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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.

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]

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]

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]

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]

A prerequisite for continuous transport systems' operation is their digital transformation, which interprets operating conditions based on the availability of a wide range of data and information in the form of measured quantities that can be obtained, for example, by experimental measurement. To implement digital transformation in continuous transport systems, it is necessary to examine and analyze the informative value of individual measured quantities in detail. Research in this area must focus on identifying addressable quantities with a clear, informative value. Such an approach enables the monitoring of continuous transport systems operation and performance of operational diagnostics, the objective of which should be identifying undesirable operating conditions. Within this paper, research will be presented aiming to verify the hypothesis that, based on a measurement of selected parameters, it is possible to identify belt mistracking in a continuous transport system. Belt mistracking is an undesirable condition that can cause a conveyor belt to converge and thus seriously turn off an entire transport system. The research results confirmed the established hypothesis. Based on this, an evaluation algorithm was created for on-time evaluation. The proposed algorithm is also suitable for the needs of a digital twin of a continuous transport system. [ABSTRACT FROM AUTHOR]

The last decade has seen a significant number of remarkable expansions in machine learning research. The field has achieved unprecedented popularity by developing new areas and increased momentum on the existing sites. Whereas the symbolic methods have been exceedingly active, the sub-symbolic approaches, especially the neural networks, have had tremendous growth. Unlike the efforts whose main concentrations were on the single learning strategies, the new trends have been to integrate various methods and for the development of cognitive learning architectures. Besides, there is an increased interest in the experimental comparisons of several methods and learning algorithms' theoretical analysis. The same data sets have often been shared amongst the researchers who have led to applying the same techniques in dealing with the same problems to understand the relative merits of various methods. There are new insights that have been brought into the complexity of the learning process through theoretical investigations. This research paper seeks to transmit the knowledge of machine learning, which has become very popular and the researchers' applications of the experience. [ABSTRACT FROM AUTHOR]

[Objective] Change in soil water content is not only an indicator of water stresses used for irrigation management but also controls biogeochemical processes in soil. In this paper, we study the feasibility of using thermal infrared imaging to estimate soil moisture dynamics. [Method] The experiment was conducted in July-August 2023 in a walnut orchard in Xinjiang. Thermal infrared images of the walnut canopy were measured continuously using a thermal infrared camera. Based on the HSV color space of the images, an improved K-means segmentation algorithm was proposed to analyze the change in canopy temperature. We also measured air temperature and humidity, illuminance, wind speed, atmospheric CO2, and soil water content in the 0-80 cm soil layer, from which we proposed an inversion model to estimate soil water dynamics. [Result] The improved K-means algorithm increased the accuracy from 82.34% to 94.55%, and the errors between the canopy temperature acquired from the images and the measured canopy temperature were in the range of 0 to 1.0. The infrared imaging method was most accurate between 14:00 pm to 16:00 pm. Our results showed that the walnut roots were most active in taking up water from the 40-60 cm soil layer 50-60 cm away horizons from the tree truck. Canopy temperature, air temperature and relative humidity, and atmospheric CO2 concentration were correlated with soil water content at significant levels; they can thus be used to estimate soil water dynamics, with a coefficient of determination of R2=0.86 and p<0.01. [Conclusion] The temperature acquired from the infrared images of the walnut canopy can be used with other metrological data and atmospheric CO2 concentration to estimate soil water dynamics in the root zone of the walnut. It provides a new method for improving soil water management in walnut orchards in Xinjiang. [ABSTRACT FROM AUTHOR]

In this work, we show that if F is a positive integer, then Sat (F) = { S ∣ S is a saturated numerical semigroup with Frobenius number F} is a covariety. As a consequence, we present two algorithms: one that computes Sat (F) , and another which computes all the elements of Sat (F) with a fixed genus. If X ⊆ S \ Δ (F) for some S ∈ Sat (F) , then we see that there exists the least element of Sat (F) containing X. This element is denoted by Sat (F) [ X ]. If S ∈ Sat (F) , then we define the Sat (F) -rank of S as the minimum of { c a r d i n a l i t y (X) ∣ S = Sat (F) [ X ] }. In this paper, we present an algorithm to compute all the elements of Sat (F) with a given Sat (F) -rank. [ABSTRACT FROM AUTHOR]

