Additivity Rule Research Articles

Exploring the interaction of alloying elements in modelling T0, Bs and Ms temperatures

Thermodynamic [Formula: see text] temperatures have been analysed for various Fe systems with additions of C, Mn, Si, Cr, Mo, Ni and Al. The findings indicate that the additivity rule is inadequate to accurately describe [Formula: see text] of ternary and higher order systems. To address this issue, a novel method accounting for the interaction of alloying elements was introduced and validated using 860 data points. The study also explored the interaction of alloying elements in modelling [Formula: see text] and [Formula: see text] temperatures, but no conclusive evidence was found due to high uncertainty in the experimental data. The models developed based on the additivity rule can accurately describe [Formula: see text] and [Formula: see text] for a wide range of alloying concentrations, demonstrating a significant improvement compared to benchmark models.

Guidance for reporting 2024 data on zoonoses, foodborne outbreaks and antimicrobial resistance

Abstract This technical report provides guidance for reporting countries on how to submit their isolate‐based quantitative antimicrobial resistance data, and data on the prevalence of zoonoses and microbiological agents and contaminants in food, food‐borne outbreaks, animal populations and disease status. To collect these data, EFSA has created the Data Collection Framework (DCF) application. This report includes data dictionaries to guide the reporting of information derived from 2024 under Directive 2003/99/EC, Regulation (EU) 2017/625, Commission Implementing Regulation (EU) 2019/627 and Commission Implementing Decision (EU) 2020/1729. The objective is to explain in detail the individual data elements of the EFSA data models that are to be used for XML data submission through the DCF. In particular, it explains the data elements to be reported, including information about the data type, a reference to the list of permitted terms and any additional business rules or requirements that may apply.

Dynamic Question Paper Generation using Bloom and NLP based Heterogeneous Feature Extraction and Machine Learning Techniques

Automatic question generation is a very difficult task in the field of Natural Language Processing (NLP) and Machine Learning (ML). The procedure requires bidirectional language processing, including both Natural Language Understanding to comprehend the input text and Natural Language Generation to produce queries in textual form. This article presents our system for generating factual inquiries from unstructured English text. The strategy used combines conventional linguistic methodologies that rely on sentence structures with several machine learning techniques. Initially, we extract lexical, syntactic, and semantic data from the input text. Subsequently, we create a hierarchical collection of patterns for each phrase. The characteristics are derived from the patterns and then used for the automatic acquisition of new transformation rules. The learning technique we use is entirely data-driven, since the transformation rules are derived from a collection of initial sentence-question pairings. The benefits of this technique are twofold. Firstly, it allows for the straightforward addition of additional transformation rules, enabling the generation of many sorts of questions. Secondly, the system may be continuously enhanced via bloom algorithm with collaboration of NLP and machine learning. The framework furthermore has a question assessment module that assesses the calibre of created questions. It functions as a filter to discern the most optimal inquiries while removing erroneous or duplicate ones. We conducted many tests to assess the accuracy of the produced questions and also compared our system with other cutting-edge technologies. Our findings recommend that the generated queries surpass the performance of existing algorithms and are on par with questions produced by people. In addition, we have developed and released an interface that includes all the datasets we have produced and the questions we have analysed.

Seismic response of monopile-supported wind turbine systems: combination laws for kinematic and inertial interaction

The dynamic soil structure interaction imposes both kinematic and inertial interactions on monopile-supported wind turbines. The two effects can be calculated individually, but there is no clear guidance in the current design standard for combining them. A theoretical study using the finite element method to model a simplified single degree of freedom system has been conducted to analyse the phase relationship between the two effects and obtain the combination laws through parametric studies using the enhanced hysteresis control soil constitutive model. The results show that when the fundamental frequency of the soil is smaller than the superstructure, an in-phase relationship is observed. Monopile’s bending moments from the two loadings have different signums. Considering the inertial loading alone with an additional 5% modification is sufficient to capture the maximum bending moment and the profile. When the natural frequency of the soil is larger than the superstructure, an out-of-phase relationship is observed. The kinematic loading has a significant domination on the lower part of the pile. The worst-case scenario can be obtained when both loadings are combined using the simple addition rule. These findings enable engineers to combine two loadings in a simple manner, avoiding performing the lengthy dynamic time-history numerical analysis and facilitating an efficient design.

