Attributes Of Different Types Research Articles

A Multi-Group Multi-Stream attribute Attention Network for fine-grained zero-shot learning

Fine-grained visual categorization in zero-shot setting is a challenging problem in the computer vision community. It requires algorithms to accurately identify fine-grained categories that do not appear during the training phase and have high visual similarity to each other. Existing methods usually address this problem by using attribute information as intermediate knowledge, which provides sufficient fine-grained characteristics of categories and can be transferred from seen categories to unseen categories. However, the learning of attribute visual features is not trivial due to the following two reasons: (i) The visual information about attributes of different types may interfere with the visual feature learning of each other. (ii) The visual characteristics of the same attribute may vary in different categories. To solve these issues, we propose a Multi-Group Multi-Stream attribute Attention network (MGMSA), which not only separates the feature learning of attributes of different types, but also isolates the learning of attribute visual features for categories with big differences in attribute appearance. This avoids the interference between uncorrelated attributes and helps to learn category-specific attribute-related visual features. This is beneficial for distinguishing fine-grained categories with subtle visual differences. Extensive experiments on benchmark datasets show that MGMSA achieves state-of-the-art performance on attribute prediction and fine-grained zero-shot learning.

Methylcellulose replacement with different enzymatically treated plant fibres as a binder in the production of plant-based meat patties

Texturised vegetable protein (TVP) is a sustainable and economical base for plant-based meat patties but requires binders to create an emulsified gel and hold the patty structure together. This study evaluated the physicochemical and sensory attributes of TVP patties incorporated with three different enzymatically-treated plant fibres i.e., pea (EPF), citrus (ECF) or apple (EAF) as a binder compared to positive control (methylcellulose, MC) and negative control (no binder, NC). All the patties with plant fibres had similar water-holding capacity compared to the MC. EAF exhibited the least fluid release and uniform surface, while ECF demonstrated the least cooking loss and shrinkage, uniform surface, hard texture, better cohesiveness, gumminess and chewiness compared to other samples. All the plant fibre-incorporated patties scored similarly for taste, texture, juiciness and overall acceptability compared to the positive control. The agglomerative hierarchical clustering revealed that the EPF had similar characteristics to the MC but the principal component analysis indicated that citrus fibre was a superior binder to pea fibre, therefore it could be used to replace methylcellulose for plant-based meat patties. Future research should explore more variations in plant-based binders to optimise the performance and sensory attributes of different types of texturised vegetable protein-based meat analogues.

Influence maximization for heterogeneous networks based on self-supervised clustered heterogeneous graph transformer

Influence maximization (IM) has drawn significant attention in recent years. Most existing IM methods primarily focus on homogeneous networks, and do not take into account the heterogeneity and the attributes of different types of nodes in heterogeneous networks. However, heterogeneous networks are ubiquitous in real world, encompassing rich semantics and complex structural information. Additionally, the clustering characteristics inherent in a network have a critical and substantial impact on the process of information diffusion, which is often overlooked in IM models designed for heterogeneous networks. To address the challenges posed by the heterogeneity and clustering structure in heterogeneous networks, we propose a novel deep learning framework based on a self-supervised clustered heterogeneous graph transformer for IM in heterogeneous networks, which we have named SCHGT-IM. SCHGT-IM aggregates the heterogeneity and clustering information in heterogeneous networks and incorporates a clustered cascade (CC) model as an information diffusion model to enhance the realism of simulations. We evaluate the performance of SCHGT-IM in comparison with that of state-of-the-art IM models using three academic heterogeneous networks extracted from the DBLP dataset. The experimental results on influence spread demonstrate that SCHGT-IM is superior to fourteen state-of-the-art algorithms and is highly effective in selecting influential seed nodes of different types from heterogeneous networks.