This paper continues the study of second-order signature properties—the characterization of the extreme 3-uniform hypergraph. Previously, bases were used to count extreme 3-uniform hypergraphs. However, the algorithm using this mechanism is extremely labor-intensive. The structure of the signature allows us to use it as a more efficient basis for the same problem. Here, we establish the nature of the mutual correspondence between the kind of second-order signature and extreme hypergraphs, and we present a new algorithm to find the power of the set of extreme 3-uniform hypergraphs through the set of their characteristic-signatures. New results obtained with the proposed tool are also presented. [ABSTRACT FROM AUTHOR]

In the rapid evolving digital landscape, the imperative to ensure data security has never been more crucial. This paper addresses the pressing challenges in data security by introducing a file encryption management system, leveraging a modified advanced encryption standard (AES) algorithm with reduced round iterations and bit permutation. This system aims to comprehensively secure various file types, providing a dependable solution for file exchange. Our findings reveal substantial improvements in both encryption and decryption processes using the reduced-round permutationbased AES (RRPBA). The adapted algorithm demonstrates a significant 38.8% acceleration in encryption time and a remarkable 44.86% improvement in decryption time, positioning it as a pivotal component for efficient file operations within the management system. Moreover, the throughput assessments showcase a remarkable 33.73% improvement in encryption and 23.72% in decryption, outperforming the original AES, emphasizing the algorithm's superior computational effectiveness, signaling positive implications for future high-performance applications. In conclusion, the study not only addresses critical security challenges but also presents a viable solution with tangible speed advantages for file encryption and decryption processes within digital file management systems. [ABSTRACT FROM AUTHOR]

Introduction: The dynamic positioning system resists the environmental forces such as wind, wave and current acting on the ship through the thruster, so that the ship can remain in the position required by the sea level as much as possible, and the operation is very convenient. But its current dynamic positioning ability can not meet people's needs. Methods: A Kalman filter based on untracked optimization was designed for dynamic positioning control system. Then the intelligent control algorithm is designed for the dynamic positioning top-level controller and thrust optimal distribution controller, which occupy an important position in the system, namely the adaptive weight variation particle swarm optimization algorithm and thrust optimal distribution algorithm. Results and Discussion: The average position error of three degrees of freedom after filter processing is 1.53 m. Compared with other mainstream controllers, the mean root error of controllers based on adaptive weight variation particle swarm optimization in environment A and B is 2.295 and 1.8 m, respectively. In environment C, the controller based on thrust optimization allocation algorithm can get the optimal solution when the full rotary thruster reaches the 7 s and the channel thruster reaches the 4 s. The thrust exclusion zone is crossed at 46 s in environment D. In the dynamic positioning capability curve of the system, the experimental hull can balance the different environmental loads at all angles. In summary, the intelligent control algorithm proposed in this paper can effectively improve the positioning ability of the dynamic positioning control system and meet the needs of people for ship navigation today. [ABSTRACT FROM AUTHOR]

The combination of lifelong learning algorithms with autonomous intelligent systems (AIS) is gaining popularity due to its ability to enhance AIS performance, but the existing summaries in related fields are insufficient. Therefore, it is necessary to systematically analyze the research on lifelong learning algorithms with autonomous intelligent systems, aiming to gain a better understanding of the current progress in this field. This paper presents a thorough review and analysis of the relevant work on the integration of lifelong learning algorithms and autonomous intelligent systems. Specifically, we investigate the diverse applications of lifelong learning algorithms in AIS's domains such as autonomous driving, anomaly detection, robots, and emergency management, while assessing their impact on enhancing AIS performance and reliability. The challenging problems encountered in lifelong learning for AIS are summarized based on a profound understanding in literature review. The advanced and innovative development of lifelong learning algorithms for autonomous intelligent systems are discussed for offering valuable insights and guidance to researchers in this rapidly evolving field. [ABSTRACT FROM AUTHOR]

