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Cloud computing and storage processing is a big service for maintaining a large number of data in centralized servers to store and retrieve data depending on use to pay as a service mode. Due to increasing storage depending on duplicate copy presence during different sceneries, the size increase leads to cost increase. To resolve this problem, efficient Deduplication techniques are implemented to reduce storage. Redundant Sparse Look-up Indexing (RSLI) based file Deduplication using Semantic Cluster Content Chunking (SCCC) for optimizing cloud storage. To optimize the cloud storage contiguous fragmentation was applied to reduce the storage blocks. Initially, preprocessing can be used to track content availability by checking file name, size, and type patterns using file space optimization techniques. Also, the file can be split into hash-indexed chunks by implementing predefined methods based on these. The content union can also be compared to other files through Sparse Lookup Content Source Duplication (SLCSD). Their use helps to identify the number of overlaps between files in different transformations. Distance Vector Weightage Correlation (DWWC) Document Similarity Weights Based on Presence Counts help to include and cluster related documents. Finally, the RCB compares the document based on the coefficient match case contents present in the cluster using the similarity weight to find the duplicate content. The simulated results demonstrated high performance in terms of precision, recall, storage efficiency, and time complexity when utilizing content file types. The proposed method enhances de-duplication performance by efficiently locating duplicate files, improving indexing, and reducing storage space in the cloud environment. The proposed method results are precision is 96.9%, recall is 97.8%, the false rate is 2.6% and storage redundant is 36.9% than other methods. [ABSTRACT FROM AUTHOR]

Copyright of South China Fisheries Science is the property of South China Fisheries Science Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Currently, the horizontal resolution of Rayleigh wave exploration is low. In this study, we propose the Born–Jordan time‐frequency distribution to analyse Rayleigh waves. The seismic signal was filtered with a wavelet transform for denoising, and the Rayleigh wave was separated in the time domain. Using the Born–Jordan time‐frequency distribution, the time waveform of each frequency comprising the Rayleigh wave from every seismic channel was obtained, and the time difference of the Rayleigh wave with the same frequency was calculated, based on which the dispersion curve between the two channels was obtained. Combined with the multichannel Rayleigh wave dispersion curve, phase velocity and frequency imaging under the seismic arrangement were obtained. Applying this method to detect abnormal geological bodies in engineering investigations showed that hard geologic bodies, such as comcrete rocks, have high velocity and frequency, whereas weak ones have low velocity and frequency. This strategy facilitated the detection of fractured zones, underground goafs and obstacles during pipe‐jacking construction near the surface. [ABSTRACT FROM AUTHOR]

Copyright of China Rural Water & Hydropower is the property of China Rural Water & Hydropower Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Purpose: This study aims to explore the optimization of Stock Pairs Trading Strategies' performance using AI techniques, with a focus on accurately evaluating stock similarities and selecting the most suitable pairs. Design/methodology/approach: A variety of AI models, including Autoencoders (AE), Vector Embeddings (VE), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRU), are utilized to assess the similarity between stocks, which is crucial in the stock pairs selection process for implementing the Pairs Trading strategy. Findings: The implementation of the Pairs Trading strategy with stock pairs selected through AI models showed higher profitability than conventional methods. Strategies utilizing LSTM models demonstrated the highest performance, achieving an approximate cumulative return of 51.25353%. This indicates that AI models are capable of accurately assessing similarities and establishing effective trading strategies. Research limitations/implications: The study highlights the potential of AI-based stock pair selection methods to enhance Pairs Trading Strategies' performance. This approach surpasses traditional statistical methods by better reflecting the stock market's complexity and dynamism, potentially offering investors more stable and higher returns. Originality/value: The research contributes to the field by demonstrating the effectiveness of AI models in the stock pair selection process, suggesting a novel approach to enhancing Pairs Trading Strategies that could provide valuable insights for investors seeking more sophisticated investment strategies in the financial markets.

Single-cell RNA sequencing (scRNA-seq) is currently an important technology for identifying cell types and studying diseases at the genetic level. Identifying rare cell types is biologically important as one of the downstream data analyses of single-cell RNA sequencing. Although rare cell identification methods have been developed, most of these suffer from insufficient mining of intercellular similarities, low scalability, and being time-consuming. In this paper, we propose a single-cell similarity division algorithm (scSID) for identifying rare cells. It takes cell-to-cell similarity into consideration by analyzing both inter-cluster and intra-cluster similarities, and discovers rare cell types based on the similarity differences. We show that scSID outperforms other existing methods by benchmarking it on different experimental datasets. Application of scSID to multiple datasets, including 68K PBMC and intestine, highlights its exceptional scalability and remarkable ability to identify rare cell populations.

Three-dimensional (3D) printing technology has been widely used to create artificial rock samples in rock mechanics. While 3D printing can create complex fractures, the material still lacks sufficient similarity to natural rock. Extrusion free forming (EFF) is a 3D printing technique that uses clay as the printing material and cures the specimens through high-temperature sintering. In this study, we attempted to use the EFF technology to fabricate artificial rock specimens. The results show the physico-mechanical properties of the specimens are significantly affected by the sintering temperature, while the nozzle diameter and layer thickness also have a certain impact. The specimens are primarily composed of SiO2, with mineral compositions similar to that of natural rocks. The density, uniaxial compressive strength (UCS), elastic modulus, and tensile strength of the printed specimens fall in the range of 1.65–2.54 g/cm3, 16.46–50.49 MPa, 2.17–13.35 GPa, and 0.82–17.18 MPa, respectively. It is capable of simulating different types of rocks, especially mudstone, sandstone, limestone, and gneiss. However, the simulation of hard rocks with UCS exceeding 50 MPa still requires validation.

