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Deviations in driving time (DT), or significant variations, occur frequently on urban public transport (PT) lines, except in subsystems with separate routes. DT variability is the main reason for disturbances in operation, leading to unstable and unreliable transport service. Moreover, it also causes variability in total user travel time, which is one of the main parameters of transport service quality. Identifying and quantifying factors that influence PT vehicle DT characteristics is significant for designing advanced prediction and passenger information systems and prioritising investments to reduce bus travel time and improve the scheduling process, and thus the level of transport service quality. An analysis of the elements of the route and other static elements of the line that influence DT was carried out in this paper. A model for determining and quantifying influential factors and methodologies for collecting all necessary data was created. The multiple regression model, developed as a result of the conducted multivariate statistical analysis using the specialised SPSS software, was applied to the selected representative set of lines in a real urban PT system. The created regression model explains between 18.2% and 97.4% of the variance of average, minimum and maximum DT and its deviation in the peak and off-peak periods.

Trip generation modelling is not a standard task as the relationship between the input and output variables is not exactly known and is often nonlinear. Furthermore, the input variables may have different types especially at the disaggregate levels. Accordingly, there is no single modelling technique that can be used for all trip generation cases. In this paper, a multiple piecewise regression technique is proposed to suit most trip generation cases. Thresholds can be identified at the discrete values in the case of categorical inputs; while, no thresholds are considered in the case of binary inputs. In addition, the locations of thresholds in case of continuous inputs are obtained to minimize the summation of squared errors and achieving the logical constraints. Thus, a broken hyperplane is determined using quadratic programming. The proposed technique is illustrated using a numerical example and then validated using three well-known examples. The results show that the proposed technique can fit the given data more accurately than the previous techniques. In addition, the proposed technique leads to more explicit and visualized models for practitioners. These findings will encourage researchers to apply the proposed technique in further applications such as trip distribution and mode choice models.

Researchers often make claims regarding the importance of predictor variables in multiple regression analysis by comparing standardized regression coefficients (standardized beta coefficients). This practice has been criticized as a misuse of multiple regression analysis. As a remedy, I highlight the use of dominance analysis and random forest, a machine learning technique, in this method showcase article to accurately determine predictor importance in multiple regression analysis. To demonstrate the utility of dominance analysis and random forest, I reproduced the results of an empirical study and applied these analytical procedures. The results reconfirmed that multiple regression analysis should always be accompanied by dominance analysis and random forest to identify the unique contribution of individual predictors while considering correlations among predictors. A web application to facilitate the use of dominance analysis and random forest among second language researchers is also introduced.

In this research, the authors aim to detect the marginal appreciation of construction and neighbourhood characteristics of property prices at three different time points: before the COVID-19 pandemic, two years after the first COVID-19 alert but before the War in Ukraine, and one year after the outbreak of the War. The marginal appreciations of the building’s features are analysed for a pilot case study in Northern Italy using a Random Forest feature importance analysis and a Multivariate Regression. Several techniques are integrated into this study, such as computer programming in Python language, multi-parametric value assessment techniques, feature selection procedures, and spatial analysis. The results may represent an interesting ongoing monitoring of how these anomalous events affect the buyer’s willingness to pay for specific characteristics of the buildings, with particular attention to the location features of the neighbourhood and accessibility.

The present study investigates the influences of stratospheric quasi-biennial oscillation (QBO) and El Niño–Southern Oscillation (ENSO) on the intensity of stratospheric isentropic mixing based on ERA-Interim and MERRA-2 reanalysis products. It is found that isentropic mixing in the stratosphere is modulated by QBO and ENSO. An analysis of the QBO basis function in the multiple regression model reveals that isentropic mixing in the lower stratosphere is suppressed in the equatorial region in the WQBO phase, while the mixing enhances in the subtropical and mid-latitude regions. This result is not consistent with the Holton–Tan mechanism. However, isentropic mixing in the mid-latitudes becomes stronger in the middle stratosphere in the EQBO phase, which agrees well with the Holton–Tan effect. Composite analysis indicates that QBO-induced changes in the direction and speed of the stratospheric zonal wind can affect wave propagation and wave breaking. In the WQBO phase, zonal wind weakens, and a planetary wave is anomalously converging near 30°N, which leads to an increase in isentropic mixing; on the contrary, wind speed becomes large, and the upward propagation of planetary wave divergence, which lead to the isentropic mixing, becomes weak near 60°N. In the EQBO phase, the wind is relatively weak around 60°N, and the isentropic mixing is strong. Multiple regression analysis reveals the ENSO impact on the intensity of isentropic mixing, which shows weak mixing in the middle and high latitudes and strong mixing in the low latitudes of the lower stratosphere in the El Niño years. In the middle stratosphere, isentropic mixing enhances in the mid-latitude region due to intensified upward propagation of planetary waves but weakens in the polar region. Composite analysis reveals a clear relationship between the mixing strength zones of the El Niño and La Niña years with the position of the polar jet and changes in zonal wind speed.

In Peru, common bean varieties adapt very well to arid zones, and it is essential to strengthen their evaluations accurately during their phenological stage by using remote sensors and UAV. However, this technology has not been widely adopted in the Peruvian agricultural system, causing a lack of information and precision data on this crop. Here, we predicted the yield of four beans cultivars by using multispectral images, vegetation indices (VIs) and multiple linear correlations (with 11 VIs) in 13 different periods of their phenological development. The multispectral images were analyzed with two methods: (1) a mask of only the crop canopy with supervised classification constructed with QGIS software; and (2) the grids corresponding to each plot (n = 48) without classification. The prediction models can be estimated with higher accuracy when bean plants reached maximum canopy cover (vegetative and reproductive stages), obtaining higher R2 for the c2000 cultivar (0.942) with the CIG, PCB, DVI, EVI and TVI indices with method 2. Similarly, with five VIs, the camanejo cultivar showed the highest R2 for both methods 1 and 2 (0.89 and 0.837) in the reproductive stage. The models better predicted the yield in the phenological stages V3–V4 and R6–R8 for all bean cultivars. This work demonstrated the utility of UAV tools and the use of multispectral images to predict yield before harvest under the Peruvian arid ecosystem.

The Covid-19 pandemic affected the industrial sector by decreasing productivity, including in private companies. Some private companies have laid off their employees to achieve company efficiency. This situation drives a threat that is arising insecurity, anxiousness, and decreasing engagement among employees. This research aims to understand the role of calling and perceived organizational support (POS) toward work engagement. The research participants are 144 employees from private companies working for at least six months, and they work from home (WFH). This study hypothesizes that POS and calling predict work engagement in a positive direction. Multiple regression analysis was utilized in this study. Results showed increased calling and POS followed by increased work engagement. The implication of this study has two directions; the first is for the organization to improve perceived organizational support, and the other hand, the employee needs to internalize the calling more.

Background: Finding trends in freight transportation activities enables businesses and policy makers to build an understanding of freight transportation patterns and their impact on logistics planning when making investments in a region’s transportation infrastructure and intermodal freight transport system. To the best of our knowledge, there is limited literature and data-driven analysis about trends in transportation mode choices and the influencing factors in Atlantic Canada. Methods: In this study, a data-driven method has been used to analyze the Canadian Freight dataset to identify trends in transportation activities within Maritime, Canada. Freight transportation mode, product categories, distance, number/weight of shipments, and revenue were examined. Results: The results revealed that the top five product categories exported from Atlantic provinces to the rest of Canada, the US, and Mexico are miscellaneous items, food products, forest products, minerals, and other manufactured goods, where Truck for Hire is the most deployed mode of transportation. A multiple linear regression analysis indicated that the weight, distance, and number of shipments are positively and rather strongly correlated with revenue generation. Conclusions: This study provides a unique overview of Canadian Freight Analysis Framework (CFAF) data with a focus on maritime activities.

Traffic models have gained much popularity in recent years, in the context of smart cities and urban planning, as well as environmental and health research. With the development of Machine Learning (ML) and Artificial Intelligence (AI) some limitations imposed by the traditional analytical, numerical and statistical methods have been overcome. The present paper shows a case study of traffic modeling with scarce reliable data. The approach we propose resorts on the advantages of ensemble learning using a large number of related features such as road and street categories, population density, functional analysis, space syntax, previous traffic measurements and models, etc. We use advanced regression models such as Random Forest, XGBoost, CatBoost etc., ranked according to the chosen evaluation metrics and stacked in a weighted ensemble for optimal fitting. After a series of consecutive data imputations we estimate the annual average daily traffic distribution in the street and road network of Sofia city and the metropolitan municipality for 2018 and 2022, and the NO2 levels for 2021 with accuracy resp. 78%, 74% and 92%, using AutoGluon and Scikit-Learn.