Advancements in new technologies, a reduction in CO2 emissions, and the rising demand for energy are causing a growth in the share of renewable energy sources. In distribution networks, an increasing number of distributed generators (DGs) makes the utility grid's protection complex and demanding. Vector surge and rate-of-change-of-frequency are the established anti-islanding protection methods, recognizing that the standard paradigm for protection, involving distributed generation, cannot be set only once but has to be continuously updated following the requirements and changes in the system. One of the requirements is active participation in the preservation of system frequency and voltage, which can be interrupted if the DG trips and disconnects from the utility grid. Anti-islanding protection and spurious tripping can be avoided by implementing new algorithms and techniques. This paper presents a novel protection scheme based on a voltage frequency differential. The proposed algorithm employs remote and local frequency measurements in such a manner that, for the occurrence of a frequency difference, it is assumed that the DG is in an islanding state. In this article, we demonstrate the feasibility of the algorithm through numerical analysis of grid events and laboratory testing emulating real grid-measured values. The test results show that the algorithm is resilient to false tripping for non-islanding events and more reliable than conventional methods in islanding detection. The algorithm can be set to low-frequency differential values, drastically reducing the non-detection zone in any DG type, regardless of its size and voltage level at the point of common coupling. Unlike standard anti-islanding methods, the algorithm supports the ability of the DG to fault-ride through demand. [ABSTRACT FROM AUTHOR]

With the transformation of transmission and distribution grids into smart grids that are more dominated by renewable energy, power electronics-based inverters that can improve power quality are becoming more visible. In order to maximize the output voltage quality and reduce the total harmonic distortion (THD), efficient operation of inverters is required. Therefore, in this paper, the problem of harmonic elimination in multilevel inverters is solved by using an adaptive grey wolf optimizer with local search. We have performed a grid search-based landscape analysis of the seven-level inverter to understand the behaviour of the proposed algorithm. For verification, the numerical results of the proposed adaptive grey wolf optimizer are compared with those of the original grey wolf optimization algorithm, a modified version of the grey wolf optimization algorithm, the particle swarm optimization algorithm, multi-verse optimization algorithm, and salp swarm algorithm. In the simulations, we solved the optimization model for three different structures of multilevel inverters (7, 11, and 15 levels) by changing the modulation indexes. It is found that the adaptive grey wolf optimization provides lower total harmonic distortion for different modulation indexes. [ABSTRACT FROM AUTHOR]

Grape dehydration is practiced widely in the food industry with large yields of sultanas produced globally. This paper proposes an investigation into the microstructure changes of grapes as they are dried by imaging specimens at intervals during dehydration at two temperatures using scanning electron microscopy. Two main methods were developed to obtain the complex boundaries of cells present in grape tissue in over 36 SEM images. Segmentation of the binary image using an adapted watershed function obtained the most consistent and accurate morphological shape. This was compared to a secondary method which used Canny's edge detection function, morphological closing and skeletonizing to outline the cellular microstructure. MATLAB was utilised to convert these boundaries into measurable areas so that quantitative data on average cell area, perimeter and cell axis lengths were acquired. It was found that over the drying time, the cell area and perimeter were reduced as expected. Some variability in the data was clear due to only single samples being analysed for each temperature and time combination. Trends in cell perimeter, diameter and shape will be used to demonstrate relationships between morphological structure, drying temperature, and duration. Detailed images of the microstructure were obtained, and a unique image processing algorithm was developed to quantitatively analyse the properties of this microstructure. The development of automatic image processing techniques and algorithms will enable quantitative data to be extracted from any image and extend to any plant/food material. [ABSTRACT FROM AUTHOR]

We study the problem of perfect tiling in the plane and explore the possibility of tiling a rectangle using integral distinct squares. Assume a set of distinguishable squares (or equivalently a set of distinct natural numbers) is given, and one has to decide whether it can tile the plane or a rectangle or not. Previously, it has been proved that tiling the plane is not feasible using a set of odd numbers or an infinite sequence of natural numbers including exactly two odd numbers. The problem is open for different situations in which the number of odd numbers is arbitrary. In addition to providing a solution to this special case, we discuss some open problems to tile the plane and rectangles in this paper. [ABSTRACT FROM AUTHOR]

