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The present study is an evaluation of published papers on machine learning as employed in mastitis research. The aim of this study was the quantitative evaluation of the scientific content and the bibliometric details of these papers. In total, 69 papers were found to combine machine learning in mastitis research and were considered in detail. There was a progressive yearly increase in published papers, which originated from 23 countries (mostly from China or the United States of America). Most original articles (n = 59) referred to work involving cattle, relevant to mastitis in individual animals. Most articles described work related to the development and diagnosis of the infection. Fewer articles described work on the antibiotic resistance of pathogens isolated from cases of mastitis and on the treatment of the infection. In most studies (98.5% of published papers), supervised machine learning models were employed. Most frequently, decision trees and support vector machines were employed in the studies described. 'Machine learning' and 'mastitis' were the most frequently used keywords. The papers were published in 39 journals, with most frequent publications in Computers and Electronics in Agriculture and Journal of Dairy Science. The median number of cited references in the papers was 39 (interquartile range: 31). There were 435 co-authors in the papers (mean: 6.2 per paper, median: 5, min.–max.: 1–93) and 356 individual authors. The median number of citations received by the papers was 4 (min.–max.: 0–70). Most papers (72.5%) were published in open-access mode. This study summarized the characteristics of papers on mastitis and artificial intelligence. Future studies could explore using these methodologies at farm level, and extending them to other animal species, while unsupervised learning techniques might also prove to be useful. [ABSTRACT FROM AUTHOR]

The present study is an evaluation of published papers on machine learning as employed in mastitis research. The aim of this study was the quantitative evaluation of the scientific content and the bibliometric details of these papers. In total, 69 papers were found to combine machine learning in mastitis research and were considered in detail. There was a progressive yearly increase in published papers, which originated from 23 countries (mostly from China or the United States of America). Most original articles (n = 59) referred to work involving cattle, relevant to mastitis in individual animals. Most articles described work related to the development and diagnosis of the infection. Fewer articles described work on the antibiotic resistance of pathogens isolated from cases of mastitis and on the treatment of the infection. In most studies (98.5% of published papers), supervised machine learning models were employed. Most frequently, decision trees and support vector machines were employed in the studies described. ‘Machine learning’ and ‘mastitis’ were the most frequently used keywords. The papers were published in 39 journals, with most frequent publications in Computers and Electronics in Agriculture and Journal of Dairy Science. The median number of cited references in the papers was 39 (interquartile range: 31). There were 435 co-authors in the papers (mean: 6.2 per paper, median: 5, min.–max.: 1–93) and 356 individual authors. The median number of citations received by the papers was 4 (min.–max.: 0–70). Most papers (72.5%) were published in open-access mode. This study summarized the characteristics of papers on mastitis and artificial intelligence. Future studies could explore using these methodologies at farm level, and extending them to other animal species, while unsupervised learning techniques might also prove to be useful.

Microplastics are among the ubiquitous contaminants in our environment. As emerging contaminants, microplastics are still facing with lots of challenges on the characterisation, including their capture, identification and visualisation, particularly from a complex background. For example, when we print documents using a laser printer, we are printing microplastics onto paper, because the plastics are the main ingredient of the toner powder mixture. Characterisation of these microplastic mixture meets an even more complicated challenge, because plastic's signals might be shielded by other toner powder ingredients such as the pigments, the dyes, the black carbon, and the paper fabrics as well. To solve this challenge, we employ various techniques, including SEM, TEM, XPS, FT-IR, TGA and Raman, to characterise the microplastics printed via the toner powders. Interestingly, we show that Raman can distinguish and visualise the distribution of the microplastics from the complex background of the mixture. We estimate the millions of toner powders, each of which is ~4–6 μm in size, are printed out per A4 sheet as microplastics. The findings send a strong warning that millions of microplastics might be generated from the printing activities in our daily lives. [Display omitted] • Laser printer prints document in our offices or homes, and prints microplastics. • SEM, TEM, XPS, FT-IR, TGA and Raman to characterise the printed microplastics. • Microplastics caught, identified and visualised. • Millions of toner powders printed out per A4 sheet as microplastics. [ABSTRACT FROM AUTHOR]