Usability and optimization of online apps in user's context.

The OptiFlow framework introduces a novel approach for enhancing usability evaluation and optimization known as OptiFlow. This framework combines heuristic evaluation with a web-based platform to provide a comprehensive method for assessing and optimizing user experiences in online applications. The architecture of OptiFlow incorporates key components, including the user, website, web service, and library, enabling seamless interaction and data exchange. A set of 240 usability guidelines, derived from a multidisciplinary expert collaboration, are systematically categorized into 15 usability categories, aligned with established design principles. Guidelines within OptiFlow are assigned implementation levels: "Green" for easily implementable guidelines, "Amber" for moderately complex ones, and "Red" for highly complex guidelines. These levels prioritize tasks based on complexity and feasibility. The framework's integration of guidelines into a structured SQL database simplifies implementation challenges, and the "execute" function systematically assesses website adherence to guidelines, resulting in True, False, or Null outcomes. Usability assessment outcomes are presented through categorized and prioritized data views for each implementation level, allowing stakeholders to address high-priority concerns efficiently. The OptiFlow framework represents an innovative approach to usability evaluation, fostering enriched user experiences and finely tuned digital interfaces. Future advancements may include additional rule types and the integration of advanced technologies for tackling intricate usability challenges. Ultimately, OptiFlow paves the way for proactive user experience enhancement and digital interface optimization in an ever-evolving digital landscape.

Polynomial Regression-Based Predictive Expert System for Enhancing Hydraulic Press Performance over a 5G Network

In industrial applications, hydraulic presses maintain workloads by controlling the hydraulic cylinder to extend and retract, ensuring optimum tracking performance in terms of position and force. Dealing with nonlinear and multinode systems, such as hydraulic systems, often requires an advanced approach that frequently includes machine learning and artificial intelligence methods. Introducing an adaptive control system to significantly improve the response of hydraulic presses is a challenge. Therefore, a polynomial regression model predictive control (PR-MPC) mechanism is proposed in this paper to compensate for external disturbances such as the forming processes and friction dynamics. Using polynomial regression modeling and least squares optimization, the approach produces highly accurate data-driven models with an R2 value of 0.948 to 0.999. The simplicity of polynomial regression facilitates the integration of smart algorithms into an expert system with additional decision-making rules. Remote adaptive control integrated within a 5G network is based on I 4.0 distributed system guidelines that provide insights into the behavior of the hydraulic press. The results of real-time experiments have shown that the PR-MPC mechanism integrated into the expert system reduces the absolute response error of the hydraulic press by up to 98.7% compared to the initial control system with a PID regulation.

More Liberty, More Rules? Abortion Policy and the Liberalization-Rule Nexus

While conventional wisdom often equates liberalization with deregulation, theoretical arguments from different scientific backgrounds suggest that states tend to increase their rule stock when they adopt more liberal policies. This article studies the case of abortion policy to better understand the liberalization-rule nexus. By examining data from 25 countries over 50years, we empirically assess the dynamics between liberalization events and corresponding regulatory changes. Our findings reveal that while the majority of countries does indeed introduce more rules upon liberalizing abortion, the extent of these regulatory changes varies considerably. The analysis suggests that the extent of rule growth depends on the conflicts that characterize liberalization processes in particular countries and time periods. Right-leaning governments, religious groups and institutional veto players are shown to have a positive influence on rule growth. On the contrary, higher levels of liberalization reduce the necessity for additional rules. By illuminating a contested empirical relationship, the article helps to better understand the implications of liberalization in modern democracies.