The purpose of this article is to present grassroots concepts and ideas about "the algorithm" in the religious context. The power and causality of algorithms are based on lines of computer code, making a society influenced by "black boxes" or "enigmatic technologies" (as they are incomprehensible to most people). On the other hand, the power of algorithms lies in the meanings that we attribute to them. The extent of the power, agency, and control that algorithms have over us depends on how much power, agency, and control we are willing to give to algorithms and artificial intelligence, which involves building the idea of their omnipotence. The key question is about the meanings and the ideas about algorithms that are circulating in society. This paper is focused on the analysis of "vernacular/folk" theories on algorithms, reconstructed based on posts made by the users of Polish Catholic forums. The qualitative analysis of online discourse makes it possible to point out several themes, i.e., according to the linguistic concept, "algorithm" is the source domain used in explanations of religious issues (God as the creator of the algorithm, the soul as the algorithm); algorithms and the effects of their work are combined with the individualization and personalization of religion; algorithms are perceived as ideological machines. [ABSTRACT FROM AUTHOR]

The built environment is a significant contributor to global energy consumption and greenhouse gas emissions. Advancements in the adoption of environmentally friendly building technology have become crucial in promoting sustainable development. These advancements play a crucial role in conserving energy. The aim is to achieve an optimal design by balancing various interrelated factors. The emergence of innovative techniques to address energy conservation have been witnessed in the built environment. This review examines existing research articles that explore different metaheuristic optimization techniques (MOTs) for energy conservation in buildings. The focus is on evaluating the simplicity and stochastic nature of these optimization techniques. The findings of the review present theoretical and mathematical models for each algorithm and assess their effectiveness in problem solving. A systematic analysis of selected algorithms using MOT is conducted, considering factors that influence wellbeing, occupant health, and indoor environmental quality. The study examines the variations among swarm intelligence MOTs based on complexity, advantages, and disadvantages. The algorithms' performances are based on the concept of uncertainty in consistently providing optimal solutions. The paper highlights the application of each technique in achieving energy conservation in buildings. [ABSTRACT FROM AUTHOR]

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In this work, we will introduce the concept of ratio-covariety, as a family R of numerical semigroups that has a minimum, denoted by min (R) , is closed under intersection, and if S ∈ R and S ≠ min (R) , then S \ { r (S) } ∈ R , where r (S) denotes the ratio of S. The notion of ratio-covariety will allow us to: (1) describe an algorithmic procedure to compute R ; (2) prove the existence of the smallest element of R that contains a set of positive integers; and (3) talk about the smallest ratio-covariety that contains a finite set of numerical semigroups. In addition, in this paper we will apply the previous results to the study of the ratio-covariety R (F , m) = { S ∣ S   is   a   numerical   semigroup   with   Frobenius   number   F   and   multiplicity m }. [ABSTRACT FROM AUTHOR]

Ensuring the protection of digital data is of utmost importance in our current reliance on network operations. However, security measures such as data encryption often result in decreased performance speed. This paper enhanced the 128-bit version of the advanced encryption standard (AES) by substituting the MixColumns function with a permutation-based approach and decreasing the overall number of rounds. The evaluation results indicate a substantial enhancement in the speed of encryption and decryption, with a 76.76% improvement in encryption time and a 55.46% improvement in decryption time. Furthermore, it is important to mention that the modifications implemented in the standard AES did not compromise its security in relation to the strict avalanche criterion. The avalanche effect of the modified AES is 52.92%, surpassing the minimum requirement of 50%. Finally, the modified AES demonstrated a 31.12% increase in throughput for encryption and a 25.50% increase for decryption when compared to the original AES, using the sample dataset. [ABSTRACT FROM AUTHOR]

