Values Of Attributes Research Articles

Exploring generative frameworks for product attribute value extraction

E-commerce platforms rely heavily on the attribute values of their products as they play a crucial role in various retail functions such as product search, recommendations, and question answering. Therefore, identifying the attribute values from unstructured product information is critical for any e-commerce retailer. This problem is challenging due to the diversity of product types and their attributes and values. Attribute value extraction deals with extracting the values of attributes from the product profile. Previous approaches for this task have formulated the attribute value extraction as a Named Entity Recognition task (NER) or a Question Answering (QA) task. In this paper, we propose to tackle the attribute value extraction task using generative frameworks. In the first task, the attribute name and the product title are used to generate the value of the attribute. In the second task, only the product title is utilized to extract both the attributes and the values jointly. The value extraction is formulated as a text-infilling task and an answer-generation task. For joint attribute value extraction, we present two types of generative paradigms, namely, word sequence-based paradigm and positional sequence-based paradigm. The pre-trained language models such as GPT-2, BART, T5, and FLAN-T5 are leveraged to perform these tasks. Our experiments show that a single general model effectively performs value extraction task over a broad set of product attributes. Experiments conducted on two datasets depict that the generative approaches achieve new state-of-the-art results, which indicates that the proposed frameworks are helpful for attribute value extraction tasks without additional tagging.

N-Dimensional intuitionistic fuzzy index matrix representation of multidimensional data partitioning methods

In the present research paper, the data partitioning operations are investigated. Their functionalities are discussed. n-Dimensional intuitionistic fuzzy index matrix is used to represent the multidimensional data partitioning. Attributes selection and values selection by predicates is presented.

Exploring the potential and viability of inland waterway transport as a sustainable passenger transportation solution in emerging countries

The mounting transportation challenges and the ever-increasing usage of private vehicles in emerging countries necessitate an investigation into alternative sustainable modes of transportation. A three-stage behavioral based methodology is proposed for evaluating the viability of Inland Waterway Transportation (IWT) as a sustainable solution to the passenger transportation challenges faced by emerging nations. The approach includes identification of intervention areas, development of econometric models and recommendation of the best operational scenarios. The intervention areas are identified based on Importance Satisfaction Analysis and econometric models are developed to quantify benefit. The study identified nine key intervention areas in terms of qualitative and quantitative attributes, which served as the starting point for subsequent analysis and evaluation. The result showed substantially higher WTP values for the qualitative attributes (safety, security, and crowding level) of IWT, which indicates the pressing need for improvements in these attributes. The study clearly shows the possibility of enhancing the service quality of IWT without putting additional financial burden on the Government. The recommended scenario offers substantial benefits to travelers as compared to the base scenario. The study offers both short-term and long-term strategies to enhance IWT services, providing improvements without fare increments in the short term. Upon adopting the short-term scenario and gaining public acceptance, the recommended long-term scenario, which includes fare increments, can be gradually implemented. Although the findings are specific to the case studied, the methodology and experience can be applied to enhance IWT services wherever applicable.

Data Mining Analysis for Improving Decision-Making in Computer Management Information Systems

In this paper a decision tree-based data mining procedure for information systems is proposed to enhance the accuracy and efficiency of data mining. An enhanced C4.5 decision tree method based on cosine similarity is suggested to evaluate the information gain rate of characteristics and the information entropy of their values. When the information entropy variance among any two values for attributes is within the threshold range, the cosine similarity of the merging attribute values is determined, and the information gain rate of the attributes is recalculated. Large-scale data sets that conventional data processing methods are unable to handle successfully have given rise to the area of data mining. The prime objective is to look into how data mining technology is used in computer management information systems. The benefits of data mining technologies in computer management information systems are examined from a variety of angles in this study. In order to analyze and comprehend huge data sets and to derive knowledge that can be utilized to enhance the decision-making process in computer management information systems, the suggested solution makes use of a number of data mining techniques, including Clustering, Classification, and Association Rule Mining. The experimental analysis indicates that the time required by the proposed method to construct a decision tree is less than the time required by the GBDT, P-GBDT method and the C5.0 decision tree Hyperion image forest type fine classification method. The minimum time is not more than 15 seconds when compared with the minimum time saving of the other two methods. The time required by the C5.0 decision tree Hyperion image forest type fine classification method is always the greatest in comparison with the minimum time saving of the C5.0 decision tree. The classification accuracy of the proposed method for various datasets exceeds 95 percent, and the data mining efficacy is high. This method enhances the precision and efficacy of data mining in order to uncover valuable information concealed behind a large volume of data and maximize its value.

