. In risk assessment models, experts' evaluations are often aggregated beforehand, and necessary computations are performed, which in turn, may cause a loss of information and valuable individual opinions. The proposed integrated IT2F-FMEA model aims to calculate risk priority numbers from the experts' evaluations and then fuse experts' judgments using a novel integrated model.Design/methodology/approachThis paper presents a novel failure mode and effect analysis (FMEA) model by integrating the fuzzy inference system, best-worst method (BWM) and weighted aggregated sum-product assessment (WASPAS) methods under interval type-2 fuzzy (IT2F) environment. The proposed FMEA approach utilizes the Mamdani-type IT2F inference system to calculate risk priority numbers. The individual FMEA results are combined by using integrated IT2F-BWM and IT2F-WASPAS methods.FindingsThe proposed model is implemented in a real-life case study in the furniture industry. According to the case study, fifteen failure modes are considered, and the proposed integrated method is used to prioritize the failure modes.Originality/valueMamdani-type singleton IT2F inference model is employed in the FMEA. Additionally, the proposed model allows experts to construct their membership functions and fuzzy rules to capitalize on the experience and knowledge of the experts. The proposed group FMEA model aggregates experts' judgments by using IT2F-BWM and IT2F-WASPAS methods. The proposed model is implemented in a real-life case study in the furniture company.

Organization model, vertical integration, and farmers' income growth: Empirical evidence from large-scale farmers in Lin'an, China.

Since China’s reform and opening-up in 1978, the income of rural residents has increased when compared with that of urban residents. However, the income growth rate of farmers is relatively low, and the income gap between urban and rural areas is widening. Using a sample of 1,325 large-scale farming households in Lin’an, this study constructs a theoretical path for how the level of vertical integration and an organization model affect farmers’ income levels and empirically tests the path using a mediation effect analysis model. The results indicate that organization models and vertical integration are important factors that affect farmers’ income levels. The total income and agricultural operation income of farmers who participate in agricultural operation organizations are greater than that of farmers who do not participate in an operation organization. In addition, the total income and agricultural operation income of farmers who produce and process and those who produce, process, and sell are higher than those of farmers who only produce. A farmers’ organization model has both a direct and an indirect positive influence on their income level, with the indirect positive influence coming through the mediating variable of vertical integration. The application of the organizational model can promote the growth of rural households’ total family income and agricultural income by 13.48% and 14.48% respectively, consisting of direct increases of 9.67% and 10.19%, and indirect increases of 3.81% and 4.29% through vertical integration. The results also show that access to credit, agricultural technology training, and the farmer’s education level have significant positive impacts on farming income levels. The findings suggest ways to increase farmers’ income by perfecting agricultural management organization systems, promoting agricultural industrialization, strengthening rural financial support, improving agricultural technical training for farmers, and increasing their level of education.

Research on the Effect of English Talents Gathering Based on Big Data Hotspot Collection Technology

Talent is the best group in human resources, and the Talents are the best group in human resources, and English-speaking talents are the most dynamic factor in productivity. In order to improve the quantitative analysis ability of the English talent aggregation effect, the English talents aggregation effect analysis model is proposed based on large-scale data collection technology. The collection information flow model of the hotspot big data of English talents aggregation effect is constructed. The high-dimensional feature grouping method is used to reconstruct the hotspot big data of the English talents aggregation effect. The piecewise linear test method is used to analyze the statistical characteristics of the hotspot big data of the English talents aggregation effect and extract the frequent vector set which reflects the hot big data category attribute of the English talents aggregation effect. According to the result of feature extraction, the fuzzy English talents aggregation is processed to realize the fusion of big data information of English talents aggregation effect hotspot. Combined with quantitative analysis method, the automatic classification of big data association rules is realized. The experimental and simulation results show that the proposed method is more accurate and effective than the traditional methods in collecting hotspot data, which is 26% and 76% higher than the traditional methods. This method has better accuracy and improved data aggregation effect in collecting hotspot data of English talents gathering and has strong collecting ability and characteristics. The research improved hotspot big data’s English talents gathering effect.

