Statistical Indicators Research Articles

Enhancing groundwater quality prediction through ensemble machine learning techniques.

Groundwater quality is assessed by conducting water sampling and laboratory analysis. Field-based measurements are costly and time-consuming. This study introduces a machine learning (ML)-based framework and innovative application of stacking ensemble learning model, for predicting groundwater quality in an unconfined aquifer located in northern Iran. The groundwater quality index (GWQI) from 250 wells was evaluated and classified. We considered various influential factors such as proximity to residential areas, evaporation, aquifer transmissivity, precipitation values, population density, distance to industrial centers, distance to water resources, and topography. Three different ML classifiers were employed to establish relationships between GWQI and the aforementioned factors: the AdaBoost classifier (ADA), quadratic discriminant analysis (QDA), and stacking ensemble learning (SEL). A novel model was introduced dubbed quadratic-ada-stacking ensemble learning (QA-SEL) to predict GWQI. The performance of these algorithms was evaluated through the receiver-operating characteristic (ROC) and multiple statistical efficiency indicators, including overall accuracy, precision, recall, and the F-1 score. All three ML algorithms displayed a high degree of accuracy in their GWQI predictions. Nonetheless, the QA-SEL method was identified as the most effective model due to its superior accuracy (overall accuracy, precision, recall = 0.95, 0.95, 0.96, ROC = 0.96, respectively). Following model optimization and testing, the QA-SEL model and a GIS were employed to map GWQI classes across the entire area. The produced GWQI map was validated by comparing the measured and predicted GWQI on the map. This study offers an economically efficient model for groundwater quality prediction, which can be replicated in other plains.

Crack detection in concrete structures using standard deviation of discrete wavelet transform

Crack detection in concrete structures is an important issue in the maintenance and repair operations of the structures. Detecting and distinguishing cracks in concrete can help determine the structure's health and prevent the possibility of structural failure. An efficient method for separating cracks in concrete is to use wavelet transform. This paper proposes a new crack detection technique based on a two-dimensional discrete wavelet transform and the standard deviation obtained from its detail signals to select an optimum wavelet function. According to our findings, there is a significant relation between the statistical index standard deviation and the desired detail signal obtained from the two-dimensional discrete wavelet transform for selecting the optimum wavelet function. Specifically, results show that as the standard deviation of the matrix of detail signal increases by a given wavelet function, crack detection resolution increases by that wavelet function.

Comparative analysis of ensemble learning algorithms in water quality prediction

ABSTRACT Water is an essential resource necessary for the survival of all life forms, yet it is continually at risk of contamination. Accurate water quality prediction is essential for protecting ecosystem health. This study aims to assess the effectiveness of ensemble learning techniques, namely AdaBoost, gradient boosting, XGBoost, CatBoost, and LightGBM, in predicting water quality parameters in the Bara River Basin, Pakistan. Initially, a random forest model was used to determine the input water quality parameters combination for the selected target water quality variable. Then, the ML models were developed for each combination of input parameters and target water quality variables. The ML model's performance was assessed via statistical performance indicators, namely R2, mean squared error, and mean absolute error. The most suitable model was highlighted using compromise programming. The results reveal that the XGBoost and gradient boosting models outperform other algorithms based on statistical indicators, displaying remarkable predictive ability with near-perfect R2 values for HCO3, CO3, and Mg on the XGBoost model and electrical conductivity, SO4, Temp, and Ca on the gradient boosting model. Whereas CatBoost and LightGBM have a more robust performance on some parameters, such as pH and dissolved solids while its performance in other water quality parameters was weak.

Assessment of Shear Strength Models for Squat Rectangular Reinforced Concrete Shear Walls

The shear strength is a critical parameter in the design of Reinforced Concrete (RC) shear walls subjected to lateral loads. Numerous design codes and published studies have proposed formulas for calculating the shear strength of squat RC walls. However, there is a discrepancy between the calculated and experimental results. This study aims to evaluate various models for the calculation of the shear strength of rectangular squat RC walls using 312 databases collected from the literature. The shear strength of the RC walls was calculated using eight code- and empirical-based models, while the input parameters were obtained from the experimental database. The results were evaluated utilizing two statistical indicators: coefficient of determination and root-mean-squared error. The analysis of the results revealed that the model of C. K. Gulec and A. S. Whittaker is the optimal model, followed by the models of S. L. Wood and Eurocode-8 (EC8).