Generating complex networks through a vertex merging mechanism: Empirical and analytical analysis

One of the most well-known mechanisms contributing to the emergence of networks with a power-law degree distribution is preferential attachment. In this study, we examined a family of network evolution models based on the merging of two arbitrary vertices, which is shown to also lead to the creation of power-law distributed networks. These models simultaneously apply rules for both node addition and merging, which reflects that many real systems exhibit the processes of growth and shrink. At each iteration, when two vertices merge, the neighbors of one of the vertices become neighbors of the other, and the vertex itself is removed from the network. In addition, at each iteration, a new vertex appears that is attached to randomly selected nodes. As an enhancement, we incorporate a triadic closure mechanism into the evolution to increase the clustering coefficient, a key characteristic of real social networks. We show that in the process of evolution any initial network converges to a stationary state with a power law degree distribution, while the number of edges, the average degree, and the average clustering coefficient saturate to a certain limit values depending on the model parameters.

Study of electron interaction with halogenated anaesthetic molecules in gas phase at 50 eV electron energy

Abstract This study addresses the environmental concerns associated with halogenated anesthetic gases, specifically halothane, sevoflurane, and isoflurane, due to their contributions to global warming and ozone depletion. Using a combination of experimental and theoretical approaches, we analyzed elastic electron scattering properties of these compounds in the gaseous phase at an electron energy of 50 eV. The experimental data were gathered using a crossed-beam setup equipped with an electron gun and channeltron detection, with argon serving as the reference gas in a relative-flow method to calibrate the absolute cross-section measurements. Theoretical calculations were performed using the Independent Atom Model with Screening Corrected Additivity Rule (IAM-SCAR+I), which includes interference effects to compare with experimental results effectively. These findings enhance understanding of the scattering dynamics of these anesthetics, highlighting their atmospheric implications and potential environmental impacts.

Variable droop gain frequency supporting control with maximum rotor kinetic energy utilization for wind-storage system

To address the emerging frequency stability issue brought by the large replacement of synchronous generators with renewable generations, wind turbine generators are required to possess frequency-supporting capability. However, existing frequency-supporting control strategies lack the assessment of the frequency support capability of wind turbine generators, leading to degraded control performance under various situations. Aiming to solve this problem, this paper proposes a variable-droop-gain control for wind turbine generators with maximum rotor kinetic energy utilization. Firstly, an analytical relationship was established between droop gain, disturbance scale, and rotor speed. Subsequently, the released energy of the wind turbine generator is evaluated, which equals the difference in the rotor kinetic energy under the initial and the post-disturbance steady-state rotor speed. It was proved that the released kinetic energy cannot exceed a certain proportion of total rotor kinetic energy. Accordingly, a variable initial gain scheme is proposed, which determines the initial droop gain as per the disturbance scale for maximizing the kinetic energy release of wind turbines. Moreover, an additional real-time droop gain adjustment rule is added to prevent the over-deceleration of wind turbines. The simulation results show that the proposed scheme may provide the maximum KE release and effectively improve the system frequency nadir while ensuring the safe operation of wind turbine generators.

Fuzzy Method for Preliminary Classification of the Micro-Electromechanical Systems Data

Data of micro-electromechanical systems (sensors) are widely used in modern multipurpose intellectual environments, autonomous mobile robots and automated systems possessing intellectual components. Since the amount of data delivered by the sensors is rather considerable and their significance varies we propose a fuzzy method of the data preliminary classification aimed at selection of the data important for the planning and control decisions while meaningless data or data that do not contain new information are discarded. As a result the amount of the resource intensive intellectual processing and the workload in the network infrastructure could be reduced significantly. The paper presents the analysis for the preliminary classification problem, the abstract model of a sensor and classification method. The method is based on the delay which is controlled by multiplicative decrease additive increase rule and expresses the expectation of the context uncertainty. The delay decreases if the variables measured by the sensor presumably change and increases if it keep its value. Numerical examples are presented as well. They consider the set of mount chest accelerometer uncalibrated measurements. We compare source data set and set of the selected values formed by an algorithm implementing fuzzy preliminary classification. Several metrics were used to compare the selected values with the source data and with CMA filter as well.