Quality analysis and antioxidant activity of different types of tea powder

As a natural product with health benefits, tea powder (TP) is widely used in food processing field. In order to explore sensory evaluation, quality components and antioxidant activities among different types of TP, matcha TP, Huangjinya green TP, oolong TP, black TP and Pu-erh TP were used as the research objects. Our results showed that total scores of sensory evaluation of unfermented TPs were higher than those of fermented TPs, whilst the colour of those unfermented TPs was more bright. Furthermore, taste attributes showed that sweetness was conducive to the formation of the taste of black TP, and aftertaste-bitterness was beneficial to matcha and Pu-erh TP. Umami was suggested as a vital contributing factor to the mellow taste of Huangjinya green TP, while sourness and bitterness were not propitious to form the taste quality of tea powder. The non-volatile quality components analysis revealed that tea powders with more fermentation usually contained fewer amino acids and catechins, but glutamate and gamma-aminobutyric acid were higher in black TP than in oolong TP. Theanine, glutamic acid and arginine were the main iconic components affecting the amino acid composition of different types of tea powder in our study. Meanwhile, the contents of ester catechins (ECG, EGC, EGCG) in black TP and Pu-erh TP were significantly lower than those in green TP and oolong TP. In addition, the free radical scavenging ability of unfermented TPs were stronger than that of fermented TPs. Therefore, to maximize the health benefits and sensory attributes of different types of TP, the use of unfermented tea powder is recommended. However, different manufacturing process of tea powder can enrich the taste of tea foods, thus giving people more choices.Graphical

A universal tool for stability predictions of biotherapeutics, vaccines and in vitro diagnostic products

It is of particular interest for biopharmaceutical companies developing and distributing fragile biomolecules to warrant the stability and activity of their products during long-term storage and shipment. In accordance with quality by design principles, advanced kinetic modeling (AKM) has been successfully used to predict long-term product shelf-life and relies on data from short-term accelerated stability studies that are used to generate Arrhenius-based kinetic models that can, in turn, be exploited for stability forecasts. The AKM methodology was evaluated through a cross-company perspective on stability modeling for key stability indicating attributes of different types of biotherapeutics, vaccines and biomolecules combined in in vitro diagnostic kits. It is demonstrated that stability predictions up to 3 years for products maintained under recommended storage conditions (2–8 °C) or for products that have experienced temperature excursions outside the cold-chain show excellent agreement with experimental real-time data, thus confirming AKM as a universal and reliable tool for stability predictions for a wide range of product types.

Nano-bioremediation: an eco-friendly and effective step towards petroleum hydrocarbon removal from environment

Global concern about petroleum hydrocarbon pollution has intensified and gained scientific interest due to its noxious nature, high persistence in environmental matrices, and low degradability. One way to address this is by combining remediation techniques that could overcome the constraints of traditional physio-chemical and biological remediation strategies. The upgraded concept of bioremediation to nano-bioremediation in this direction offers an efficient, economical, and eco-friendly approach to mitigate petroleum contaminants. Here, we review the unique attributes of different types of nanoparticles and their synthesis procedures in remediating various petroleum pollutants. This review also highlights the microbial interaction with different metallic nanoparticles and their consequential alteration in microbial as well as enzymatic activity which expedites the remediating process. Besides, the latter part of the review explores the application of petroleum hydrocarbon degradation and the application of nano supports as immobilizing agents for microbes and enzymes. Further, the challenges and the future prospects of nano-bioremediation have also been discussed.

Sketch+ for Visual and Correlation-Based Exploratory Data Analysis: A Case Study with COVID-19 Databases

The amount of data daily generated by different sources grows exponentially and brings new challenges to the information technology experts. The recorded data usually include heterogeneous attribute types, such as the traditional date, numerical, textual, and categorical information, as well as complex ones, such as images, videos, and multidimensional data. Simply posing similarity queries over such records can underestimate the semantics and potential usefulness of particular attributes. In this context, the Exploratory Data Analysis (EDA) technology is well-suited to understand data and perform knowledge extraction and visualization of existing patterns. In this paper, we propose Sketch+ , a technique and a corresponding supporting tool to compare electronic health records (provided by hospitals) by similarity, supporting correlation-based exploratory analysis over attributes of different types and allowing data preprocessing tasks for visualization and knowledge extraction. Sketch+ computes partial and overall data correlation considering distance spaces induced by the attributes. It employs both ANOVA and association rules with lift correlations to study relationships between variables, allowing extensive data analysis. Among the tools provided, a pixel-oriented one drives the analysts to observe visual correlations among dates, categorical and numerical attributes. As a running case study, we employed three open databases of COVID-19 cases, showing that specialists can benefit from the inference modules of Sketch+ to analyze electronic records. The study highlights how Sketch+ can be employed to spot strong correlations among tuples and attributes, with statistically significant results. The exploratory analysis has been shown to be an essential complement for similarity search tasks, identifying and evaluating patterns from heterogeneous attributes.

