Different Levels Of Importance Research Articles

An empirical framework for soft CSFs of TQM in Chinese manufacturing companies

Purpose The purpose of this paper is to frame an empirical model with the priority of implementing soft critical successful factors (CSFs) of total quality management (TQM) in Chinese manufacturing companies. Design/methodology/approach In total, 23 hypotheses regarding the relationships among TQM factors and business results have been developed through literature review and are tested using the structural equation modelling (SEM). The data used in this study are collected from 228 manufacturing companies in the central area of China. Findings Of the 23 hypotheses, 11 were statistically significant, and according to the empirical results a conceptual framework complying to Chinese condition is constructed and discussed in the finding parts to group soft CSFs of TQM into different levels of importance. Research limitations/implications Researchers can use the framework to further study soft CSFs of TQM in detail, and extend it to explore their impact mechanism for a clearer and deeper understanding of the TQM. Practical implications The empirical framework for soft CSFs of TQM provides a new perspective for managers to implement the soft CSFs and optimise the quality management system of their manufacturing companies. Originality/value This research uses multiple, distinct indicators for each soft factor to test an elaborate SEM model of the relationships among them, according to the empirical results constructs a holistic framework classifying the importance level of soft CSFs of TQM, which is novel and subjected with Chinese condition and manufacturing characteristics.

AN ANALYSIS ON KRI IMPROVEMENT PROJECT IN INDONESIAN NAVAL MAIN BASE NO. V SURABAYA FASHARKAN (NAVY’S BATTLESHIPS MAINTENANCE AND REPAIRING FACILITIES USING FUZZY FMEA METHOD

The FMEA method is a tool used to identify the consequences or consequences of a system or process failure and reduce or eliminate failure. In determining risk factors and system improvement priorities, traditional FMEA still has weaknesses, where FMEA traditionally places severity factors, occurrence, and detection at the same level of importance, even though in reality different levels of importance and the importance of FMEA assessment teams are ignored. In this study, the fuzzy method is integrated into FMEA where the severity factor, occurrence, and detection are assessed in linguistic form. In this fuzzy method, the weight of the interests of the FMEA assessment team is taken into account to do ranking and repair priorities. The application of Fuzzy methods to FMEA to determine significant risk factors and prioritizing improvements from various alternatives chosen for the process of repairing KRI in Surabaya Lantamal V, so that it is expected that the application of this method can improve the operational performance of Fasharkan to eliminate or reduce the risks that occur in the repair process ships in the eastern region of the fleet.
 Keywords: Risk Management, Failure Modes and Effect Analysis (FMEA), Fuzzy Method

Ethnobotanical Study of the Mexican Laurel in El Chico National Park, Mexico: A Quantitative Perspective

This study was conducted in two villages of El Chico National Park (ECNP), Mexico, to document the uses of Litsea glaucescens (Mexican laurel) by the local population and to identify actors with knowledge about the species using quantitative ethnobotanical techniques. Fifty-five semi-structured interviews were conducted to obtain a free-list about the specific uses of the laurel, to analyze its importance among the social group under study, and to use social networking to identify individuals within the community who had particular knowledge about the plant. We found a total of 25 specific uses for the plant, which have different levels of importance for the people of the ECNP. The most common use was seasoning, while medicinal and cultural uses had a lesser importance. Use of the seed of the laurel as a material for handcrafts is recorded for the first time in this study. The social network showed that there was a relationship between the actors interviewed from the two communities. They are identified as having some relationship to the plant or knowledge about it, but the actors who produce it are the most prominent. An analysis of the specific uses of the laurel and those with knowledge about it is considered indispensable for generating specific management and harvesting strategies for the species, which will be able to contribute a local perspective to its conservation.