Speed of sound measurements of binary n-octane+ ethylcyclohexane mixture at liquid-gas phase transition curve

Bio-jet fuel is a key element in the aviation industry to reduce operating costs and environmental impacts. Bio-jet fuel is a complex mixture of four hydrocarbons (n-alkanes, isoalkanes, cycloalkanes and aromatics). In the present work, the speed of sound in pure n-octane, ethylcyclohexane, and their mixtures with six selected compositions of (0.3004, 0.4191, 0.4999, 0.5538, 0.6991, and 0.7852 mole fraction of ethylcyclohexane) has been measured along the l-G saturation curve in the temperature ranges from (286 to 443) K using the pulse method with a constant (acoustic) sounding base. The combined expanded absolute and relative uncertainties (0.95 level of confidence, k = 2) of the temperature, concentration, and speed of sound measurements are estimated to be 20 mK, 0.0006 mole fraction, and 0.2 %, respectively. The measured speed of sound data together with our previous reported density data for the pure component (ethylcyclohexane) and the mixture were used to calculate derived thermodynamic properties, such as isentropic compressibility kS and heat capacity ratios CPCV as a function of temperature along the l-G saturation curve for the pure components and the mixture for selected concentration of x = 0.8 mole fraction of ethylcyclohexane. The deviation of the measured speed of sound data for the mixture from the linear additive rule has been determined using the pure component data.

Theoretical basics of the self-learning system of intelligent locomotive decision support systems

Analysis of works in the field of artificial intelligence allows to make an assumption that today there is a sufficiently developed theoretical basis for the development of intelligent control systems for locomotive control. This will minimize the risks associated with the human factor on the railways. The paper presents the theoretical rationale for the development of a knowledge base for intelligent locomotive control systems. The approach and structure of the self-learning system of intelligent DSS is proposed, the advantage of which is the presence of a fuzzy classifier that works according to the set criteria and determines a fuzzy image of the current train situation. Learning a fuzzy classifier consists in finding a vector K that minimizes the distance between the results of logical inference and experimental data from the sample. The knowledge base is implemented using linguistic variables formalized by methods of fuzzy logic. The use of linguistic values makes it possible to design the base using the usual language of communication, which greatly simplifies both the design process itself and the analysis of the system's performance. Also, the knowledge base has the possibility of constant self-improvement. This happens in two ways. The first is by adding new rules to the knowledge base in case the current situation does not match the existing ones in the base, in which case an additional rule is created and checked for adequacy. The second way is a mechanism for ranking rules in the knowledge base. If the control action of the locomotive driver coincided with the recommendation of DSS in the current situation, then the rating of this recommendation (rule) increases, and in the future the rule selection algorithm will choose one or another control action for the current situation that has the highest rating (that is, it has already been verified several times person). The experiment has shown that the use of intelligent DSS has positive results. On average, the DSS made the correct train control decisions faster than the locomotive driver.

Improving Performance of Cluster Heads Selection in DEC Protocol Using K-Means Algorithm for WSN.

Wireless sensor networks (WSN) have found more and more applications in remote control and monitoring systems. Energy management in the network is crucial because all nodes in the WSN are energy constrained. Therefore, the design and implementation of WSN protocols that reduce energy depletion in the network is still an open scientific problem. In this paper, we propose a new clustering protocol that combines DEC (deterministic energy-efficient clustering) protocol with K-means clustering, called DEC-KM (deterministic energy-efficient clustering protocol with K-means). DEC is a very energy-efficient clustering protocol that outperforms its predecessors, such as LEACH and SEP. K-means ensures more effective clustering and shorter data transmission distances within the network. The shorter distances improve the network's lifetime and stability and reduce power consumption. Additional heuristic rules in DEC-KM ensure improved cluster head selection, taking into account node energy level and position and minimising the risk of premature cluster head exhaustion. The simulation results for the DEC-KM protocol using MATLAB show that cluster heads have shorter distances to nodes in cluster areas than for the original DEC protocol. The proposed protocol ensures reduced energy consumption, outperforms the standard DEC, and extends the stability period and lifetime of the network.

