Massive online reviews provide consumers with the convenience of obtaining product information, but it is still worth exploring how to provide consumers with useful and reliable product rankings. The ...existing ranking methods do not fully mine user information, rating, and text comment information to obtain scientific and reasonable information aggregation methods. Therefore, this study constructs a user credibility model and proposes a large-scale user information aggregation method to obtain a new product ranking method. First, in order to obtain the aggregate weight of large-scale users, this paper proposes a consistency modeling method of text comments and star ratings by mining the associated information of user comments, including user interaction information and user personalized characteristics information, combined with sentiment analysis technology, and then constructs a user credibility model. Second, a double-layer group division mechanism considering user regions and comment time is designed to develop the large-scale group ratings aggregation approach. Third, based on the user credibility model and the large-scale ratings aggregation approach, a product ranking method is developed. Finally, the feasibility and effectiveness of the proposed method are verified through a case study for automobile ranking and a comparative analysis is furnished. The analysis results of the application case of automobile ranking show that there is a significant difference between the ranking results obtained by the ratings aggregation method based on the arithmetic mean and the ranking results obtained by this method. The method in this study comprehensively considers user credibility and group division, which can be reflected in user aggregation weights and the group aggregation process, and can also obtain more scientific and reasonable decision results.
Sustainable supplier selection is the essential core of sustainable supply chain management, which can directly influence the manufacturer’s performance and can enormously enhance the manufacturer’s ...competitiveness in the international market. However, most of the previous studies concerning sustainable supplier selection have less focused on the reliability of the decision-makers judgments and the application of regret theory. To fill this gap, we presented an integrated sustainable supplier selection model based on regret theory and QUALItative FLEXible multiple criteria method (QUALIFLEX) under a 2-dimensional uncertain linguistic variable (2-DULV) environment. In the proposed model, 2-DULV including the reliability of evaluation information is employed to handle the uncertainty and vagueness of decision-makers judgments. A similarity-based method is used to derive the decision-makers’ weight, and a maximizing deviation model is established to calculate the weights of evaluation criteria. Then an improved QUALIFLEX method based on regret theory is presented to obtain the ranking order of sustainable suppliers. The proposed approach integrates both the superiority of 2-DULV in effectively handling the uncertainty, vagueness, and reliability of evaluation information and the merit of regret theory in dealing with decision-maker’s bounded rationality. Finally, a numerical example concerning an automobile manufacturer is provided to validate the effectiveness and feasibility of the presented model.
The scientific and reasonable evaluation of the carrying capacity of water resources is of guiding significance for solving the issues of water resource shortages and pollution control. It is also an ...important method for realizing the sustainable development of water resources. Aiming at an evaluation of the carrying capacity of water resources, an evaluation model based on the cloud model theory and evidential reasoning approach is studied. First, based on the existing indicators, a water resources evaluation index system based on the pressure-state-response (PSR) model is constructed, and a classification method of carrying capacity grade is designed. The cloud model theory is used to realize the transformation between the measured value of indicators and the degree of correlation. Second, to obtain the weight of the evaluation index, the weight method of the index weights model based on the entropy weight method and evidential reasoning approach is proposed. Then, the reliability distribution function of the evaluation index and the graded probability distribution of the carrying capacity of water resources are obtained by an evidential reasoning approach. Finally, the evaluation method of the carrying capacity of water resources is constructed, and specific steps are provided. The proposed method is applied to the evaluation of water resources carrying capacity for Hunan Province, which verifies the feasibility and effectiveness of the method proposed in the present study. This paper applies this method of the evaluation of the water resources carrying capacity of Hunan Province from 2010 to 2019. It is concluded that the water resources carrying capacity of Hunan Province belongs to III~V, which is between the critical state and the strong carrying capacity state. The carrying capacity of the province’s water resources is basically on the rise. This shows that the carrying capacity of water resources in Hunan Province is in good condition, and corresponding protective measures should be taken to continue the current state.