This paper presents an algorithm for organizing and grouping data related to the expenditures by education levels in Bulgaria. The built database is used as a source from which the necessary information is searched depending on the entered indicators and time interval. The considered indicators include the public and private expenditures by education levels for the period2012-2020. The extracted subsets of elements are processed and analyzed. In this connection, four groups of variables are calculated and discussed. The results show that the expenditures in four of the education levels (primaryand pre-primary education as well as upper and lower secondary education) grow over the last five years (2016-2020). The quoted indicator inpost-secondary, non-tertiary education isreduced gradually in 2016-2019. During 2015, as compared to 2014, the expenditures in the considered 7 levels of education decreased in the range from 0.29% to 7.81%. This work calculates the share of expenditure for tertiary educationbachelor, master and doctor in comparison with the total expenditures for education in the indicatedtime period. The obtained value of the variable for this education level is the highest. It is 29.74%. The values of this variable are significantly lower for the following two levels of education - tertiary education-professional bachelor and postsecondary, non-tertiary education. They are 1.54% and 0.29% respectively. [ABSTRACT FROM AUTHOR]

With the rapid advancement of Unmanned Aerial Vehicle (UAV) technologies in recent years, their uses have been increasingly adopted in the architecture, engineering, and construction industries. To satisfy the needs of various types of construction projects, a considerable amount of research work has been performed to implement and refine the operations, safety, and accuracy of UAVs. This paper presents the findings of a comprehensive literature review that focuses on UAV research in construction management during the timeframe of 2016 to 2021. A total of 95 papers were identified and collected from a list of 21 relevant journals and conference proceedings, and were then categorized by their research topic, sensor types, and targeted structures. The results of 47 exemplary studies were reported in two categories, namely UAV uses and construction uses. The research topics identified for UAV uses include algorithm, applications, operations, framework, and training, while research topics identified for construction use include inspection, surveying, safety, and monitoring. The connection between the research topics, sensor types, targeted structures, and other advanced technologies were also discussed. This paper summarizes the current results of UAV research in construction management, reviews the methodology, benefits, and limitations of the reviewed literature, and provides valuable knowledge for the future trend of UAV applications in the civil, infrastructure, and construction industries. [ABSTRACT FROM AUTHOR]

The widely used discrete-element particle flow software PFC's (PFC 7.0 and previous versions) algorithm for calculating porosity is not sufficiently accurate. Because of this, when the particles are densely packed, the solution to the equation produces an algorithm exception for odd calculations of porosity, which results in the inability to calculate the results. This paper, based on a Darcy seepage model of fluid flow through a granular bed, analyzed the shortcomings of the two porosity calculation methods of PFC and the function analysis method. Combining this analysis with the theory of computer graphics, a new and efficient porosity calculation algorithm was proposed. The result showed that the new proposed porosity calculation algorithm calculated a more accurate and reasonable porosity field and made the iterative solution of the CFD equation more stable. This method makes porosity-related models of PFC more accurate. The algorithm can be not only used to calculate porosity, but also applied to other fields. [ABSTRACT FROM AUTHOR]

Two curves are affinely equivalent if there exists an affine mapping transforming one of them onto the other. Thus, detecting affine equivalence comprises, as important particular cases, similarity, congruence and symmetry detection. In this paper we generalized previous results by the authors to provide an algorithm for computing the affine equivalences between two parametric curves of certain types, in any dimension. In more detail, the algorithm is valid for rational curves, and for parametric curves with nonrational but meromorphic components, it admits an also meromorphic, and in fact rational, inverse. Unlike other algorithms already known for rational curves, the algorithm completely avoids polynomial system solving, and instead uses bivariate factoring as a fundamental tool. The algorithm has been implemented in the computer algebra system Maple and can be freely downloaded and used. [ABSTRACT FROM AUTHOR]

An improved algorithm for diffractive optical element (DOE) with high imaging quality is proposed in this paper. The algorithm is designed based on amplitude division between signal and noise regions, further subdivides the noise region into two distinct parts. The image quality in the signal region will be effectively improved by employing a partition-constraint strategy, which imposes amplitude freedom on the first noise region while enforcing strict amplitude constraints on the second noise region. The principle of the algorithm, simulation analysis, and experimental results are presented. The simulation and experimental results demonstrate that the algorithm is feasible. [ABSTRACT FROM AUTHOR]