Comprehensive models to estimate the Isobaric heat capacity of deep eutectic solvents based on Machine learning algorithms

Deep eutectic solvents (DESs) have recently gained significant attention due to their sustainable and environmentally friendly properties. Acquiring exact knowledge regarding the isobaric heat capacity (Cp) of DESs is essential for energy-related processes, in which these green solvents are utilized. Hence, this study deals with the development of comprehensive models for estimating the Cp of DESs. To reach this target, 682 experimental data, encompassing the Cp of 36 different DESs over widespread ranges of pressure and temperature, were assembled from the literature. The foregoing data were employed to establish new models based on four machine learning techniques, including Gaussian process method (GPM), Adaptive neuro-fuzzy inference system (ANFIS), Radial basis function neural network (RBF-NN) and Multilayer perceptron neural network (MLP-NN). The evaluations performed based on the statistical indices and graphical tools demonstrated that although all suggested models present excellent estimations, that designed based on the MLP-NN method yields the highest accuracy with Mean absolute percentage error (MAPE) and coefficient of determination (R2) values being 0.4% and 99.93, respectively, for the validation data. A comparison between the results of the literature correlations and those of novel models confirmed the obvious superiority of the latter. Moreover, the proposed models properly described the Cp variations of different DESs versus pressure and temperature. The order of importance of various factors in controlling the Cp of DESs was also determined based on the sensitivity analysis. Eventually, the intelligent models showed excellent performance in estimating the Cp of unseen DESs.

Incorporating changes in land surface temperature improves BESS evapotranspiration estimates under water-deficit conditions: A case study for US Midwest and Great Plains grasslands

Evapotranspiration (ET) is a critical climate and ecosystem variable that interconnects water, energy, and carbon cycles. Breathing Earth System Simulator (BESS) is one of the state-of-the-art biophysical models capable of producing spatio-temporal continuous ET results. However, we found that since the BESS model does not use an explicit constraint on soil moisture (SM), it has a relatively lower performance under drier conditions. Given that changes in land surface temperature (LST) are closely associated with surface water status and sensible heat energy, we hypothesize that integrating LST changes could explicitly add the soil moisture constraints and thus enhance BESS’s ability to estimate ET. Here we used the morning rise rate of LST (Trate) as a proxy of LST change because of the low noise level in Trate as well as Trate’s close relationship with daily mean sensible heat. To test the hypothesis, this study first assessed whether the performance of BESS ET can be explained by the LST change, targeting grassland sites of the AmeriFlux network in the US Midwest and Great Plains. Specifically, the ET deviation (i.e., the difference between BESS-modeled ET and field-measured ET) and Trate deviation, as well as their relationships, were investigated under different conditions of precipitation, SM, and vapor pressure deficit (VPD) at the AmeriFlux sites. Results indicated that BESS ET exhibited consistently higher performance under well-watered conditions than water-deficit conditions. Also, the deviations of ET and Trate became more negatively correlated under water-deficit conditions. Leveraging the empirical relationship between ET and Trate deviations, this study developed a new way to calibrate BESS ET based on Trate calculated from LST diurnal observations, particularly under soil or atmospheric water-deficit conditions. After calibrating BESS ET, the statistical indicators between the calibrated ET and the ground measurements showed meaningful improvements relative to those before calibration. Specifically, in the Midwest (Great Plains), R2 increased from 0.42 to 0.51 (from 0.45 to 0.46), and RMSE and absolute bias decreased by 12% and 42% (11% and 45%), respectively. This study highlights that the morning rise rate of LST can effectively constrain the ET models that have no SM constraints under water-deficit conditions.

