Effect Analysis Model Research Articles

Interactive Visual Simulation Modeling for Structural Response Prediction and Damage Detection

Cameras capture the dynamic behaviors of vibrating structures or systems in videos, and the object in videos can respond to new forces to explore more dynamics by visual simulation methods. However, existing visual simulation models usually suffer from strong noise interference and lack rigorous verification of the simulation accuracy in the field of structural dynamics analysis. In this article, we present an interactive visual simulation modeling (IVSM) method, aiming to build an effective and accurate visual simulation model for structure dynamic response analysis. The IVSM endows the object in videos with the ability to directly and visually respond to new forces, in a way of human–computer interaction. We demonstrate the excellent performance of the IVSM in two application scenarios, i.e., the response prediction and damage detection. In response prediction, experiments, and IVSM simulations are compared to verify the effectiveness and accuracy of the proposed IVSM. The synthesized animations of the IVSM also present high fidelity to the actual experiment. In damage detection, we propose a novel damage magnification technique to build the IVSM. The IVSM simulation results demonstrate the superior performance in detecting and visualizing abnormal behaviors caused by damage.

Analysis on the Mutation and Time-Varying Characteristics of Coal Price System Evolution from the Perspective of Finance

Coal is essential to ensure China’s energy security. The sudden or gradual change of coal price reflects the degree of disequilibrium or expected disequilibrium of coal supply and demand, which will not be conducive to energy security. Therefore, it is important to analyze the change points of coal price and explore the reason of the price fluctuation. This paper analyses the coal price from January 2008 to June 2019 as the perspective of the financial market. Firstly, the PPM-DBSCAN model is used to identify the mutation point of coal price fluctuation. Secondly, path analysis is used to extract the core driving factors that affect coal price. Thirdly, the authors construct a time-varying and time-lag effect analysis model for structural changes of coal price based on the TVP-VAR model. The results show that there are 11 mutation points of coal price fluctuation. Financial market factors, coal supply and demand and alternative factors are the reasons of coal price mutation. The authors find that the imbalance of coal supply and demand in traditional view cannot fully explain the fluctuation of coal price. The impact of the financial market and non-thermal power generation have more influence on the coal price.

Emotion Wheel Attention-Based Emotion Distribution Learning

Emotion distribution learning is an effective multi-emotion analysis model proposed in recent years. Its core idea is to record the expression degree of examples on each emotion through emotion distribution, which is suitable for handling emotion analysis tasks with emotional ambiguity. To solve the problem that the prior knowledge of emotion psychology is seldom considered in the existing emotion distribution learning methods, we propose an Emotion Wheel Attention based Emotion Distribution Learning (EWA-EDL) model. EWA-EDL generates a prior emotion distribution describing the relevance of emotional psychology for each basic emotion, and then directly integrates the prior knowledge based on the emotion wheel into the deep neural network through the attention mechanism. The deep network of EWA-EDL is trained using an end-to-end approach to learn both emotion distribution prediction and emotion classification tasks. The EWA-EDL architecture includes five main parts: input layer, convolutional layer, pooling layer, attention layer and multi-task loss layer. Extensive comparative experiments on eight commonly used textual emotion datasets show that EWA-EDL outperforms the comparison emotion distribution learning methods on both emotion distribution prediction and emotion classification task.

Integrating FMEA and the Kano Model to Improve the Service Quality of Logistics Centers

This study uses the logistics center of a large organic retail store in Taiwan to analyze service blueprint and workflow, identifying the potential points of failure and thus serving as a basis for quality improvement. The failure mode and effect analysis (FMEA) model is an effective problem prevention methodology that can easily interface with many engineering and reliability methods. The utilized method integrates the failure mode and effect analysis (FMEA) and the Kano model to explore the possible occurrence of failures in the internal workflow and services of the studied logistics center. A two-stage survey was conducted. In the first stage, an investigation was conducted by 20 logistics experts on the FMEA’s key service failures. In the second stage, a questionnaire was filled out by 220 store staff to summarize the logistics service quality factors found in the Kano model. The results show that the degree of attention and satisfaction in the priority improvement items when there were service failures vary among the opinions of different internal employees and customers. The participants jointly believed that the items that need improvement are “Damaged incoming goods” and “A shortfall in the quantities of delivered goods”.