Exploring buyer’s preferences to propose strategies to improve dog welfare in Brazil

ABSTRACT This study aimed to investigate trends and concerns related to the commercialization of dogs in Brazil. The authors analyzed 1,500 ads for the sale of Miniature Schnauzer, Chihuahua, and German Spitz breeds using the methodology of hedonic price analysis. Marginal and relative values of attributes were calculated for puppy sex, coat, age, pedigree, vaccination and deworming, seller type and region, using ordinary least squares. The most valued attributes for each breed were identified as selling by kennels, pedigree, and coat color. The results suggest that this market niche is willing to pay for guarantees of better dog breeding, which represents an opportunity for the development of certification labels for origin and good practices for kennels. Such labels could become an effective strategy to ensure greater transparency and better welfare standards in the Brazilian dog market. This study also provides valuable insights for the development of guidelines and public policies aimed at improving animal welfare and reducing information asymmetry in the pet market.

Therapeutic potential of Cordyceps militaris extracts and its structural effects on the cultured milk model

The study was the production of the probiotic cultured milk enriched with the edible mushroom crude extract and its evaluation to gain insight about the prebiotic potential of Cordyceps militaris. During the 28–day shelf life; microbial, physico–chemical, textural, and sensorial properties were investigated. The results indicated that the probiotic milk with extract was influential on values of gel firmness, color and sensorial attributes . The interaction between extract and probiotic culture, and hence increased acidity, has an impact on textural properties. Moreover, in terms of taste the extract left a dominant aroma in–mouth and bitterness in throat, and in terms of odor it suppressed the characteristic flavor of probiotic cultured milk. Consequently, the results suggested that extract showed a positive stimuli on growth and activity of probiotics, and it could be considered as potential prebiotic source as its impact had been evaluated on fermentation by a cultured model.

Technical evaluation of steel roof trusses for a large pool basin

In this work, we carry out a technical evaluation of steel roof trusses for a large basin of the Del’fin pool in the city of Ust-Ilimsk with the purpose of determining their further operability. This task involves establishing the damage degree of building structures and the category of their technical condition based on a comparison between the actual values of quantifiable attributes and those established by the project or regulatory documents. The studied objects are represented by steel trusses with a span of 15 m, located in plan with a spacing of 6.0 m and conditionally divided into 2 groups: reinforcement trusses (F-1 and F-4) and existing trusses without reinforcement (F-2 and F-3). We analyze the technical, as-built, and operational documentation, and carry out visual and measuring control procedures. The verification calculation of the most loaded truss elements is presented (not provided in the article). According to the analysis of the obtained data, the condition of the studied steel trusses can be concluded as unacceptable and emergency. The emergency technical condition of the F-2 and F-3 trusses is due to the unprovided bearing capacity of support brace struts and insufficient length of welds, fastening the support brace struts. The unacceptable technical condition of the reinforcement trusses F-1 and F-4 is due to the insufficient length of the welds, fastening the support brace struts, as well as the deviations in a number of technical solutions, outlined in the reinforcement project, from the current norms. Recommendations on the further operability of roof structures are provided.

Obese people are more likely to exhibit unhealthy food decisions when sated

Food decision-making plays a key role in weight loss and obesity prevention. It involves the value and integration of food attributes, such as health and taste. However, how homeostatic status affects the value and integration of food attributes, especially in obese people, remains unknown. To address this question, we investigated how metabolic states (sated vs. fasting) may affect food decision-making and brain response. This study encompassed a multifaceted approach, investigating hormone levels, decision-making processes relating to food, and fNIRS data. We recruited 20 obese and 20 normal-weight subjects. We found that there were no differences between obese and normal weights in health and taste ratings. However, the weight of health ratings was higher in sated normal weight people than sated obese people. The dlPFC activity showed a similar pattern with the weight of health ratings. The functional connectivity between the vmPFC and dlPFC was lower in obese people when sated than during fasting, while this connectivity remained relatively constant in counterparts. Sated normal weight people had stronger associations between ghrelin levels and the dlPFC activation than sated obese people. Our results highlight the role of the dlPFC with regard to the homeostatic signaling regulation of food decision-making and suggest that obese people are more likely to exhibit unhealthy eating behavior when sated, which may be related to the reduced ability of the dlPFC to downregulate the vmPFC.