Look Beyond Additivity and Extensivity of Entropy for Black Hole and Cosmological Horizons.

We present a comparative analysis of the plethora of nonextensive and/or nonadditive entropies which go beyond the standard Boltzmann-Gibbs formulation. After defining the basic notions of additivity, extensivity, and composability, we discuss the properties of these entropies and their mutual relations, if they exist. The results are presented in two informative tables that are of strong interest to the gravity and cosmology community in the context of the recently intensively explored horizon entropies for black hole and cosmological models. Gravitational systems admit long-range interactions, which usually lead to a break of the standard additivity rule for thermodynamic systems composed of subsystems in Boltzmann-Gibbs thermodynamics. The features of additivity, extensivity, and composability are listed systematically. A brief discussion on the validity of the notion of equilibrium temperature for nonextensive systems is also presented.

Evaluating non-linear optical behavior in ionic liquids: Combining ab-initio polarizability with additivity rule

In the field of non-linear Optics, the additivity rule, which both experimental and theoretical researchers utilize, provides a simple and reliable method for calculating polarizability. However, this model neglects crucial factors, such as the solvent, the interaction distance, and external optical fields, leading to inaccurate results. In this study, the role of these parameters on the non-linear optical behavior of the ionic liquid BMIM+⋯BF4−, a system extensively studied for its intriguing non-linear optical properties, is investigated. To do that, first-principles calculations based on the density functional theory are developed; the Lennard-Jones potential is used as the reference model. The results of this study reveal the significant influence of the solvent on the second- and third-order non-linear optical responses, which are strongly affected by ion interactions, underscoring the limitations of the additivity rule in terms of molecular polarizability. The EFISHG values for each solvent are inversely proportional to the dielectric constant; for all considered solvents, it observed a clear dependence of polarizability values on the external field, ranging from 1.675 to 1.638× 10−23 esu. It was also found high sensitivity of the static polarizability to changes in the interaction distance and the solvent choice, ranging from 1.57 to 2.05× 10−30 esu, while considering the additivity rule, this parameter changed to 1.59× 10−30 esu. This information can serve as a reference for both theoretical and experimental researchers.

Impact of implicit vs. explicit Instructions on tactical performance in under-20 soccer players.

This study aimed to verify whether implicit and explicit informational constraints generate differences in tactical performance and behavior in U-20 national-level soccer players. Thirty-two under-20 male athletes from two clubs participated. Four 4-a-side small-sided games (SSG) protocols were used: R1 - explicit rule for high-press marking, R2 - implicit rule for high-press marking, R3 - game with both previous rules simultaneous and FR - free game, without additional rules. SSGs comprised 4 vs. 4+ goalkeepers games on a 42 m × 29 m field. Position data from 10 Hz Global Positioning System (GPS) devices were used to evaluate individual and collective tactical behavior (spatial occupation metrics) and performance (interpersonal coordination). MANOVA was used for normally distributed variables, and Friedman's with Dunn or Bonferroni post hoc was used for variables without normal distribution. For SEI, an ANOVA was used with Bonferroni post hoc. The R1 protocol showed higher SEI, length, width, and LpWratio than the FR protocol (p = 0.009). There was an effect of the different protocols in SEI values (p < 0.001). Under the explicit rule, players also showed the highest in-phase interpersonal coordination values (p < 0.001). Providing players with explicit tactical instructions improves tactical performance acutely for high-pressing defensive actions.

Rapidly destructive hip osteoarthritis: a diagnosis not to miss.