External Quality Assurance programs for processing methods provide evidence on impact of preanalytical variables

An annual External Quality Assurance (EQA) program has been provided to processing laboratories over the last ten years, allowing them to assess the performance of their processing methods, such as nucleic acid extractions or peripheral blood mononuclear cell (PBMC) isolation and cryopreservation. The objective of this study was to perform a global analysis on almost 1000 EQA scheme/participant data in order to assess (i) the impact of critical preanalytical factors on quantitative or qualitative attributes of different types of specimens and (ii) laboratory performance pattern over time. Statistical analysis was performed within each EQA scheme based on categorized preanalytical data provided by the participants and on centralized measurements of relevant quality attributes of the produced specimens (z-scores): DNA, cell-free (cf)DNA or RNA extraction from blood, DNA or RNA extraction from formalin fixed tissue, DNA or RNA extraction from frozen tissue, DNA extraction from saliva or stool, viable PBMC isolation and cryopreservation. The most critical preanalytical factors in nucleic acid extraction schemes were the nucleic acid extraction method and kit, the elution buffer, the enzymes used during extraction, the input material quantity and the storage temperature. Several indications of laboratory performance improvement over time could be seen. The conclusions are that EQA for processing methods provides unique evidence-based insights into the impact of preanalytical factors and the comparative performance of different processing methods and kits, while supporting laboratories in validating their processing methods.

Intrusion Detection Model Based on Rough Set and Random Forest

Currently, redundant data affects the speed of intrusion detection, many intrusion detection systems (IDS) have low detection rates and high false alert rate. Focusing on these weakness, a new intrusion detection model based on rough set and random forest (RSRFID) is designed. In the intrusion detection model, rough set (RS) is used to reduce the dimension of redundant attributes; the algorithm of decision tree(DT) is improved; a random forest (RF) algorithm based on attribute significances is proposed. Finally, the simulation experiment is given on NSL-KDD and UNSW-NB15 dataset. The results show: attributes of different types of datasets are reduced using RS; the detection rate of NSL-KDD is 93.73%, the false alert rate is 1.02%; the detection rate of NSL-KDD is 98.92%, the false alert rate is 2.92%.

On account of trans fatty acids and cardiovascular disease risk – There is still need to upgrade the knowledge and educate consumers

AimsTrans fatty acids (TFAs) are unsaturated lipids either of industrial origin or naturally occurring in ruminant meat and milk. TFAs generated through food processing (industrial) is the main source in our diet and studies provide converging evidence on their negative effect on cardiovascular health. Since April 2021, the European Commission has put into effect a regulation for TFAs providing maximum 2% of total fat in all industrially produced foods. In light of this development, we review the evidence regarding the health attributes of different types of TFAs, their dietary sources and current intakes, and we describe the history of TFA-related legislative actions in an attempt to anticipate the efficiency of new measures. Data synthesisThe PubMed database was searched including original research (observational and intervention studies), systematic reviews and meta-analyses. Scientific reports of competent authorities and organizations have also been screened. ConclusionsTrans-fat elimination provides a fine example of how evidence has led to the application of horizontal regulatory measures regarding legal food ingredients in order to protect consumers’ health. In EU Member States, TFAs currently provide on average less than 1% of energy (1%E) and intakes marginally exceed recommendations primarily among young adults. Large dietary surveys however provide evidence for additional, less-well known sources of TFAs in the diet. Raising public awareness of “hidden” trans-fat found naturally in foods such as cheese, as well as of the trans-fat generated through traditional cooking practices is needed, if the goal to eliminate trans-fat from the diet is to be met.

Research on an intelligent disassembling method for multi-type mobile phones based on rough set theory

Waste mobile phones (WMPs), as a type of electronic waste, must be properly disposed of, so as not to pollute the environment. WMPs should be non-destructively dismantled, to recycle useful resources and produce considerable economic benefits, as they contain many rare metals. Dismantling equipment, though, is hard to adapt to multiple types of mobile phones, since many models are produced each year. A smart classification and dismantling method for mobile phones is thus proposed here. The feature attributes of different types of mobile phones are collected, and the weight of feature attributes for mobile phone disassembly is obtained via rough set theory. With many pipelines already designed, mobile phones without existing disassembly schemes can be assigned a scheme by calculating their similarity with multiple existing pipelines based on the feature attributes of the phone. The disassembly scheme most similar to the mobile phone to be disassembled is selected for disassembly and the disassembly scheme of the mobile phone is designed according to that disassembly situation. This article analyzes the disassembly station appropriate for the mobile phone rear cover. The results show that the similarity judgment of the scheme is reasonable. It is assumed that there is an available assembly line database. As time goes on, the database can be updated by continuously collecting the disassembly information of new mobile phones, to further increase the stability of assembly line selection.