Vaginal progesterone for prevention of preterm birth in asymptomatic high-risk women with a normal cervical length: a systematic review and meta-analysis protocol

BackgroundPreterm birth (PTB) is estimated to affect 14.9 million babies globally every year. Global rates of PTB continue to increase from 9.8 to 10.6% over a 15-year period from 2000 to 2014. Vaginal progesterone is commonly used by clinicians as a prevention strategy, with recent evidence affirming the benefit of vaginal (micronised) progesterone to prevent PTB in women with a shortened cervix (< 25 mm). Given the low incidence of a short cervix at mid-gestation in high-risk populations further evidence is required. The objective of this review is to determine if vaginal progesterone reduces spontaneous preterm birth (sPTB) before 37 weeks in asymptomatic high-risk women with a singleton pregnancy with a normal mid-gestation cervical length.MethodsStudies will be sourced from MEDLINE, Embase and Cochrane Register of Trials (CENTRAL) from their inception onwards with the search terms ‘progesterone’ and ‘preterm birth’. Studies will be screened and included if they assess vaginal progesterone compared to placebo in women with a normal cervical length. The primary outcome will be sPTB < 37 weeks, with secondary outcomes of sPTB < 34 weeks.Two independent reviewers will conduct study screening at abstract and full text level, data extraction and risk of bias assessment with disagreements resolved by an experienced researcher. The Mantel-Haenszel statistical method and random effects analysis model will be used to produce treatment effect odds ratios and corresponding 95% confidence intervals.DiscussionThis review will assess the current body of evidence and provide clarity regarding the potential benefits and best practice of use of vaginal progesterone in asymptomatic women with high-risk singleton pregnancies and normal cervical length.Trial registrationPROSPERO CRD42020152051

Effect of Chronic Resistance Training on Circulating Irisin: Systematic Review and Meta-Analysis of Randomized Controlled Trials.

Irisin seems to play an important role in several chronic diseases, however, the interactions between chronic training and irisin are still unclear. The purpose of this systematic review and meta-analysis was to examine the effect of chronic resistance training on circulating irisin in adults. Literature search was conducted in PubMed, Web of Science and EBSCOhost (Academic Search Complete) until December 2020. Randomized controlled trials researching irisin levels after a resistance training program for at least 8 weeks among an adult population were eligible. Other inclusion criteria comprised recruiting a control group and reporting circulating irisin through ELISA kits. Cohen’s d effect size and subgroup analyses (95% confidence level) were calculated using a random effects analysis model. Data of the seven included studies comprising 282 individuals showed an increasing and non-significant tendency after a resistance training program (d = 0.58, 95% CI: −0.25 to 1.40, p = 0.17). Subgroup analyses showed significant increases for the older adults group (p < 0.001) and when training is demanding and progressive in terms of intensity (p = 0.03). Data suggest that resistance training programs seem to increase circulating irisin, especially in older adults and in demanding and progressive training programs. However, more studies should be conducted using robust measurement methods, such as mass spectrometry, to better understand the interaction between chronic resistance exercise and irisin.

Difficulties in the Construction of Subway Tunnel Underpass Project and Research on Ecological Restoration Technology

Subway construction is a high-risk construction project, because there are many uncertain factors such as Undercrossing urban hinterland and geological conditions, which lead to the difficulty of organization and management and frequent safety accidents. Therefore, there is an urgent need for effective safety risk analysis model and risk management means to prevent accidents. Aiming at the mechanical problems of surrounding rock and supporting structure of subway tunnel under different construction methods, the dynamic numerical simulation of three pilot tunnel method and upper and lower step method is carried out by using finite element method. The transformation relationship between stress field and displacement field of subway tunnel under different construction procedures is established, and the two construction methods are compared. The test results show that the internal force, axial force and bending moment model of the subway tunnel support structure system established in this paper has a certain guiding significance for the design and safe construction of the subway tunnel.