Information and communication technologies are spreading rapidly due to their fast proliferation in many fields. The number of Internet users has led to a spike in cyber-attack incidents. E-commerce applications, such as online banking, marketing, trading, and other online businesses, play an integral role in our lives. Network Intrusion Detection System (NIDS) is essential to protect the network from unauthorized access and against other cyber-attacks. The existing NIDS systems are based on the Backward Oracle Matching (BOM) algorithm, which minimizes the false alarm rate and causes of high packet drop ratio. This paper discussed the existing NIDS systems and different used pattern-matching techniques regarding their weaknesses and limitations. To address the existing system issues, this paper proposes an enhanced version of the BOM algorithm by using multiple pattern-matching methods for the NIDS system to improve the network performance. The proposed solution is tested in simulation with existing solutions using the Snort and NSL-KDD datasets. The experimental results indicated that the proposed solution performed better than the existing solutions and achieved a 5.17% detection rate and a 0.22% lower false alarm rate than the existing solution. [ABSTRACT FROM AUTHOR]

How to strengthen emergency management and improve the ability to prevent and respond to emergencies is an important part of building a harmonious socialist society. This paper proposes a domain emotion dictionary construction method for network public opinion analysis of public emergencies. Using the advantages of corpus and semantic knowledge base, this paper extracts the seed words based on the large-scale network public opinion corpus and combined with the existing emotion dictionary, trains the word vector through the word2vec model in deep learning, expands the emotion words, and obtains the candidate emotion words according to the semantic similarity calculation, So as to generate a domain emotion dictionary. The accuracy rate of emotion discrimination by the emotion dictionary constructed in this paper is 0.86, the recall rate is 0.92. Through the verification of accuracy and recall rate, the construction method proposed in this paper has good accuracy and reliability. Because of the great differences in different experiences and situations of different groups, there will be great differences in views and perspectives on the same event. The key to prevent the public from blindly following the crowd should be to reach groups close to emotional distance, and targeted prevention and control of public opinion can be conducted according to different characteristics of different groups. [ABSTRACT FROM AUTHOR]

The quality of cotton seed is of great significance to the production of cotton in the cotton industry. In order to reduce the workload of the manual sorting of cotton seeds and improve the quality of cotton seed sorting, this paper proposed an image-detection method of cotton seed damage based on an improved YOLOv5 algorithm. Images of cotton seeds with different degrees of damage were collected in the same environment. Cotton seeds of three different damage degrees, namely, undamaged, slightly damaged, and seriously damaged, were selected as the research objects. Labeling software was used to mark the images of these cotton seeds and the marked images were input into the improved YOLOv5s detection algorithm for appearance-based damage identification. The algorithm added the lightweight upsampling operator CARAFE to the original YOLOv5s detection algorithm and also improved the loss function. The experimental results showed that the mAP_0.5 value of the improved algorithm reached 99.5% and the recall rate reached 99.3% when the uncoated cotton seeds were detected. When detecting coated cotton seeds, the mAP_0.5 value of the improved algorithm reached 99.2% and the recall rate reached 98.9%. Compared with the traditional appearance-based damage detection approach, the improved YOLOv5s proposed in this paper improved the recognition accuracy and processing speed, and exhibited a better adaptability and generalization ability. Therefore, the proposed method can provide a reference for the appearance detection of crop seeds. [ABSTRACT FROM AUTHOR]