A Failure Mode Assessment Model Based on Neutrosophic Logic for Switched-Mode Power Supply Risk Analysis

Reducing the potential risks in the manufacturing process to improve the reliability of the switched-mode power supply (SMPS) is a critical issue for the users’ safety. This paper proposes a novel failure mode and effects analysis (FMEA) model based on hybrid multiple criteria decision-making (MCDM), which adopts neutrosophic set theory into the proposed model. A developed neutrosophic Best Worst method (NBWM) is used to evaluate the weights of risk factors and determine their importance. Secondly, the neutrosophic Weight Aggregated Sum Product Assessments (NWASPAS) method is utilized to calculate the Risk Priority Number (RPN) of the failure modes. The proposed model improves the shortcomings of traditional FMEA and improves the practical applicability and effectiveness of the Best Worst method (BWM) and Weight Aggregated Sum Product Assessments (WASPAS) methods. In addition, this study uses neutrosophic logic to reflect the true judgments of experts in the assessment, which considers authenticity, deviation, and uncertainty to obtain more reliable information. Finally, an empirical case study from an SMPS company headquartered in Taiwan demonstrates the effectiveness and robustness of the proposed model. In addition, by comparing with two other FMEA models, the results show that the proposed model can more clearly reflect the true and effective risks in the assessment. The results can effectively help power supply manufacturers to assess risk factors and determine key failure modes.

Granulized Z-VIKOR Model for Failure Mode and Effect Analysis

In this article, we have developed an improved failure mode and effect analysis (FMEA) model by leveraging the concepts of Z-number, rough number (RN), and probabilistic distance measure. Two new concepts, namely, double upper approximated rough number (DUARN) and granulized Z-number (gZN), a new scheme for measuring distance between two gZNs using weighted similarity and average linkage method, a new risk prioritization model, named, granulized Z-VIKOR, and a scheme for uncertainty assessment using box-plot are proposed. DUARN embodies the notion of double sided upper approximation of an ordinal decision class. gZN is developed using Z-number and DUARN. The distance between two gZNs is computed using the maximum entropy principle that captures the relationship between the A (opinion) and B (reliability of A) parts of gZN. The granulized Z-VIKOR involves synergistic integration of gZN and VIKOR. Both objective and subjective risk measures are computed and a combined risk measure is defined, which considers the interactions among the risk criteria using λ-Shapley index. Two case studies are conducted. Sensitivity and comparative analysis is carried out to demonstrate the applicability, effectiveness, and robustness of the proposed model, as well as its superiority to existing models.

An Effect Analysis Model for Corporate Marketing Mix Based on Artificial Neural Network

Under the boom of market economy in China, marketing mix is being updated constantly to facilitate the all-round development of various industries. To accurate evaluate the effect of corporate marketing mix, this paper designs an effect analysis model for corporate marketing mix based on artificial neural network (ANN). Firstly, a complete evaluation index system (EIS) was created for corporate marketing mix, and the weight of each index was assigned through analytic hierarchy process (AHP). Then, a marketing mix of big marketing + service marketing was proposed, and subject to fuzzy comprehensive evaluation (FCE). Finally, an FCE model was constructed for the effect of corporate marketing mix, based on three-layer backpropagation neural network (BPNN). The effectiveness of the model was verified through experiments. The research findings provide a reference for the application of ANN in other fields of effect analysis.

The Impact of Fossil-Fuels and Biomass Combustion on Outdoor PM2.5 Mass Associations with Respiratory Emergency Department Visits in Dhaka, Bangladesh