CLAIM MANAGEMENT FRAMEWORK UNDER FIDIC 2017: CONTRACTOR CLAIM SUBMISSION

Purpose of the study: The objective of this research is to develop a framework for managing the claim document. The contractual and management issues will be considered in this framework to enhance the Claims Management System (CMS). The framework includes the mechanism of claim submission based on the clauses and procedures of Fédération Internationale Des Ingénieurs Conseils (FIDIC, 2017).
 Methodology: Qualitative methodology has been selected for this paper as the topic requires a collection of sensitive information from an experienced professional. The proposed Claims Management (CM) framework is developed on the basis of a study conducted to rank the feature required for CMS. This study has been conducted among 43 experts in CM field working on contractor firm’s category A. Then, the framework was verified by seven experts who participated in the first study.
 Main Findings: Eleven features were required for CMS that can enhance contractor claim submission. These factors have different levels of importance. The top three factors are Tracking Claim Status (99.5%), Supporting all types of documents (96.3%), and having a Centralized Database (93.0%). Based on these features, the proposed framework was developed to improve contractor claim submission.
 Applications of this study: Applying the proposed framework reduces human efforts in getting documents related to claims by its systematic recording, transparency, reminder feature, contractual guide, user-friendliness, and other features of the system. Moreover, it provides the contractual support pursuant to FIDIC 2017 clauses.
 Novelty/Originality of this study: The framework will improve contractor claim submission and the contractor will be satisfied by claim resolution and engineer determination. Side by side, the framework will save about 50% of time consumed by the claims analysts that are usually spent in collecting, screening, and identifying information related to claim event in the project’s documentation.

Mapping stocks of soil total nitrogen using remote sensing data: A comparison of random forest models with different predictors

Estimation of soil total nitrogen (STN) is important for quantifying the spatial distribution of soil N (Nitrogen) stocks, determining the amount absorbed by crops and avoiding yield losses and environmental pollution. The objective of this research was to determine the spatial distribution of STN using a remote sensing approach and to investigate the importance of different predictors under two conditions: the bare soil condition and the vegetated condition. We also investigated whether the Sentinel-2A red-edge bands and relative spectral indices were suitable for STN estimation. Soil data were collected from the topsoil (0–20 cm) at 104 sampling sites on 06/15/2016 from farmland in a black soil region in Northeast China. Two Sentinel-2A Multispectral Instrument (MSI) remote sensing images were acquired on 05/26/2016 and 08/03/2016 to represent the two conditions. Environmental variables included the terrain attributes, temperature and precipitation. Then, 21 predictors, including the original bands (O), normal spectral indices (S), red-edge indices (R) and environmental variables (E), were employed to estimate the spatial distribution of the STN content using a random forest (RF) model. Finally, different predictors were combined to construct RF models, and the prediction model with the best performance was selected to determine the spatial pattern of the STN content. The results showed that the predictors had different levels of importance under the two conditions. However, most environmental variables and normal spectral indices always play a significant role in the estimation of STN. The model with the combination of the original bands, normal spectral indices, red-edge indices and environmental variables (O + S + E + R) under the bare soil condition had the best prediction performance, and the combination of the original bands, normal spectral indices and red-edge indices (O + S + R) model had a performance similar to the O + S + E + R model. Therefore, the selection of suitable predictors is necessary to predict STN. The spatial pattern of STN was related to the crop type and elevation of the study area. The results of this study suggested that the proposed RF-based remote sensing method was able to accurately capture the variation in STN and that the performance of the prediction model can be improved by providing enough types of suitable predictors.

Centrality-based identification of important edges in complex networks.

Centrality is one of the most fundamental metrics in network science. Despite an abundance of methods for measuring centrality of individual vertices, there are by now only a few metrics to measure centrality of individual edges. We modify various, widely used centrality concepts for vertices to those for edges, in order to find which edges in a network are important between other pairs of vertices. Focusing on the importance of edges, we propose an edge-centrality-based network decomposition technique to identify a hierarchy of sets of edges, where each set is associated with a different level of importance. We evaluate the efficiency of our methods using various paradigmatic network models and apply the novel concepts to identify important edges and important sets of edges in a commonly used benchmark model in social network analysis, as well as in evolving epileptic brain networks.