To address the challenges posed by the poor sensitivity of traditional degradation indices to the onset of bearing degradation and the inability to accurately predict the remaining using life (RUL) of wind turbine bearings due to trend shifts in degradation indices, a biexponential method for predicting bearing RUL was proposed. This method was based on the combination of Teager's energy arithmetic-ratio of failures to energy (TFER) and degradation trend (DT) similarity detection. Firstly, the TFER value of the original signal was calculated to determine the bearing degradation starting point following the 4σ principle. The trend feature of the TFER value was then extracted as a degradation indicator. Subsequently, a bi-exponential degradation model was fitted using historical TFER values. Then, the best DT segment was chosen by analyzing the similarity between the latest TFER values and the fitted curve. Finally, the wind turbine bearing RUL was predicted by extrapolating the DT segment with the best similarity to the failure threshold. The experimental results show that the prediction method achieves 98% accuracy in detecting the degradation onset time point, and improves 10. 04%, 6. 29%, and 5. 22% in bearing RUL prediction accuracy compared with the original exponential model, the long-and short-term memory neural network (LSTM), and the support vector regression (SVR), respectively. This method not only improves the accuracy of predictive maintenance of wind turbine bearings, but also provides strong support for reducing operating costs and improving maintenance efficiency. [ABSTRACT FROM AUTHOR]

In the plastics industry, CFD simulation has been used for many years to support mold design. However, using simulation as a substitute for experimentation remains a major challenge to this day. This is due to the unknown congruence between simulation and experiment. The present work focuses on a comparison between simulation (generated with the software Moldflow Insight Ultimate from Autodesk Inc., San Francisco, CA, USA) and experiment by using molds of different complexity, where, in contrast to a large number of previous investigations, both the characteristics of the parts and the time series of the process parameters were compared with each other. For this purpose, the high-resolution time series of the process parameters injection pressure, flow rate, and cavity pressure as well as the mass and the dimensions of the manufactured parts were acquired during the experiments and the results were compared with the computations obtained from the simulation. In addition, potential causes like the material data, mesh and solver parameter, and the machine-specific behavior were analyzed to assess which of these causes may be decisive for a deviation between simulation and experiment. [ABSTRACT FROM AUTHOR]

The word 'threat' currently proliferates in the public sphere, which contributes to the large-scale information spread of societal threats of our time. However, the difference of common sense that laypeople attribute to the concepts of 'threat', 'risk' and 'danger' remains unclear. Therefore, this research aims to fill the social psychological knowledge gap and investigate laypeople's common sense of 'societal threat'. Participants (N = 186) were asked to complete the word association task, and their narratives (N = 1,146) were analysed using two complementary research methods: the automatized methods of multidimensional analysis and the classic method of qualitative content analysis. The concept of 'societal threat' has been associated most frequently with specific large-scale societal issues that are tainted by uncertainty, and a range of primarily negative feelings and emotions. Additionally, the conceptually and semantically close concepts of 'risk' and 'danger' have not been associated, on the lexical basis, with the concept of 'societal threat'. The implications for an improved systemic understanding of actual societal threats' features and related social psychological applications are discussed. [ABSTRACT FROM AUTHOR]

Inward flow radial (IFR) supercritical CO2 (sCO2) turbines are smaller in size and operate at considerably higher speeds in comparison to similar capacity conventional gas or steam turbines. The compact size and high speed of IFR turbines result in significant parasitic (leakage and disk friction) losses at the rotor backface. This paper presents CFD investigations to understand the mechanism of parasitic losses of IFR turbines in the kW to MW power scales. The first part of the paper presents a parametric study to quantify the effect of backface flowpath dimensions, rotor inlet radius, and rotational speed on the magnitude of parasitic losses. Subsequently, the paper proposes the implementation of a radial labyrinth seal on the rotor backface to curtail the parasitic losses. The second part of the paper examines how the power scale of the turbine and its design parameters, specific speed and velocity ratio, influence the magnitude of parasitic losses. The investigation reveals that parasitic losses cause an efficiency drop of 8%–15% for 100 kW and 4%–9% for 1 MW IFR sCO2 turbines. In addition, IFR turbines designed for low specific speeds and high velocity ratios result in notably higher parasitic losses, leading to a decline in turbine efficiency. Finally, the paper presents optimal turbine design parameters accounting for the parasitic losses to maximize turbine efficiency. [ABSTRACT FROM AUTHOR]

Product variety, in combination with today's unstable market environment, brings strong challenges to manufacturing firms. Single-product assembly systems become difficult to implement as the return on investment is difficult to obtain with decreasing lot sizes and unstable customer demands. Multi-product assembly systems are a possible solution to this problem, as they allow to compensate demand fluctuations throughout the product mix. However, variety is a strong barrier to this kind of production systems. A new method is presented in this paper to identify multi-product assembly system architectures adapted to a product family, based on the identification of components for common positioning. This method has been applied to a supplier in the automotive industry and a return of experience of this case study is provided, highlighting its potentials and barriers. [ABSTRACT FROM AUTHOR]