The aim of the article is to establish a regression equation that determines the influence of technological factors on the content of flavonoids in the extract for further use in optimizing the technology for obtaining an extract based on horse chestnut. The task of the research was to identify a mathematical model to describe the effect of technological parameters of extraction on the target quality indicators of a given dosage form, in particular, on the quantitative content of flavonoids. Materials and methods. The proposed approach is based on mathematical processing of experimental results obtained according to plan 23using the computer program Mathcad 14 and MS Excel. To establish a mathematical description, an analysis of the separate influence of technological factors on the target indicator was carried out and the possibility of forming a geometric mean function was determined using the corresponding linear regression equations. Results. The general problem of identification was solved, when it was necessary to reveal both the mechanism of influence of technological factors on the value of the target indicator, and to give a quantitative assessment of the unknown parameters of the regression equation. Based on the results of experimental observations, an adequate mathematical model was established in the form of a linear multiple regression equation with the interaction of factors. Conclusions. The obtained mathematical description makes it possible to analyze the influence of technological factors on the quantitative content of the complex of flavonoids in the herbal extract in the range of the investigated factor space, and also to optimize the technological parameters of extraction

Research on carbon emission is an important basis for solving global climate problems, and it is also one of the ways to quantitatively assess the impact of human activities. Cities are one of the main bearing spaces of human activities, and reasonable urban form is conducive to reducing energy consumption in human activities. This paper takes 50 clusters within Zhuhai, China, as the research object, quantifies the landscape spatial form index and social spatial form index of each cluster and establishes the index set of urban spatial form, so as to analyze the influence of different urban spatial form index on carbon emission. The main conclusions are as follows: (1) From the spatial distribution of each index, the urban cluster size and residential building area of each cluster in Zhuhai are generally large, and the distribution is basically consistent with that of densely populated areas. The urban clusters with high dominance are mainly located in the main urban area of Xiangzhou District, and the urban compactness, dispersion and industrial building area are generally high in the west and low in the east. (2) The size of urban clusters, industrial building area and residential building area have a strong promoting effect on carbon emission, while the compactness, dispersion and dominance of urban clusters have a strong inhibiting effect on carbon emission. (3) Based on the above conclusions, the low-carbon urban spatial form optimization strategy should be proposed from three aspects: urban development boundary control, promoting industrial structure transformation and compact urban development.

Agriculture is one of the most important sectors and driving factors of the economy of Bangladesh, which plays a significant role in the prosperity of large rural communities by increasing productivity, income, and creating employment. Presently, this sector has faced a severe challenge in its production, due to the construction of unplanned infrastructure in rural areas. This study investigates the effect of rural infrastructure on agricultural production in Bangladesh. Using the purposive sampling technique, 50 respondents were interviewed through a structured questionnaire to collect primary data from six unions of Sadar Upazila in the Kushtia district. Statistical methods of multiple regression and paired-sample t-test have been utilized to analyze the collected data. The results of the multiple regression model show that the co-efficient of cultivable and infrastructural land size is statistically significant at 1 percent of level, which depicts cultivable land positively affects agricultural production, whereas infrastructural land negatively affects agricultural production in the study area. This means that infrastructure built on cultivable land has reduced agricultural production. Paired-sample t-test result also shows that the mean difference between agricultural production before and after constructing infrastructure is TK.134847.94 per year. The primary reasons for the construction of infrastructure in the study area are unanticipated population expansion, urbanization, unplanned human settlement, and a rise in nuclear families. Lastly, suitable policies have been offered to develop the infrastructure as well as agricultural production in rural areas., {"references":["Ahmed, Z., & Marjuk, O. A. (2013). Horizontal Expansion of Housing in Rural Areas of Bangladesh: Does Population Growth Matter? Developing Country Studies, 3(10), 66–77.","Bangladesh Economic Review. (2021). Finance Division, Ministry of Finance, Government of the People's Republic of Bangladesh.","BBS. (2011a). Population & Housing Census, Zila Report: Kushtia. Bangladesh Bureau of Statistics, Statistics & Informatics Division (SID), Ministry of Planning, Government of the People's Republic of Bangladesh, Dhaka, Bangladesh.","BBS. (2013). Economic Census 2013 District Report. Bangladesh Bureau of Statistics, Statistics & Informatics Division (SID), Ministry of Planning, Government of the People's Republic of Bangladesh, Dhaka, Bangladesh.","Beckers, V., Poelmans, L., Van Rompaey, A., & Dendoncker, N. (2020). The impact of urbanization on agricultural dynamics: A case study in Belgium. Journal of Land Use Science, 15(5), 626–643. https://doi.org/10.1080/1747423X.2020.1769211","Bhuiyan, M. (2003a). Has urbanization caused the agricultural land loss? The Daily Star.","Islam, R. (2013). Causes and consequences of Agricultural land losses of Rajshahi District, Bangladesh. IOSR Journal Of Environmental Science, Toxicology And Food Technology, 5(6), 58–65. https://doi.org/10.9790/2402-0565865","Fazal, S. (2000). Urban expansion and loss of agricultural land—A GIS based study of Saharanpur City, India. Environment & Urbanization, 12, 133–149.","Halim, M. A., Rahman, M. M., & Hassan, M. Z. (2014). Agricultural Land Conversion in the Sub-Urban Area: A Case Study of Rajshahi Metropolitan City. Journal of Life and Earth Science, 8, 21–30. https://doi.org/10.3329/jles.v8i0.20136","Hasnat, G. N. T., & Siddik, A. (2018). Land Use Changing Pattern and Challenges for Agricultural Activities: A Study on Dumki Upazila, Patuakhali. J. Patuakhali Sci. and Tech. Uni, 1(2), 67–76.","Islam, R., & Hassn, D. Z. (2013). Losses of Agricultural Land due to Infrastructural Development: A Study on Rajshahi District. International Journal of Scientific & Engineering Research, 4(11), 391–397.","Islam, S. (2014). Study on Impact of Land Fragmentation in Agriculture-A Case of Rajshahi District, Bangladesh. International Journal of Recent Research in Social Sciences and Humanities (IJRRSSH), 1(1), 54–61.","Kolakar, S., R., R. (1993). The Future of Our Land: Facing the Challenge. Hall of India Pvt. Ltd., New Delhi.","Mahbub, A. (2003b). Agricultural land loss and food security: An assessment. IRRI, Manila, The Philippines.","Rahman, M.T, H., M. N. (2013). Assessment of shifting of agricultural land to nonagricultural land in Bangladesh. SRDI, Ministry of Agriculture, Government of Bangladesh.","Rana, S., Faruk, O., Begum, N., & Tabassum, M. (2020). Trend Analysis of Land Utilization in Bangladesh. International Journal of Engineering Technologies and Management Research, 6(2), 39–46. https://doi.org/10.29121/ijetmr.v6.i2.2019.354","World Bank Report. (2018). Food and Agriculture Organization, Electronic Files and Website."]}

We have comparatively assessed five regression performance metrics namely, Mean Absolute Error, Mean Square Error, Root Mean Square Error, and . A total of twelve datasets were used for the study inclusive of nine real-world datasets and three simulated datasets. Apart from the and , analysis shows that the output of other performance metrics are not data dependent. Regarding the simulated datasets which complied with all the regression model assumptions, we discovered that any of the five performance metrics can be used on the data, to assess the strength or otherwise of a regression model. This is based on the fact that the individual outputs of the performance metrics on simulated data are identical. Keywords: Regression, Multiple Regression, Performance Metrices for Regression, Machine Learning. Title: A Comparative, Data-Based Assessment of Several Performance Metrics for Regression Author: Jude Chukwura Obi, Obimuanya Ijeoma International Journal of Novel Research in Physics Chemistry & Mathematics ISSN 2394-9651 Vol. 10, Issue 1, January 2023 - April 2023 Page No: 20-24 Novelty Journals Website: www.noveltyjournals.com Published Date: 10-March-2023 DOI: https://doi.org/10.5281/zenodo.7715854 Paper Download Link (Source) https://www.noveltyjournals.com/upload/paper/A%20Comparative,%20Data-Based%20Assessment-10032023-3.pdf, International Journal of Novel Research in Physics Chemistry & Mathematics, ISSN 2394-9651, Novelty Journals, Website: www.noveltyjournals.com