Optimization of traffic sensor location for complete link flow observability in traffic network considering sensor failure

The full link flow observability problem is to identify the minimum set of traffic sensors to be installed in links in a road traffic network. The sensors are used to both monitor the flow of observed links and to provide flow information for the link flow inference of unobserved links. Unavoidably, the traffic sensors deployed in a traffic network are subject to failure which leads to missing the link flow observation of observed links as well as the inability to infer the link flow of unobserved links. This study aims to identify the minimum set of links in a traffic network to be instrumented with two different types of counting sensors (basic and advanced sensors) to reach full link flow observability while minimizing the effect of sensor failure on the link flow inference of unobserved links. Mathematically, we formulate two objective functions including min-max and min-sum functions. The first function attempts to minimize the maximum effect of sensor failure on the link flow inference of unobserved links while the second one minimizes the expected number of unobserved links where flow cannot be inferred due to the failure of sensors. We select the genetic algorithm (GA) as a well-known heuristic to solve the proposed optimization model. The results recommend minimizing the number of sensors required for the link flow inference of each unobserved link as well as installing advanced sensors on links involved in the link flow inference of multiple unobserved links. We also develop a new objective function to reflect that links in a traffic network can be either minor or major roads with different levels of importance. The results suggest installing more advanced sensors on the major roads as well as minimizing the number of major roads included in the set of unobserved links. Concerning the availability of route flow information in a network, we consider the effect of this information on evaluating the sensor deployment in a network. To maintain full link flow observability of a traffic network if any sensor fails, we study the location and type of additional sensors introduced as redundant sensors, which are more than the minimum required for full link flow observability. Finally, we discuss the applicability of the proposed model for the partial observability problem in which the full link flow observability conditions are not satisfied.

An improved MULTIMOORA method with combined weights and its application in assessing the innovative ability of universities

AbstractInnovative ability plays a critical role in the sustainable development of universities. Although the assessment of universities' innovative ability is a significant undertaking, it is difficult work. This challenge can be addressed as a typical multiple attribute decision making (MADM) problem, in which multiple attributes should be considered with different levels of importance. This paper aims to propose an integrated MADM method to solve this issue. To do so, we first introduce the least square method with the hesitant fuzzy linguistic term set to determine the subjective attribute weights. Considering that the selected attributes are not always in conflict with each other due to the complexity of objective things, we further present a correlation coefficient‐based method to calculate another kind of attribute weight. The final weights are the combined form of these two types of attribute weights. In addition, we enhance the robust ranking method, MULTIMOORA, with the Borda rule to calculate the utility values of universities and derive their rankings. Finally, after establishing an index system, the assessment of the innovative ability of 26 world‐class construction universities in China is conducted by using the proposed method. The advantages and disadvantages of the assessed universities are analysed.

Spatial–Temporal Attention-Based Human Dynamics Retrospection

Motivated by impressive success of deep recurrent neural networks (RNNs), sequence-to-sequence (seq2seq) architecture has been widely adapted to tackle human motion prediction. However, forecasting in longer time horizons always leads to implausible human poses or converges to mean poses. To address these challenges, we dig into the root causes and lay emphasis on two key principles. First, error can be easily accumulated on seq2seq architecture without modifications and thus RNNs cannot recover from its own mistakes in longer time horizons. Second, all the frames or joints are treated equally, whereas both of them often have different levels of importance in human motion. To mitigate this gap, we propose to retrospect human dynamics with attention. We design a retrospection module built upon seq2seq architecture to recollect previous subsequences and correct mistakes in time which enables a self-correction ability. This assists the original seq2seq architecture to eliminate error accumulation which improves significantly both short-term and long-term performances. Besides, we present two attention techniques to explore correlations among different joints as well as different frames in both spatial and temporal domains, which successfully capture key properties of different actions and enable our model to generate more realistic human poses. Quantitative and qualitative experiments have been both conducted to evaluate the our proposed model. Experimental results clearly demonstrate the superiority of proposed model over other baselines.