To forecast chloride transport in site by applying accelerated test results, it is necessary to investigate the similarity of chloride diffusivity under different environments. However, limited by test period and cost expenses, a large amount of tests in field and simulated environment are hard to conduct, and a limit test data will make prediction on chloride diffusivity of concrete less precise. To solve this problem, machine learning methods are explored to predict chloride transport. Based on the existing chloride concentration test data of fly ash concrete in natural and accelerated simulated environment, this paper establishes a chloride concentration predictive model based on the one-dimensional convolutional neural network (1D-CNN) method at first, and verifies the stability and accuracy of the established model. Second, similarity models for concentration ratio and diffusion time ratio based on multivariate nonlinear model and based on theoretical Fick’s second law are constructed, and the model accuracy is also tested. Finally, the chloride concentration data predicted by the 1D-CNN model is applied to the constructed similarity models respectively, and the application results are compared and analyzed to determine the optimal similarity model. Results indicate that the concentration predicted by the 1D-CNN method has a strong robustness and a small prediction error. Besides, based on multivariate nonlinear model, the error of concentration predicted by the similarity models for concentration ratio and diffusion time ratio is less than 16 % and 30 %, respectively. Similarly, according to the theoretical model based on Fick’s second law, the error of concentration predicted by the similarity models for concentration ratio based on the time dependent chloride diffusion coefficient and time dependent peak chloride concentration is less than 21 % and 36 %, respectively. The similarity model for concentration ratio based on multivariate nonlinear model has the best prediction effect.

Natural Language Processing (NLP) technology is rapidly evolving, and various large language models have been widely applied in Legal Artificial Intelligence (AI). However, low accuracy in Similar Case Matching (SCM) persists in the most popular case recommendation systems. It hinders the practical application of case recommendations in Legal Judgment Prediction (LJP). Developing effective methods to extract features from long texts and improve the accuracy of SCM is an urgent matter that requires attention. Therefore, the paper proposes a SCM method based on deep text comprehension. A fine-tuned BERT model is used to extract text information, and a combination of global attention and self-attention is employed to represent the features of long texts deeply. A dual-channel similar text-matching approach is used after candidate texts are pre-encoded to reduce the SCM model’s training time and improve accuracy. Experiments on the China AI and Law (CAIL) competition dataset show that the proposed method achieves the highest accuracy in SCM compared to the recent methods.

Phishing is an electronic fraud through which an attacker can access user credentials. Phishing websites are the ones that mimic legitimate websites. Fraudsters can replace them within hours to evade their detection. The effects of phishing attacks exhibit the need for anti-phishing mechanisms. Several approaches were there to recognize the phishing websites, the white list approach, blacklist approach, machine learning, and heuristic-based approach. Earlier studies have shown that classifiers may be subject to evasion attacks although this point has only been explored on a small scale. As a result, the study covers evasion attacks and their detection within the context of website classifiers, which is rarely explored. In response to the inadequacies, the proposed technique includes extracting information from URLs and classifying webpages using various machine learning methods. The methodology involves crafting adversarial samples targeting classification features, with a focus on maintaining the functionality and appearance of phishing websites. The appearance is evaluated using image distortion metrics named mean squared error. Then a resemblance approach is utilized for the aim of detecting assaults that happened as a result of evasion attacks. This research introduces a novel defense mechanism against evasion attacks, marking a significant contribution to the field.

ABSTRACTAs the melting of Arctic ice accelerates, the Arctic Passage continues to grow. Geopolitical influence has increased the strategic value of the Passage, attracting the attention of countries around the world. The geopolitical environment of the Arctic Passage is not affected by single factors but is instead determined by the combined effects of multiple factors. This paper analyses the multi-dimensional geopolitical environment factors of the Arctic Passage, and extracts features to construct a multi-dimensional geopolitical environment index system, which takes the Northern Sea Route as an example to construct the Arctic Passage Geo-potential Model, and quantitatively analyse the geo-potential for different states in Eurasia with the development and utilization of the Arctic Passage. This paper combines clustering and similarity analysis to explore the geo-spatial pattern of the Northern Sea Route, and identifies two interest groups that are represented by Russia and the United States. We further analyse the preference of stakeholders in the jurisdiction of the Northern Sea Route. This exploration addresses a gap in the current discussion on the Arctic Passage’s geopolitical environment, which only considers a single factor, and it helps to explore new methods and ideas that will assist the quantitative evaluation and pattern analysis of the Arctic Passage’s geopolitical environment, especially in Eurasia. Meanwhile, it will provide a comprehensive reference for the development and utilization of the Arctic Passage about the geopolitical environment patterns.

Graphics Processing Units (GPUs) are employed for their parallel processing capabilities, which are essential to train deep learning (DL) models with large datasets within a reasonable time. However, the diverse GPU architectures exhibit variability in training performance depending on DL models. Furthermore, factors such as the number of GPUs for distributed training and batch size significantly impact training efficiency. Addressing the variability in training performance and accounting for these influential factors are critical for optimising resource usage. This paper presents a scheduling policy for DL training tasks in a heterogeneous GPU cluster. It builds upon a model-similarity-based scheduling policy by implementing a round-based mechanism and job packing. The round-based mechanism allows the scheduler to adjust its scheduling decisions periodically, whereas job packing optimises GPU utilisation by fitting additional jobs into a GPU that trains a small model. Results show that implementing a round-based mechanism reduces the makespan by approximately 29%, compared to the scenario without it. Additionally, integrating job packing further decreases the makespan by 5%. [ABSTRACT FROM AUTHOR]