This paper presents a study aimed at effectively implementing a deterministic random bit generator (DRBG) IP in verilog language, based on the standard encryption algorithm. By controlling the existing round generation and key generation blocks, the internal modules of the counter mode deterministic random bit generator (CTR-DRBG) were successfully implemented and operated, ensuring the secure and efficient generation of random bit sequences. The research focused on parallel operation of modules and optimized module placement to achieve improved clock frequencies. By concurrently operating two modules in the derivation and internal update modules of CTR-DRBG, the processing speed was enhanced compared to the conventional algorithm. Additionally, integrating the reseeding and initialization modules of CTR-DRBG into a single module successfully reduced size. Furthermore, this IP supports the special function register (SFR) interface. The safety of the CTR-DRBG was validated through known answer test (KAT) verification utilizing test vectors from certification. Future research should explore additional studies on CTR-DRBG operating on real FPGA or ASIC, not only using normal algorithm but also employing other block cipher algorithms. [ABSTRACT FROM AUTHOR]

Background: Differences in the preparation, staining and scanning of digital pathology slides create significant pre-analytic variability. Algorithm-assisted tools must be able to contend with this variability in order to be applicable in clinical practice. In a previous study, a decision support algorithm was developed to assist in the diagnosis of Hirschsprung's disease. In the current study, we tested the robustness of this algorithm while assessing for pre-analytic factors which may affect its performance. Methods: The decision support algorithm was used on digital pathology slides obtained from four different medical centers (A-D) and scanned by three different scanner models (by Philips, Hamamatsu and 3DHISTECH). A total of 192 cases and 1782 slides were used in this study. RGB histograms were constructed to compare images from the various medical centers and scanner models and highlight the differences in color and contrast. Results: The algorithm was able to correctly identify ganglion cells in 99.2% of cases, from all medical centers (All scanned by the Philips slide scanner) as well as 95.5% and 100% of the slides scanned by the 3DHISTECH and Hamamatsu brand slide scanners, respectively. The total error rate for center D was lower than the other medical centers (3.9% vs 7.1%, 10.8% and 6% for centers A-C, respectively), the vast majority of errors being false positives (3.45% vs 0.45% false negatives). The other medical centers showed a higher rate of false negatives in relation to false positives (6.81% vs 0.29%, 9.8% vs 1.2% and 5.37% vs 0.63% for centers A-C, respectively). The total error rates for the Philips, Hamamatsu and 3DHISTECH brand scanners were 3.9%, 3.2% and 9.8%, respectively. RGB histograms demonstrated significant differences in pixel value distribution between the four medical centers, as well as between the 3DHISTECH brand scanner when compared to the Philips and Hamamatsu brand scanners. Conclusions: The results reported in this paper suggest that the algorithm-based decision support system has sufficient robustness to be applicable for clinical practice. In addition, the novel method used in its development – Hierarchial-Contexual Analysis (HCA) may be applicable to the development of algorithm-assisted tools in other diseases, for which available datasets are limited. Validation of any given algorithm-assisted support system should nonetheless include data from as many medical centers and scanner models as possible. [ABSTRACT FROM AUTHOR]

Featured Application: This approach is suitable for diagnostics of other systems in terms of real-time fault identification and mitigation. It will also be useful in the field of digital twin applications. Many different types of aircraft designs have flight control systems (FCS) powered by hydraulic systems. With respect to the torques, moments, surface areas, and opposing forces to be acted upon, components introduce faults into the hydraulic system when these components are aging or degrading. The diagnostics of a hydraulically powered flight control actuation system (HPFCAS) rely on the faults produced within the subsystem components as well as the entire system's mechanism itself. In this paper, a model for an HPFCAS is developed to analyze faults where the HPFCAS was approached as a system of systems (SOS). The identified faults were injected into the system. It is established that some of the faults from the different subsystems had similar characteristic effects and were propagated with attendant consequences. For instance, a measured decrease in the pressure value is observed because of the decrease in the pump speed. A similar characteristic is observed if there is leakage on the line or if there is a clogging valve. These form complex integrated responses in determining where the fault is coming from if only one component is analyzed since it involves components serving different subsystems. Results show that only models that can describe the real characteristics or attributes of the specific systems, due to their defined components, are sufficient for effective diagnostics. This is because the data obtained are more accurate at predicting the behavior of components. [ABSTRACT FROM AUTHOR]