Soil erodibility and its influencing factors on the Loess Plateau of China: a case study in the Ansai watershed

Abstract. The objectives of this work were to identify the best possible method to estimate soil erodibility (K) and understand the influencing factors of soil erodibility. In this study, 151 soil samples were collected during soil surveys in the Ansai watershed of the Loess Plateau of China. The K values were estimated by five methods: erosion-productivity impact model (EPIC), nomograph equation (NOMO), modified nomograph equation (M-NOMO), Torri model and Shirazi model. The main conclusions of this paper are (1) K values in the Ansai watershed ranged between 0.009 and 0.092 t ⋅ hm2 ⋅ h/(MJ ⋅ mm ⋅ hm2), and the maximum values were 1.9–7.3 times larger than the corresponding minimum values, and the Shirazi and Torri models were considered the optimal models for the Ansai watershed. (2) Different land use types had different levels of importance; the principal components (PCs) accounted for 100 % (native grassland), 48.88 % (sea buckthorn), 62.05 % (Caragana korshinskii), and 53.61 % (pasture grassland) of the variance in soil erodibility. (3) The correlations between soil erodibility and the selected environmental variables differed among different vegetation types. For native grasslands, soil erodibility had significant correlations with terrain factors. For the most artificially managed vegetation types (e.g., apple orchards) and artificially restored vegetation types (e.g., sea buckthorn), soil erodibility had significant correlations with the growing conditions of vegetation. Soil erodibility had indirect relationships with both environmental factors (e.g., elevation and slope) and human activities, which potentially altered soil erodibility.

Strategic allocation of working memory resource

Visual working memory (VWM), the brief retention of past visual information, supports a range of cognitive functions. One of the defining, and largely studied, characteristics of VWM is how resource-limited it is, raising questions about how this resource is shared or split across memoranda. Since objects are rarely equally important in the real world, we ask how people split this resource in settings where objects have different levels of importance. In a psychophysical experiment, participants remembered the location of four targets with different probabilities of being tested after a delay. We then measured their memory accuracy of one of the targets. We found that participants allocated more resource to memoranda with higher priority, but underallocated resource to high- and overallocated to low-priority targets relative to the true probability of being tested. These results are well explained by a computational model in which resource is allocated to minimize expected estimation error. We replicated this finding in a second experiment in which participants bet on their memory fidelity after making the location estimate. The results of this experiment show that people have access to and utilize the quality of their memory when making decisions. Furthermore, people again allocate resource in a way that minimizes memory errors, even in a context in which an alternative strategy was incentivized. Our study not only shows that people are allocating resource according to behavioral relevance, but suggests that they are doing so with the aim of maximizing memory accuracy.

The structural roles of sectors and their contributions to global carbon emissions: A complex network perspective

Carbon dioxide (CO2) emissions have a significant impact on global climate change. Previous studies have mostly focused on the impact of economic performance of sectors on carbon emissions, however, there is a lack of information about the influence of the structural roles on the full industrial chain. This article first built global embodied carbon emission transfer networks from 2000 to 2015, combining multi-regional input-output analysis and complex network theory, to explore the structural characteristics and recognize the structural roles of sectors. Then, panel regression analysis was applied to quantify the contributions of the structural roles of sectors to their carbon emissions. The results first showed that there were significant scale-free network characteristics in the global embodied carbon emissions transfer networks. The majority of global carbon emission transfers was concentrated on a limited number of sectors over time. Moreover, the network analysis revealed that sectors played different roles with different levels of importance in the global embodied carbon emission transfer networks. The results also suggested that the carbon emissions of sectors related to economic activity were significantly determined by some structural roles in the global embodied carbon emission transfer networks. Degree-in, strength-out and closeness-out and betweenness had significant positive influences, while degree-out, strength-in and closeness-in had significant negative impacts. These results indicated that carbon emissions mainly were increased by the domestic markets rather than international trades. Thus, adjusting the domestic consumption structures can be helpful for global carbon emission reduction. It also indicated that carbon emissions were not only driven by direct production demand from downstream sectors, but also were significantly influenced by indirect production demand in the industrial chain. Therefore, reducing carbon emissions should be established in both the direct and the indirect relationships in the production process.

White Paper for Global Palliative Care Advocacy: Recommendations from a PAL-LIFE Expert Advisory Group of the Pontifical Academy for Life, Vatican City.