Modern web applications offer various APIs for data interaction. However, as the number of these APIs increases, so does the potential for security threats. Essentially, more APIs in an application can lead to more detectable vulnerabilities. Thus, it is crucial to identify APIs as comprehensively as possible in web applications. However, this task faces challenges due to the increasing complexity of web development techniques and the abundance of similar web pages. In this paper, we propose APIMiner, a framework for identifying APIs in web applications by dynamically traversing web pages based on web page state similarity analysis. APIMiner first builds a web page model based on the HTML elements of the current web page. APIMiner then uses this model to represent the state of the page. Then, APIMiner evaluates each element's similarity in the page model and determines the page state similarity based on these similarity values. From the different states of the page, APIMiner extracts the data interaction APIs on the page. We conduct extensive experiments to evaluate APIMiner's effectiveness. In the similarity analysis, our method surpasses state-of-the-art methods like NDD and mNDD in accurately distinguishing similar pages. We compare APIMiner with state-of-the-art tools (e.g., Enemy of the State, Crawlergo, and Wapiti3) for API identification. APIMiner excels in the number of identified APIs (average 1136) and code coverage (average 28,470). Relative to these tools, on average, APIMiner identifies 7.96 times more APIs and increases code coverage by 142.72%. [ABSTRACT FROM AUTHOR]

Researchers from different fields have studied the effects of the COVID-19 pandemic and published their results in peer-reviewed journals indexed in international databases such as Web of Science (WoS), Scopus, PubMed. Focusing on efficient methods for navigating the extensive literature on COVID-19 pandemic research, our study conducts a content analysis of the top 1000 cited papers in WoS that delve into the subject by using elements of natural language processing (NLP). Knowing that in WoS, a scientific paper is described by the group Paper = {Abstract, Keyword, Title}; we obtained via NLP methods the word dictionaries with their frequencies of use and the word cloud for the 100 most used words, and we investigated if there is a degree of similarity between the titles of the papers and their abstracts, respectively. Using the Python packages NLTK, TextBlob, VADER, we computed sentiment scores for paper titles and abstracts, analyzed the results, and then, using Azure Machine Learning-Sentiment analysis, extended the range of comparison of sentiment scores. Our proposed analysis method can be applied to any research topic or theme from papers, articles, or projects in various fields of specialization to create a minimal dictionary of terms based on frequency of use, with visual representation by word cloud. Complementing the content analysis in our research with sentiment and similarity analysis highlights the different or similar treatment of the topics addressed in the research, as well as the opinions and feelings conveyed by the authors in relation to the researched issue. [ABSTRACT FROM AUTHOR]

To verify the accuracy and rationality of discrete fracture network modeling, a comprehensive method for evaluating the similarity between two-dimensional fracture maps is proposed in this study. Five evaluation indicators for two-dimensional discrete fracture networks are proposed, including spacing, length, angle, position, and density of rock fractures. Based on the quantitative characteristics of each indicator, methods for evaluating data distribution similarity based on the Wasserstein distance function and matrix similarity based on vector cosine values are proposed. Considering the grouping characteristics of trace lines that are neglected in existing similarity systems, the similarity between each indicator is calculated based on the grouping matching of trace line maps. This achieves the comprehensive calculation of grouping similarity and overall similarity indicators. The importance and size effects of indicators are discussed through fracture model examples. The results show that the indicators of grouping similarity are more rigorous and suitable for engineering modeling with strict requirements compared to those for overall similarity. The proposed method effectively alleviates the difficulty of comprehensive distribution similarity evaluation and achieves a more objective similarity evaluation of position and density indicators. Finally, the established similarity evaluation system is applied to real tunnel construction in Hebei Province, China, providing application value for the accuracy of fracture modeling in practical engineering. Highlights: A comprehensive method for evaluating similarity between 2D fracture maps is proposed Five indicators are suggested, including spacing, length, angle, position, and density. Grouping characteristics of trace lines are considered in similarity calculation. Proposed method achieves a more objective evaluation of position and density indicators. [ABSTRACT FROM AUTHOR]

In this study, we aimed to identify how Information Technology professionals make sense of digital transformation, in order to compare this perception with the scientific literature on the topic. To conduct the research, we adopted the Social Representation Theory. Thus, via an online tool, we applied the words evocation technique as well as an ancillary questionnaire comprising open and closed questions. The expressions collected were then semantically treated and categorized, being then analyzed vis-à-vis their frequency of evocation, relevance, connectedness, and cooccurrence. Then, we performed similarity and content analyses for the open answers received. In doing so, we detected dissonance between scientific production on the concept of digital transformation and the perception of Information Technology professionals on this construct, concluding that such professionals have a technocentric view of digital transformation, based mainly on its technological aspects, which can prevent digital transformation initiatives from fully achieving the desired objectives.

Copyright of Técnica Industrial: Revista Cuatrimestral de Ingeniería, Industria e Innovación is the property of Fundacion Tecnica Industrial and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study explored the ecological niche and interspecific relationships among the main fish species in the coastal waters of Hainan Island based on data from fishery stock surveys conducted in the spring and autumn of 2022. The methods of chi-square test, percentage of co-occurrence (PC), association coefficient (AC), cluster analysis, and redundancy analysis were used to analyze the interspecific associations and influence of environmental factors on the ecological niches of fish communities. According to the cluster analysis of niche breadth, 20 main fishes could be divided into broad, medium, and narrow niche species. There were four and eight broad niche species in spring and autumn, respectively. The ranges of niche overlap values were 0.001–0.91 in spring and 0–0.87 in autumn, indicating that the species differed and were similar in their ability to utilize resources, survive in habitats, and prey. According to the variance ratio and statistic value W, the main fishes in spring showed a significant positive association, whereas those in autumn showed a positive association, but not at a significant level, indicating that the main fishes in spring were more closely associated with each other. There were 56 species pairs in the 2 seasons that were significantly associated (χ2 ≥ 3.841). AC and PC tests revealed that the interspecific association was strong and tended to be positive. According to the redundancy analysis, environmental factors such as surface temperature, water depth, and pH significantly affected the main fishes in spring, while environmental factors such as dissolved oxygen, bottom temperature, surface salinity, and pH significantly affected those in autumn. [ABSTRACT FROM AUTHOR]

