Random Factors Research Articles

Seeds in the guts: can seed traits explain seed survival after being digested by wild ungulates?

Plants inhabiting open landscapes are often dispersed by ungulates and are expected to be adapted to this type of dispersal through their seed traits. To find which traits help seeds survive the passage through digestion of wild ungulates, we conducted a comprehensive feeding experiment with almost forty species of plants and three species of ungulates. We fed specified numbers of seeds to the animals, collected the dung, and germinated the dung content. We explored whether seed morphological traits and seed nutrient contents are good predictors of seed survival after passage through the ungulate digestive system. We also tested how the seed survival differed after the passage through different ungulate species. To find answers, we used GLMM with beta-binomial distribution and animal and plant species as random factor, respectively. We found that species survival and germination success were negatively correlated to seed elongation and the thickness of the seed coat. Even though phylogenetically correct GLMM did not yield significant results, when we tested species from commonly represented families, separately (legumes and grasses compared to all other species) different traits had statistically significant effects. In the case of seed elongation, the effect changed direction from negative to positive when legumes and grasses were left out. Our results suggest that seed traits enabling species survival after passage through the digestive tract are strongly phylogenetically conserved and different groups of plants evolved different ways of adapting to grazing pressure and utilize it for dispersal.

Resilience assessment of trade network in copper industry chain and the risk resistance capacity of core countries: Based on complex network

Copper is a critical mineral for the energy transition, facing significant risks of supply disruptions. To explore the copper industry chain's resistance to such risks, this article constructs a trade network for the global copper industry chain based on complex network methods, utilizing 2022 data on copper ores and concentrates, blister copper, refined copper, copper semis, and copper waste and scrap. It assesses the industry's resilience from both static and dynamic perspectives, and analyzes the risk resistance capacity of core countries. The results indicate that: (1) in the copper industry chain, copper semis, copper waste and scrap exhibit a relatively high level of overall resilience, but copper ores and concentrates, blister copper, and refined copper display a lower level of overall resilience. This indicates that the upstream of the copper industry chain is more susceptible to external factors than that of the downstream. (2) Targeted factors such as economic sanctions and trade disputes have a more serious impact on the copper industry chain than random factors such as natural disasters and transportation interruptions. Meanwhile, targeted factors have a stronger impact on the transmission efficiency and connectivity of the network. (3) There are significant communities in the copper industry chain, with some core countries playing an important role in connecting the network. In terms of connectivity, influence, and control, developed countries represented by the United States and the United Kingdom are stronger than the other two groups; emerging developed countries like Japan and South Korea exhibit lower propagation and intermediary capacities; emerging industrialized countries represented by Brazil and South Africa fall somewhere between the first two. Additionally, China and India demonstrate relatively important influence within the copper industry chain. Finally, this article provides policy recommendations for maintaining the stability of the copper industry chain and enhancing its resilience.

Predicting the flux of various volatile fatty acids mixtures in a membrane distillation

This study aims to predict the initial flux of various volatile fatty acids (VFAs) mixtures in direct contact membrane distillation (DCMD), focusing on understanding the interaction between the acids and improving a previous model to better suit the VFAs mixtures. The DCMD experiments were conducted under conditions of feed temperatures of 40, 50, and 60 °C, pH conditions of 3, 4.8, and 6, and VFA concentrations within the feed of 1000–4000 mg/L. The experiments investigated the influence of acids in the VFAs mixture. It was verified whether the ionization fraction of VFAs, even in multi-acid solutions, is proportionate to the flux. The correction coefficient and the overall mass transfer coefficient were introduced to enhance the model that previously used an empirical equation. The correction coefficients were derived, reflecting the interaction between acids and the hydrophobic membrane in the VFAs mixtures. A random factor experiment was conducted to verify the accuracy of the prediction model. Additionally, the sum of each VFA transport can be estimated and expressed using a common water quality measure, chemical oxygen demand. The modeling work can provide further insights into the relationship among the flux, ionization, hydrophobicity, and interactions among acids.

Saponin Extracts Utilization as Dietary Additive in Ruminant Nutrition: A Meta-Analysis of In Vivo Studies.

