scholarly journals Technical and Economic Aspects of Stone Pine (Pinus pinea L.) Maintenance in Urban Environments

2021 ◽  
Author(s):  
Marcello Biocca ◽  
Pietro Gallo ◽  
Giulio Sperandio
Keyword(s):  
Video Recording ◽  
Urban Forestry ◽  
Multiple Linear Regression Model ◽  
Observation Time ◽  
Urban Environments ◽  
Pinus Pinea ◽  
P Value ◽  
Stone Pine ◽  
Residual Biomass ◽  
The City

The Italian Stone Pine (Pinus pinea L.) is one of the most employed ornamental trees in towns with Mediterranean climates. For example, in the city of Rome, Pinus is the most common genus, with more than 51,000 trees. This study investigates technical and economic features of maintenance operations of Stone Pines and evaluates the productivity and costs of the observed yards. Pruning and felling are the most frequent management operations of trees in towns and this study analyzes the features of these operations carried out in 14 work sites. The operations were carried out either with aerial platforms (19 trees) or ascending the crown by tree-climbing (6 trees). The operations were sampled with time studies (12 trees for pruning and 13 for felling). Work time was measured from the beginning of operations to the transport of the residual biomass to the collection and loading point, using centesimal stopwatches and video recording. The total residual biomass was weighed or assessed. Total observation time amounted to 63.1 hours. The evaluation of the costs of each work site considered the fixed and the variable costs and the costs for the labor force. A Multiple Linear Regression model (statistics: determination coefficient R2: 0.74, adjusted R2: 0.67, p-value < 0.001) which utilizes four regressors easily evaluable before the work, was adopted to predict the gross time of the operations. This paper can contribute to optimize trees maintenance methods in urban sites and to assess the potential residual wood biomass attainable from urban forestry maintenance in the city of Rome.

scholarly journals Real Estate Values and Urban Quality: A Multiple Linear Regression Model for Defining an Urban Quality Index

2021 ◽  
Vol 13 (24) ◽  
pp. 13635
Author(s):  
Sebastiano Carbonara ◽  
Marco Faustoferri ◽  
Davide Stefano
Keyword(s):  
Linear Regression ◽  
Real Estate ◽  
Linear Regression Model ◽  
Quality Index ◽  
Property Values ◽  
Multiple Linear Regression Model ◽  
Urban Environments ◽  
Urban Quality ◽  
The City

Urban quality, real estate values and property taxation are different factors that participate in defining how a city is governed. Real estate values are largely determined by the characteristics of urban environments in which properties are located and, thus, by quality of the location. Beginning with these considerations, this paper explores the theme of urban quality through a study of property values that seeks to define all physical (and thus measurable) characteristics that participate in defining urban quality. For this purpose, a multiple linear regression model was developed for reading the residential real estate market in the city of Pescara (Italy). In addition to the intrinsic characteristics of a property (floor area, period of construction/renovation, level, building typology and presence of a garage), input also included extrinsic data represented by the Urban Quality Index. Scientific literature on this theme tells us that many independent variables influence real estate prices, although all are linked to a set of intrinsic characteristics (property-specific) and to a set of extrinsic characteristics (specific to the urban context in which the property is located) and, thus, to the quality of urban environments. The index developed was produced by the analytical and simultaneous reading of four macrosystems with the greatest impact on urban quality: environment, infrastructure, settlement and services (each with its own subsystems). The results obtained made it possible to redefine proportional ratios between various parts of the city of Pescara, based on a specific Urban Quality Index, and to recalculate market property values used to calculate taxes in an attempt to resolve the inequality that persists in this field.

scholarly journals Development of Models for Children—Pedestrian Crossing Speed at Signalized Crosswalks

2021 ◽  
Vol 13 (2) ◽  
pp. 777
Author(s):  
Irena Ištoka Otković ◽  
Aleksandra Deluka-Tibljaš ◽  
Sanja Šurdonja ◽  
Tiziana Campisi
Keyword(s):  
Neural Network ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Network Model ◽  
Neural Network Model ◽  
Multiple Linear Regression Model ◽  
Urban Environments ◽  
The Neural Network ◽  
The City ◽  
Traffic Behavior