Topological Indices of Pent-Heptagonal Nanosheets via M-Polynomials

The combination of mathematical sciences, physical chemistry, and information sciences leads to a modern field known as cheminformatics. It shows a mathematical relationship between a property and structural attributes of different types of chemicals called quantitative-structures’ activity and qualitative-structures’ property relationships that are utilized to forecast the chemical sciences and biological properties, in the field of engineering and technology. Graph theory has originated a significant usage in the field of physical chemistry and mathematics that is famous as chemical graph theory. The computing of topological indices (TIs) is a new topic of chemical graphs that associates many physiochemical characteristics of the fundamental organic compounds. In this paper, we used the M-polynomial-based TIs such as 1st Zagreb, 2nd Zagreb, modified 2nd Zagreb, symmetric division deg, general Randi c ´ , inverse sum, harmonic, and augmented indices to study the chemical structures of pent-heptagonal nanosheets of V C 5 C 7 and H C 5 C 7 . An estimation among the computed TIs with the help of numerical results is also presented.

Are consumer preferences for attributes of alternative vehicles sufficiently accounted for in current policies?

International research illustrates some attributes of electro-mobility such as charging infrastructure and range which are crucial determinants of market development. Between 2017 and 2020 the number of battery electric vehicles in Germany has increased much more compared to the number of charging points. High preferences for these attributes in combination with insufficient infrastructure require further clarification to explain why the development of markets for electro-mobility in Germany during the last years is not as expected. This study aims to examine the willingness to pay for attributes of different types of electric vehicles and for car sharing in order to derive recommendations for marketing and policy. The representative study is based on a survey of 405 car users in Germany using a discrete choice experiment with the attributes price, power, running costs, bonus, range and availability of charging stations. 12 choice situations were presented to each respondent. A latent class model was used to analyze socio economic determinants of the willingness to pay for single attributes. The results confirm findings in the literature indicating low preferences for battery electric vehicles in general. Due to high shares of house owners in sub-urban regions this consumer group should be more focused in local sustainability concepts and marketing of battery electric vehicles and other alternative vehicles.

Portable near Infrared Spectroscopy as a Tool for Fresh Tomato Quality Control Analysis in the Field

Portable spectrometers are promising tools that can be an alternative way, for various purposes, of analyzing food quality, such as monitoring in a few seconds the internal quality during fruit ripening in the field. A portable/handheld (palm-sized) near-infrared (NIR) spectrometer (Neospectra, Si-ware) with spectral range of 1295–2611 nm, equipped with a micro-electro-mechanical system (MEMs), was used to develop prediction models to evaluate tomato quality attributes non-destructively. Soluble solid content (SSC), fructose, glucose, titratable acidity (TA), ascorbic, and citric acid contents of different types of fresh tomatoes were analyzed with standard methods, and those values were correlated to spectral data by partial least squares regression (PLSR). Fresh tomato samples were obtained in 2018 and 2019 crops in commercial production, and four fruit types were evaluated: Roma, round, grape, and cherry tomatoes. The large variation in tomato types and having the fruits from distinct years resulted in a wide range in quality parameters enabling robust PLSR models. Results showed accurate prediction and good correlation (Rpred) for SSC = 0.87, glucose = 0.83, fructose = 0.87, ascorbic acid = 0.81, and citric acid = 0.86. Our results support the assertion that a handheld NIR spectrometer has a high potential to simultaneously determine several quality attributes of different types of tomatoes in a practical and fast way.

Quantitative determination of sugar profiles in peach fruit during storage by an integrating sphere system

This work describes the optical approach for the quantitative description of sugar profiles in peaches during storage by using single integrating sphere (SIS). The spectral profiles for the optical properties (light absorption coefficient μa and reduced scattering coefficient μ’s) were measured. Correlation analysis between the optical properties and the sugar profiles showed that higher correlated values, with r > 0.640, were found. Furthermore, quantitative models were constructed by Partial Least Squares Regression (PLSR) based on μa and μ’s datasets to predict the sugar composition in peach flesh. The predicted results for the sucrose content based on μa datasets by PLSR were better than those based on the μ’s. For fructose and glucose contents, the predicted models were not very satisfactory due to low concentration in peach flesh, with Rp2 of 0.721, RMSEP of 0.817 g kg−1 for fructose and Rp2 of 0.753 and RMSEP of 0.604 g kg−1 for glucose, respectively. The models established by SIS present a simple and efficient strategy for quantitative analysis and may be used as alternative methods for evaluating the quality attributes of different types of fruit.