Background: The Pontifical Academy for Life (PAV) is an academic institution of the Holy See (Vatican), which aims to develop and promote Catholic teachings on questions of biomedical ethics. Palliative care (PC) experts from around the world professing different faiths were invited by the PAV to develop strategic recommendations for the global development of PC (“PAL-LIFE group”).Design: Thirteen experts in PC advocacy participated in an online Delphi process. In four iterative rounds, participants were asked to identify the most significant stakeholder groups and then propose for each, strategic recommendations to advance PC. Each round incorporated the feedback from previous rounds until consensus was achieved on the most important recommendations. In a last step, the ad hoc group was asked to rank the stakeholders' groups by order of importance on a 13-point scale and to propose suggestions for implementation. A cluster analysis provided a classification of the stakeholders in different levels of importance for PC development.Results: Thirteen stakeholder groups and 43 recommendations resulted from the first round, and, of those, 13 recommendations were chosen as the most important (1 for each stakeholder group). Five groups had higher scores. The recommendation chosen for these top 5 groups were as follows: (1) Policy makers: Ensure universal access to PC; (2) Academia: Offer mandatory PC courses to undergraduates; (3) Healthcare workers: PC professionals should receive adequate certification; (4) Hospitals and healthcare centers: Every healthcare center should ensure access to PC medicines; and (5) PC associations: National Associations should be effective advocates and work with their governments in the process of implementing international policy framework. A recommendation for each of the remaining eight groups is also presented.Discussion: This white paper represents a position statement of the PAV developed through a consensus process in regard to advocacy strategies for the advancement of PC in the world.

Knowledge, Skills, and Abilities in the Precision Agriculture Workforce: An Industry Survey

Core Ideas Needed skills are changing for agriculturalists working in precision farming. It is often difficult for agribusiness to find qualified candidates to fill positions. Different sets of skills are needed for equipment operators, agronomists, equipment technicians, technical support, and sales positions. Precision agriculture encompasses a set of related technologies aimed at better utilizing crop inputs, increasing yield and quality, reducing production risks, and enabling information flow throughout the crop supply and end‐use chains. As agricultural businesses invest in precision offerings, their capacity to provide these products and services will depend on their ability to hire and retain employees with appropriate proficiency as defined in their knowledge, skills, and abilities (KSAs). A 2015 survey of agricultural retailers examined the minimum educational requirements retailers were seeking in their hires, along with the importance of a list of KSAs for the various positions that they customarily fill. The positions included equipment operators, sales specialists, technical support, and agronomists. The KSAs included specifics, such as the ability to install, calibrate, troubleshoot, and repair equipment; knowledge of precision agriculture software; and also more broad skills such as effective written and verbal communication and in making agronomy recommendations. As expected, retailers expressed different educational minimums and different levels of importance for various positions. Overwhelmingly, the retailers indicated difficulty in finding qualified candidates and a predominance of candidates with low or deficient proficiency in areas they rated important. The survey was accomplished using email lists from both CropLife and the Certified Crop Adviser program.

Participation of adults with visual and severe or profound intellectual disabilities: Analysis of individual support plans

BackgroundThe extent of participation of adults with visual and severe or profound intellectual disabilities (VSPID) is unclear. AimsTo explore participation of adults with VSPID and the association between occurrence and importance of aspects of participation. MethodsIndividual support plans (ISPs) of 40 adults with VSPID were analyzed: selected text fragments were categorized according to 125 previously operationalized statements that had different levels of importance and were divided into seven participation clusters. ResultsThe ISPs contained 2791 text fragments that related to a statement. All clusters were covered: the clusters ‘Experience and discover’ (91.7%), ‘Involvement’ (90%), and ‘Social relations’ (87.5%) were well covered. ‘Inclusion’ (53.6%) and ‘Leisure and recreation’ (57.1%) were mentioned less often. Among the 36 high-importance statements, two related to ‘Inclusion’, ‘Involvement’ and ‘Social Relations’ each, three to ‘Communication and being understood’, and five to ‘Self-management and autonomy’ had at least 30 text fragments. ConclusionsThe participation domains ‘Experience and discover’, ‘Involvement’, and ‘Social relations’ are well-documented, suggesting that adults with VSPID participate in those areas. However, domains such as ‘inclusion in society’ and ‘leisure in society’ were not documented. This overview of participation offers residential facilities the opportunity to determine in which areas participation can be improved.