Ensemble species distribution models reveal Javan leopard’s preference for areas with high prey species diversity

Like many other large carnivores worldwide, the Javan leopard (Panthera pardus melas) faces significant threats from prey depletion due to human disturbance and habitat loss, yet the distribution of its prey across Java and the relationship between prey diversity and leopard presence remain poorly understood at broad spatial scales. This study predicted the distribution of eight prey species for the Javan leopard across Java using an ensemble species distribution model and identified key factors influencing prey distribution. A prey diversity map was generated to evaluate the relationship between prey diversity and Javan leopard occurrence. The results showed that model accuracy for each prey species, as measured by the True Skill Statistic (TSS) and continuous Boyce index (BCI), ranged from 0.609 to 0.951 and 0.628 to 0.965, respectively, with distance to primary and secondary forests as the most influential predictor. The results also showed that areas with high prey diversity were primarily concentrated in western and eastern Java, and approximately 94.3% of leopard occurrence points were found in regions with five or more prey species. This is the first comprehensive spatial prediction of prey diversity for Javan leopards, emphasizing the predator’s preference for habitats with high prey diversity. These findings suggest that in landscapes harboring large carnivores as apex predators, areas with elevated prey diversity may serve as key indicators of potential core habitats for these carnivores.

A spatio-temporal assessment of seawater quality in Miri-Sibuti Coral Reef: Evaluating for metal pollution, sources and dynamics

Coral environments are sensitive to seawater quality, and their sustenance depends on understanding the dynamics of geochemical variables, especially in trace levels. Miri-Sibuti Coral Reef National Park (MSCRNP) is a unique marine reserve in the state of Sarawak, NW Borneo, Malaysia, which attracts tourists worldwide. In this study, seawater samples were collected from MSCRNP in three different diving sites (Eve’s Garden (EG), Anemone Garden (AG) and North Siwa (NS)) at different depths for three seasons (pre-southwest monsoon (PRSWM), post-southwest monsoon (POSWM) and northeast monsoon (NEM). Insitu parameters (temperature, pH, salinity and turbidity) were measured in the field. The concentration of trace metals was determined in the water column (sea surface layer, middle layer and bottom water) using standard procedure. Statistics and pollution indices were used to identify the possible sources of metals and the pollution status of the seawater. From the analytical results, the vertical distribution of trace metal concentrations with respect to the locations and seasons was identified. pH ranging from 7.8 to 8.4 with a depth-wise increasing trend in NS during NEM. Similarly, a decreasing trend of turbidity was observed in AG and EG in all the seasons with respect to depth. A higher concentration of trace metals was observed during NEM, particularly at EG and NS. Fe, Ni and Pb are the dominant metals in all the seasons. Fe concentration varies from 789 to 1,478µg/L, averaging 1,112µg/L irrespective of seasons and locations. The suspended solids/sediments discharged by the river runoff were the main contributor to the distribution of metals. The release of metals from the suspended particles through desorption at the surface layers and the exchange of metals between water-sediment interface at the bottom water were observed. In addition, coastal development and other anthropogenic activities in this region were also contributed to the metal pollution.

Investigation and optimisation of performance and emission data of CI engine ternary blends under distinct conditions

This study investigates the significance of isoamyl alcohol (IAA) in a CI engine by blending with watermelon seed biodiesel (WSB) at preheating and varying load, injection timing (IT), and exhaust gas recirculation (EGR). Adaptive-Network Based Fuzzy Inference Systems (ANFIS), and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods were employed to analyze the test results. From statistical indices data, the ANFIS models predicted more accurately. TOPSIS technique confirmed the preheated optimum ternary blend (POTB) as load-12 kg, WSB-5 %, IAA-15 %, IT-25° bTDC, and EGR-21 %. The closeness coefficient values model R2 = 0.9736. In comparison to diesel, thePOTB blend (WSB5IAA15) offers lower viscosity, density, CN, and heating value, with higher oxygen and latent heat of vaporization (Lv), most of which contribute to improved atomized spray and better combustion. Hence, at load-80 %, POTB to diesel results decrease BTE by 0.11 %, CO by 25 %, CO2 by 16.39 %, NOx by 37.08 %, and smoke by 16.16 %, while increasing BSEC by 0.40 %, and HC by 39.83 %. Further, at load-100 %, the results of POTB to diesel diminish BTE by 2.12 %, CO by 14.29 %, CO2 by 32.89 %, NOx by 58.12 %, and smoke by 38.13 %, while elevating BSEC by 7.57 %, and HC by 31.30 %. IAA exhibits elevated levels of oxygen, energy density, Lv, and OH active radicals, accompanied by reduced heating value and CN. IAA is crucial in lowering NOx and smoke levels, which are difficult for WSB to control on its own. Lastly, plots are illustrated for each response parameter to dominant (EGR and WSB) concerning ternary blends and diesel data for specific conditions. Ultimately, the POTB demonstrated that it can serve as an effective diesel fuel substitute.