The present meta-analysis aimed to determine the underlying effects of different saponins extracted from different sources on the production performance, milk yield, digestibility, rumen fermentation, blood metabolites, and nitrogen utilization of ruminants. A total of 26 papers comprising 66 in vivo studies (148 data points of dietary treatments) were evaluated in the present study. The databases were statistically analyzed using the mixed model procedure of SAS, where experiments considered random effects and tannin-related factors were treated as fixed effects. Statistical procedures were then continued in comparing different sources of saponin extract through Mixed Model analysis, where experiments were also random factors and sources of saponin extract were fixed factors. The evidence revealed in the present meta-analysis that saponin supplementation of up to 40 g/kg DM appears to have no detrimental impact on feed intake across ruminant types, suggesting that it does not significantly affect diet palatability. However, the results indicated that there are species-specific responses to saponin supplementation, particularly in relation to palatability and nutrient absorption efficiency, with larger ruminants being better able to tolerate the bitterness induced by saponin extracts. Furthermore, the study found that saponin extracts can influence nutrient digestibility and rumen fermentation dynamics, with different effects observed in large and small ruminants. While some saponin extracts can enhance average daily weight gain and milk yield, others can have adverse effects, highlighting the importance of considering both saponin sources and animal physiological condition when developing nutritional strategies. Additionally, optimization of ruminant production by utilizing saponin extracts is necessary to avoid negative health implications, such as increased blood creatinine levels. Different saponin extracts utilization in ruminant nutrition and environmental management, have a distinct understanding associated to their various bioactive properties. However, among the saponin sources, saponin extracted from Quilaja saponaria is more likely to improve large ruminant production performance while maintaining ruminant health and metabolism, but negatively affect small ruminants. Further research is needed to unravel the intricate effects of different saponin sources on ruminant health and productivity, emphasizing the importance of tailored dietary strategies that consider the unique physiological and metabolic characteristics of the target livestock.

Advanced VTCDREM for dynamic reliability evaluation of railway systems: Integration of fully probabilistic track irregularities and multifaceted random factors

Advanced VTCDREM for dynamic reliability evaluation of railway systems: Integration of fully probabilistic track irregularities and multifaceted random factors

DID-HVC-based Web3 healthcare data security and privacy protection scheme

In response to the current situation that Web2 Internet medical giants monopolize data elements and deprive medical users of their data rights and interests, self-sovereign identity(SSI) in the open environment of “Web3+Medicine” will make it possible for users to control and manage their e-medical data independently. However, SSI is a new identity management paradigm, and in the face of frequent interactions between identity credentials and application services, the existing research schemes have a coarse granularity of identity privacy protection, so there is an urgent need for attestation methods that selectively minimize the disclosure of identity attributes. In this study, we propose a SSI attribute selectively disclosable scheme based on a decentralized identifiers-hash verifiable credentials (DID-HVC) and design a model, that enables medical users not only to selectively minimize the disclosure of identity attribute data using verifiable credentials (VCs) with hash claims and combines with DID incorporating random obfuscation factors, to prevent overexposure of personal and healthcare privacy information, but also to safeguard users’ VCs are not linked and analysed. Finally, we provide a security and practicality analysis of our scheme and do simulation experiments based on Ethernet. The experimental results show that our scheme can more securely solve the problem of excessive privacy leakage of medical users within a feasible timeframe.

Development of a model for controlling chemical oxygen consumption in pulp production

An approach to the development of a model for controlling chemical oxygen consumption in pulp production based on fuzzy logic is proposed. Since it is not satisfactory to formalize the pulp cooking process strictly mathematically due to the presence of a large number of random factors causing dynamic disturbances of the continuous process, a class of models has been selected that are intuitive to humans and the use of which is not difficult to teach.