Stress concentration often occurs around bolt holes in load-bearing joint structures of large complex equipment, ships, aerospace and other complex machinery fields, which is an important mechanical factor leading to the failure of joint structures. It is of great engineering significance to study the phenomenon of stress concentration on connected structures for the safety of large and complex equipment; meanwhile, the layout of bolts seriously affects the stress around holes. Many scholars have studied the layout optimization of multi-bolted structures through experiments and simulations, but few algorithms have been applied to the layout optimization of bolted structures. And most of the studied types of multi-bolt structures are symmetrical. Therefore, in this paper, the gray wolf algorithm is used to optimize the layout of nickel steel plate connectors with a bolt layout in triangular position, and the optimal objective function is found based on the hole circumferential stress of the nickel steel plate, maximum shear stress of the bolt and bending stress of the nickel steel plate. Comparing the optimal values obtained by the fruit fly optimization algorithm, particle swarm optimization algorithm, gray wolf optimization algorithm, multiverse optimization algorithm and wind driven optimization algorithm, the accuracy of selecting the gray wolf algorithm for optimization is verified. A multi-bolt connection structure model was established in ABAQUS, and the surface stress before and after optimization was compared to verify the correctness of the gray wolf algorithm applied to the structure layout optimization of the nickel steel flat bolt connection. The results show that under the force of 15 KN, compared with the original bolt structure layout, the optimized upper side nickel steel plate bore peripheral stress is reduced by 73.1 MPa, and the optimization rate is 24%; bolt stress is reduced by 47.7 MPa, and the optimization rate is 12.5%; when the load is less than 18 KN, the optimization effect of both the upper nickel steel plate and bolt group is more than 10%. When the load is greater than 18 KN, the optimization effect is reduced, and when the load is greater than 21 KN, the nickel steel plate has exceeded the yield limit. Due to the existence of fixed constraints, the optimization of the lower nickel steel plate is not obvious. The results of this study can provide data and theoretical support for the layout optimization of the nickel steel flat bolt connection structure, and help to improve reliability analysis and health monitoring in complex assembly fields such as large complex equipment and aerospace. [ABSTRACT FROM AUTHOR]

Drying processes are among the most energy-intensive industrial processes. There is a need for development of the efficient methods needed for estimating, measuring, and reducing energy use. Different machine learning algorithms might provide some of the answers to these issues in a faster and less costly way, without the need for time-consuming and expensive experiments done at different scales of the dryers. The aim of this paper was to provide a comprehensive overview of machine learning applications for addressing energy-related challenges by exploration of different energy types and energy reduction opportunities. Also, the analysis of the applied algorithms, their specific applications and a critical evaluation of the obtained results are provided. The paper is based on the necessity of the improvements in energy use needed for drying related on the existing data. The overview of the ways for such achievements, and a general classification of machine learning algorithm are the background of the paper. The methods used are the machine learning techniques employed in different energy-related issues for drying processes. The paper focuses on the applications of artificial neural networks and other machine learning algorithms and models for different energy-related issues, including different energy types applications, challenges associated with energy consumption, and opportunities for energy reduction. Not only the applied algorithms, but also their specific applications, and the statistical analysis of the obtained results are also overviewed. Finally, a critical evaluation of the findings highlighting the potentials of machine learning algorithms in addressing energy-related challenges (such as estimation of energy consumption, opportunities for energy reduction, and use of different energy sources) is provided. The presented analysis underscored the effectiveness of machine learning applications for these purposes. [ABSTRACT FROM AUTHOR]

Many datasets including social media data and bibliographic data can be modeled as graphs. Clustering such graphs is able to provide useful insights into the structure of the data. To improve the quality of clustering, node attributes can be taken into account, resulting in attributed graphs. Existing attributed graph clustering methods generally consider attribute similarity and structural similarity separately. In this paper, we represent attributed graphs as star-schema heterogeneous graphs, where attributes are modeled as different types of graph nodes. This enables the use of personalized pagerank (PPR) as a unified distance measure that captures both structural and attribute similarities. We employ DBSCAN for clustering, and we update edge weights iteratively to balance the importance of different attributes. The rapidly growing volume of data nowadays challenges traditional clustering algorithms, and thus, a distributed method is required. Hence, we adopt a popular distributed graph computing system Blogel, based on which, we develop four exact and approximate approaches that enable efficient PPR score computation when edge weights are updated. To improve the effectiveness of the clustering, we propose a simple yet effective edge weight update strategy based on entropy. In addition, we present a game theory based method that enables trading efficiency for result quality. Extensive experiments on real-life datasets offer insights into the effectiveness and efficiency of our proposals. [ABSTRACT FROM AUTHOR]