Assessment of reference evapotranspiration forecasting in the Mediterranean climate of Central Chile using the ASCE standardized Penman-Monteith equation, the Hargreaves-Samani equation, and weather predictions from the Global Forecast System model

Accurate estimation of crop evapotranspiration (ETc) is essential for effective agricultural water resource planning. To determine ETc it is common multiplying the reference evapotranspiration (ETref) by an appropriate crop coefficient (Kc). Forecasting ETref could be particularly beneficial for irrigation scheduling. In this work, daily and cumulative (7 days) ETref predictions were estimated with the Penman-Monteith (PM) and the Hargreaves-Samani (HS) models using environmental variables obtained from the Global Forecast System (GFS). Results were compared with ETref calculated with the ASCE (American Society of Civil Engineers) PM equation from measured daily values of solar radiation, air temperature, relative humidity and wind speed. Four weather stations located in Central Chile during 2020 to 2022 were used. The best results were obtained using the HS equation, for all weather stations, the statistical indicators for daily ETref predictions fall within the following ranges: Accuracy (defined as the percentage of daily ETref predictions with an absolute error within 1mmd−1) varies between 72.8% and 91.8%; the root mean square error (RMSE) ranges from 0.59mmd−1 to 0.98mmd−1; the normalized root mean square error (NRMSE) is between 16.1% and 24.1%; the mean bias error (MBE) is between 0.10mmd−1 and 0.36mmd−1; and the normalized mean bias error (NMBE) varies from 2.5% to 8.3%. On the other hand, the indicators for the 7-day cumulative ETref forecasts were: RMSE from 2.86mm to 5.08mm; NRMSE from 11.0% to 16.3%; MBE from 0.66mm to 2.57mm; and NMBE from 2.4% to 8.3%. These results suggest that HS ETref values predicted from GFS temperature forecasts could be effective for foreseeing near-future water demand and enhancing the accuracy of irrigation scheduling and management in Central Chile.

High-resolution 3D hydrodynamic modeling and comprehensive assessment of tidal current energy resources

As a clean and renewable resource, tidal current holds significant development potential. However, accurately assessing tidal current energy resources has been a persistent challenge. This study proposes a comprehensive assessment method using a small-scale, high-precision, multi-criteria three-dimensional hydrodynamic model to assess resource reserves applied to the Zhairuoshan Waterway. The hydrodynamic model was employed to simulate critical parameters such as tidal level, flow velocity, and flow direction. Multiple statistical indicators were used to evaluate the model's accuracy, showing consistency. The study analysed the flow speed distribution over a complete cycle, revealing an average flow speed of 1.51 m/s, with a maximum speed reaching 3.83 m/s. The generally high flow speeds within the waterway, coupled with the stability of the tidal current, indicate the significant resource potential of the study area. A quantitative analysis of the spatial distribution of tidal current energy was conducted using energy flux density, and the results showed that high energy density areas are widely distributed. Furthermore, the theoretical tidal energy resources of the waterway were calculated, showing that the maximum resource reserves could reach 9.98 MW at the selected cross-sections. Coupled with the analysis of unit-width energy flux, the high-potential areas within the waterway were further identified.