The advancement of the Internet of Things (IoT) has increased the demand for large-scale intelligent sensing systems. The periodic replacement of power sources for ubiquitous sensing systems leads to significant resource waste and environmental pollution. Human staffing costs associated with replacement also increase the economic burden. The triboelectric nanogenerators (TENGs) provide both an energy harvesting scheme and the possibility of self-powered sensing. Based on contact electrification from different materials, TENGs provide a rich material selection to collect complex and diverse data. As the data collected by TENGs become increasingly numerous and complex, different approaches to machine learning (ML) and deep learning (DL) algorithms have been proposed to efficiently process output signals. In this paper, the latest advances in ML algorithms assisting solid–solid TENG and liquid–solid TENG sensors are reviewed based on the sample size and complexity of the data. The pros and cons of various algorithms are analyzed and application scenarios of various TENG sensing systems are presented. The prospects of synergizing hardware (TENG sensors) with software (ML algorithms) in a complex environment and their main challenges for future developments are discussed. [ABSTRACT FROM AUTHOR]

In recent years, with the rapid development of modern information technology, intelligent algorithms have been deeply embedded in all aspects of social life, which not only promotes the continuous development of human economy and society to intelligence, but also promotes the overall pattern of society to intelligence, providing technical guidance for the efficient and orderly operation of national governance and becoming an important driving force for the modernization of national governance level and governance capacity. In this context, how to promote the algorithm technology to be more effectively embedded in government governance at this stage, how to counter the origin of technology while giving full play to technical benefits, and how to better achieve the goal of maximizing public value become an urgent proposition of the times. In this paper, an analytical framework is built from the three dimensions of "technology-power-rules" to study the embedding of algorithms into government governance. Taking the practice of embedding algorithms into different scenes of government governance as the observation window, the operational logic and value implication of enabling government governance by algorithms are explored, and it is proposed that algorithm technology can promote public decision-making to be more rational, algorithm power can promote governance behavior to be more inclusive, and algorithm rules can promote government governance to be more fair. At the same time, it focuses on the risks and challenges that may be brought by the application of algorithms with technical, power and rule attributes in government governance practice, such as the black box of algorithm technology blocking the identification of public responsibility, the power of algorithm weakening the autonomy of government and people, and the preference of algorithm rules leading to the loss of governance justice. Combined with the government governance practice in China, it is proposed that in order to promote the intelligent algorithm to be more effectively embedded in government governance and better realize the goal of maximizing public benefits, efforts should be made to promote the moderate transparency of algorithm technology to clarify the chain of responsibility, standardize the operation of algorithm power and adhere to the humanistic governance concept. Different from the "negative theory of algorithm" and "positive theory of algorithm" held by the existing research results, this paper recognizes that the algorithm has both positive and negative external benefits to government governance from the two dimensions of efficiency and value, and focuses on how to avoid or reduce the possible risks and challenges of the algorithm to government governance from the three levels of technology, power and rules, and amplify the positive benefits of the algorithm to the governance process, so that the intelligent algorithm can be truly and effectively embedded in government governance, and promote the quality and efficiency of social governance. [ABSTRACT FROM AUTHOR]

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In order to improve the effect of intelligent fusion recommendation under the background of social network, this paper combines the fuzzy perception algorithm to research the intelligent fusion recommendation algorithm of social network. Moreover, this paper proposes a task offloading scheme that relies on V2V communication to utilize idle computing resources in a "resource pool". In addition, this paper formulates the computational task execution time as a min-max problem to reduce the storage overhead to optimize the total task execution time. Numerical results show that the proposed scheme greatly reduces the task execution time. The introduced particle swarm optimization algorithm also proves the convergence speed and accuracy of the optimization problem. The research verifies that the intelligent fusion recommendation algorithm for social network based on fuzzy perception has good social network data fusion effect and can effectively improve the effect of intelligent fusion recommendation. [ABSTRACT FROM AUTHOR]