Uterine tone influences fertility of Merino ewes following laparoscopic artificial insemination

Artificial insemination (AI) plays a critical role in facilitating rapid genetic and production gains within the sheep industry. However, variable rates of AI success remain a concern for the industry and a barrier to adoption. Furthermore, the degree to which female factors influence the success of intrauterine laparoscopic AI rather than natural mating remains unknown. As such, this study investigates the effect of several factors collected during the time of AI, on the success of intrauterine laparoscopic AI. Data was generously donated by artificial breeding companies and stud breeders during routine commercial AI operations. AI data was collected over 3 breeding seasons during commercial AI programs (N = 24 programs) using Merino ewes (N = 24,700). Sire ID (N = 253), time of AI following progesterone removal (approx. 43–59 h post removal), uterine tone and intra-abdominal fat (both scored 1–5) as well as age of the ewe were all recorded at the time of AI. Transcutaneous ultrasound subsequently determined pregnancy rate approximately 55 days post-AI. A multivariate regression analysis was performed and revealed pregnancy success to increase when semen was inseminated into a ewe with a uterine tone score of 4 or 5 (P < 0.001). The remaining factors fell short of significance within the multivariate model. An interclass coefficient variation matrix was also used to determine the proportion of variation contributed to AI success by random factors allocated in the model; site, sire, AI date and breeding season (45.99 %, 29.94 %, 15.15 % and 8.92 %, respectively). These results highlight the influence of uterine tone on ewe fertility following laparoscopic AI, but also that program location and the sire used can further modify this influence on pregnancy rate. These factors must now be considered in combination with semen factors per individual sire used during AI to ascertain the contribution of several factors to the success of laparoscopic AI in Australia.

An Improved Slack Based Measure Model for Evaluating Green Innovation Efficiency Based on Asymmetric Data

Nowadays, one of the main challenges facing green innovation management is how to enhance the performance of innovation processes by utilizing asymmetric input and output data. Therefore, this paper develops an improved SBM model analysis framework for evaluating the green innovation efficiency of asymmetric input and output data. The framework is applied to assess the technical (TE), managerial (PTE), and scale (SE) efficiencies of new energy companies under three input variables (R&amp;D personnel input, R&amp;D capital input, and comprehensive energy consumption input), two desirable output variables (green technology output and economic output), and one undesirable output variable (greenhouse gas emissions). Then, environmental factors and random factors are eliminated from the obtained input slack variables based on the SFA model, placing decision-making units in a homogeneous environment. The results demonstrate that TE, PTE, and SE are improved after eliminating environmental factors and random factors. Subsequently, based on the entropy method, this paper classifies companies’ green innovation patterns into four categories and provides targeted solutions. The purpose of this paper is to provide an evaluation method for new energy companies to understand green innovation efficiency and assist decision makers in identifying the most optimal resource allocation approach. The proposed improved SBM model contributes to the literature and to industry practice by (1) providing a reliable evaluation of green innovation efficiency under asymmetric input and output data; (2) determining effective improvement actions based on a slack analysis of environmental variables and random variables that lead to improved process performance; and (3) making fuzzy innovation performance efficient to facilitate understanding and managing innovation resource allocation quality.

Effectiveness of Higher Education Financing: DEA and SFA Modelling

The article aims to assess the effectiveness of financial expenditures in higher education for 31 provinces in China, namely overall, technical and scale efficiency, and to determine whether the returns to scale are increasing or decreasing for each province. The main method used is the three-stage DEA analysis / Data Envelopment Analysis, and software DEAP2.1 and Frontier 4.1 are used for modelling. Financial expenditures on higher education form an input parameter in the DEA model. The output parameters (determining the efficiency of higher education) are the number of students, the number of full-time teachers and the new value added to fixed assets in higher education institutions. The modelling was carried out for 2008-2013 and 2014-2018 (the COVID-19 pandemic is not considered to avoid uncommon trends and unpredictable disturbances in the model). A comparison of the analysis results for these periods allowed the identification of new trends and patterns. The study takes into account three main groups of parameters: 1) endogenous (inefficiency of internal management processes); 2) exogenous (gross regional product per capita of each province in China; the extent of university clustering, which is determined by political and market decisions and does not depend on educational institutions; and the degree of fiscal decentralisation, which is estimated in the article as the ratio of local and state budget expenditures per capita, taking into account the distribution of total budget expenditures for each district and the total population of the respective territory); 3) random disturbances. At the first stage of the study, a BCC model focused on inputs (to assess the technical efficiency of financial expenditures on higher education in different provinces) was built. It does not consider exogenous factors and random disturbances. In the second stage, SFA (stochastic frontier analysis) was applied to account for the impact of all three groups of factors (endogenous, exogenous and statistical noise) on the efficiency of financial expenditures in higher education for each province in China. In the third stage, the adjusted and standardised input and output parameters are incorporated into the BCC models, and the actual value of efficiency caused by exogenous parameters and the influence of random factors is determined. A comparison of the results of each modelling stage for each stage in the study and each province in China allowed the establishment of how the influence of the determinants of higher education financing efficiency changed in chronological and spatial contexts.