Introduction: Developed world studies report associations between particle air pollution and increased risk of emergency department (ED) visits for respiratory diseases, but few were conducted in the south Asia region, where PM2.5 composition is different. Differences are from greater biomass and residue crop-burning, which may have differing health impacts from past PM2.5 studies. Despite that fact, an “equal toxicity” assumption has been made in most global PM2.5 effect estimates. We evaluated this assumption in Dhaka.Methods: The acute effects of PM2.5 and source component’s on 38,233 respiratory ED visits during 2014-2017 were assessed using time-series modeling, adjusted for temporal trend, day of week, holidays, relative humidity, and temperature. Daily counts of respiratory ED visits were obtained from National Institute of Chest Diseases and Hospital. Analyses were conducted to test for seasonal effect modification by including dummy variables for monsoon vs. non-monsoon seasons.Results: PM2.5 yielded statistically significant association with increased respiratory ED visits [Excess Risk, ER=0.88%, 95%CI: 0.37%, 1.39%] per 10 μg/m3 increase. The effect of PM2.5 air pollution per 10 µg/m3 varies with season, with the highest during monsoon season [2.84%, 95%CI: 0.74, 4.91] vs. non-monsoon season [0.77%, 95%CI: 0.23%, 1.31%], suggesting that the effect size per unit mass is much larger when fossil-fuel combustion sources dominated exposures, with effect per unit mass declining when crop-burning and dust sources dominated exposures. A source-specific health effect analysis model using elemental source tracers provided consistent results, with fossil-fuel PM2.5 having a larger ER per 10 μg/m3 than biomass-combustion PM2.5.Conclusion: This study found that the respiratory health in Dhaka is significantly affected by particle air pollution, with greater impact by fossil-fuel PM2.5. This disparity in health impact by PM2.5 source origin should be considered in PM2.5 risk assessment, PM2.5 mitigation measure prioritization, and clinical advice.

Analytical formulation and finite-element implementation technique of a rigorous two-parameter foundation model to beams on elastic foundations

ABSTRACT A simple way of computer implementation of an effective two-parameter subgrade model for the routine analysis of flexible foundations on an elastic subgrade is presented. It is based on a rigorous analytical subgrade model recently proposed by one of the authors that takes full account of the soil–structure interaction. The details of both finite element (FE) and analytical implementation of the model to beams are presented. Fully established closed-form relationships are provided for the subgrade parameters. The essence in the method is that the soil shear resistance within the loaded region is transferred to the foundation element as an additional rigidity in form of a geometric stiffness, thereby leaving the Winkler spring bed to take care of the vertical compression as accustomed. Additional edge springs are introduced to account for the edge forces due to the shear continuity extending beyond the edges. By so doing, the attractiveness of the long-enduring Winkler’s model is maintained, whereas the missing shear interaction is taken care of through the introduction of the geometric stiffness and the edge springs. The FE discretisation is thus limited to the foundation unit alone as initially intended. Subject classification codes: soil–foundation interaction

Effective Macroeconomic Model for GDP Analysis, Albanian Case

The study aims to create a macroeconomic market model, through which to analyze nominal and real GDP, to analyze the impact of prices on GDP, create a model of aggregate demand curve, using price deflator, etc. It aims to calculate the Price Deflator and real GDP starting from 1990, as the first year of calculating the macroeconomic indicators of the Albanian economy. More detailed analysis is focused on the years 2000-2017. It has been proven that the deflator calculated by INSTAT is almost equal to the nominal GDP average of the price increase. This data was used to construct the market model with Albanian economy data, with Aggregate Price and real GDP. The new model of the aggregate market differs from the existing one. It expresses the nominal GDP curve as a listing of real GDP at and has a positive slope. This model enables a detailed analysis of nominal and real GDP can be used by anyone, and for any economy. It is recommended for the government and the Albanian institutions to apply this model, as it is effective. Statistical, econometric, analysis, synthesis, comparison, etc. methods were used in the analysis.

Intracranial hypertension in patients with aneurysmal subarachnoid hemorrhage: a systematic review and meta-analysis.