Modeling the behavior of pedestrians is an important tool in the analysis of their behavior and consequently ensuring the safety of pedestrian traffic. Children pedestrians show specific traffic behavior which is related to cognitive development, and the parameters that affect their traffic behavior are very different. The aim of this paper is to develop a model of the children-pedestrian’s speed at a signalized pedestrian crosswalk. For the same set of data collected in the city of Osijek—Croatia, two models were developed based on neural network and multiple linear regression. In both cases the models are based on 300 data of measured children speed at signalized pedestrian crosswalks on primary city roads located near a primary school. As parameters, both models include the selected traffic infrastructure features and children’s characteristics and their movements. The models are validated on data collected on the same type of pedestrian crosswalks, using the same methodology in two other urban environments—the city of Rijeka, Croatia and Enna in Italy. It was shown that the neural network model, developed for Osijek, can be applied with sufficient reliability to the other two cities, while the multiple linear regression model is applicable with relatively satisfactory reliability only in Rijeka. A comparative analysis of the statistical indicators of reliability of these two models showed that better results are achieved by the neural network model.

Minimetrò in Perugia as leverage for urban regeneration

TERRITORIO ◽  
2020 ◽  
pp. 148-163
Author(s):  
Luca Fondacci
Keyword(s):  
Urban Regeneration ◽  
Urban Environments ◽  
Low Carbon ◽  
Sustainable Mobility ◽  
Field Surveys ◽  
Peripheral Area ◽  
Alternative Means ◽  
Considerable Impact ◽  
The City ◽  
Historical Centre

In the 1970s, the fragile historical centre of the city of Perugia was a key area where the binomial of sustainable mobility and urban regeneration was developed and applied. At the turn of the xxi century, the low carbon automatic people-mover Minimetrò broadened that application from the city's historical centre to the outskirts, promoting the enhancement of several urban environments. This paper is the outcome of an investigation of original sources, field surveys and direct interviews, which addresses the Minimetrò as the backbone of a wide regeneration process which has had a considerable impact on the economic development of a peripheral area of the city which was previously devoid of any clear urban sense. The conclusion proposes some solutions to improve the nature of the Minimetrò as an experimental alternative means of transport.

scholarly journals Kinetic and Microhydrodynamic Modeling of Fenofibrate Nanosuspension Production in a Wet Stirred Media Mill

Pharmaceutics ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1055
Author(s):  
Gulenay Guner ◽  
Dogacan Yilmaz ◽  
Ecevit Bilgili
Keyword(s):  
Rate Constant ◽  
Kinetic Models ◽  
Multiple Linear Regression Model ◽  
Empirical Correlation ◽  
P Value ◽  
Order Kinetic ◽  
Stirrer Speed ◽  
Predictive Capability ◽  
Breakage Rate ◽  
The Impact

This study examined the impact of stirrer speed and bead material loading on fenofibrate particle breakage during wet stirred media milling (WSMM) via three kinetic models and a microhydrodynamic model. Evolution of median particle size was tracked via laser diffraction during WSMM operating at 3000–4000 rpm with 35–50% (v/v) concentration of polystyrene or zirconia beads. Additional experiments were performed at the center points of the above conditions, as well as outside the range of these conditions, in order to test the predictive capability of the models. First-order, nth-order, and warped-time kinetic models were fitted to the data. Main effects plots helped to visualize the influence of the milling variables on the breakage kinetics and microhydrodynamic parameters. A subset selection algorithm was used along with a multiple linear regression model (MLRM) to delineate how the breakage rate constant k was affected by the microhydrodynamic parameters. As a comparison, a purely empirical correlation for k was also developed in terms of the process/bead parameters. The nth-order model was found to be the best model to describe the temporal evolution; nearly second-order kinetics (n ≅ 2) was observed. When the process was operated at a higher stirrer speed and/or higher loading with zirconia beads as opposed to polystyrene beads, the breakage occurred faster. A statistically significant (p-value ≤ 0.01) MLRM of three microhydrodynamic parameters explained the variation in the breakage rate constant best (R2 ≥ 0.99). Not only do the models and the nth-order kinetic–microhydrodynamic correlation enable deeper process understanding toward developing a WSMM process with reduced cycle time, but they also provide good predictive capability, while outperforming the purely empirical correlation.