Identifying Careless Responses in Computer-Adaptive Affective Surveys Using Person Fit Analysis

ABSTRACT Careless responding is a pervasive concern in research using affective surveys. Although researchers have considered various methods for identifying careless responses, studies are limited that consider the utility of these methods in the context of computer adaptive testing (CAT) for affective scales. Using a simulation study informed by recent research, we explored the impact of careless response patterns on three types of response quality indicators: model-based person fit statistics, CAT-specific statistical process control indicators, and standalone indices of carelessness. We focused on over-consistency and random responding as forms of carelessness. All three types of indicators were sensitive to carelessness, with some differences related to CAT termination rules and item bank size. Our findings suggest that person fit analysis techniques may also be useful in affective CAT contexts to evaluating response quality from a measurement model perspective, including identifying carelessness. We discuss the implications of our work for research and practice.

Intelligent hybrid approaches utilizing time series forecasting error for enhanced structural health monitoring

Over the past decade, the growing importance of machine learning-based structural health monitoring (SHM) for early-stage damage detection has become evident. Time series forecasting, using deep learning, has emerged as a key focus, significantly contributing to improving damage detection, localization, and quantification processes. Researchers in SHM have conducted numerous studies utilizing neural networks based on time series forecasting, grounded in traditional methods. This study diverges from existing research by directly incorporating neural network prediction errors in time series for detecting, localizing, and quantifying damage. The proposed methods are well-suited for online structural monitoring. They eliminate the need for data classification methods and damage-sensitive feature extraction techniques by relying solely on training the neural network with data from structurally sound conditions. However, the testing process does require data from damaged conditions. To address the non-linear and non-stationary characteristics of the signals, the Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) method is applied for signal processing. This method processes response signals (i.e., time series) from three well-known benchmark structures: the University of Central Florida structure, the Qatar University Grandstand Simulator, and the Z24 Bridge. Subsequently, the first intrinsic mode function (IMF) obtained from signal decomposition is independently input into Long-Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) neural networks for time series prediction. Optimal parameter values for the LSTM and GRU neural networks are chosen using the Bayesian Optimization (BO) algorithm before the prediction process. By introducing three indices—Statistical Distance Function (SDF), error index, and accuracy index—the evaluation not only emphasizes the accuracy of the methods but also explores the localization and quantification of damage. The results demonstrate that both ICEEMDAN-BO-LSTM-SDF and ICEEMDAN-BO-GRU-SDF methods have successfully achieved accurate detection, localization, and quantification without the need for data classification and damage-sensitive feature extraction methods, and merely by utilizing data from healthy states for neural network training.

A predictive model of shear thrombus growth for the coronary arteries using integrated numerical simulation and artificial intelligence

Cardiovascular disorders are among the most common causes of death worldwide. The formation of a thrombus alters blood flow patterns and impairs blood flow. Therefore, blood flow prediction can aid in an improved understanding of various medical disorders, and developing innovative diagnostic procedures and therapies. Numerical simulations are time-consuming and computationally expensive, and this approach for complex computational domains is prohibitively costly. Therefore, the neural network method is an effective approach for the solution of the corresponding problems. This study aimed to forecast wall shear stress in arteries with varying degrees of stenosis. The prediction approach involved a multi-layer perceptron, with the hyperparameters tuned by the Slime Mould algorithm throughout the training phase. The Hemocell model was used by combining the lattice Boltzmann and immersed boundary methods to generate input datasets for the prediction model. R-squared and root-mean-squared error statistical indices were used to evaluate the outcomes of the predictions. The results indicate that the R-squared values for the testing data were 0.965, 0.959, 0.955, 0.929, and 0.903, respectively, for stenosis levels of 90%, 80%, 60%, 40%, and 20%. The article demonstrates thrombus growth relative to stenosis location across varying levels of blockage. Notably, thrombus growth predominantly occurs before the stenosis peak in lower blockage cases, while higher blockages exhibit growth primarily after the peak.