Organic molecules have been extensively utilized in various applications including materials science, chemical, and biomedical fields. Traditionally, the design of organic molecules is achieved through experimental approaches, guided by conceptual insights, intuition, and experience. Although these experimental approaches have been successfully utilized to unveil various high‐performance materials, these methods show serious limitations due to vast design space and the ever‐increasing demand for organic molecules (new materials). Artificial intelligence with computer science is used by modern researchers to design materials with better performance and for predicting the properties of new materials. Herein, benzofuran‐based building blocks are used as a standard molecule to search for new building blocks. Similarity analysis is performed to screen/search potential candidates for photodetectors based on the chemical structural similarity. Extended‐connectivity fingerprints (ECFPs) are used for the similarity analysis. The virtual libraries of unique monomers are enumerated. The breaking retro‐synthetically interesting chemical substructures (BRICS) method is also used to design building blocks by automatically decomposing and combining monomers in enumerated libraries. Moreover, this work offers a potential way to identify new monomers for photodetectors cost‐effectively and rapidly. [ABSTRACT FROM AUTHOR]

This article analyses, from a statistical point of view, the titles of the articles published in Análise Social (from 1963 to 2022), by comparing the words used over these six decades. Starting from the premise that a certain choice of words reflect a diverse set of "textual worlds", which are organised according to different motivations and interests, these words, produced under certain historical contexts and mediated by the work of the journal's editorial teams reveal both the continuity and the thematic changes that shaped its lexicon over time. [ABSTRACT FROM AUTHOR]

Experimental data under a wide range of conditions are essential for the optimization of combustion kinetic models. However, some laboratory measurements under given conditions may not be conducted due to the constraint of existing techniques. It is thus needed to employ the experimental data obtained under alternate conditions to improve the model predictions for the desired conditions. In this work, an active subspace-based similarity analysis is proposed as an experimental design method to find substitutes for experiments (or measurements) that are difficult to conduct. The eigenvalues and eigenvectors of the matrix that contains the gradient information of a model output with respect to inputs (matrix C of the active subspace) are used to calculate the cosine-based similarity of key reactions of two model targets. The method is demonstrated in three combustion systems, i.e. , ignition of hydrogen/oxygen mixture, premixed flame of the dimethyl ether (DME), and C 2 H 6 /O 2 systems in different reactors. The results show that if the similarity coefficient is large, the key reactions for the two model targets are similar, and the measurement of one target can improve the model prediction of the other target. In addition to designing experimental targets or conditions with strong constraint effects beforehand, this method can also be used to classify potential experimental targets/conditions. [ABSTRACT FROM AUTHOR]

Abstract Virtual personal assistants (VPAs), such as Amazon Alexa and Google Assistant, are software agents designed to perform tasks or provide services to individuals in response to user commands. VPAs extend their functions through third-party voice apps, thereby attracting more users to use VPA-equipped products. Previous studies demonstrate vulnerabilities in the certification, installation, and usage of these third-party voice apps. However, these studies focus on individual apps. To the best of our knowledge, there is no prior research that explores the correlations among voice apps.Voice apps represent a new type of applications that interact with users mainly through a voice user interface instead of a graphical user interface, requiring a distinct approach to analysis. In this study, we present a novel voice app similarity analysis approach to analyze voice apps in the market from a new perspective. Our approach, called SkillSim, detects similarities among voice apps (i.e. skills) based on two dimensions: text similarity and structure similarity. SkillSim measures 30,000 voice apps in the Amazon skill market and reveals that more than 25.9% have at least one other skill with a text similarity greater than 70%. Our analysis identifies several factors that contribute to a high number of similar skills, including the assistant development platforms and their limited templates. Additionally, we observe interesting phenomena, such as developers or platforms creating multiple similar skills with different accounts for purposes such as advertising. Furthermore, we also find that some assistant development platforms develop multiple similar but non-compliant skills, such as requesting user privacy in a non-compliance way, which poses a security risk. Based on the similarity analysis results, we have a deeper understanding of voice apps in the mainstream market.

In the plastics industry, CFD simulation has been used for many years to support mold design. However, using simulation as a substitute for experimentation remains a major challenge to this day. This is due to the unknown congruence between simulation and experiment. The present work focuses on a comparison between simulation (generated with the software Moldflow Insight Ultimate from Autodesk Inc., San Francisco, CA, USA) and experiment by using molds of different complexity, where, in contrast to a large number of previous investigations, both the characteristics of the parts and the time series of the process parameters were compared with each other. For this purpose, the high-resolution time series of the process parameters injection pressure, flow rate, and cavity pressure as well as the mass and the dimensions of the manufactured parts were acquired during the experiments and the results were compared with the computations obtained from the simulation. In addition, potential causes like the material data, mesh and solver parameter, and the machine-specific behavior were analyzed to assess which of these causes may be decisive for a deviation between simulation and experiment.