Aneurysmal subarachnoid hemorrhage (aSAH) is a devastating and life-threatening condition with high mortality and morbidity. Even though there is an association with intracranial pressure (ICP) raise and aSAH, there is a lack of recommendations regarding the indications for ICP monitoring in patients with aSAH. Defining what patients are at a higher risk to develop intracranial hypertension and its role in the functional outcome and mortality in patients with aSAH will be the purpose of the following systematic review and meta-analysis. The primary endpoint is to determine the prevalence and impact on mortality of ICP in patients with aSAH. Secondary endpoints aim to describe the variables related to the development of ICP and the relationship between traumatic and aneurysmal etiology of intracranial hypertension. PubMed, Embase, Central Cochrane Registry of Controlled Trials, and research meeting abstracts were searched up to August 2019 for studies that performed ICP monitoring, assessed the prevalence of intracranial hypertension and the mortality, in adults. Newcastle Ottawa scale (NOS) was used to assess study quality. The statistical analysis was performed using the Mantel-Haenszel methodology for the prevalence and mortality of intracranial hypertension for reasons with a randomized effect analysis model. Heterogeneity was assessed by I2. A total of 110 bibliographic citations were identified, 20 were considered potentially eligible, and after a review of the full text, 12 studies were considered eligible and 5 met the inclusion criteria for this review. One study obtained 7 points in the NOS, another obtained 6 points, and the rest obtained 5 points. Five studies were chosen for the final analysis, involving 793 patients. The rate of intracranial hypertension secondary to aSAH was 70.69% (95% CI 56.79-82.84%) showing high heterogeneity (I2 = 92.48%, p = < 0.0001). The results of the meta-analysis of mortality rate associated with intracranial hypertension after aSAH found a total of four studies, which involved 385 patients. The mortality rate was 30.3% (95% CI: 14.79-48.57%). Heterogeneity was statistically significant (I2 = 90.36%; p value for heterogeneity < 0.001). We found that in several studies, they reported that a high degree of clinical severity scale (Hunt and Hess or WNFS) and tomographic (Fisher) were significantly correlated with the increase in ICP above 20mmHg (P < 0.05). The interpretation of the results could be underestimated for the design heterogeneity of the included studies. New protocols establishing the indications for ICP monitoring in aSAH are needed. Given the high heterogeneity of the studies included, we cannot provide clinical recommendations regarding this issue.

Block-Constraint Laplacian-Regularized Low-Rank Representation and Its Application for Cancer Sample Clustering Based on Integrated TCGA Data

Low-Rank Representation (LRR) is a powerful subspace clustering method because of its successful learning of low-dimensional subspace of data. With the breakthrough of “OMics” technology, many LRR-based methods have been proposed and used to cancer clustering based on gene expression data. Moreover, studies have shown that besides gene expression data, some other genomic data in TCGA also contain important information for cancer research. Therefore, these genomic data can be integrated as a comprehensive feature source for cancer clustering. How to establish an effective clustering model for comprehensive analysis of integrated TCGA data has become a key issue. In this paper, we develop the traditional LRR method and propose a novel method named Block-constraint Laplacian-Regularized Low-Rank Representation (BLLRR) to model multigenome data for cancer sample clustering. The proposed method is dedicated to extracting more abundant subspace structure information from multiple genomic data to improve the accuracy of cancer sample clustering. Considering the heterogeneity of different genome data, we introduce the block-constraint idea into our method. In BLLRR decomposition, we treat each genome data as a data block and impose different constraints on different data blocks. In addition, graph Laplacian is also introduced into our method to better learn the topological structure of data by preserving the local geometric information. The experiments demonstrate that the BLLRR method can effectively analyze integrated TCGA data and extract more subspace structure information from multigenome data. It is a reliable and efficient clustering algorithm for cancer sample clustering.

Oral cannabinoid for the prophylaxis of chemotherapy-induced nausea and vomiting-a systematic review and meta-analysis.

Chemotherapy-induced nausea and vomiting (CINV) is a burdensome adverse event frequently associated with chemotherapy treatment of cancer. Evidence suggests that cannabinoid CB2 receptors are present in brainstem neurons, and thus, there may exist a role for cannabinoids to counter CINV. The aim of this paper is to conduct a systematic review and meta-analysis of the efficacy and safety of oral cannabinoids compared with other treatments as documented in randomized controlled trials (RCTs). A literature search was conducted using Ovid MEDLINE up until December 31, 2018; Embase Classic and Embase up until 2018 week 53; and Cochrane Central Register of Controlled Trials up until November 2018. Study data were extracted and included in this meta-analysis if they reported on at least one of the following efficacy endpoints: no nausea and no vomiting, no nausea, and no vomiting. The Mantel-Haenszel method and random effects analysis model were used, to generate odds ratio (OR) and accompanying 95% confidence intervals (CI). In the setting of prophylactic treatment against both nausea and vomiting, oral cannabinoid was more efficacious than placebo or other studied antiemetic treatments. When controlling for vomiting, oral cannabinoid was equally as efficacious as others. Against nausea, oral cannabinoid was equally as effective as other treatments. A greater percentage of patients administered oral cannabinoid for CINV experienced dysphoria, euphoria, and sedation. Although there exists some evidence suggesting that oral cannabinoids may have a role in controlling for emesis from a neurophysiological perspective, these conclusions are currently not mirrored in the published RCTs to date. However, there exists only a limited number of RCTs, comparisons with older treatment regimens and a lack of standard reporting practice across published literature. Further RCTs should investigate the efficacy and safety of oral cannabinoids, to secure a better picture of the efficacy of oral cannabinoids against CINV.