Protectionism and Sanctions Policy of the USA towards the Gas Industry of the Russian Federation in the Period 2014–2022

We studied the impact of US protectionism and sanctions policy on the Russian gas industry in the period 2014–2022. Information on global energy production and the level of natural gas production in various countries for 2014–2022 was taken from the database of the international research company Enerdata. Statistical indicators of the activities of PJSC Gazprom were extracted from the organisation’s annual statistical reports. During the statistical analysis, a database was created, growth rates, total and proportional values were determined, and a correlation and regression analysis was conducted. It was established that the leading countries in gas production in 2014–2022 were the USA and the Russian Federation. The average annual increase in gas production in the above observation period was 4.4 % in the USA and 1.3 % in the Russian Federation Restrictions on the access of Russian natural gas to global markets led to an increase in the share of gas produced in the USA in global energy production by 0.97 % (from 5.05 % in 2014–2018 to 6.02% in 2019–2022), the Russian Federation’s indicators were 4.36 and 4.58 % respectively (an increase of 0.22 %). Diversification of gas imports from the Russian Federation to friendly countries is hampered by insufficient pipeline capabilities, sanctions on the supply of equipment for the development of gas liquefaction capacities, and the growth of their own gas production. In China, the average annual increase in natural gas production in 2014–2022 was 6.8 %. A targeted policy to stimulate domestic gas demand in the Russian Federation as a response to restrictions on foreign markets has made it possible to achieve an average annual increase in gas consumption in our country of 1.2 %. Thus,the US sanctions protectionism slows down the development of the gas industry in the Russian Federation and allows the US to fill the vacant niche in the global gas market.

Predicting Wall Pressure Fluctuations on Aerospace Launchers Through Machine Learning Approaches

Artificial intelligence (AI) can be used to optimize the prediction of pressure fluctuations over the external surfaces of aerospace launchers and minimize the number of wind tunnel tests. In the present research, various machine learning (ML) techniques capable of predicting the acoustic load were tested and validated. The methods included decision trees, Gaussian Process Regression (GPR), Support Vector Machines (SVMs), artificial neural networks (ANNs), linear regression, and ensemble methods such as bagged and boosted trees. These algorithms were trained using experimental data from an extensive wind tunnel test campaign conducted to support the design of a VEGA (Advanced Generation European Vehicle) launcher vehicle and provide wall pressure fluctuations in many configurations. The main objective of this study was to identify, among several algorithms, the most suitable method able to process such complex databases efficiently and to provide reliable predictions. Different statistical indices, including the root mean square error (RMSE), the mean square error (MSE), and a correlation coefficient (R-squared), were employed to evaluate the performance of the ML methods. Among all the methods, the bagged tree algorithm outperformed the others, providing the most accurate predictions, with low RMSE and high R-squared values across all test cases. Other methods, such as the ANNs and GPR, exhibited higher errors, indicating their reduced suitability for this dataset. The results demonstrate that ensemble decision tree methods are highly effective in predicting acoustic loads, offering reliable predictions, even for configurations outside the training database. These findings support the application of ML-based models to optimize experimental campaigns and enhance the design of aerospace launch vehicles.

A novel hybrid fuzzy analytical hierarchy process–game theory model for prioritizing factors affecting the deterioration of water pipelines

Water pipes face significant aging and degradation problems due to several pipe-related, soil-related, operational, and environmental factors. Hence, the paramount objective of this research paper is to prioritize the criticality of the factors affecting the deterioration of water pipes in Hong Kong. The framework of the developed model is envisioned based on two main modules, namely weight computation and weight aggregation. The first module incorporates identifying and categorizing water deterioration factors. Then, the relative importance priorities of water deterioration factors are scrutinized using seven weight computation methods. These methods encompass analytical hierarchy process (AHP), Monte Carlo AHP, fuzzy AHP, magnitude-based fuzzy AHP, total difference-based fuzzy AHP, spherical fuzzy AHP and Pythagorean fuzzy AHP. In this regard, fuzzy-based and Monte Carlo-based methods are leveraged to circumvent the critical shortcomings of classical AHP. The performances of weight computation methods are analyzed using statistical evaluation indicators of satisfactory index (SAT) and distance between weights (WD). The second module is a hybrid meta-heuristic-based game theory model designated for compiling the importance weights of deterioration factors obtained from the first module. In this context, a set of widely acclaimed meta-heuristics are exploited and examined for optimizing the significance of deterioration factors. Analytical results exemplified that soil-related factors implicate the deterioration process more than pipe-related, operational-related, and environmental-related factors. It was also inferred that water pressure (6.64%) is the most significant factor influencing water pipe deterioration followed by internal corrosion and protection method (6.11%), and then soil corrosivity (6.05%). On the other hand, length (1.93%), rain deficit (1.97%), and street block length (2.33%) constitute the least influencers on water pipe deterioration. Results also demonstrated that spherical FAHP outperformed other variants of AHP accomplishing SAT and WD of 0.065 and 0.057, respectively. Comparative analysis revealed that particle swarm optimization-based game theory is a better mechanism than the remainder of meta-heuristic-based game theory models in obtaining a more accurate compromised-based weighting vector to the experts’ judgments. It is envisaged that this research can assist the water supplies department in identifying, assessing, and prioritizing the impairment causes of water pipelines in Hong Kong. It can also aid in establishing more accurate deterioration models and more cost-effective maintenance intervention programs.