In this paper, algorithms for different types of trigonometric power sums are developed and presented. Although interesting in their own right, these trigonometric power sums arise during the creation of an algorithm for the four types of twisted trigonometric power sums defined in the introduction. The primary aim in evaluating these sums is to obtain exact results in a rational form, as opposed to standard or direct evaluation, which often results in machine-dependent decimal values that can be affected by round-off errors. Moreover, since the variable, m, appearing in the denominators of the arguments of the trigonometric functions in these sums, can remain algebraic in the algorithms/codes, one can also obtain polynomial solutions in powers of m and the variable r that appears in the cosine factor accompanying the trigonometric power. The degrees of these polynomials are found to be dependent upon v, the value of the trigonometric power in the sum, which must always be specified. [ABSTRACT FROM AUTHOR]

Operator precedence grammars (OPG) are context-free grammars (CFG) that are characterized by the absence of two adjacent non-terminal symbols in the body of each production (right-hand side). Operator precedence languages (OPL) are deterministic and context-free. Three possible precedence relations between pairs of terminal symbols are established for these languages. Many CFGs are not OPGs because the operator precedence cannot be applied to them as they do not comply with the basic rule. To solve this problem, we have conducted a thorough redefinition of the Left and Right sets of terminals that are the basis for calculating the precedence relations, and we have defined a new Leftmost set. The algorithms for calculating them are also described in detail. Our work's most significant contribution is that we establish precedence relationships between terminals by overcoming the basic rule of not having two consecutive non-terminals using an algorithm that allows building the operator precedence table for a CFG regardless of whether it is an OPG. The paper shows the complexities of the proposed algorithms and possible exceptions to the proposed rules. We present examples by using an OPG and two non-OPGs to illustrate the operation of the proposed algorithms. With these, the operator precedence table is built, and bottom-up parsing is carried out correctly. [ABSTRACT FROM AUTHOR]

The study described in this paper develops a new technique which permits the execution of an open straddle strategy based on the superior volatility forecast for analyzing historical data. We extend the current litearure by measuring the volatility of an underlying asset in the last predefined period and comparing the actual volatility in currency with historical volatility in currency to make predictions of implied volatility. We calculated stock price volatility through an optimal holding period (OHP) and set up bars of volatility in currency. To obtain this, we solved optimization equations to find maximum and minimum movements in the volatility in currency within the defined range. We placed volatility in currency into percentile rankings and designed a straddle trading strategy based on the last OHP's volatility in currency. The technique allows for an investor (or trader) to open either short or long positions based on calculations for a selected OHP's volatility in currency. We applied this strategy to 130 stocks which are traded on CBOE. We developed a trading algorithm which can be used by institutional as well as individual investors. The algorithm is set to determine historical volatility in currency and forecast upcoming volatilities in currency through the understanding of the market sentiment. The empirical findings show that the stocks analyzed with the algorithm generate positive returns along a spectrum of changing volatilities of the underlying assets. [ABSTRACT FROM AUTHOR]

Protecting crops from pests is a major issue in the current agricultural production system. The agricultural digital twin system, as an emerging product of modern agricultural development, can effectively achieve intelligent control of pest management systems. In response to the current problems of heavy use of pesticides in pest management and over-reliance on managers' personal experience with pepper plants, this paper proposes a digital twin system that monitors changes in aphid populations, enabling timely and effective pest control interventions. The digital twin system is developed for pest management driven by data and model fusion. First, a digital twin framework is presented to manage insect pests in the whole process of crop growth. Then, a digital twin model is established to predict the number of pests based on the random forest algorithm optimized by the genetic algorithm; a pest control intervention based on a twin data search strategy is designed and the decision optimization of pest management is conducted. Finally, a case study is carried out to verify the feasibility of the system for the growth state of pepper and pepper pests. The experimental results show that the virtual and real interactive feedback of the pepper aphid management system is achieved. It can obtain prediction accuracy of 88.01% with the training set and prediction accuracy of 85.73% with the test set. The application of the prediction model to the decision-making objective function can improve economic efficiency by more than 20%. In addition, the proposed approach is superior to the manual regulatory method in pest management. This system prioritizes detecting population trends over precise species identification, providing a practical tool for integrated pest management (IPM). [ABSTRACT FROM AUTHOR]