Biodiversity conservation plays an important role in maintaining the function of urban ecosystem. In this study, 39 species of key protected plants in Xiamen were selected as the objects. MaxENT, a ...species distribution model is used to predicate the potential distribution of species. Zonation, a quantitative tool for Spatial Conservation Priority, is used to identify the areas that are suitable for the survival of key protected plants and can ensure the landscape connectivity, so as to obtain the multi species landscape protection level of local key protected plants. According to the 2020 global biodiversity target, 17% of the areas with the highest level of landscape protection will be regarded as the spatial conservation prioritization areas of multi species. Combined with the weighted extinction risk curve with landscape loss generated by zonation model, 8% of the areas with the highest level of protection are classified as primary conservation areas, and the areas with the protection level ranging from 8%
The hair follicle is a skin accessory organ that regulates hair development, and its activity varies on a regular basis. However, the significance of metabolites in the hair follicle cycle has long ...been unknown.
Targeted metabolomics was used in this investigation to reveal the expression patterns of 1903 metabolites in cashmere goat skin during anagen to telogen. A statistical analysis was used to investigate the potential associations between metabolites and the hair follicle cycle. The findings revealed clear changes in the expression patterns of metabolites at various phases and in various feeding models. The majority of metabolites (primarily amino acids, nucleotides, their metabolites, and lipids) showed downregulated expression from anagen (An) to telogen (Tn), which was associated with gene expression, protein synthesis and transport, and cell structure, which reflected, to some extent, that the cells associated with hair follicle development are active in An and apoptotic in An-Tn. It is worth mentioning that the expression of vitamin D3 and 3,3',5-triiodo-L-thyronine decreased and then increased, which may be related to the shorter and longer duration of outdoor light, which may stimulate the hair follicle to transition from An to catagen (Cn). In the comparison of different hair follicle development stages (An, Cn, and Tn) or feeding modes (grazing and barn feeding), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis revealed that common differentially expressed metabolites (DEMs) (2'-deoxyadenosine, L-valine, 2'-deoxyuridine, riboflavin, cytidine, deoxyguanosine, L-tryptophan, and guanosine-5'-monophosphate) were enriched in ABC transporters. This finding suggested that this pathway may be involved in the hair follicle cycle. Among these DEMs, riboflavin is absorbed from food, and the expression of riboflavin and sugars (D-glucose and glycogen) in skin tissue under grazing was greater and lower than that during barn feeding, respectively, suggesting that eating patterns may also alter the hair follicle cycle.
The expression patterns of metabolites such as sugars, lipids, amino acids, and nucleotides in skin tissue affect hair follicle growth, in which 2'-deoxyadenosine, L-valine, 2'-deoxyuridine, riboflavin, cytidine, deoxyguanosine, L-tryptophan, and guanosine-5'-monophosphate may regulate the hair follicle cycle by participating in ABC transporters. Feeding practices may regulate hair follicle cycles by influencing the amount of hormones and vitamins expressed in the skin of cashmere goats.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Credit scoring is an efficient tool for financial institutions to implement credit risk management. In recent years, many novel machine learning models have been developed for credit scoring. Among ...the existing machine learning models, the heterogeneous ensemble model receives much attention because of its superior performance. This paper presents a new heterogeneous ensemble model based on the generalized Shapley value and the Choquet integral. To do this, the model first uses the fuzzy measure to express the interactive characteristics between any two coalitions of base learners. Based on the accuracy and diversity objective function, a linear programming model for determining the fuzzy measure is built. To retain the original information as much as possible in the training stage, the normal fuzzy number is employed to express the base learner predicted values. Then, the generalized Shapley Choquet integral (GSCI) aggregation operator is defined to calculate the comprehensive predicted value of the ensemble model. Based on the defined aggregation operator and linear programming model, a GSCI approach is proposed for ensemble credit scoring. To illustrate the efficiency and feasibility of the GSCI approach, an experiment of thirteen machine learning models over four public credit scoring datasets and three real-world P2P leading datasets with large volumes of samples is made. Furthermore, robust tests and comparatives analysis are made to demonstrate the adaptability and performance of the GSCI-based ensemble model.