The study of traits in crops enables breeders to guide strategies for selecting and accelerating the progress of genetic breeding. Although the simultaneous evaluation of characteristics in the plant breeding programme provides large quantities of information, identifying which phenotypic characteristic is the most important is a challenge facing breeders. Thus, this work aims to quantify the best approaches for prediction and establish a network of better predictive power in flood-irrigated rice via methodologies based on regression, artificial intelligence, and machine learning. Multiple regression, computational intelligence, and machine learning were used to predict the importance of the characteristics. Computational intelligence and machine learning were notable for their ability to extract nonlinear information from model inputs. Predicting the relative contribution of auxiliary characteristics in rice through computational intelligence and machine learning proved to be efficient in determining the relative importance of variables in flood-irrigated rice. The characteristics indicated to assist in decision making are flowering, number of grains filled by panicles and length of panicles for this study. The network with only one hidden layer with 15 neurons was observed to be efficient in determining the relative importance of variables in flooded rice. The study of traits in crops enables breeders to guide strategies for selecting and accelerating the progress of genetic breeding. Although the simultaneous evaluation of characteristics in the plant breeding programme provides large quantities of information, identifying which phenotypic characteristic is the most important is a challenge facing breeders. Thus, this work aims to quantify the best approaches for prediction and establish a network of better predictive power in flood-irrigated rice via methodologies based on regression, artificial intelligence, and machine learning. Multiple regression, computational intelligence, and machine learning were used to predict the importance of the characteristics. Computational intelligence and machine learning were notable for their ability to extract nonlinear information from model inputs. Predicting the relative contribution of auxiliary characteristics in rice through computational intelligence and machine learning proved to be efficient in determining the relative importance of variables in flood-irrigated rice. The characteristics indicated to assist in decision making are flowering, number of grains filled by panicles and length of panicles for this study. The network with only one hidden layer with 15 neurons was observed to be efficient in determining the relative importance of variables in flooded rice.

Agricultural topsoils are characterized by a heterogeneous mixture of structural units with a considerable difference in soil mechanical properties. Mechanistic modeling of compaction for this layer is challenging because of the need to parameterize model input parameters. This study quantified soil deformation during loading in semi-confined conditions and searched for drivers decisive for observed strains with no a priori assumptions of material properties. Undisturbed soil samples were collected in the plough layer of eleven sandy and loamy soils in Denmark. The samples were adjusted to either of the following matric potentials: − 30, − 50, − 75, − 100, − 160 and − 300 hPa (pF from ∼1.5 to ∼2.5). Cores at each water condition were loaded with an annulus covering one third of the soil surface at either of the following normal loads: 30, 60, 90, 120, 150, 180 kPa. Multiple regression was performed to estimate the best model describing the variation in soil compressibility. Measured strain, ε, decreased with increase in soil organic matter, bulk density and pF, but the effect of pF was affected by soil clay content. The model explained ∼84 % of the variation in data and predicted well measured strain for two independent data sets. The regression model is suggested for prediction of soil deformation of arable topsoils in field traffic. A procedure is described for the prediction, including calculation of the relevant stress based on loading characteristics.

The primary goal of this research was to investigate the role of service quality in mediating the relationship between corporate social responsibility (CSR) and customer-based brand equity (CBBE) in tourism businesses in the Davao Region. The study utilized the quantitative non-experimental descriptive and correlational research design. There were 176 tourism enterprises surveyed for the independent variable of CSR determined by proportionate stratified sampling. Meanwhile, 384 tourists were also surveyed for the mediator variable of Service Quality and the independent variable of CBBE derived through snowball sampling. Google forms were employed to gather the primary data on the three variables of the study. The multiple regression analysis indicated that CSR predicted CBBE, CSR predicted Service Quality, and combined CSR and Service Quality statistically predicted CBBE. In the relationship between CSR and CBBE, the mediation study revealed partial mediation of Service Quality. The Medgraph pathway illustrated that Service Quality intervened with the effects that CSR had on CBBE. Service Quality statistically significantly reduced the total effect of CSR on CBBE. Thus, not only should CSR be improved, but Service Quality also needs to be enhanced for Davao Region to become a top tourism destination in the Philippines and Southeast Asia., {"references":["Ahn, J. & Back, K., 2018.Antecedents and consequences of customer brand engagement in integrated resorts, International Journal of Hospitality Management, vol. 75, pp. 144-152, https://doi.org/10.1016/j.ijhm.2018.05.020.","Ahrholdt, D.C., Gudergan, S.P. &Ringle, C.M., 2017. Enhancing service loyalty: the roles of delight, satisfaction, and service quality. Journal of Travel Research, vol. 56, no. 4, pp. 436-450, https://doi.org/10.1177/0047287516649058.","Al-Ababneh, J., 2017. Service quality in the hospitality industry, Journal of Tourism & Hospitality, vol. 6, no. 1, DOI: 10.4172/2167-0269.1000e133.","Ali, Z., & Bhaskar, S. B., 2016. Basic statistical tools in research and data analysis. Indian Journal of Anaesthesia, vol. 60, no. 9, pp. 662–669. https://doi.org/10.4103/0019-5049.190623","Almalki, S., 2016. Integrating Quantitative and Qualitative Data in Mixed Methods Research—Challenges and Benefits, vol. 5, no. 3, DOI:10.5539/jel.v5n3p288.","Arellano, A., 2019. The image of The Philippines as a tourism destination in Finland (Masters' thesis). Retrieved from https://www.theseus.fi/bitstream/handle/10024/342306/Thesis%20- %20Amarie%20Arellano%20%28Final%29.pdf?sequence=2.","Asian Development Bank, 2020.Reviving tourism amid the COVID-19 pandemic (ADB Briefs No. 150). http://dx.doi.org/10.22617/BRF200245-2","Asia-Pacific Economic Cooperation, 2017. Strengthening tourism business resilience against the impact of terrorist attack. Report on the APEC Counter-Terrorism Working Group's Workshop on 9-10 May 2017, Bali, Indonesia. Retrieved from https://www.apec.org/Publications/2017/09/Strengthening-Tourism-BusinessResilience-against-the-Impact-of-Terrorist-Attack"]}

Building fires are preventable incidents that have proven to be both deadly and costly. Addressing their root causes will lead to safer neighbourhoods for families and businesses to live and operate in. Multiple studies have established the effect of residents’ socioeconomic compositions on an area’s building fire rates; however, the existing model based on the classical stepwise regression procedure has several limitations. This paper aims to construct a more accurate predictive model of building fire rates based on a set of explanatory socioeconomic variables. In building the socioeconomic model, a backward elimination by Robust Final Predictor Error (RFPE) criterion is proposed to enhance the forecasting capability of the model. The proposed method has been implemented on the census data and the fire incident data of the South East Queensland region in Australia. A cross-validation was then conducted to assess the model’s accuracy. In addition, comparative analyses of other elimination criteria, such as p-value, adjusted R-squared, Akaike’s Information Criterion (AIC), Bayesian Information Criterion (BIC) and predicted residual error sum of squared (PRESS), were conducted. The cross-validation analyses demonstrate that the proposed criterion is a more accurate predictive model based on a couple of goodness-of-fit measures. All in all, the RFPE equation was found to be a suitable criterion for the backward elimination procedure in the socioeconomic modelling of building fires.

Countries are drastically impacted by financial and fiscal crises. Financial crises have the worst impact on not only society, but also the economy. The Canadian economy underwent financial crises and recessions several times during the last century. In this paper, daily closing stock prices of five large Canadian banks were studied during the last five crisis periods. It is aimed to determine the most effective or dominant index prices on the daily closing stock price of the banks during the crisis periods. The five periods were selected from secondary data from January 1975 to December 2020 by using the graphs and the crises in the literature. Multiple linear regression was performed to analyze the impact of price indexes during crisis periods. Findings show that “price index—financials” had a positive impact on the daily closing price of banks during the last five economic crises in Canada. Since the banks have different investment tools in their portfolio, the impacts of price indexes on the daily closing prices depend on these portfolios, which ultimately could have led to the economic crises.

Статья посвящена исследованию взаимосвязи показателя «стоимость квартиры» с различными факторами, влияющими на его формирование. Используя возможности табличного редактора, позволяющего быстро получать решение, был проведен корреляционно-регрессионный анализ. Данные были взяты из открытых источников по Липецкой области за период 2014–2021 годы, показатели рассматриваются в среднем. В результате проведенного исследования был сделан вывод, что наиболее значимым фактором, который влияет на стоимость квартиры, остается площадь квартиры. Полученная множественная регрессия отвечает требованиям, которые предъявляются экономико-математическим моделям и, с учетом значения ошибки аппроксимации, может быть использована для дальнейшего исследования и прогнозирования., The paper is devoted to the study of the interrelation between the indicator "cost of an apartment" and various factors, which can affect the cost. Using the capabilities of the tabular editor, which allows you to get a solution much faster, a correlation-regression analysis was carried out. The data were taken from open sources in the Lipetsk region and covered the period 2014-2021, indicators are considered on average. The study showed that the most significant factor, which affects the cost of the apartment remains the area of the apartment. The resulting multiple regression meets the requirements of economic and mathematical models and, taking into account the average relative error, can be used for the further research and forecasting.