Fabrication of Three Bio-Adsorbents from Different Parts of Rape Straw

This work describes the novel approach for the quantitative description of sugar profiles in peaches during storage by using single-integrating sphere (SIS). The spectral profiles for the optical properties (light absorption coefficient μa and reduced scattering coefficient μ's) were measured. Correlation analysis between the optical properties and the sugar profiles showed that higher correlated values, with r > 0.640, were found. Furthermore, quantitative models were constructed by partial least squares regression (PLSR) based on μa and μ's datasets to predict the sugar composition in peach flesh. The predicted results for the sucrose content based on μa datasets by PLSR were better (Rp2 = 0.796 at wavelengths of 600 to 1050 nm) than those based on the μ's datasets. The models established by SIS present a simple and efficient strategy for quantitative analysis and may be used as alternative methods for evaluating the quality attributes of different types of fruit.

Foreign trade and its impact on the nutritional balance of the most important leguminous crops in Egypt

The Industrial Location Policies Research Project (RPO 672-91) produced a series of UDD discussion papers in the following three categories: (1) descriptive studies on observed location pattern and policy planning implementation experiences; (2) theoretical development and policy simulation studies; and (3) empirical studies of the firm's location behavior. This paper as part of the empirical studies describes the survey of 500 manufacturing establishments conducted in the Seoul region and presents most salient findings from the survey. The results show the attributes of different types of firms and their site characteristics, and reveal important factors that determine individual firms' locational choice. The paper summarizes these findings by firm size and location tenure, i.e., newly established firms, relocated firms, and mature firms, those stayed at the same location.

Variations in postharvest attributes of different types of tomatoes stored under shelf-life conditions

Variations in postharvest attributes of different types of tomatoes stored under shelf-life conditions

Taking into account Qualitative and Textual Variables in Hierarchical Ascending Clustering (HAC)

In Machine Learning, the clustering methods are the mains unsupervised methods. Their objectives is to partition a set of objects in some homogeneously groups. Clustering methods in general and more particularly Hierarchical Ascending Clustering (HAC) techniques are based on metrics and ultra-metrics. Metrics are used to evaluate the similarities between two objects; and ultra-metrics are used to estimate the similarity of two groups or the similarity of an element and a group. The main characteristic of these metrics and ultra-metrics is the fact that they are only adapted to numerical variables or can be reduced to them. With the advent of Data Mining and Data Science, most of the datasets to be analyzed contain different types of variables. In the same dataset, we can find numeric attributes, qualitative variables and free text fields very often together. Despite this diversity of variables in the same dataset, the existed clustering methods are generally build to use only an unique kind of attribute. In this paper, we propose an approach to take account different types of attributes in the same clustering method. The method proposed is a variant of HAC methods that can take into account both numerical, qualitative and textual data. Our approach is based on a metric call Phi-Similarity we developed in order to estimate the proximity of two objects, each of them is describe by a vector of attributes of different types. The developed method will be implemented with the scientific computing language R and applied to real survey data. A comparison of the results will be made with HAC techniques based on classical metrics with the Ward criterion as aggregation criteria. For classical algorithms, we will limit ourselves to the variables of the database compatible with them. This work of comparison will highlight the gain in precision in terms of classification brought by our method compared to the classic versions of HAC

Winner determination of loss-averse buyers with incomplete information in multiattribute reverse auctions for clean energy device procurement

Clean energy is an important source to mitigate the pressure of resources shortage and environmental effects. While multiattribute reverse auction is frequently adopted for the procurement efficiency of clean energy devices, the associated winner determination problem is explicitly studied for a utility company/buyer to select the most appropriate supplier in the clean energy market. Considering the buyer's loss-averse behavior with incomplete information and suppliers' conflicting attributes in different types evaluated by multiple experts, we incorporate an IULOWAf (induced uncertain linguistic ordered weighted averaging) operator and a revised TODIM (an acronym in Portuguese of Interactive and Multi-attribute Decision Making) measure that is consistent with prospect theory into the “benefits, opportunities, costs and risks” (BOCR) framework to develop a novel BOCR-uRTODIM solution method for multi-attribute decision making. Numerical experiments illustrate the effectiveness and robustness of the proposed method by comparing it with some known methods. An interesting result indicates that the risk aversion degree of the buyer increases as the variance of suppliers' attributes expands. The BOCR-uRTODIM could be a useful tool for utility companies to avoid losses and for associated suppliers to improve their attributes for a win in the clean energy industry.