Combining Multiple Plant Attributes to Reveal Differences in Community Structure in Two Distant Deserts in Central Asia.

International interest is growing in biodiversity conservation and sustainable use in drylands. Desert ecosystems across arid Central Asia are severely affected by global change. Understanding the changes in a plant community is an essential prerequisite to revealing the community assembly mechanism, vegetation conservation, and management. The knowledge of large-scale spatial variation in plant community structure in different Central Asian deserts is still limited. In this study, we selected the Taukum (TD, Kazakhstan) and the Gurbantunggut (GD, China) deserts as the research area, with similar latitudes despite being nearly 1000 km apart. Thirteen and 15 sampling plots were set up and thoroughly investigated. The differences in community structure depending on multiple plant attributes (individual level: plant height, canopy diameter, and plant volume, and community level: plant density, total cover, and total volume) were systematically studied. TD had a better overall environmental status than GD. A total of 113 species were found, with 68 and 74 in TD and GD, respectively. The number of species and plant attributes was unequally distributed across different families and functional groups between deserts. The values of several plant attributes, such as ephemerals, annuals, dicotyledons, and shrubs with assimilative branches in GD, were significantly lower than those in TD. The Motyka indices of six plant attributes (26.18-38.61%) were higher between the two deserts than the species similarity index (20.4%), indicating a more robust convergence for plant functional attributes. The community structures in the two deserts represented by different plant attribute matrices demonstrated irregular differentiation patterns in ordination diagrams. The most variance in community structure was attributed to soil and climatic factors, while geographic factors had the smallest proportion. Consequently, the community structures of the two distant deserts were both different and similar to an extent. This resulted from the long-term impacts of heterogeneous environments within the same region. Our knowledge is further deepened by understanding the variation in community structure in different deserts on a large spatial scale. This therefore provides valuable insights into conserving regional biodiversity in Central Asia.

Enhancing Citizens’ Perceived Restoration Potential of Green Facades through Specific Architectural Attributes

Research on restorative environments has suggested green facades as a promising model for natural spaces in urban and densely populated areas. However, the impact of architectural design attributes of green building facades on perceived restoration potential is insufficiently researched. Therefore, this study aimed to investigate the relationship between architectural design attributes of green building facades and perceived restoration potential. A discrete choice experiment was used. First, seven architectural design attributes (Value, Symmetry, Material, Balcony, Variety, Configuration, and Shape) were identified based on the Content Identifying Method. In the next step, a sample of 204 participants randomly selected a block and evaluated the perceived restoration potential of six pairs of designed images of different green facade scenarios based on the seven architectural design attributes. The results showed that low diversity in greenery was associated with greater influence, and people chose asymmetric greenery more often. Horizontal and scattered greenery was preferred over vertical and concentrated greenery, which had no significant effect on perceived restoration potential. Stone and cement used in green facades were the most influential factors in perceived restoration potential. These findings can aid designers in designing the green facade of residential buildings to increase perceived restoration potential.

Quality attributes of Pitanga (Eugenia uniflora L.) during postharvest storage as affected by ripening stage and storage temperature

BACKGROUND: Pitanga (Eugenia uniflora L.) is a South American berry with a sweet flavor and is rich in bioactive compounds and antioxidants. However, its high water and sugar content, susceptibility to spoilage, and bruising limit its shelf life. OBJECTIVE: This study aimed to investigate the physical and chemical changes of pitanga during storage and to identify the optimal harvest time and storage conditions. METHODS: Pitangas were harvested at mid or full-ripeness stages, stored at 2°C, 10°C, or 20°C, and analyzed for physical and chemical quality attributes using a kinetic approach. RESULTS: The storage temperature of pitanga significantly affected the kinetics of various quality attributes, including weight loss, percentage of damaged and decayed fruit, fruit hardness, total soluble solid content, and color change. However, the ripening stage during harvest only influenced the initial values of a few quality attributes. The bioactive compound content and antioxidant capacity of pitanga were not significantly affected by either storage temperature or ripening stage, except for vitamin C. CONCLUSIONS: Pitanga can be preserved at 2°C for 20 days, resulting in a four-fold increase in shelf life compared to current postharvest practices. This finding offers valuable insights into optimizing the postharvest technology of pitanga, thereby increasing its utilization and promoting sustainable production practices.