Many optimization problems in engineering and industrial design applications can be formulated as optimization problems with highly nonlinear objectives, subject to multiple complex constraints. Solving such optimization problems requires sophisticated algorithms and optimization techniques. A major trend in recent years is the use of nature-inspired metaheustic algorithms (NIMA). Despite the popularity of nature-inspired metaheuristic algorithms, there are still some challenging issues and open problems to be resolved. Two main issues related to current NIMAs are: there are over 540 algorithms in the literature, and there is no unified framework to understand the search mechanisms of different algorithms. Therefore, this paper attempts to analyse some similarities and differences among different algorithms and then presents a generalized evolutionary metaheuristic (GEM) in an attempt to unify some of the existing algorithms. After a brief discussion of some insights into nature-inspired algorithms and some open problems, we propose a generalized evolutionary metaheuristic algorithm to unify more than 20 different algorithms so as to understand their main steps and search mechanisms. We then test the unified GEM using 15 test benchmarks to validate its performance. Finally, further research topics are briefly discussed. [ABSTRACT FROM AUTHOR]

This study provides a conceptual exploration of an innovative telemedicine-enhanced team-based care (TETC) model, tailored to prenatal care, integrating a multidisciplinary team approach with advanced telemedicine technologies. The algorithm developed for TETC aims to optimize communication and coordination among healthcare professionals, including obstetricians, midwives, nutritionists, and mental health experts. This cohesive team structure ensures a comprehensive care plan encompassing all facets of maternal and fetal health. Leveraging telemedicine tools like video conferencing and digital health records, the model supports remote consultations and coordinated care, proving particularly advantageous during pandemics or in regions with limited healthcare access. Central to the TETC model is patient-centered care, focusing on personalized care plans attuned to the individual needs, health status, and socioeconomic backgrounds of pregnant women. This approach not only enhances accessibility and convenience by diminishing the necessity for physical consultations but also ensures continuity of care throughout pregnancy. This continuity is crucial for consistent health parameter tracking and early risk identification. The paper discusses the model's design, operational workflow, and ethical and legal considerations, providing implementation guidelines and potential applications. The TETC model, rooted in current technological capabilities and healthcare frameworks, underscores the need for close collaboration with healthcare professionals to adhere to medical standards and address real-world requirements effectively. [ABSTRACT FROM AUTHOR]

In this paper, a nonlocal boundary value problem for the Benjamin-Bona-Mahony-Burgers equation is studied in a rectangular domain. By introducing new functions, the nonlocal boundary value problem for a nonlinear third-order partial differential equation is reduced to a boundary value problem for a second-order hyperbolic equation with a mixed derivative and functional relations. Before using the approximate method, the nonlinear problem under consideration is examined for the presence of solutions, it is necessary to clarify where these solutions are located, that is, to find the region of isolation of solutions. The isolation area of the solution in our case is a ball in which there is a unique solution to the problem. Next, an algorithm for finding a solution to a nonlocal boundary value problem is proposed. In terms of the initial data, conditions for the convergence of the algorithms are established, which simultaneously ensure the existence and isolation of a solution to a nonlinear nonlocal boundary value problem. Estimates between the exact and approximate solutions of the problem under consideration are obtained. The results obtained are of a theoretical nature and can be used in the construction of computational algorithms for solving nonlocal boundary value problems for the Benjamin-Bona-Mahony-Burgers equation. [ABSTRACT FROM AUTHOR]

* Context.--Automated prostate cancer detection using machine learning technology has led to speculation that pathologists will soon be replaced by algorithms. This review covers the development of machine learning algorithms and their reported effectiveness specific to prostate cancer detection and Gleason grading. Objective.--To examine current algorithms regarding their accuracy and classification abilities. We provide a general explanation of the technology and how it is being used in clinical practice. The challenges to the application of machine learning algorithms in clinical practice are also discussed. Data Sources.--The literature for this review was identified and collected using a systematic search. Criteria were established prior to the sorting process to effectively direct the selection of studies. A 4-point system was implemented to rank the papers according to their relevancy. For papers accepted as relevant to our metrics, all cited and citing studies were also reviewed. Studies were then categorized based on whether they implemented binary or multi-class classification methods. Data were extracted from papers that contained accuracy, area under the curve (AUC), or k values in the context of prostate cancer detection. The results were visually summarized to present accuracy trends between classification abilities. Conclusions.--It is more difficult to achieve high accuracy metrics for multiclassification tasks than for binary tasks. The clinical implementation of an algorithm that can assign a Gleason grade to clinical whole slide images (WSIs) remains elusive. Machine learning technology is currently not able to replace pathologists but can serve as an important safeguard against misdiagnosis., The adoption of whole slide image (WSI) scanners in clinical practice was accelerated by US Food and Drug Administration approval in 2017, which allowed primary pathologic diagnoses to be made [...]