Thai Science and Technology Journal, 31, 3, 32-45

Horticultural farms operate in a complex environment that is shaped, amongst other things, by the different stakeholders (e.g., consumers, trade, politics). Public approval of production and working methods is an important prerequisite for companies’ actions. Therefore, knowledge of a society’s expectations and perceptions of horticultural products, services and production methods is an important strategic resource for aligning business strategies and active communication with society. The aim of this work is to examine society’s expectations and perceptions using the examples of “sustainability” and “reputation” in order to support this challenge. The first study of this thesis provides a broad overview of different sustainability attributes for flowers and ornamental plants (non-food) as well as for fruits and vegetables (food) from the con-sumers’ point of view. Sustainability attributes collected from the literature were grouped together in a first step. Based on these results, an exploratory online consumer survey was conducted in a second step for both food and non-food horticultural products. A confirmatory factor analysis con-firmed the four dimensions of the literature research and highlighted the particular relevance of the ecological sustainability characteristics. The additional studies comprise an analysis of the reputation of the horticultural sector. The initial focus of these additional studies was the development of a structural model for measuring reputa-tion, specifically for horticulture. Based on a literature analysis, terms that are often used synony-mously (image, identity) were defined and indicators of reputation measurement approaches from different areas (e.g., companies, branches) were collected. Reputation is epistemically related to the indicators (formative vs. reflective) that are represented in a “multiple indicators and multiple causes” (MIMIC) model and can be supplemented by the influence of moderating variables. The following chapter describes how the theoretically elaborated model was evaluated and com-pleted with the help of experts (n = 102). The statistical analysis using multiple regression and explorative factor analysis resulted in a model with a total of 15 indicators. A reputation map illustrat-ing the greater influence of the service segments on the reputation of horticulture compared to the production segments was created. The final study focuses on the evaluation of the model using the example of gardening and land-scaping. Based on a consumer sample (n = 752), it could be shown that the reputation of gardening and landscaping was assessed as positive. The reputation of the segment is mainly shaped by production methods, economic performance and industry development. This shows that only some of the indicators are relevant for reputation management., Gartenbaubetriebe agieren in einem komplexen Umfeld, das sich aus verschiedenen Anspruchs-gruppen (z. B. Konsument, Handel, Politik) zusammensetzt. Eine öffentliche Zustimmung zur Arbeits- und Produktionsweise ist eine wichtige Voraussetzung für das unternehmerische Handeln. Deshalb ist Wissen über die Erwartungen der Gesellschaft und die Wahrnehmung der gärtneri-schen Produkte, Dienstleistungen und Produktionsweisen eine wichtige strategische Ressource für eine aktive Kommunikation mit der Gesellschaft. Ziel dieser Arbeit ist es, die gesellschaftliche Erwartung und Wahrnehmung an den Beispielen „Nachhaltigkeit“ und „Reputation“ zu untersuchen. Zunächst gibt eine erste Studie einen Überblick über verschiedene Nachhaltigkeitsmerkmale für Blumen und Zierpflanzen (Non-Food) sowie für Obst und Gemüse (Food) aus Sicht der Verbraucher. Anschließend werden die aus der Literatur gesammelten Nachhaltigkeitsmerkmale gruppiert und in einer explorativen Online-Verbraucherbefragung bewertet. Eine konfirmatorische Faktorenanalyse bestätigt die vier Dimensionen der zugrundeliegenden Literatur und stellt die besondere Relevanz der ökologischen Nachhaltigkeitsmerkmale heraus. Weitere Studien fokussieren sich auf die Messung der Reputation des Gartenbausektors. Basierend auf einer Literaturanalyse werden zunächst vielfach synonym verwendete Begriffe (Image, Identität) definiert und Indikatoren von Reputationsmessansätzen aus verschiedenen Bereichen (z. B. Unternehmen, Branchen) gesammelt. Die Reputation steht zu den Indikatoren in einer epistemischen Beziehung (formativ vs. reflektiv), die in einem „multiple indicators and multiple causes“ (MIMIC) Modell dargestellt wird und durch den Einfluss von moderierenden Variablen ergänzt werden kann. Im darauffolgenden Kapitel wird das theoretisch ausgearbeitete Modell mit Hilfe von Expert*innen (n = 102) evaluiert und vervollständigt. Die statistische Analyse mittels multipler Regression und explorativer Faktorenanalyse führt zu einem Modell mit insgesamt 15 Indikatoren. Eine Reputationslandkarte verdeutlicht den größeren Einfluss der Dienstleistungssegmente auf die Reputation des Gartenbaus im Vergleich zu den Produktionssparten. Eine abschließende Studie konzentriert sich auf die Evaluierung des Modells am Beispiel des Garten- und Landschaftsbaus. Anhand einer Konsumentenbefragung (n = 752) konnte gezeigt werden, dass der Ruf des Garten- und Landschaftsbaus als positiv bewertet wird. Das Ansehen der Sparte wird vor allem durch die Produktionsweise, die wirtschaftliche Leistungsfähigkeit und die Branchenentwicklung geprägt. Daraus wird deutlich, dass nur wenige Indikatoren für das Reputationsmanagement relevant sind.

The current study aims to explore personal nostalgia’s role in reducing suicidal ideation when mediated by the increase of meaning in life. All three variables will be measured using their respective questionnaires and a writing task recalling fond personal memories in the past will be given to induce nostalgia. Mediation analysis will be performed through Baron and Kenny's (1986) method of multiple regression.

This study will examine possible correlations between rigid perfectionism, a PID-V trait, and conscientiousness, a BFAS trait. A four-wave longitudinal data set will be used in which participants and informants answered questionnaires regarding participants. The surveys relevant to this study are the Big Five Aspect Scale and the Personality Inventory for DSM-V.

Ultrasonic pulse velocity (UPV) is a non-destructive measurement technique with which the quality of any concrete element can be evaluated. It provides information on concrete health and for assessing the need for repair in a straightforward manner. In this paper, the relationship is studied between UPV readings and the mechanical behavior of self-compacting concrete (SCC) containing coarse, fine, and/or powdery RA. To do so, correlations and simple- and multipleregression relationships between compressive strength, modulus of elasticity, splitting tensile strength, flexural strength, and UPV readings of nine SCC mixes were assessed. The correlations showed that the relationship of UPV with any mechanical property was fundamentally monotonic. The inverse square-root model was therefore the best-fitting simple-regression model for all the mechanical properties, although for bending-tensile-behavior-related properties (splitting tensile strength and flexural strength) the estimation accuracy was much lower than for compressive-behavior-related properties (compressive strength and modulus of elasticity). Linear-combination multiple-regression models showed that the properties related to bending-tensile behavior had a minimal influence on the UPV value, and that their introduction resulted in a decreased estimation accuracy. Thus, the multiple-regression models with the best fits were those that linked the compressive-behavior-related properties to the UPV readings. This therefore enables the estimation of the modulus of elasticity when the UPV and compressive strength are known with a deviation of less than ±20% in 87% of the SCC mixes reported in other studies available in the literature., This research was funded by the Spanish Ministry of Universities, MICIN, AEI, EU, ERDF, and NextGenerationEU/PRTR, grant numbers PID2020-113837RB-I00, 10.13039/501100011033, and TED2021-129715B-I00; the Junta de Castilla y León (Regional Government) and ERDF, grant number UIC-231; and the University of Burgos, grant numbers SUCONS, Y135.GI.