In this paper, considering the low cost and high effectiveness-cost ratio of near space airships, as well as the insufficiency of theoretical methods for verifying space-ground equivalents, flight control technology validation of a typical airship was considered, and a method for describing the airship motion characteristics is presented in detail. According to the similarity and π theorems, dimensional analysis of the prototype airship's motion characteristics was completed, similarity criteria were tabulated, and the similarity index was determined. The design of the scaled parameters under the single-value constraint of "geometric similarity, kinematic similarity, and dynamical similarity" between the prototype airship and its low-altitude scaled model was performed, and a low-altitude scaled flight test platform and flight test were developed. Theoretical methods for the similarity analysis of the prototype airship and its low-altitude scaled model, as well as the basics of flight test engineering applications, were systematically introduced, describing a low-cost, repeatable low-altitude scaled flight test verification method for key technology verification of high-cost and high-risk near space airships. This study provides a foundation for follow-up studies of relevant similar theories and applications. [ABSTRACT FROM AUTHOR]

Copyright of Food Research & Development is the property of Food Research & Development Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The utilization of primary and secondary woody biomass resources, despite controversies, is being promoted to reduce dependence on fossil fuels and due to the need to diversify energy sources and ensure energy security in European Union countries. Forest biomass is one of the renewable and sustainable energy sources that can be used for electricity, heat, and biofuel production. In the context of the ongoing energy crisis in Europe, an attempt was made to analyze the production and consumption of woody biomass for energy purposes (fuel wood, chips, and pellets). Specifically, an analysis of similarities between European countries in terms of biomass utilization was conducted. The analysis was complemented by a forecast of primary biomass production in selected European countries. The similarity analysis was conducted using the Ward method. Artificial neural networks (ANNs), including multi-layer feedforward perceptron (MLP) and radial basis function (RBF) models, were used to predict fuelwood extraction. The study showed that woody biomass remains an important source of bioenergy in Europe, and its significance as a strategic resource guaranteeing energy security is likely to increase. Fuel wood harvesting in Europe generally shows an upward trend, particularly in the Czech Republic, Germany, Estonia, Denmark, and the UK. A decreasing trend was observed in France, Spain, Greece, and Cyprus. The analysis revealed differences between countries in terms of woody biomass consumption. The ANN-based forecasts of fuelwood supply generally showed an increase in primary biomass harvesting. [ABSTRACT FROM AUTHOR]

Traditional Chinese Medicine (TCM) prescriptions are organically composed of compatible herbs according to the TCM theory. The complex ingredients of TCM could act on multiple targets through various pathways simultaneously to exert pharmacological effects, making TCM an unrivaled gem in the medical world. However, due to a lack of comprehensive and standard study methods, the research of TCM products has been quite limited. A novel paradigm that could aid in the discovery of the material basis and fully clarify the mechanism of TCM prescriptions is urgently needed. In this study, a similarity analysis based on molecular fingerprints was adopted to explore the representative molecules of the Tiaoxin recipe, a Chinese patent formula approved by the National Medical Products Administration (NMPA) for the treatment of mild-to-moderate Alzheimer's disease (AD), and 38 out of 1047 chemicals were finally screened out. Next, we tried to define a new concept of a "functional molecule cluster" for chemicals with similar pharmacological effects to elucidate how the chemical mixture from TCMs produce their therapeutic effects. Four anti-AD functional molecule clusters from the Tiaoxin recipe were identified: an anti-inflammatory cluster, an anti-ROS cluster, an anti-AChE activity cluster, and an anti-A β aggregation cluster. Furthermore, the chemicals from the anti-inflammatory cluster and anti-ROS cluster were proved to display their multi-target and multi-pathway roles partially or mainly through molecules of the TLR4-MYD88-NF- κ B and Keap1-Nrf2-ARE pathways. The functional molecule clusters may be vital to the explanation of the efficacy of the Tiaoxin recipe, which could give us a more profound understanding of TCM prescriptions. Our paradigm may open a novel path for TCM research. [ABSTRACT FROM AUTHOR]

In this study, eight cultivars, all of which are Vitis vinifera L., are grown in Aksaray province were determined. Identification material was selected approximately 20 years old, own roots vines in the producer vineyards. Ampelographic characteristics are defined by 123 descriptive characters in the OIV (The International Organization of Vine and Wine) list of grape and grapevine rootstock varieties identification criteria. With the numerical codes of the OIV identification criteria, similarity analysis was made in the IBM SPSS Statistics software (SPSS) package program, and the similarity relationships of the cultivars were visualized with a dendrogram. Aşeri and Mor Üzüm varieties defined in Aksaray province are not registered in the European Catalogue, but all varieties are Vitis vinifera L. Subsp. vinifera is registered as Turkey varieties. All the cultivar excluding Aşeri, and Emir names defined here are included in the Turkey grapevine genetic resources list, and several cultivars containing the same name or synonyms have been defined in different studies. The ampelographic descriptions of Aşeri and Emir grape varieties were made for the first time. The ampelographic definitions of Ak Dimrit, Çavuş, Kalecik Karası, Mor Üzüm, Parmak Üzümü and Sergi Karası cvs were confirmed with previous descriptions made in different provinces. The similarities and differences of the varieties we identified were reflected in the similarity dendrogram. Six of the eight grape varieties are hermaphrodite, and two of them (Çavuş and Sergi Karası) are functional female flowers. In the international variety catalogue records, the most common Aksaray variety on a global scale is Çavuş variety originating from Turkey. The cv. Parmak Üzümü was a separate ecotype due to its differences from previous identification records. It would be appropriate to protect the Aşeri, Emir, and Parmak Üzümü varieties, which we described for the first time, in the grapevine genetic resources of Turkey. [ABSTRACT FROM AUTHOR]