We propose an approach for Bayesian inference in time-varying structural vector autoregressions (SVARs) identified with sign restrictions. The linchpin of our approach is a class of rotation-invariant time-varying SVARs in which the prior and posterior densities of any sequence of structural parameters belonging to the class are invariant to orthogonal transformations of the sequence. Our methodology is new to the literature. In contrast to existing algorithms for inference based on sign restrictions, our algorithm is the first to draw from a uniform distribution over the sequences of orthogonal matrices given the reduced-form parameters. We illustrate our procedure for inference by analyzing the role played by monetary policy during the latest inflation surge. JEL classification: C11, C51, E52, E58 Key words: time-varying parameters, structural vector autoregressions, identification, 1 Introduction Structural vector autoregressions (SVARs) featuring time-varying parameters are commonly used in empirical macroeconomics to study a wide range of classical questions such as the economic consequences of policy [...]

This paper introduces the geometric distribution in the context of neutrosophic statistics. The research outlines the essential properties of this new distribution and introduces novel algorithms for generating imprecise geometric data. The study explores the practical applications of this distribution in the industry, highlighting differences in data generated under deterministic and indeterminate conditions using detailed tables, simulation studies, and real-world applications. The results indicate that the level of uncertainty has a substantial impact on data generation from the geometric distribution. These findings suggest updating classical statistical algorithms to better handle the generation of imprecise data. Therefore, decision-makers should exercise caution when using data from the geometric distribution in uncertain environments. [ABSTRACT FROM AUTHOR]

Breathing pattern assessment holds critical importance in clinical practice for detecting respiratory dysfunctions and their impact on health and wellbeing. This systematic literature review investigates the efficacy of inertial sensors in assessing adult human breathing patterns, exploring various methodologies, challenges, and limitations. Utilizing the PSALSAR framework, incorporating the PICOC method and PRISMA statement for comprehensive research, 22 publications were scrutinized from the Scopus, Web of Science, and PubMed databases. A diverse range of sensor fusion methods, data signal analysis techniques, and classifier performances were investigated. Notably, Madgwick's algorithm and the Principal Component Analysis showed superior performance in tracking respiratory movements. Classifiers like Long Short-Term Memory Recurrent Neural Networks exhibited high accuracy in detecting breathing events. Motion artifacts, limited sample sizes, and physiological variability posed challenges, highlighting the need for further research. Optimal sensor configurations were explored, suggesting improvements with multiple sensors, especially in different body postures. In conclusion, this systematic literature review elucidates methods, challenges, and potential future developments in using inertial sensors for assessing adult human breathing patterns. Overcoming the challenges related to sensor placement, motion artifacts, and algorithm development is essential for progress. Future research should focus on extending sensor applications to clinical settings and diverse populations, enhancing respiratory health management. [ABSTRACT FROM AUTHOR]

In today's interconnected world, teamwork and collaboration are becoming essential competencies across all disciplines. This review examines various pedagogical strategies aimed at nurturing these skills, with a specific focus on integrating algorithms into educational practices. While traditional approaches classify teamwork strategies as either instructor-led or student-led, this review introduces a third method that is based on ML algorithms, which are promising methods for optimizing team composition based on both static and dynamic student characteristics. We investigate the effectiveness of these algorithms in enhancing collaborative learning outcomes compared to conventional grouping methods. In fact, this review synthesizes the findings from 20 key studies on the implementation of these technologies in educational settings, evaluating their impact on learning outcomes, student motivation and overall satisfaction. Our findings suggest that computer-enhanced strategies not only improve the academic and collaborative experience but also pave the way for more personalized and dynamic educational environments. This review aims to provide educators and curriculum developers with comprehensive insights into leveraging advanced computational tools to foster effective teamwork and interdisciplinary collaboration, thereby enhancing the overall quality of education and preparing students for the collaborative demands of the professional world. [ABSTRACT FROM AUTHOR]