THE GENETIC VARIABILITY IN TOMATO GENOTYPES L-11/53 AND V 5 GROWN IN PROTECTED SPACES

The tomato genotypes (L-11/53 and V 5) were subjected to an analysis regarding the variability of the main quantitative fruit characters (fruit weight, fruit height, pericarp thickness). The registered biometric data were statistically processed, calculating for each analyzed character the mean (x̄), the standard deviation (s), the coefficient of variability (s%) and the range of variability (k = x̄ ± s).The calculation and analysis of the variability of the characters studied in the tomato genotypes L-11/53 and V 5, revealed on average in the two years of the study a small variability for the height of the fruit (s% = 9.37% for L-11/53 and 7.52% for V 5) and medium for pericarp thickness (s% =16.59% for L-11/53 and 15.47% for V 5).The values of the calculated statistical indices, of the characters taken in the study, require the continuation of the selection process in order to maintain them within the specific limits of variability.

Book Trade: Factors Influencing Demand of Population

The goal of the present article is to identify macro-economic factors affecting customer demand for books by using tools of correlation analysis. The authors formulated a number of hypotheses and made an attempt to check their rightfulness by tools. As empiric data the authors used information of the Federal Service of State Statistics, annual reports of the Ministry of Digital Development and Mass Communications of the Russian Federation ‘Book Market in Russia. Situation, Trends and Prospects’, as well as of the Ministry of Culture of the Russian Federation in 2017– 2023. As research methods to check the mentioned-above hypotheses statistic indices of correlation were used. As a result the authors’ assumptions were proved that people incomes are a serious factor influencing the amount of book purchases. At the same time it was pointed out that between the amount of book purchases and the cover of the population by library services a certain feed-back can be observed. The authors’ ideas about the impact on the amount of book purchase on the part of people access to the Internet and their skills in information and communication technologies have not been proven.

Estimation of Future Climate Change in Low Folded Zone, Iraq with the LARS-WG and Five GMS Models under CMIP5 Scenarios

This study investigates the trends of climate change in the Low Folded Zone region of Iraq and provides future projections for daily maximum and minimum temperatures, as well as precipitation. Using the LARS-WG model and five General Circulation Models (GCMs) under three Representative Concentration Pathways (RCP) scenarios, the research aims to provide valuable insights into the anticipated changes in climate variables. The calibration and validation of the model demonstrate its capability to simulate future climatic data, with statistical indices indicating a strong correlation between observed and generated data. The projected future temperatures showed a consistent increase across all selected stations, with average annual maximum temperatures expected to rise by 1.06 to 5.48°C by the end of the twenty-first century. The highest increase in temperatures was predicted under the high-emission scenario RCP8.5. The results also indicated spatial and temporal variations in precipitation patterns, with the annual percentage increase in precipitation ranging from 7.07% to 10.75% for RCP 2.6, 0% to 2.2% for RCP4.5, and 3.7% to 6.8% for RCP8.5. The findings reveal a projected increase in annual temperatures and variable precipitation patterns, highlighting the urgency of proactive measures to address the challenges posed by climate change in the region. The results of this study are crucial for informing decision-makers and planners in developing strategies for climate change adaptation and resource management, particularly in water resource management and agricultural planning.