Context: Selecting high performance polymer materials for organic solar cells (OSCs) remains a compelling goal to improve device morphology, stability, and efficiency. To achieve these goals, machine learning has been reported as a powerful set of algorithms/techniques to solve complex problems and help/guide exploratory researchers to screen, map, and develop high performance materials. In present work, we have applied machine learning tools to screen data from reported studies and designed new polymer acceptor materials, respectively. Quantitative structure-activity relationship (QSAR) models were generated using machine learning-assisted simulation techniques. For this purpose, 3000 molecular descriptors are generated. Consequently, molecular descriptors having key effect on power conversion efficiency (PCE) were identified. Moreover, numerous regression models (e.g., random forest and bagging regressor models) were developed to predict the PCE. In particular, new materials were designed based on the similarity analysis. The GDB17 chemical database consisting of 166 million organic molecules in an ordered form is used for performing similarity analysis. A similarity behavior between GDB17 materials and the materials reported in literature is studied using RDKit (a cheminformatics software). Noteworthily, 100 monomers proved to be unique and effective, and PCEs of these monomers are predicted. Among these monomers, four monomers exhibited PCE higher than 14%, which is better than various reported studies. Our methodology provides a unique, time- and cost-efficient approach to screening and designing new polymers for OSCs using similarity analysis without revisiting the reported studies. Methods: To perform machine learning analysis, data from reported studies and online databases was collected. Different molecular descriptors were generated for polymer materials utilizing Dragon software. 3D structures of studied molecules were applied as input (SDF; structure data file format). Importantly, about 3000 molecular descriptors were generated. Comma-separated value (.csv) file format was used to export these molecular descriptors. To shortlist best descriptors, univariate regression analysis was performed. These descriptors were further utilized for training machine learning models. Moreover, necessary packages of Python for data analysis and visualization were imported such as Matplotlib, Numpy, Pandas, Scikit-learn, Seaborn, and Scipy. Random forest and bagging regressor models were applied for performing machine learning analysis. A cheminformatics software, RDKit, was applied for similarity analysis. [ABSTRACT FROM AUTHOR]

Virtual personal assistants (VPAs), such as Amazon Alexa and Google Assistant, are software agents designed to perform tasks or provide services to individuals in response to user commands. VPAs extend their functions through third-party voice apps, thereby attracting more users to use VPA-equipped products. Previous studies demonstrate vulnerabilities in the certification, installation, and usage of these third-party voice apps. However, these studies focus on individual apps. To the best of our knowledge, there is no prior research that explores the correlations among voice apps.Voice apps represent a new type of applications that interact with users mainly through a voice user interface instead of a graphical user interface, requiring a distinct approach to analysis. In this study, we present a novel voice app similarity analysis approach to analyze voice apps in the market from a new perspective. Our approach, called SkillSim, detects similarities among voice apps (i.e. skills) based on two dimensions: text similarity and structure similarity. SkillSim measures 30,000 voice apps in the Amazon skill market and reveals that more than 25.9% have at least one other skill with a text similarity greater than 70%. Our analysis identifies several factors that contribute to a high number of similar skills, including the assistant development platforms and their limited templates. Additionally, we observe interesting phenomena, such as developers or platforms creating multiple similar skills with different accounts for purposes such as advertising. Furthermore, we also find that some assistant development platforms develop multiple similar but non-compliant skills, such as requesting user privacy in a non-compliance way, which poses a security risk. Based on the similarity analysis results, we have a deeper understanding of voice apps in the mainstream market. [ABSTRACT FROM AUTHOR]

In order to explore the flavor characteristics of different colored millet porridges, the differences in volatile organic compounds in black, green, white and yellow millet porridges were analyzed by gas chromatography-ion mobility spectrometry (GC-IMS). The results showed that a total of 48 volatile organic compounds were identified from all samples, including 22 aldehydes, 11 ketones, 9 alcohols, 2 furans, 2 esters and 2 ethers, accounting for 68.58%–80.66%, 6.52%–9.89%, 5.99%–14.91%, 2.09%–5.66%, 1.13%–1.72% and 0.39%–0.69% of the total amount of volatile organic compounds, respectively. The major volatile organic compounds identified were aldehydes, alcohols and ketones, followed by furans, esters and ethers. The relative contents of aldehydes, ketones and esters in black millet porridge were higher, and the relative content of ketones in green millet porridge was highest. The relative contents of alcohols and furans in white millet porridge were highest. The relative contents of aldehydes and esters in yellow millet porridge were highest. Principal component analysis (PCA) and Euclidean distance analysis showed that the GC-IMS data of volatile organic compounds could be used to distinguish different colored millet porridge samples. A prediction model with good stability was established using orthogonal partial least squares-discriminant analysis (OPLS-DA), and 13 differential volatile organic compounds (variable importance in the projection (VIP) >1) were identified, which could be used for classification of different colored millet porridges. This study provides useful information for enriching the eating quality characteristics of different colored millet.