Machine-Learning-Based Imputation Method for Filling Missing Values in Ground Meteorological Observation Data

Ground meteorological observation data (GMOD) are the core of research on earth-related disciplines and an important reference for societal production and life. Unfortunately, due to operational issues or equipment failures, missing values may occur in GMOD. Hence, the imputation of missing data is a prevalent issue during the pre-processing of GMOD. Although a large number of machine-learning methods have been applied to the field of meteorological missing value imputation and have achieved good results, they are usually aimed at specific meteorological elements, and few studies discuss imputation when multiple elements are randomly missing in the dataset. This paper designed a machine-learning-based multidimensional meteorological data imputation framework (MMDIF), which can use the predictions of machine-learning methods to impute the GMOD with random missing values in multiple attributes, and tested the effectiveness of 20 machine-learning methods on imputing missing values within 124 meteorological stations across six different climatic regions based on the MMDIF. The results show that MMDIF-RF was the most effective missing value imputation method; it is better than other methods for imputing 11 types of hourly meteorological elements. Although this paper applied MMDIF to the imputation of missing values in meteorological data, the method can also provide guidance for dataset reconstruction in other industries.

Fuzzy Rule-Based Explainer Systems for Deep Neural Networks: From Local Explainability to Global Understanding

Explainability of deep neural networks has been receiving increasing attention with regard to auditability and trustworthiness purposes. Of the various post-hoc explainability approaches, rule extraction methods assist to understand the logic that underpins their functioning. Whereas the rule-based solutions are directly managed and understood by practitioners, the use of intervals or crisp values in the antecedents that rely on numerical values might not be intuitive enough. In this case, the benefits of a linguistic representation based on fuzzy sets/rules are straightforward, as these semantically meaningful components ease the model understanding. This paper proposes fuzzy rule-based explainer systems for deep neural networks. The algorithm learns a compact yet accurate set of fuzzy rules based on features' importance (i.e., attribution values) distilled from the trained networks. These systems can be used for both local and global explainability purposes. The evaluation results of different applications revealed that the fuzzy explainers maintained the fidelity and accuracy of the original deep neural networks while implying lower complexity and better comprehensibility.

English

This study was conducted with the objective to estimate the implicit values for various olive oil attributes in Riyadh, Saudi Arabia. A hedonic price model was applied to examine 558 olive oil samples taken from nine hypermarkets with different olive oil brands. The attributes under consideration include the package size, extra virgin label, level of acidity, organic certification, purity, country of origin, place of retail market, and packaging type. Results show agreement between signs and magnitudes for the implicit values for the most attributes. The significant attributes of olive oil identified in this study are package size, level of acidity, and variables describing the country of origin, place of retail market, and packaging type. The results reveal a negative relationship between (i) the price and the size of container and (ii) the price and the level of acidity for olive oil. Furthermore, olive oil products with organic and pure label have a higher price compared to those without them. The country of origin and the type of packaging may also influence the olive oil price

The Effect of Conscious Cosmetics Selection Attributes of Generation Z on Perceived Value and Eco-friendly Consumption Efficacy

The purpose of this research is to point out that problems such as abnormal weather and global warming are caused by environmental pollution caused by human activities due to recent changes in ecosystems and environments around the world. In this study, we studied the impact of Gen Z consumers' conscious cosmetic selection attributes on perceived value and eco-friendly consumption efficiency. This survey was conducted by a group of 297 conscious cosmetics consumers as part of the post-purchase experience from March 2-31, 2023. Gen Z conscious cosmetic choice attributes had a statistically significant effect on perceived value, and Gen Z conscious cosmetic choice attributes had a significant effect on eco-friendly consumption efficiency. In conclusion, this study is meaningful in analyzing the perceived value of consumer choice attributes of conscious cosmetics and the eco-friendly consumption effect, focusing on Generation Z, which companies are paying attention to. Combined with the above, the purpose of this study is to provide a basis for establishing marketing and advertising strategies for the conscious cosmetics industry market.