Stochastic Models of Electricity Consumption

The article considers a stochastic model of electricity consumption based on the convolution theory. It is assumed that the processes of switching on and off energy consumption on a city scale depend on a large number of independent random factors and have the form of a normal probability distribution. The functions of switching on and off the load are presented, graphs are plotted. The comparison of the intelligent power supply system with the existing power supply system in the Russian Federation is carried out. The reasons for the slow introduction of smart grids in the Russian Federation are revealed. At the moment, smart grids are not very popular in the Russian Federation, while in the countries of the European Union, the USA and China, projects related to the development, production of components and the widespread introduction of smart grids are actively receiving state financial support. Thus, the European Union allocates more than a billion dollars annually for projects in the field of electric power and smart grids. These funds are allocated according to the European Green Deal strategy. At the same time, the State Grid Corporation of China has introduced the concept of the "Global Energy Internet", which, in accordance with the government's expectation, will stimulate the smart grid market in the country. As in other countries of the world, the US electric power industry is also facing problems related to rising utility prices, fluctuations in peak load and the need to reduce carbon dioxide emissions. The consequence of this was the adoption at the end of 2021 of an "infrastructure" law providing for large-scale investments in green energy projects.

A COMPARATIVE ANALYSIS OF INJURIES AND DEATHS CAUSED BY ROAD TRAFFIC ACCIDENTS IN POLAND AND SELECTED EU COUNTRIES

The article examines the relationship between three dependent variables: the quantity of accidents on roads in Poland and the quantities of those hurt and killed as a result. Data were obtained from police websites between 2016 and 2021. Multidimensional comparative analyses were used for the research, which, by grouping data and compiling them dynamically in various scales depending on the needs, allowed us to observe trends such as seasonality on a monthly basis (data in a month - Fig. 2) and a downward trend (data in years Fig. 3). This became a premise for building a multiple regression model, which allowed us to dynamically observe the correlation between the quantity of accidents in Poland and the quantity of people hurt and killed as a result. The above-mentioned regularity was described by the function: Y = ‒67,9212 + 1,3341*v(2) + 0,7332*v(3) + +28,4305*v(4) and the built model was analyzed and evaluated. Then, information on the quantity of road accidents and quantities of people killed and hurt in the capital city of Warsaw from January 2021 to December 2022 were used for the research and compared with the data observed throughout Poland. To observe the impact of the random factor, which was a strong increase in the scale of fines in 2022, especially for exceeding the limit of speed by approximately more than 31 km/h. Dynamic indices on a constant base were calculated for the research. It was found that in 2022, compared to 2021, there was a visible decline in the quantity of people killed in road accidents in Warsaw from 42 to 30 people. A similar regularity can be observed in the data on fatalities in road accidents throughout Poland in the identical time period [11]. Moreover, there was no correlation between the time series of fatalities in road accidents in Warsaw and the quantity of accidents and injuries in 2022.