Abstract A probable problem of disconnection between chemical fingerprints and drug effects for TCMs would be contrary to the original intention of fingerprint research, and limits the development and application of fingerprints. In this study, Shenmai injection, as a treatment dosage form of coronary heart disease, shock, and viral myocarditis clinically, was applied as the research object. The fingerprint of Shenmai injection was constructed, and the pharmacodynamic test of antioxidant effect was carried out to obtain quantitative characteristics and pharmacodynamic data. On this basis, a monitoring model based on the HPLC pharmacodynamic fingerprint was established to evaluate the quality of Shenmai injections from different batches and different manufacturers. Results showed that the optimized HPLC method had good repeatability, precision, and stability. A total of 28 characteristic peaks were identified to provide more chemical information. Furthermore, 13 ginsenosides and notoginsenoside have been selected as characteristic components of LC/MS fingerprint method. 8 peaks closely related to antioxidant properties by multiple linear regression method, which were identified as Rg1, Re, Rf, Rb1, and some other ginsenosides using MS analysis. The monitoring model based on HPLC pharmacodynamic fingerprint could successfully identify quality differences for Shenmai injections. Based on the case study of Shenmai injection, the novel and practical fingerprint analytical strategy could be further applied to monitor or predict the quality of TCMs.

In this article, we explore the concept and measurement of the degree of economic development pattern (DEDP) of economy, which refers to the extent to which the development of an economy can serve as a reference for other economies. Utilizing 76 macroeconomic indicators across 217 economies, the economic development paths in a standardized space of economy is compared to identify variations in DEDP through the regression analysis on the relationship between the similarity of development paths and the growth rate on gross domestic product (GDP) per capita. To measure DEDP of economy from different perspective, two types of metrics are constructed. One is the determination coefficient of regression analysis, which exhibits significant positive correlations with population size of economy, uncovering differences of development paths among economies of varying population sizes. The other type of metrics is based on the consistency on regression coefficients and effectively explains disparities among economies in the growth rate on GDP per capita, economic complexity index and economic fitness. These findings reveal the differences in development paths among different countries from the perspective of referentiality for development patterns, suggesting the potential existence of the paths with more universal meaning to economic development.

Identification of the plant parts and genuine species is one of the most important steps for the quality control of raw material used in herbal medicines. In the present study, comparative analysis of TLC, HPLC and FTIR-ATR fingerprinting in conjunction with chemometric method was done to select the most appropriate fingerprinting method for quality control of Berberis aristata and its related species. To achieve this, extracts were first utilized to obtain TLC, HPLC and FT-IR fingerprinting data. The data was analyzed by chemometric methods and similarity analysis. The PCA model obtained with HPLC fingerprinting method could successfully classify the Berberis species as well plant parts viz. root, stem and leaves which was not possible with the PCA model of TLC or FT-IR fingerprints. Similarity analysis based on the mean HPLC fingerprinting chromatogram could also appropriately assess the chemical variability and identification of different Berberis species. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNNs), a representative type of deep neural networks, are used in various fields. There are problems that should be solved to operate CNN in the real-world. In real-world operating environments, the CNN's performance may be degraded due to data of untrained types, which limits its operability. In this study, we propose a method for identifying data of a type that the model has not trained on based on the neuron cluster, a set of neurons activated based on the type of input data. In experiments performed on the ResNet model with the MNIST, CIFAR-10, and STL-10 datasets, the proposed method identifies data of untrained and trained types with an accuracy of 85% or higher. The more data used for neuron cluster identification, the higher the accuracy; conversely, the more complex the dataset's characteristics, the lower the accuracy. The proposed method uses only the information of activated neurons without any addition or modification of the model's structure; hence, the computational cost is low without affecting the classification performance of the model. [ABSTRACT FROM AUTHOR]

Managing large volumes of business news data is important for companies. However, identifying relevant data can be challenging due to the dynamic nature of business environments and varying market needs. The article acknowledges this challenge and suggests that the lack of a standardized definition for relevant data makes it even more difficult for companies to prioritize and manage their data effectively, which can lead to missed opportunities. To address this challenge, the article proposes a text relevance hierarchy framework consisting of five levels that assess the relevance of business news texts to a company's specific operations and interests. The framework uses criteria such as specific topics, organizations, people, locations, and financial figures involved in the article to evaluate the importance of texts. Natural Language Processing (NLP) approaches such as entity recognition, topic modeling, and similarity analysis can be leveraged to implement the text relevancy hierarchy. By using this framework, companies can prioritize and manage their business news information efficiently, focus on the most important and relevant texts, and identify new areas of interest based on changing market needs. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) power generation is the most widely adopted renewable energy source. However, its inherent unpredictability poses considerable challenges to the management of power grids. To address the arduous and time-consuming training process of PV prediction models, which has been a major focus of prior research, an improved approach for PV prediction based on neighboring days is proposed in this study. This approach is specifically designed to handle the preprocessing of training datasets by leveraging the results of a similarity analysis of PV power generation. Experimental results demonstrate that this method can significantly reduce the training time of models without sacrificing prediction accuracy, and can be effectively applied in both ensemble and deep learning approaches. [ABSTRACT FROM AUTHOR]

Precipitation images play an important role in meteorological forecasting and flood forecasting, but how to characterize precipitation images and conduct rainfall similarity analysis is challenging and meaningful work. This paper proposes a rainfall similarity research method based on deep learning by using precipitation images. The algorithm first extracts regional precipitation, precipitation distribution, and precipitation center of the precipitation images and defines the similarity measures, respectively. Additionally, an ensemble weighting method of Normalized Discounted Cumulative Gain-Improved Particle Swarm Optimization (NDCG-IPSO) is proposed to weigh and fuse the three extracted features as the similarity measure of the precipitation image. During the experiment on similarity search for daily precipitation images in the Jialing River basin, the NDCG@10 of the search results reached 0.964, surpassing other methods. This indicates that the method proposed in this paper can better characterize the spatiotemporal characteristics of the precipitation image, thereby discovering similar rainfall processes and providing new ideas for hydrological forecasting. [ABSTRACT FROM AUTHOR]