Introduction: Left ventricular (LV) diastolic dysfunction and structural abnormalities are common cardiac changes in hypertension (HTN), and several factors other than high blood pressure (BP) may play a role in these changes. The aim of this study was to reveal the relationship between triglyceride glucose (TyG) index, a novel parameter for insulin resistance (IR), with LV diastolic function and structure in hypertensive patients. Material and method: A total of 119 newly diagnosed, untrated hypertensive patients free of diabetes and/or cardiovascular complications were included in this study. IR was estimated with the TyG index calculated from ln [fasting TG (mg/dL) × fasting blood glucose (mg/dL)/2]. Two-dimensional and Doppler echocardiographic examinations were performed to assess LV diastolic functions and structure. Results: Based on median TyG index, 51 patients was assigned as group I (8.7). In patients with high TyG index, left atrial volume index (LAVi) (p .05). Stepwise multiple regression analysis demonstrated that the relationship of TyG index with LV diastolic function and structure was independent of potential confounders (all p

V prispevku na primeru jelke predstavljamo sedem regresijskih modelov za modeliranje priraščanja dreves s podatki periodičnih meritev na stalnih vzorčnih ploskvah. Poleg polinomske regresije, modela z dodanim šumom in mešanega linearnega modela, predstavljamo regresijo z naravnimi zlepki in tri modele z omejenimi odvisnimi spremenljivkami: truncated regression, tobit regression in grouped data regression. Modele lahko uporabimo, kadar se zaradi načina merjenja in zaokroževanja podatkov ter hierarhičnosti podatkov srečamo z rezanimi ali krnjenimi slučajnostnimi spremenljivkami, nezveznostjo odvisne spremenljivke in pristransko oceno prirastka. Pri pojasnitvi debelinskega priraščanja 21.013 jelk na 4.405 ploskvah v obdobju 1990–2014 v raznomernih gozdovih v dinarskih jelovo-bukovjih so vsi modeli pokazali podoben vpliv prsnega premera, sestojne temeljnice, temeljnice debelejših dreves, raznomernosti, nagiba, nadmorske višine in le manjše razlike v regresijskih koeficientih in merah prileganja. Največje povprečne napovedi prirastka daje tobit model, mešani model pa se najbolj prilega podatkom. V primerjavi z drugimi modeli model z zlepki kaže na počasnejše zmanjševanje prirastka zelo debelih jelk po kulminaciji prirastka. We present seven alternative statistical models for modelling tree diameter increment with data from permanent sampling plots. In addition to the polynomial regression model, we present a regression model with added random noise, a mixed linear model, regression with natural splines, and three models with limited dependent variables: truncated regression, tobit regression and grouped data regression. The models may be used when dealing with truncated or censored variables, biased estimation of the increment due to censoring and rounding down, or when having multilevel data. The parametrization of the models was done using 21,013 fir trees on 4,405 plots in the period 1990–2014 in uneven-aged Dinaric fir-beech forests. All models showed a similar effect of tree diameter, stand basal area, basal area of larger trees, diameter structure diversity, altitude and slope. There were only minor differences in the regression coefficients and fit measures. The highest increment predictions were given by the tobit model. The mixed model fit the data best and, compared to the other models, predicted a slower decrease in the growth of large-diameter trees after growth culmination.

As Photovoltaic (PV) energy is impacted by various weather variables such as solar radiation and temperature, one of the key challenges facing solar energy forecasting is choosing the right inputs to achieve the most accurate prediction. Weather datasets, past power data sets, or both sets can be utilized to build different forecasting models. However, operators of grid-connected PV farms do not always have full sets of data available to them especially over an extended period of time as required by key techniques such as multiple regression (MR) or artificial neural network (ANN). Therefore, the research reported here considered these two main approaches of building prediction models and compared their performance when utilizing structural, time-series, and hybrid methods for data input. Three years of PV power generation data (of an actual farm) as well as historical weather data (of the same location) with several key variables were collected and utilized to build and test six prediction models. Models were built and designed to forecast the PV power for a 24-hour ahead horizon with 15 min resolutions. Results of comparative performance analysis show that different models have different prediction accuracy depending on the input method used to build the model: ANN models perform better than the MR regardless of the input method used. The hybrid input method results in better prediction accuracy for both MR and ANN techniques, while using the time-series method results in the least accurate forecasting models. Furthermore, sensitivity analysis shows that poor data quality does impact forecasting accuracy negatively especially for the structural approach.

Body condition is an important concept in behaviour, evolution and conservation, commonly used as a proxy of an individual's performance, for example in the assessment of environmental impacts. Although body condition potentially encompasses a wide range of health state dimensions (nutritional, immune or hormonal status), in practice most studies operationalize body condition using a single (univariate) measure, such as fat storage. One reason for excluding additional axes of variation may be that multivariate descriptors of body condition impose statistical and analytical challenges. Structural equation modelling (SEM) is used in many fields to study questions relating multidimensional concepts, and we here explain how SEM is a useful analytical tool to describe the multivariate nature of body condition. In this 'Research Methods Guide' paper, we show how SEM can be used to resolve different challenges in analysing the multivariate nature of body condition, such as (a) variable reduction and conceptualization, (b) specifying the relationship of condition to performance metrics, (c) comparing competing causal hypothesis and (d) including many pathways in a single model to avoid stepwise modelling approaches. We illustrated the use of SEM on a real-world case study and provided R-code of worked examples as a learning tool. We compared the predictive power of SEM with conventional statistical approaches that integrate multiple variables into one condition variable: multiple regression and principal component analyses. We show that model performance on our dataset is higher when using SEM and led to more accurate and precise estimates compared to conventional approaches. We encourage researchers to consider SEM as a flexible framework to describe the multivariate nature of body condition and thus understand how it affects biological processes, thereby improving the value of body condition proxies for predicting organismal performance. Finally, we highlight that it can be useful for other multidimensional ecological concepts as well, such as immunocompetence, oxidative stress and environmental conditions.

This article addresses the possibility of improving the traditional bus passenger demand forecasting models by leveraging additional data from relevant big data systems and proposes a conceptual framework for developing big data-based forecasting models. Based on the data extracted from available big data systems, the authors have developed a conceptual procedural framework for determining the significance of statistical indicators that can potentially be used as predictor variables for forecasting future passenger demand. At the first stage of the proposed framework, the statistical significance of partial linear correlations between observed statistical indicators and bus ridership demand are determined. All statistical indicators identified as potentially significant are further tested for multicollinearity, homoscedasticity, autocorrelation and multivariate normality to determine the suitability of their inclusion in the final equation of the prediction model. The final formulation of the predictive model was developed using stepwise regression. The R programming language was used to implement the proposed procedural framework to develop a model suitable for predicting passenger demand on the Prizren-Zagreb international bus route. Two predictor variables identified as the most statistically significant are the population of Kosovo and the annual number of Kosovo citizens crossing the Croatian border by bus.

Chlorophyll a (chl-a) concentration is an important parameter for evaluating the degree of water eutrophication. Monitoring it accurately through remote sensing is thus of great significance for early warnings of water eutrophication, and the inversion of water quality from UAV images has attracted more and more attention. In this study, a regression method to estimate chl-a was proposed; it used a small multispectral UAV to collect data and took the vegetation indices as intermediate variables. For this purpose, ten monitoring points were selected in Erhai Lake, China, and two months of monitoring and data collection were conducted during a cyanobacterial bloom period. Finally, 155 sets of valid data were obtained. The imaging data were obtained using a multispectral UAV, water samples were collected from the lake, and the chl-a concentration was obtained in the laboratory. Then, the images were preprocessed to extract the information from different wavebands. The univariate regression of each vegetation index and the regression using band information were used for comparative analysis. Four machine learning algorithms were used to build the model: support vector machine (SVM), random forest (RF), extreme learning machine (ELM), and convolutional neural network (CNN). The results showed that the effect of estimating the chl-a concentration via multiple regression using vegetation indices was generally better than that via regression with a single vegetation index and original band information. The CNN model obtained the best results (R2 = 0.7917, RMSE = 8.7660, and MRE = 0.2461). This study showed the reliability of using multiple regression based on vegetation indices to estimate the chl-a of surface water.

Nowadays, sensor-based air pollution sensing systems are widely deployed for fine-grained pollution monitoring. In-field calibration plays an important role in maintaining sensory data quality. Determining the model structure is challenging using existing methods of variable global fitting models for in-field calibration. This is because the mechanism of interference factors is complex and there is often insufficient prior knowledge on a specific sensor type. Although Artificial-Neuron-Net-based (ANN-based) methods ignore the complex conditions above, they also have problems regarding generalization, interpretability, and calculation cost. In this paper, we propose a clustering-based segmented regression method for particulate matter (PM) sensor in-field calibration. Interference from relative humidity and temperature are taken into consideration in the particulate matter concentration calibration model. Samples for modeling are divided into clusters and each cluster has an individual multiple linear regression equation. The final calibrated result of one sample is calculated from the regression model of the cluster the sample belongs to. The proposed method is evaluated under in-field deployment and performs better than a global multiple regression method both on PM2.5 and PM10 pollutants with, respectively, at least 16% and 9% improvement ratio on RMSE error. In addition, the proposed method is insensitive to reduction of training data and increase in cluster number. Moreover, it may bear lighter calculation cost, less overfitting problems and better interpretability. It can improve the efficiency and performance of post-deployment sensor calibration.