Inter-rater reliability in performance status assessment among healthcare professionals: an updated systematic review and meta-analysis.

Survival prediction for patients with incurable malignancies is invaluable information during end-of-life discussions, as it helps the healthcare team to appropriately recommend treatment options and consider hospice enrolment. Assessment of performance status may differ between different healthcare professionals (HCPs), which could have implications in predicting prognosis. The aim of this systematic review and meta-analysis is to update a prior systematic review with recent articles, as well as conduct a meta-analysis to quantitatively compare performance status scores. A literature search was carried out in Ovid MEDLINE, Embase, and Cochrane Central Register of Controlled Trials, from the earliest date until the first week of August 2019. Studies were included if they reported on (1) Karnofsky Performance Status (KPS), Eastern Cooperative Oncology Group (ECOG) Performance Status, and/or Palliative Performance Scale (PPS) and (2) assessment of performance status by multiple HCPs for the same patient sets. The concordance statistics (Kappa, Krippendorff's alpha, Kendall correlation, Spearman rank correlation, Pearson correlation) were extracted into a summary table for narrative review, and Pearson correlation coefficients were calculated for each study and meta-analyzed with a random effects analysis model. Analyses were conducted using Comprehensive Meta-Analysis (Version 3) by Biostat. Fourteen articles were included, with a cumulative sample size of 2808 patients. The Pearson correlation coefficient was 0.787 (95% CI: 0.661, 0.870) for KPS, 0.749 (95% CI: 0.716, 0.779) for PPS, and 0.705 (95% CI: 0.536, 0.819) for ECOG. Four studies compared different tools head-to-head; KPS was favored in three studies. The quality of evidence was moderate, as determined by the GRADE tool. The meta-analysis's Pearson correlation coefficient ranged from 0.705 to 0.787; there is notable correlation of performance status scores, with no one tool statistically superior to others. KPS is, however, descriptively better and favored in head-to-head trials. Future studies could now examine the accuracy of KPS assessment in prognostication and focus on model-building around KPS.

A Novel Failure Mode and Effects Analysis Model Using Triangular Distribution-Based Basic Probability Assignment in the Evidence Theory

Failure mode and effects analysis (FMEA) is a typical risk assessment and prevention technology, in which different experts provide different assessments on the target system to identify the risk grades of its components. Sometimes, the assessments contain conflict information. How to manage and fuse the conflict assessment information is an open issue. We propose a triangular distribution-based basic probability assignment (TDBPA) method to model and fuse the conflict risk level coming from different experts' assessments in the frame of Dempster-Shafer evidence theory. First, the subjective assessments of risk analysis from domain experts are modeled with belief structure in Dempster-Shafer evidence theory. Then, the assessments are transformed as the TDBPA function. Thirdly, the conflict risk assessments from the FMEA team for failure analysis can be fused with Dempster rule of combination. After that, the modified risk priority number (RPN) model based on fused assessment can be calculated for ranking of failure modes. Finally, recommended actions should be taken for prevention of potential risk items. We verify the rationality and efficiency of the proposed method with a case study in the blades of an aircraft turbine. In short, the presented FMEA methodology procedure in this paper is well organized so that we can apply it in a more simple and understandable way. Utilizing the character of triangular distribution, taking adjacent values into account, TDBPA method can smooth the conflict assessment for information fusion. In addition, the shortcoming of repeating values in classical RPN is eliminated in the proposed method, which improves the ability for risk assessment of FMEA.