Objective: To characterize and validate the landscape of algorithms that use International Classification of Disease (ICD) codes to identify low‐acuity emergency department (ED) visits. Data Sources: Publicly available ED data from the National Hospital Ambulatory Medical Care Survey (NHAMCS). Study Design: We systematically searched for studies that specify algorithms consisting of ICD codes that identify preventable or low‐acuity ED visits. We classified ED visits in NHAMCS according to these algorithms and compared agreements using the Jaccard index. We then evaluated the performance of each algorithm using positive predictive value (PPV) and sensitivity, with the reference group specified using low‐acuity composite (LAC) criteria consisting of both triage and clinical components. In sensitivity analyses, we repeated our primary analysis using only triage or only clinical criteria for reference. Data Collection: We used the 2011–2017 NHAMCS data, totaling 163,576 observations before survey weighting and after dropping observations missing a primary diagnosis. We translated ICD‐9 codes (years 2011–2015) to ICD‐10 using a standard crosswalk. Principal Findings: We identified 15 papers with an original list of ICD codes used to identify preventable or low‐acuity ED presentations. These papers were published between 1992 and 2020, cited an average of 310 (SD 360) times, and included 968 (SD 1175) codes. Pairwise Jaccard similarity indices (0 = no overlap, 1 = perfect congruence) ranged from 0.01 to 0.82, with mean 0.20 (SD 0.13). When validated against the LAC reference group, the algorithms had an average PPV of 0.308 (95% CI [0.253, 0.364]) and sensitivity of 0.183 (95% CI [0.111, 0.256]). Overall, 2.1% of visits identified as low acuity by the algorithms died prehospital or in the ED, or needed surgery, critical care, or cardiac catheterization. Conclusions: Existing algorithms that identify low‐acuity ED visits lack congruence and are imperfect predictors of visit acuity. [ABSTRACT FROM AUTHOR]

The paper presents the methodology to compute modulo multiplication with the moduli set 2n, 2n-1, 2n+1. In addition to this, designs of the modulo multipliers, namely 2n, 2n-1, and 2n+1 (with n = 4, 8, and 16), have been proposed which are based on half adders, full adders, 4:3 compressor, 7:3 compressor, and the multi-column compressor namely 5,5:4. The gate level design of 4:3 compressor is carried out by solving the truth table using the K-map reduction. To verify the functionalities we have implemented the proposed modulo multipliers using VHDL coding in Xilinx 14.2 design suite. Simulation using Virtex-6 device has been performed to estimate delay, power consumption, and power-delay product (PDP). Moreover, the modulo multipliers are simulated in Cadence RC compiler using 0.18 μm technology to estimate the area. One of the major contributions to the arts of this work is in the partial product reduction stage which utilizes the multi-column 5,5:4 compressor to reduce power and area. The modulo 2n-1 multiplier of operand size 4-bit shows an improvement of 66.34% in terms of area over the best-reported paper. On the other hand, the modulo 2n+1 multiplier of operand size 4-bit shows an improvement of 58.59% terms of in area and the same of operand size 8-bit shows an improvement of 22.72% over the best-reported paper. The proposed algorithms of moduli multiplication are applicable to Booth multiplication of signed numbers. [ABSTRACT FROM AUTHOR]

Vehicular ad hoc networks (VANETs) play a crucial role in intelligent transportation systems (ITS), enabling seamless communication between vehicles and other entities. VANETs provide a wide range of services, allowing vehicles to communicate with each other and with roadside infrastructure. With the increasing amount of data generated by VANETs, machine learning approaches have emerged as valuable tools to address complex challenges in this domain. This paper presents a comprehensive literature review on the application of machine learning in VANETs. The paper discusses the potential challenges and future research directions in the field, emphasizing the need for more accessible machine learning solutions for VANETs. This review emphasizes the significant role of machine learning approach in advancing the capabilities of VANETs and shaping the future of intelligent transportation systems. [ABSTRACT FROM AUTHOR]

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