Paleoecological reconstructions of hydrological regimes in tropical peatlands during the Holocene are important for the estimation of their responses to changing environments. However, the application of some widely used proxies, such as testate amoebae, is hampered by poor knowledge of their morphology and ecological preferences in the region. The aim of this study is to describe the morphospecies composition of sub-fossil testate amoebae in deposits of a tropical peatland in Central Sumatra (Indonesia) during the Holocene and reconstruct the hydrological regime using morphospecies- and functional-trait-based approaches. In total, 48 testate amoeba morphospecies were observed. Based on morphospecies composition, we distinguished three main periods of peatland development (13,400–8000, 8000–2000, 2000 cal yr BP–present). The application of the morphospecies-based transfer function provided a more reliable reconstruction of the water regime in comparison to the functional trait-based one. The weak performance of the latter might be related to the poor preservation of shells and the greater variation in the functional traits in sub-fossil communities as compared to the training set and linear modeling approach. These results call for future studies on the functional and morphospecies composition of testate amoebae in a wider range of tropical peatlands to improve the quality of hydrological reconstructions.

A recently developed solid-state joining technique called “friction stir spot welding” (FSSW) is used to combine metal alloys with low weldability. Significant experimental work for this study is concentrated on the FSSW of AA6063 and AA5083. The study involved fixture setup and welding pattern parameters for the sizes of the material, all of which were done on a milling machine. A cylindrical shoulder with a concave (10°) square pin profile was taken into consideration using various process parameters. The tool material is heat-treated high-carbon steel. The Taguchi L9 orthogonal array is employed for the experimental model. The influence of FSSW factors including spindle speed, time, and depth was studied, along with the relationship between tensile strength, hardness at the contact region, and hook development, which would be a nominal metallurgical pledge established in the welding zone among the interacting sheet plate. The hook structure was found in the area of contact, and thermomechanical effects were seen in the spot-welding area. It has been determined that the influencing variables are spindle speed and plunge depth. As a consequence, it was determined that a tool spindle speed of 1200 rpm, a time of 25 seconds, and a plunge depth of 0.10 mm offer better mechanical properties. The microstructural analysis of welded region was carried out using a scanning electron microscope (SEM).

Urban rail transit (URT) promotes sustainable urban development by alleviating traffic congestion and environmental degradation. However, many cities have developed URT projects recently, often encumbering local governments with debt. Land value capture (LVC) is an important theory that explores the new financing modes for urban rail transit. Despite prior studies that have found a positive relationship between URT and property prices based on LVC, limited empirical studies explore sustainable paths to better examine the effect of URT on property prices. This study collects 1036 properties in Xuzhou, China. Meanwhile, multiple regression models are established to analyze the impact mechanism of URT on property prices, and to further examine the combination effects of multidimensional neighborhood infrastructure and URT on property prices from the perspective of sustainable development. The results show that the coefficients of URT in all models are negative, indicating that the property prices decrease as the distance from the URT to the property increases, and the positive coefficient of TRANS implies that the transfer station would raise house prices. Combining Park, School (and Hospital) variables with URT, respectively, the negative coefficients of URT increase from 0.0435 to 0.0846 and 0.0525, and these URT variables are significant, indicating that parks, schools, and hospitals can enhance the effect of URT on property prices. However, adding the Shopping variable, the negative value of URT drops from 0.0435 to 0.0192, and is not significant, which means shopping centers have a restraining impact. Moreover, the combination of parks and schools (and hospitals) can better enhance the effect of URT on property prices (the highest URT negative coefficient is 0.0870). This study can provide a reference for the government to facilitate URT planning and better decision-making.

The Compressional-wave (Vp) data are useful for reservoir exploration, drilling operations, stimulation, hydraulic fracturing employment, and development plans for a specific reservoir. Due to the different nature and behavior of the influencing parameters, more complex nonlinearity exists for Vp modeling purposes. In this study, a statistical relationship between compressional wave velocity and petrophysical parameters was developed from wireline log data for Jeribe formation in Fauqi oil field south Est Iraq, which is studied using single and multiple linear regressions. The model concentrated on predicting compressional wave velocity from petrophysical parameters and any pair of shear waves velocity, porosity, density, and fluid saturation in carbonate rocks. A strong linear correlation between P-wave velocity and S-wave velocity and between P-wave velocity and density rock was found. The resulting linear equations can be used to estimate P-wave velocity from the S-wave velocity in the case of both. The results of multiple regression analysis indicated that the density, porosity, water-saturated, and shear wave velocity (VS) are strongly related to Vp.

Whether intellectually gifted children have a greater emotional response when tested is still unclear. This may be due to the marked heterogeneity of this particular population, and the fact that most studies lack the power to reduce the noise associated with this heterogeneity. The present study examined the relationship between performance and emotional response in 468,423 Italian fifth-graders taking a national test on mathematics and language. Analyses were performed using statistical models with polynomial terms. Special attention was paid to estimating the mean emotional response of the children who were gifted (1.5-2.5 standard deviations above the mean) or highly gifted (more than 2.5 standard deviations above the mean). The results showed that, although a lower emotional response correlated with a higher achievement, this relationship is nonlinear, and the estimates for gifted and highly gifted children were virtually the same. Girls showed a greater emotional response than boys on all levels of performance. The theoretical and practical implications of these findings are discussed.

Percentage of body fat, age, weight, height, and 14 circumference measurements (e.g., waist) are given for 184 women aged 18–25. Body fat, one measure of health, was accurately determined by an underwater weighing technique which requires special equipment and training of the individuals conducting the process. Modeling body fat percentage using multiple regression provides a convenient method of estimating body fat percentage using measures collected using only a measuring tape and a scale. This dataset can be used to show students the utility of multiple regression and to provide practice in model building.

The interannual changes in ice coverage in the Greenland Sea for the winter (December–April), spring (May– June), summer (July–September), and autumn (October–November) seasons for the period 1950-2018 are considered. The presence of negative linear trends and the polycyclic oscillations of the ice coverage variability for all seasons has been confirmed. Using spectral analysis, the dominant fluctuations from 5 to 22 years were identified. The cross-correlation method allowed us to determine the significant relationship of the Greenland Sea ice coverage with hydrometeorological and astrogeophysical factors. The statistically significant relationship of the ice coverage for a concrete year with similar characteristics for a previous period persisting up to three years had been noted. The highest cross-correlation coefficients were noted in the winter and spring seasons. The ice coverage of the autumn season demonstrates the persistence of the inertia of ice conditions for up to two years. The analysis of correlations with astrogeophysical parameters revealed the closest relationship between the ice coverage and the longitude coordinate of the Earth's pole position, the nutation parameters of the Earth's axis, and the distance between the Earth and the Sun. When constructing the multi-regression equations, we investigated the informativeness of various hydrometeorological and astrogeophysical factors in the models of the ice coverage variability for each season. The following estimates of quality of the models were obtained: correlation coefficients (up to 0.89), determination coefficients (to up 0.80), and a model reliability which depends on the admissible forecast error and includes the mean square deviation of the investigated value) – up to 99%). The informativeness of various factors was estimated and the contribution to the total variance was revealed: hydrometeorological factors – up to 70%; astrogeophysical factors – up to 50%. The obtained statistical equations can be used for the diagnosis and for development of methods for the very-long-term forecast of the Greenland Sea ice coverage.

Within urban green spaces, tree species diversity is believed to correlate with aboveground biomass, though there is some disagreement within the literature on the strength and directionality of the relationship. Therefore, we assessed the relationship between the biodiversity of woody species and the aboveground biomass of woody plant species in the tropical, coastal city of Haikou in southern China. To accomplish this, we obtained comprehensive tree and site data through field sampling of 190 urban functional units (UFUs, or work units) corresponding to six types of land uses governmental‐institutional, industrial‐commercial, park‐recreational, residential, transport infrastructure, and undeveloped area. Based on our field data, we investigated the relationship between tree species diversity and aboveground biomass using multiple regression, which revealed significant relationships across all five types of land uses. Aboveground biomass in green spaces was also correlated with anthropogenic factors, especially time since urban development, or site age, annual maintenance frequency by human caretakers, and human population density. Among these factors, maintenance is the strongest predictor of aboveground biomass in urban green space. Therefore, this study highlights the critical role of maintenance of urban green space in promoting both aboveground biomass and woody biodiversity in urban ecosystems and, consequently, on urban ecosystem services. Our findings contribute to a deeper understanding of the ecosystem services provided by communities of woody plant species in urban areas., We investigated the relationship between tree species diversity and aboveground biomass using multiple regression, which revealed significant relationships across all five types of land uses. Aboveground biomass in green spaces was also correlated with anthropogenic factors, especially time since urban development, or site age, annual maintenance frequency by human caretakers, and human population density. Among these factors, maintenance is the strongest predictor of aboveground biomass in urban green space. Therefore, this study highlights the critical role of maintenance of urban green space in promoting both aboveground biomass and woody biodiversity in urban ecosystems and, consequently, on urban ecosystem services. Our findings contribute to a deeper understanding of the ecosystem services provided by communities of woody plant species in urban areas.