Influence of sustainable packaging material and packaging conditions on physicochemical, microbiological, and sensorial properties of cheeses

The aim of this study was to evaluate the influence of different packaging materials [standard foil: BOPP (biaxially oriented polypropylene)/PET (polyester)/PE (polyethylene) for upper layer, and APET (polyethylene terephthalate)/PE for bottom layer; foil 1: PP (polypropylene)/PET/PE/EVOH (ethylene-vinyl alcohol copolymer)/PE upper layer, and PP/PE/EVOH/PE bottom layer; foil 2: PP/PET/PE/EVOH/PE upper layer, and PA (polyamide)/EVOH/PE bottom layer; foil 3: PP/PET/PE upper layer, and PA/EVOH/PE bottom layer; foil 4: PP/PET/PE upper layer, and PA/PE bottom layer; foil 5: PP upper layer, and PP/PP bottom layer] on the quality of 3 different ripening rennet cheeses packed under different modified atmosphere (MAP) conditions as reflected in particular physicochemical, microbiological, and sensorial changes. The changes were monitored during a period of 90 d of storage at 2°C or 8°C. For Gouda cheese, CO2 content of the headspace of the packages was in the range 35% to 45%, whereas for Maasdamer and Sielski Klasyczny cheeses it was 55% to 65%. Three-way ANOVA showed that the foil type influenced the moisture content of Gouda cheese stored for 90 d at 2°C and for Sielski Klasyczny cheese at 8°C, whereas the moisture content was not dependent on MAP conditions during storage. Moreover, the foil type had a significant effect on free fatty acid changes for Gouda and Sielski Klasyczny cheeses stored at 2°C for 90 d. Sensory attributes changed significantly over storage time at 2°C for all studied cheeses as affected by foil type, whereas there was no effect of MAP conditions. In general, the cheeses packed in standard foil and foil 4 were characterized by the highest values of mean sensory attributes. Time was the most significant factor influencing most changes in physicochemical and sensory attributes of cheeses stored at 2°C and 8°C. The storage temperature did not affect the moisture of the samples during storage. In general, we found an increase in the pH value during storage regardless of storage temperature. It was possible to decrease the thickness of the packaging material from initial 103 and 250 µm (standard foil; lid and bottom, respectively) to 98 and 100 µm (foil 4) without affecting sensory attributes of the product.

Preparation of a Pt(II)-3-Hydroxy-2-tolyl-4H-chromen-4-one Complex Having Antimicrobial, Anticancerous, and Radical Scavenging Activities with Related Computational Studies.

A novel benzopyran-based platinum (II)-3-hydroxy-2-tolyl-4H-chromen-4-one (HToC) complex has been prepared and studied by UV-visible spectrophotometry. The study is based on the colored complexation between Pt(II) and HToC in the pH range of 8.92-9.21, resulting in the formation of a stable binary yellow complex exhibiting λmax at 509-525 nm. The formed complex maintains linearity between 0.0 and 1.8 μg Pt(II) mL-1. The well-known qualitative analytical methods, including Job's method of continuous variations and the mole ratio approach, have both proven that the stoichiometry of the complex is 1:2 [Pt(II)/HToC]. Hence, the analytical results suggest that the formed platinum complex exhibits a square planar geometry. The values of various attributes corresponding to spectrophotometric studies and statistical calculations, such as the molar extinction coefficient (6.790 × 104 L mol-1 cm-1), Sandell's sensitivity (0.0029 μg Pt(II) cm-2), standard deviation (± 0.0011), RSD (0.317%), limit of detection (0.0147 μg mL-1) and correlation coefficient (0.9999), show that the performed study satisfies all of the criteria for good sensitivity, versatility, and cost-effectiveness. In order to have an apprehension of the molecular geometry and other structural specifics of the complex, DFT studies have been carried out. The in vitro anticancer potential of the ligand and its platinum complex in the human breast cancer cell line (T-27D), as determined by the MTT assay, reveals that the complex has better antiproliferative potential than the ligand. The antimicrobial potential of the complex has been successfully tested against both Gram-positive and -negative bacteria. Antioxidant capacity results suggest the better radical scavenging capacity of the complex than that of the ligand.