The m-delay Autoregressive Model with Application

The classical autoregressive (AR) model has been widely applied to predict future data using m past observations over five decades. As the classical AR model required m unknown parameters, this paper implements the AR model by reducing m parameters to two parameters to obtain a new model with an optimal delay called as the m-delay AR model. We derive the m-delay AR formula for approximating two unknown parameters based on the least squares method and develop an algorithm to determine optimal delay based on a brute-force technique. The performance of the m-delay AR model was tested by comparing with the classical AR model. The results, obtained from Monte Carlo simulation using the monthly mean minimum temperature in Perth Western Australia from the Bureau of Meteorology, are no significant difference compared to those obtained from the classical AR model. This confirms that the m-delay AR model is an effective model for time series analysis.

Visual Sentiment Analysis With Active Learning

Visual Sentiment Analysis (VSA) has attracted wide attention since more and more people are willing to express their emotion and opinions via visual contents on social media. Meanwhile, extensive datasets drive the rapid development of deep neural networks for this task. However, the annotation of large-scale datasets is very expensive and time consuming. In this paper, we propose a novel active learning framework, which uses few labeled training samples to achieve an effective sentiment analysis model. First, we attach a new branch to the traditional Convolution Neural Network (CNN), which is named ”texture module”. The affective vector will be obtained by computing inner products of feature maps from different convolutional blocks in this branch. We will utilize this vector to distinguish affective images. Second, the query strategy is formed by the classification scores from both the traditional CNN and the texture module. Then, we can use samples obtained by utilizing the query strategy to train our model. Extensive experiments on four public affective datasets show that our approach uses few labeled training samples to achieve promising results for VSA.

An Interaction Investigation of the Contributing Factors of the Bullwhip Effect Using a Bi-Level Social Network Analysis Approach

The bullwhip effect refers to the phenomenon of the increase in demand variability from supply chain downstream members to upstream members. This effect is critically important for the different sectors involved in a supply chain and is one of the main causes of inefficiency, material waste, and low sustainability in supply chain management. In order to help minimize the bullwhip effect and achieve sustainable development in a supply chain, this study identifies the influential factors of the bullwhip effect in a supply chain and the interactive relationships among them. A bi-level bullwhip effect analysis model was established to evaluate the causes of the bullwhip effect at different levels by using a social network analysis approach. Furthermore, to help achieve efficient and sustainable development, the implications of mitigating the impacts of the bullwhip effect in a supply chain were explored based on the results of the inter-relationships analysis among the contributing factors of the bullwhip effect.

Regional effect of monetary policy based on GVAR model and machine learning

Monetary policy is an important means for a country to regulate macroeconomic operations and achieve established economic goals. Moreover, a reasonable monetary policy improves the efficiency of financial operations on a global scale and effectively resolves the financial crisis. At present, scholars from various countries have begun to pay attention to the issue of differentiated formulation of monetary policy among regions. This paper combines machine learning to construct a monetary policy differentiation effect analysis model based on the GVAR model. Moreover, this paper uses the gray correlation analysis method to obtain the gray correlation matrix between industries, and then introduces the industry’s own characteristics, industry relevance and macroeconomic factors into the macro stress test of credit risk. In addition, this paper constructs a conduction model based on the industry GVAR model, and uses the first-order difference sequence of GDP growth rate, CPI growth rate and M2 growth rate of each economic region to construct a GVAR model to test the impulse response function. The results of the test show that the monetary policy shocks of various economic regions are significantly different. All in all, the research results show that the performance of the model constructed in this paper is good.

Thermo‐Hydro‐Mechanical Combined Effect Analysis Model for Early‐Age Concrete Bridges and Its Application

The early cracking of concrete beam bridges remains a concern in civil engineering. An analytical model considering the combined effect of thermo‐hydro‐mechanical processes forms the basis for assessing the cracking risk of girders during construction. Based on the equivalent hydration theory, the temperature and moisture conduction processes and the evolution of the mechanical properties of concrete were modeled as a function of the equivalent age. A coupling model for the temperature and moisture fields was established, and a theoretical framework for analyzing the thermo‐hydro‐mechanical combined effect was presented. Based on this, a numerical analysis method was proposed and implemented into ABAQUS; the results were validated with some typical tests. Finally, a long‐span prestressed concrete (PC) box girder bridge with balanced cantilever construction was taken as an example, and the causes of web cracking and its impact degree were analyzed. The results show that the rate of moisture conduction is significantly lower than the rate of temperature conduction; even for thin‐walled components, there exists a significant humidity gradient on the surface layer. The humidity‐induced shrinkage and restraint of the precast members are the main causes of web cracking.