In this research we examined the effects of domain-specific knowledge on the relationships between creative mindsets and creative performance in the context of ESL writing

The COVID-19 pandemic suddenly and forever changed the lives of many undergraduate students around the globe, forcing them to switch to online learning while undergoing social confinement within their homes. It is now well-established that this prolonged period of uncertainty impacted students’ well-being, health, and academic achievement. However, how student-related factors, such as coping strategies as well as sociodemographic, contextual, and technological variables, are linked to digital education factors is currently less understood. Using multiple regression analysis, this study investigates the results of an online questionnaire administered to students from two universities in southern Italy, differing in positioning and size, as well as policies and attitude towards digital learning. The results of this study show the positive effects of expertise with digital devices and university digital learning policies on students’ perceived general health. Conversely, isolation and lack of relational connectedness negatively impacted students’ health. Furthermore, this study highlights the role of different coping strategies, demonstrating that active forms of coping have a positive effect on students’ health, whereas avoidance strategies have the opposite effect. Taken together, this study provides crucial links between the many factors influencing students’ experience with online learning and health, and provides useful indications to promote the uptake of and adaptation to online learning.

En la sociedad chilena, la educación constituye un valor muy apreciado por la población, que ve en ella la única forma de progresar en la vida. Por ello, la mayoría de las personas se encuentran dispuestas a realizar grandes esfuerzos para obtener un buen nivel educacional (Oliva, 2018). Sin embargo, existen factores vinculados a desigualdades socioeconómicas y demográficas de origen que obstruyen la posibilidad de dedicar más tiempo de vida a los estudios, de obtener un buen desempeño educativo, o de prevenir la deserción o retraso. La presente investigación busca analizar en que medida las variables nivel de escolaridad alcanzado por la madre, sexo y pobreza multidimensional, influyen en los años de escolaridad de las personas, entendiendo que dichas variables proveen información sobre las circunstancias socioeconómicas que caracterizarán la trayectoria educacional. Se elaborará un modelo de regresión múltiple a partir de los datos proporcionados por la encuesta CASEN 2017.

Across the globe, obesity has increased almost threefold over the past half a century (World Health Organization, 2020). Over half of the global adult population was overweight or obese in 2016. This is a huge problem given that obesity is connected to adverse effects on both health and the economy (Ford Runge, 2007; Hruby et al., 2016). Therefore, it is noteworthy that levels of obesity are not increasing equally across the globe. 27% of the UK adult population was obese in 2016, whilst only 7% of the Chinese adult population was obese (OECD, 2017). Even across regions, there are vast disparities in obesity rates. In Africa, the obesity rate varies from 4.5% in Ethiopia to 32.5% in Libya (OECD, 2016). The question therefore arises: what explains this disparity? Countries with high levels of food insecurity are for obvious reasons not facing rising levels of obesity. However, it is curious that countries in similar developmental stages have vastly different obesity rates. For example, the Republic of Korea and Luxembourg are almost the same on the Human Development Index (UNDESA, 2019). However, Luxembourg has a rate of obesity four times that of the Republic of Korea (OECD, 2016). What is it about particular countries and cultures that make the population more resistant to gaining weight? There are numerous examples in the literature of cultural differences surrounding food. These differences are often found when comparing cultures that differ along an individualist/collectivist axis (Triandis, 1988). In individualist cultures, the values, attitudes, and goals of an individual determine social behaviours. In collectivist cultures, the values, attitudes, and goals of the group determine social behaviours. Cross-cultural studies comparing individualist and collectivist cultures have reported differences. For example, in Canada and New Zealand (both individualist cultures), people tend to overestimate the amount of food on a small dish (Peng et al., 2017). This phenomenon has been named the plate size effect. However, the effect was not found in Chinese and Korean samples (both collectivist cultures). Similarly, in Korea and Japan (collectivist cultures), eating is only thought of as late after 20:00-21:00 (Hernandez et al., 2016, Watanabe et al., 2016). In the UK and US (individualist cultures), eating is thought of as late one hour earlier, after 19:00-20:00. Such behavioural differences affect BMI in the long run: living in an individualist culture is significantly associated with higher BMI relative to living in a collectivist culture (Masood et al., 2019). The primary aim of this study is to look at the relationship of mindful eating, episodic memory, and cognitive distraction with BMI. The current evidence base for these individual differences in eating behaviours is in Western, Educated, Industrialised, Rich, and Democratic (WEIRD) populations (Henrich et al., 2010). This unrepresentative sampling means the findings cannot be generalised justifiably. Therefore, this study will look at samples in the UK, an individualist culture and China, a collectivist culture. All three of the predictors have both associative and experimental evidence suggesting a significant relationship with BMI. Mindfulness is the non-judgemental awareness of the present (Framson et al., 2009). Higher levels of mindfulness while eating are associated with a lower BMI (Hinton et al., in preparation). One study used an audio recording that encouraged participants to focus their attention on their senses whilst eating lunch (Seguias & Tapper, 2018). These participants consumed significantly less of a snack than the control group, two hours after their lunch. Episodic memory is the memory of our own experiences. Experimental evidence shows that the hunger experienced after a meal, two and three hours later, is related to perceived rather than actual consumption (Brunstrom et al., 2012). Associative evidence suggests a negative correlation between episodic memory and uncontrolled eating (Martin et al., 2018), and between episodic memory and BMI (Cheke, Simons & Clayton, 2016). Cognitive distraction is the lack of attention paid to the task at hand. Eating lunch and watching television simultaneously results in increased subsequent consumption of cookies, relative to a control group who did not watch television (Higgs & Woodward, 2009). Playing computer games whilst eating lunch leads to increased subsequent snack consumption and decreased memory of the lunch (Oldham-Cooper et al., 2011). The secondary aim of this study is to look at the relationship of eating rate, the social facilitation effect and models of hunger with BMI. There is a close relationship between eating rate and obesity (e.g. Sakurai et al., 2012; Sasaki et al., 2003). Eating fast will increase weight, while slow eating can control weight (Ohkuma et al., 2015). The social facilitation effect is the phenomenon that people tend to consume more food when surrounded by other people (Hernan, 2015; Hetherington et al., 2006). Social behaviour is more influenced by the individual in individualist cultures. Therefore, we expect that the social facilitation effect will be weaker in individualist cultures. On the other hand, social behaviour is more influenced by the group in collectivist cultures. Therefore, we expect that the social facilitation effect will be stronger in collectivist cultures. Models of hunger assess the extent to which an individual has set-point beliefs (Assanand et al., 1998). An example of a set-point belief is, "It is unhealthy for me to stop eating if I am feeling hungry". The original research had a solely Canadian sample. However, there are cross-cultural differences in eating motivations, namely physical, environmental, or emotional (Hawks et al., 2003). Therefore, it is important to investigate models of hunger, or the prevalence of set-point beliefs, across cultures. In summary, this study will investigate the relationships between mindful eating, episodic memory, cognitive distraction and BMI as its primary aim. This study will examine the relationships between eating rate, the social facilitation effect, models of hunger and BMI as its secondary aim. We will recruit samples from the UK and China and collect data using an online survey. Separately analysing data from the UK and China will enable us to test associations of mindful eating, episodic memory, and cognitive distraction with BMI in non-WEIRD populations for the first time. Comparing data from the UK and China will enable us to investigate differences and similarities in eating behaviours between the UK, an individualist culture, and China, a collectivist culture.