Relativistic isotropic stellar model in [formula omitted] gravity with Durgapal- IV metric

In this work, a new static, non-singular, spherically symmetric fluid model has been obtained in the background of f(R,T) gravity. Here we consider the isotropic metric potentials of Durgapal-IV (Durgapal, 1982) solution as input to handle the Einstein field equations in f(R,T) environment. For different coupling parameter values of χ, graphical representations of the physical parameters have been demonstrated to describe the analytical results more clearly. It should be highlighted that the results of General Relativity (GR) are given by χ=0. With the use of both analytical discussion and graphical illustrations, a thorough comparison of our results with the GR outcomes is also covered. The numerical values of the various physical attributes have been given for various coupling parameter χ values in order to discuss the impact of this parameter. Here we apply our solution by considering the compact star candidate LMC X-4 (Rawls et al., 2011) with mass =(1.04±0.09)M⊙ and radius =8.301−0.2+0.2 km. respectively, to analyze both analytically and graphically. To confirm the physical acceptance of our model, we discuss certain physical properties of our obtained solution such as energy conditions, causality, hydrostatic equilibrium through a modified Tolman–Oppenheimer–Volkoff (TOV) conservation equation, pressure–density ratio, etc. Also, our solution is well-behaved and free from any singularity at the center. From our present study, it is observed that all of our obtained results fall within the physically admissible regime, indicating the viability of our model.

Data-driven multi-step prediction and analysis of monthly rainfall using explainable deep learning

Monthly rainfall prediction is a crucial topic for the management of water resources and prevention of hydrological disasters. To make a multi-step monthly rainfall prediction and discover the primary factors affecting rainfall, this study presents an explainable deep learning approach that integrates four consecutive modules, namely the Gated-Recurrent-Unit-based (GRU-based) encoder module, attention mechanism module, GRU-based decoder module, and expected-gradient-based explanation module, respectively. The first three modules constitute an encoder-decoder with an attention mechanism which could predict monthly rainfall in multiple continuous months in the future, while the explanation module computes attribution values for the input weather and climate features to quantify their importance. A case study is conducted on monthly rainfall data collected from Darwin and Perth, Australia, from Jan 1921 to Dec 2020. The results mainly indicate that: (1) Compared with the baseline methods, the multi-step monthly rainfall predictions made by the encoder-decoder with an attention mechanism show better agreement with the ground truth. (2) For January rainfall in Darwin, the most important weather factors are last January and February rainfall, while the most critical climate index is last February’s Southern Oscillation Index, with its high values inhibit while low values promote January rainfall in Darwin. (3) The most significant feature for June rainfall in Perth is last June’s solar radiation whose feature value is weakly negatively correlated with its attribution value. The study’s significance lies in enhancing the accuracy of multi-step rainfall prediction and providing interpretability through identification of influential factors, which facilitates long-term planning of water resources and deeper understanding of complex weather systems.

Regional differences in water beetle communities networks settling in dystrophic lakes in northern Poland

The relationships between the species that form the networks in small dystrophic lakes remain poorly recognised. To investigate and better understand the functioning of beetle communities in different ecosystems, we created three network models that we subjected to graph network analysis. This approach displays correlation–based networks of connections (edges) between objects (nodes) by evaluating the features of the whole network and the attributes of nodes and edges in the context of their roles, expressed by centrality metrics. We used this method to determine the importance of specific species in the networks and the interspecific relationships. Our analyses are based on faunal material collected from 25 dystrophic lakes in three regions of northern Poland. We found a total of 104 species representing different ecological elements and functional trophic groups. We have shown that the network of relationships between the biomass of species differs considerably in the three study regions. The Kashubian Lakeland had the highest cohesion and density, while the network in the Suwalki Lakeland was the thinnest and most heterogeneous, which might be related to the fractal structure and the degree of development of the studied lakes. Small–bodied predators that congregated in different clusters with species with similar ecological preferences dominated all networks. We found the highest correlations in the Masurian Lakeland, where we obtained the highest centralisation of the network. Small tyrphophiles typically occupied the central places in the network, while the periphery of the network consisted of clusters with different habitat preferences, including large predators. The species that were most important for network cohesion and density were mainly tyrphophilous species, such as Anacaena lutescens, Hygrotus decoratus, Enochrus melanocephalus and Hydroporus neglectus. The values of attributes determining the role of species in community networks were influenced by both biotic and environmental factors.