► Eutrophication has been accelerated by human activities worldwide. ► TN:TP mass ratios in nutrient sources and river water were compared in study sites. ► Both external loading and internal ...nutrient release contributed to algal blooms. ► Damming altered internal nutrient cycles and fueled eutrophication of river waters.
The natural process of eutrophication is accelerated by human activities worldwide that interrupt nutrient biogeochemical cycles. Three algal bloom events have been monitored in the northern tributary of the Jiulong River since 2009. The inflection points in a robust locally-weighted regression analysis (LOESS) of the relationship between TN and TP concentrations in the river water, and a TN:TP comparison with nutrient source loadings, suggested that both external loading and internal nutrient cycling contributed to these algal blooms. Nutrient release from the sediments may have played an important role in regulating the nutrients in the overlying water column. In particular, excessive nutrient inputs from various sources and ubiquitous river damming caused further accumulation of the nutrient loading. In-situ autochthonous primary production was enhanced in this relatively stable “river” to “lake” water body. Thus, attention must be paid to the effects of river damming and the consequent internal nutrient release.
•We propose a new method for ordinal regression with deep neural networks.•We address the rank inconsistency issue of other ordinal regression frameworks.•Our approach was evaluated on several face ...image datasets for age prediction.•Our method is compatible with other state-of-the-art deep neural networks.
In many real-world prediction tasks, class labels include information about the relative ordering between labels, which is not captured by commonly-used loss functions such as multi-category cross-entropy. Recently, the deep learning community adopted ordinal regression frameworks to take such ordering information into account. Neural networks were equipped with ordinal regression capabilities by transforming ordinal targets into binary classification subtasks. However, this method suffers from inconsistencies among the different binary classifiers. To resolve these inconsistencies, we propose the COnsistent RAnk Logits (CORAL) framework with strong theoretical guarantees for rank-monotonicity and consistent confidence scores. Moreover, the proposed method is architecture-agnostic and can extend arbitrary state-of-the-art deep neural network classifiers for ordinal regression tasks. The empirical evaluation of the proposed rank-consistent method on a range of face-image datasets for age prediction shows a substantial reduction of the prediction error compared to the reference ordinal regression network.
Perfluorooctanoic acid (PFOA) that accumulates in wastewater and excess sludge interact with the anaerobes and deteriorate the energy recovery and pollutants removal performance in the anaerobic ...digestion (AD) system. However, the interaction between PFOA and microbial metabolism in the AD systems remains unclear. This study aimed to clarify the effects and mechanism of PFOA on the AD process as well as the removal pathways of PFOA in an AD system. The results showed that the methane recovery efficiency was inhibited by 7.6–19.7% with the increased PFOA concentration of 0.5–3.0 mg/L, and the specific methanogenesis activity (SMA) was inhibited by 8.6–22.3%. The electron transfer system (ETS) was inhibited by 22.1–37.3% in the PFOA-containing groups. However, extracellular polymeric substance (EPS) gradually increased due to the toxicity of PFOA, and the ratio of protein to polysaccharide shows an upward trend, which led to the formation of sludge aggregates and resistance to the toxic of PFOA. The PFOA mass balance analysis indicated that 64.2–71.6% of PFOA was removed in the AD system, and sludge adsorption was the main removal pathway, accounting for 36.1–61.2% of the removed PFOA. In addition, the anaerobes are proposed to have the potential to reduce PFOA through biochemical degradation since 10.4–28.2% of PFOA was missing in the AD system. This study provides a significant reference for the treatment of high-strength PFOA-containing wastes.
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