The film industry has great importance for both global and local economies. In the process, movie studios and producers try to predict box office success due to financial risk management. It is necessary to increase knowledge about the factors affecting the box office of a movie and the relationship between factors. The purpose of this paper is to analyze the determinants of box office revenue in the movie industry in Turkey. The study sample consists of 1,178 movies released and the time spans between 2015 and 2019. Based on the findings, an increasing number of theaters and weeks, sequels and school breaks have a positive and significant effect on total revenues. In addition, if movies are released in November, December, January, and February, they show good financial performance. The results also indicate audiences prefer science-fiction, fantasy, and biography movies. Finally, the movies in the market distributed by Warner Bros. Pictures (WB) and United International Pictures (UIP) have a positive and significant effect on total revenues., Film endüstrisi hem küresel hem de yerel ekonomiler için büyük önem taşımaktadır. Bu süreçte, film stüdyoları ve yapımcılar finansal risk yönetimi nedeniyle gişe başarısını tahmin etmeye çalışmaktadırlar. Bir filmin gişesini etkileyen faktörler ve faktörler arasındaki ilişkiler hakkında bilgi birikiminin arttırılması gerekmektedir. Bu makalenin amacı, Türkiye’de sinema sektöründe gişe hasılatı faktörlerini analiz etmektir. Çalışmanın örneklemini 2015-2019 yılları arasında vizyona giren 1.178 film oluşturmaktadır. Analiz sonuçlarına göre sinema salonlarının ve haftalarının, devam filmlerinin ve tatil sayılarının artması filmlerin toplam gelirlerini pozitif ve önemli ölçüde etkilemektedir. Ayrıca, filmler Kasım, Aralık, Ocak ve Şubat aylarında yayınlandıklarında, iyi finansal performans göstermektedirler. Sonuçlar ayrıca bilim kurgu, fantastik ve biyografi filmlerinin, filmlerin toplam geliri üzerinde pozitif ve önemli bir etkiye sahip olduğunu göstermektedir. Son olarak Warner Bros Pictures (WB) ve United International Pictures (UIP) tarafından piyasada dağıtılan filmler, gelir üzerinde istatistiksel olarak önemli ve pozitif bir etkiye sahiptir.

Forest biomass is a key index to comprehend the changes of ecosystem productivity and forest growth and development. Accurate acquisition of single tree scale biomass information is of great significance to the protection, management and monitoring of forest resources. LiDAR technology can penetrate the forest canopy and obtain information on the vertical structure of the forest. Aerial photography technology has the advantages of low cost and high speed, and can obtain information on the horizontal structure of the forest. Therefore, in this study, multispectral imagery and LiDAR data were integrated, and a part of the Zengcheng Forest Farm in Guangdong Province was selected as the study area. Large-scale and high-precision Eucalyptus biomass estimation research was gradually carried out by screening influencing factors and establishing models. This study compared and analysed the performance of multiple stepwise regression methods, random forest algorithms, support vector machine algorithms and decision tree algorithms for Eucalyptus biomass estimation to determine the best method for Eucalyptus biomass estimation. The results demonstrated that the accuracy of the model established by the machine learning method was higher than that of the linear regression model, and in the machine learning model, the random forest model had the best performance on both the training set (R2 = 0.9346, RMSE = 8.8399) and the test set (R2 = 0.8670, RMSE = 15.0377). RF was more suitable for the biomass estimation of Eucalyptus in this study. The spatial resolution of Eucalyptus biomass distribution was 0.05 m in this study, which had higher accuracy and was more accurate. It can provide data reference for the details about biomass distribution of Eucalyptus in the majority of provinces, and has certain practical reference significance.

Vrana Lake on the karst island of Cres (Croatia) is the largest freshwater lake in the Mediterranean islands. The lake cryptodepression, filled with 220 million m3 of fresh drinking water, represents a specific karst phenomenon. To better understand the impact of water level change drivers, the occurrence of meteorological and hydrological droughts was analysed. Basic machine learning methods (ML) such as the multiple linear regression (MLR), multiple nonlinear regression (MNLR), and artificial neural network (ANN) were used to simulate water levels. Modelling was carried out considering annual inputs of precipitation, air temperature, and abstraction rate as well as their influential lags which were determined by auto-correlation and cross-correlation techniques. Hydrological droughts have been recorded since 1986, and after 2006 a series of mostly mild hot to moderate hot years was recorded. All three ML models have been trained to recognize extreme conditions in the form of less precipitation, high abstraction rate, and, consequently, low water levels in the testing (predicting) period. The best statistical indicators were achieved with the MNLR model. The methodologies applied in the study were found to be useful tools for the analysis of changes in water levels. Extended monitoring of water balance elements should precede any future increase in the abstraction rate.

Submitted by Edineia Teixeira (edineia.teixeira@unioeste.br) on 2022-11-09T12:22:03Z No. of bitstreams: 2 Ana_Silva.2022.pdf: 5065902 bytes, checksum: 24aba4b896547a003e97cf1ac240bb42 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Made available in DSpace on 2022-11-09T12:22:03Z (GMT). No. of bitstreams: 2 Ana_Silva.2022.pdf: 5065902 bytes, checksum: 24aba4b896547a003e97cf1ac240bb42 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2022-08-16 Due to physical or economic factors, the absence or insufficiency of long and reliable fluviometric data series is one of the main challenges faced in hydrological studies. In order to overcome this problem, the present work aimed at applying the regionalization method of flow permanence curves for the part of the Iguaçu River basin located in the State of Paraná. In this context, the study region was divided into hydrologically homogeneous regions (RHH), defined by the methods of hierarchical clustering of Ward and fuzzy Fuzzy C-Means, and regional models of permanence flow curves were formulated. The Euclidean distance was used as a similarity measure, as well as the explanatory flow variables (the drainage area (A - km²), the length (L - km) and the river gradient (H - m), and the average basin slope (D - %)) as input data. The permanence curves were constructed for each river gauge station distributed in their respective regions and calibrated against six mathematical models (linear, power, exponential, logarithmic, quadratic, and cubic); linear and multiple regression were then applied. The correlation matrix showed that A and L, A and H, and H and L had a positive linear relationship. In the Ward method, the formation of HRH was spatially associated with the geomorphology of the watershed. On the other hand, the Fuzzy C-Means formed more clusters than Ward, verifying sharpness in the formation of the groups by the spatialization and proximity of the stations along the basin. However, in both methods, there was the presence of clusters composed of few fluviometric stations (below 4 units) and with correlated variables (correlation above 0.85), and the regional equations were not applied to them. Regarding the mathematical models for adjustment, the logarithmic method stood out in Ward and Fuzzy C-Means, with an average coefficient of multiple determination (R²) above 85%. In the regional models, only one equation showed R² and adjusted coefficient of determination (R²_a) above 60% in the Ward grouping and two in Fuzzy C-Means, but all with high Mean Square Error (MSE), outliers, and non-randomly distributed residuals. Finally, no model proved efficient in the validation for both methods tested, with an underestimation in the predicted values. A ausência ou a insuficiência de dados fluviométricos de séries longas e confiáveis, devido a fatores físicos e/ou econômicos, é um dos principais desafios enfrentados em estudos hidrológicos. Com o intuito de contornar esse problema, o presente trabalho teve como objetivo aplicação do método de regionalização de curvas de permanência de vazões para a parte da bacia hidrográfica do rio Iguaçu localizada no Estado do Paraná. Nesse contexto, dividiu-se a região de estudo em regiões hidrologicamente homogêneas (RHH), definidas pelos métodos de agrupamento hierárquico de Ward e difuso Fuzzy C-Means, e, para cada um, formularam-se modelos regionais de curvas de permanência de vazões. A distância euclidiana foi utilizada como medida de similaridade, bem como as variáveis explicativas da vazão (a área de drenagem (A – km²), o comprimento (L – km) e o desnível do rio (H – m) e declividade média da bacia (D – %)) como dados de entrada. As curvas de permanência foram construídas para cada uma das estações fluviométricas distribuídas em suas respectivas regiões e calibradas em função de seis modelos matemáticos (linear, potência, exponencial, logarítmico, quadrático e cúbico); posteriormente, foi aplicada regressão linear e múltipla. Por meio da matriz de correlação, conheceu que A e L; A e H; e H e L possuíram uma relação linear positiva entre elas. No método Ward, a formação de RHH ficou associada espacialmente com a geomorfologia da bacia hidrográfica. Já o Fuzzy C-Means, formou mais agrupamentos que Ward, verificando nitidez na formação dos grupos pela espacialização e proximidade das estações ao longo da bacia. Entretanto, em ambos os métodos, houve a presença de agrupamentos compostos por poucas estações fluviométricas (abaixo de 4 unidades) e com variáveis correlacionadas (correlação acima de 0,85), não se aplicando nelas as equações regionais. Já em relação aos modelos matemáticos para ajuste, o método logarítmico destacou em Ward e Fuzzy C-Means, com coeficiente de determinação múltipla (R²) médio acima de 85%. Já nos modelos regionais, apenas uma equação apresentou R² e coeficiente de determinação ajustado (R²_a) acima de 60% no agrupamento Ward e duas em Fuzzy C-Means, mas todas com elevado Erro Quadrático Médio (MSE), outliers e resíduos não distribuídos aleatoriamente. Por fim, nenhum modelo mostrou-se eficiente na validação para ambos os métodos testados, havendo uma subestimação nos valores preditos.