Development of a yield monitoring model based on analysis of surveys and images of the field

The study is devoted to the construction of a mathematical model of the yield of agricultural crops, which includes three components: trend, seasonal, and random. The developed model shows the dependence of yield on phenological indicators, the quality of land resources, management efficiency, and other random factors. The trend and seasonal components of the yield model do not depend on random factors and can therefore be used to predict yield. It is proposed to consider the trend component of the model as a piecewise linear function, and the seasonal component of the model as a linear harmonic regression. A method for analyzing multispectral images with consideration of geoinformation data has been developed to assess phenological indicators. This method includes determining the threshold value using Otsu method to find the density of the agricultural crop in the field. Data on crop density, supplemented with geodata about the plot boundaries, are used to calculate the yield. A comparison of yield forecasts for three crops in the Chernihiv region was made using observations of the phenological indicators of crops throughout the year and over 3 months. It was found that yield is significantly determined by plant development in the first months after germination. The comparison of yield forecasts was made with data from the State Statistics Service of Ukraine and forecasts made using the WOFOST simulation model. It was established that the average relative error of yield prediction using the developed model is 2.96% when using observations of the phenological indicators of crops throughout the year and 4.51% when observing over 3 months. This accuracy is sufficient and comparable to the average accuracy of predictions based on the WOFOST model, which is 3.62%).

Application of the Randomized Earned Value Method to assess the advancement of the construction of the office building under the unstable implementation conditions

The REVM method is a modernized option of classical EVM method. The new method has been developed for applying in unstable condition of works implementation. When the works can be accidentally disturbed and the impact of random disruption factors on course and results of works must be taken into consideration. Next, Randomized Budgeted Duration to Completion and Randomized Budgeted Cost to Completion that is duration and cost of works remaining to execution after each inspection, as well as the Randomized Budgeted Duration at Completion and Randomized Budgeted Cost at Completion that is duration and cost of all works of the project completion after the site inspection. Moreover, the risk of durations and costs overrun of works are evaluated. It is important that input data required for the REVM method are the similar and are measured in the same way as in typical control of advancement works. But results of the application consist new decision information. Control of the investment under deterministic conditions, without taking into account the risk of disruptions, resulted in a final deviation from the planned budget of over 7%, and from the planned completion of the investment by almost 12%. Without analysing the factor related to disruptions at the investment implementation stage, the material and financial schedule was completely outdated. On the other hand, when controlling the investment under risk conditions and introducing organizational and technological changes adequate to the inspection reports, the final deviation from the planned budget was less than 2%, and slightly more than 2% from the planned completion date. Researches confirm that the results received by using the REVM method well reflect real situation of works implementation.

Online group streaming feature selection based on fuzzy neighborhood granular ball rough sets

Online group streaming feature selection holds significant research value in large-scale streaming data processing scenarios, and the related work based on rough set theory has attracted academic interest. Nevertheless, most relevant algorithms come with parameters, and performing a grid search for the optimal parameters reduces the efficiency. Moreover, the existing online group streaming feature selection frameworks cannot effectively fit the situation in practice. Focusing on these issues, this paper investigates an online group streaming feature selection method based on fuzzy neighborhood granular ball rough sets. First, Canopy clustering is introduced to granular ball computing, and the adaptive neighborhood of samples is generated based on the granular ball distribution. Second, we construct a fuzzy neighborhood granular ball rough set (FNGBRS) model and propose the integrated dependence degree to achieve maximal dependency and minimum classification error. Then, the purity of granular balls is considered as the weight of features, and some uncertainty measures based on FNGBRS are presented. Finally, we define a random factor to control the size of streaming groups and design an online group streaming feature selection algorithm. Comparative experimental results on sixteen public datasets demonstrate that the proposed algorithm exhibits superior and stable classification performance, coupled with increased efficiency from its parameter-free design.

Stability of stochastic delayed differential systems with average-random-delay impulses

This paper studies the pth moment exponential stability of stochastic delayed differential systems (SDDSs) with random-delay impulses. By using average random delay (ARD) and average impulsive estimation (AIE) approaches, we obtain a sufficient condition for the pth moment exponentially stable (p-ES) of SDDSs. Compared with some previous results, our work has a wider application since we consider the effects of random factors for systems and impulses. Furthermore, the time delays of the impulses are allowed to be longer than the delay of the system, which shows a big improvement. Finally, the correctness of the results is verified by an example.