A hybrid robust possibilistic approach for a sustainable supply chain location-allocation network design

ABSTRACTIn this paper, a multi-objective multi-period sustainable location-allocation supply chain network model will be presented. Different levels of technology for vehicle fleet, which leads to different monetary and environmental costs, and different released CO2 emission for each potential location of facilities to reach a green supply chain network, were considered. In addition, back order of products, which leads to dissatisfaction of customers, was noted and for different customers, different levels of importance are assumed to consider social impacts. To handle the problem of unpredicted parameters, a novel approach of uncertainty, which is named Hybrid Robust Possibilistic Programming-II (HRPP-II), is used. At last, a case study will be solved by the Improved Augmented ϵ-Constraint method (AUGMECON2) to achieve Pareto solutions, and some sensitivity analysis will be done. New social and environmental considerations in location-allocation supply chain models and using a novel robust approach to overcome the uncertainty of parameters are contributions to the previous models.

Perceptions of Caring Between Slovene and Russian Members of Nursing Teams.

To measure the perceptions of caring between Slovene and Russian members of nursing teams and compare the results with earlier findings in other European Union countries. A cross-sectional study that included nurses and nursing assistants in Slovenia ( n = 294) and Russia ( n = 531). Data were collected using the 25-item Caring Dimensions Inventory. The most endorsed item for Slovene and Russian members of nursing teams was an item related to medication administration. All items that were endorsed by Russian participants were also endorsed by Slovenian participants; however, they ascribed a different level of importance to individual aspects of caring. Compared with other European Union countries, such as the United Kingdom and Spain, Slovenian and Russian members of nursing teams endorsed more technical aspects of nursing duties as caring, suggesting cultural differences and previous influences of the biomedical model on nursing education and practice.

Distinct non-invasive evaluation values of tumor-derived cell-free microRNAs, circulating microvesicles and exosomal microRNAs after renal carcinoma cryoablation

The detection of peripheral circulating tumor-derived components, such as cell-free microRNAs, circulating microvesicles, and exosomal microRNAs, has been shown as a promising noninvasive strategy. However, the different roles of these components in tumor therapy evaluations have remained largely undefined. In this paper, we employed an in vivo model of the human clear cell renal cell carcinoma line Caki-1-bearing mice to evaluate the therapeutic effects of cryoablation, which is a new minimally invasive treatment for renal cell carcinoma. At different times after cryoablation, we found that the levels of the cell-free microRNAs miR-122, miR-155 and miR-210 were first increased and then decreased. Additionally, the number of large-sized microvesicles was increased after cryoablation, but the number of small-sized circulating microvesicles did not change. Furthermore, the exosomal microRNAs miR-126–3p, miR-17–5p, and miR-21–3p rapidly decreased one day after cryoablation, an effect that was well correlated with the treatment degree. Therefore, we suggest that these circulating components may have different levels of importance in the evaluation of the efficacy of renal cell cryoablation, furthermore, exosomal microRNAs may be more suitable for the early postoperative judgment of tumor elimination effects, which are worth further exploration in clinical practice.

Triggers to Word of Mouth and Revisit Intention to Chinese Food Restaurant

The purpose of this study is to probe the differences between the main triggers to revisit intention (RI) and word of mouth (WOM) intention of the Chinese Restaurants´ consumers. The research method is quantitative; 525 consumers were surveyed in Tijuana. Ten indicators of the consumer behavioural intention were included to carried out multiple linear regressions. It was found that empathy, food taste and physical environment are related to both, but with different levels of importance and effect. Nevertheless, price ratio, and time to receive the food have a statistically relationship with RI and food aroma and time to receive the restaurant bill are significant just to the WOM.

Triggers to Word of Mouth and Revisit Intention to Chinese Food Restaurant

The purpose of this study is to probe the differences between the main triggers to revisit intention (RI) and word of mouth (WOM) intention of the Chinese Restaurants´ consumers. The research method is quantitative; 525 consumers were surveyed in Tijuana. Ten indicators of the consumer behavioural intention were included to carried out multiple linear regressions. It was found that empathy, food taste and physical environment are related to both, but with different levels of importance and effect. Nevertheless, price ratio, and time to receive the food have a statistically relationship with RI and food aroma and time to receive the restaurant bill are significant just to the WOM.