This review illustrates the imaging features of rapidly destructive hip osteoarthritis (RDHO) across different imaging modalities. RDHO is a rare joint disease of unknown aetiology resulting in rapid deterioration of the hip joints. Patients often present with severe hip pain and can progress to complete joint destruction in 6 months to 3 years causing disability. Since its clinical and imaging features often overlap with other conditions, the diagnosis is easily missed in daily practice. The purpose of this paper is to outline the characteristic imaging features of RDHO, aiming to enhance awareness of this disease entity and promote timely treatment for patients. Sequential radiographs are particularly important in demonstrating the rapid progression of radiographic findings and with the early recognition of characteristic features, diagnosis can be made to avoid treatment delay. Although CT scan is not necessary for diagnosis, it is useful in evaluating the extent of joint destruction and guiding surgical planning. MRI can provide additional information and rule out other pathologies with similar symptoms. Overall, radiographs, CT and MRI are common modalities used in the evaluation of RDHO.

Stepwise Construction and Integration of Ecological Network in Resource-Based Regions: A Case Study on Liaoning Province, China

Ecological networks are an effective strategy to maintain regional ecological security. However, current research on ecological network construction in areas with large-scale resource extraction is limited. Moreover, classic ecological network construction methods do not perform satisfactorily when implemented in heavily damaged mining landscapes. Taking the example of Liaoning Province, China, a framework for stepwise renewal of ecological networks was proposed, which integrates basic ecological sources and other sources that include mining areas. The framework was based on multi-source ecological environment monitoring data, and all potential ecological sources were extracted and screened using an MSPA model and the area threshold method. Further, ecological sources were classified into two types and three levels based on the influence of abandoned mines and the characteristics of ecosystem services in the ecological sources. Ecological corridors were extracted using the MCR model. An ecological corridor optimization process based on combining the gravity model with addition and removal rules of corridors was proposed. The results indicated that the basic ecological network in Liaoning Province included 101 ecological sources and 162 ecological corridors, and the supplementary ecological network included 28 ecological sources and 67 ecological corridors. The ecological sources were divided into two types, and corridors were divided into three types. The basic ecological network exhibited a spatial distribution of discrete connections in the west and close connections in the east. Changes in ecological network topological indicators indicated that a supplementary ecological network strengthened the structural performance of the regional ecological network, expanding spatial coverage, filling hollow areas, and enriching local details of the regional ecological network. Regulation strategies were proposed for ecological sources with different connection modes. The number of ecological sources implementing restrictive development, pattern optimization, and protective development were 101, 12, and 16, respectively. This paper provides a constructing framework of ecological networks adapted for resource-based regions. This method can support decisions for the environmental governance of mines, thus contributing to a balance between resource exploitation and ecological protection in regions.

IMPLEMENTASI PERATURAN PRESIDEN NO 72 TAHUN 2021 TENTANG PERCEPATAN PENURUNAN STUNTING DI KABUPATEN KEPULAUAN MENTAWAI

This research aims to determine the implementation of Presidential Regulation No. 72 of 2021 concerning the Acceleration of Reducing Stunting in the Mentawai Islands Regency, especially in the Betumonga area. This research is motivated by stunting problems which are increasing in the Mentawai Islands Regency, especially in the Betumonga Area. This research uses qualitative methods by conducting field surveys and interviews with related parties such as service staff and local service heads. Based on the results of interviews using the theory presented by Marile a Grindle, the results obtained from the implementation of Presidential Decree number 72 of 2021 concerning the acceleration of stunting reduction in the Mentawai Islands Regency in the Betumonga area can be said to have been implemented well out of a total of 105 achievement indicators in Presidential Decree number 72 of 2021, 27 indicators which is not fulfilled properly and there are only 80 indicators that are fulfilled. achievement indicators that have not met targets. 27 indicators that have not been met, several indicators that are not yet close to the development line and are not in line with the targets for 2024 in the Betumonga area, the government has made efforts to overcome the problem of stunting, but the community still sometimes does not want to accept the reality and some still refuse to handle stunting prevention. The research results show that this incident is caused by the need for sustainable programs, especially in remote areas that are difficult to reach due to their wide reach so that access to transportation is limited. The implementation of Presidential Decree number 72 of 2021 in the Mentawai Islands Regency requires additional rules or efforts in handling stunting cases related to the determinant factors that cause stunting for families at risk of stunting, especially in special cases such as the Betumonga area as well as additional regulations for preventing stunting.