Assessing the Performance of Handheld Laser Scanning for Individual Tree Mapping in an Urban Area

Precise individual tree or sample-based inventories derived from 3D point cloud data of mobile laser scanning can improve our comprehensive understanding of the structure, function, resilience, biodiversity, and ecosystem services of urban forests. This study assessed the performance of a handheld laser scanning system (HLS) for the extraction of tree position, diameter at breast height (DBH), and tree height (H) in an urban area. A total of 2083 trees of 13 species from 34 plots were analyzed. The results showed that the registration of tree positions using ground control points (GCPs) demonstrated high accuracy, with errors consistently below 0.4 m, except for a few instances. The extraction accuracy of DBH for all trees and individual species remained consistently high, with a total root mean square error (RMSE) of 2.06 cm (6.89%) and a bias of 0.62 cm (2.07%). Notably, broad-leaved trees outperformed coniferous trees, with RMSE and bias values of 1.86 cm (6%) and 0.76 cm (2.46%), respectively, compared to 2.54 cm (9.46%) and 0.23 cm (0.84%), respectively. The accuracy of H extraction varied significantly among different species, with R2 values ranging from 0.65 to 0.92. Generally, both DBH and H were underestimated compared to ground measurements. Linear mixed-effects models (LMEs) were applied to evaluate factors affecting the performance of HLS with the plot as a random factor. LME analysis revealed that plant type and terrain significantly influenced the accuracy of DBH and H derived from HLS data, while other fixed factors such as plot area, tree density, and trajectory length showed no significance. With a large sample size, we concluded that the HLS demonstrated sufficient accuracy in extracting individual tree parameters in urban forests.

RWESA-GNNR: Fusing Random Walk Embedding and Sentiment Analysis for Graph Neural Network Recommendation

A graph neural network-based recommendation system treats the relationship between user items as a graph, and achieves deep feature mining by modelling the graph nodes. However, the complexity of the features of graph neural network-based recommendation systems brings poor interpretability and suffers from data sparsity problems. To address the above problems, a graph convolutional neural network recommendation model (RWESA-GNNR) based on random walk embedding combined with sentiment analysis is proposed. Firstly, a random walk-based matrix factorization is designed as the initial embedding. Secondly, the user and item nodes are modelled using a convolutional neural network with an injected attention mechanism. Then, sentiment analysis is performed on the review text, and attention mechanism is introduced to fuse text sentiment features and semantic features. Finally, node features and text features are aggregated to generate recommendation results. The experimental results show that our proposed algorithm outperforms traditional recommendation algorithms and other graph neural network-based recommendation algorithms in terms of recommendation results, with an improvement of about 2.43%-5.75%.

Simulation model of a flat plate air solar collector

Simulation model of a flat plate air solar collector

A multi-objective optimization model for medical waste recycling network design under uncertainties

Transporting medical waste (MW) generated by medical institutions (MIs) is a process that poses potential threats to the environment and public safety. Therefore, it is vital to find a safe and efficient way to transport this type of waste to disposal centers (DCs). However, there are challenges in the transportation of MW due to random factors such as the generation of waste in an unpredictable manner and unforeseen travel times. In this paper, we propose a bi-level optimization model to minimize site selection costs, transportation costs, time-window penalties costs, and transportation risks under uncertainties. The concepts of “loading reliability”, “travel time reliability” and “transportation risk” are adopted in the proposed optimization model. The simulated annealing algorithm (SA) is employed to address the optimal location problem (Upper level), which involves minimizing the construction cost, transportation cost (DC-CP-DC), and transportation risk (estimated using Bayesian method). To tackle the capacity-constrained vehicle routing problem considering transportation risk and time window penalties (Lower level), we propose an improved genetic algorithm named harmony search algorithm (IHSGA). Subsequently, the results from the lower level are looped back to the upper level, fostering mutual influence between the two stages. We demonstrate the effectiveness and correctness of the proposed model and algorithm using S city in China as an illustrative example. Furthermore, a series of sensitivity analyses were conducted to examine the impact of various factors. The findings highlight the pivotal roles of both travel time reliability and loading reliability in designing the medical waste recycling network. In comparison to the general Genetic Algorithm (GA) and CPLEX solver, the modified IHSGA presented in this paper exhibits superior performance.