scholarly journals Facilitating fine-grained intra-urban dengue forecasting by integrating urban environments measured from street-view images

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Kang Liu ◽  
Ling Yin ◽  
Meng Zhang ◽  
Min Kang ◽  
Ai-Ping Deng ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Risk ◽  
Feature Vector ◽  
Urban Environments ◽  
Environmental Feature ◽  
Fine Grained ◽  
Environmental Features ◽  
Street View ◽  
The City ◽  
Dengue Epidemic

Abstract Background Dengue fever (DF) is a mosquito-borne infectious disease that has threatened tropical and subtropical regions in recent decades. An early and targeted warning of a dengue epidemic is important for vector control. Current studies have primarily determined weather conditions to be the main factor for dengue forecasting, thereby neglecting that environmental suitability for mosquito breeding is also an important factor, especially in fine-grained intra-urban settings. Considering that street-view images are promising for depicting physical environments, this study proposes a framework for facilitating fine-grained intra-urban dengue forecasting by integrating the urban environments measured from street-view images. Methods The dengue epidemic that occurred in 167 townships of Guangzhou City, China, between 2015 and 2019 was taken as a study case. First, feature vectors of street-view images acquired inside each township were extracted by a pre-trained convolutional neural network, and then aggregated as an environmental feature vector of the township. Thus, townships with similar physical settings would exhibit similar environmental features. Second, the environmental feature vector is combined with commonly used features (e.g., temperature, rainfall, and past case count) as inputs to machine-learning models for weekly dengue forecasting. Results The performance of machine-learning forecasting models (i.e., MLP and SVM) integrated with and without environmental features were compared. This indicates that models integrating environmental features can identify high-risk urban units across the city more precisely than those using common features alone. In addition, the top 30% of high-risk townships predicted by our proposed methods can capture approximately 50–60% of dengue cases across the city. Conclusions Incorporating local environments measured from street view images is effective in facilitating fine-grained intra-urban dengue forecasting, which is beneficial for conducting spatially precise dengue prevention and control.

Stone pine (Pinus pinea L.): an interesting species for agroforestry in Chile

2017 ◽  
Vol 93 (2) ◽  
pp. 703-713 ◽  
Author(s):  
Verónica Loewe ◽  
Claudia Delard
Keyword(s):  
Pinus Pinea ◽  
Stone Pine ◽  
Interesting Species

scholarly journals Prediction limits of mobile phone activity modelling

2017 ◽  
Vol 4 (2) ◽  
pp. 160900 ◽  
Author(s):  
Dániel Kondor ◽  
Sebastian Grauwin ◽  
Zsófia Kallus ◽  
István Gódor ◽  
Stanislav Sobolevsky ◽  
...  
Keyword(s):  
Mobile Phone ◽  
Spatial Scales ◽  
Urban Environments ◽  
Full Spectrum ◽  
Left Behind ◽  
Social Events ◽  
Prediction Limits ◽  
Spatio Temporal ◽  
City Structure ◽  
The City

Thanks to their widespread usage, mobile devices have become one of the main sensors of human behaviour and digital traces left behind can be used as a proxy to study urban environments. Exploring the nature of the spatio-temporal patterns of mobile phone activity could thus be a crucial step towards understanding the full spectrum of human activities. Using 10 months of mobile phone records from Greater London resolved in both space and time, we investigate the regularity of human telecommunication activity on urban scales. We evaluate several options for decomposing activity timelines into typical and residual patterns, accounting for the strong periodic and seasonal components. We carry out our analysis on various spatial scales, showing that regularity increases as we look at aggregated activity in larger spatial units with more activity in them. We examine the statistical properties of the residuals and show that it can be explained by noise and specific outliers. Also, we look at sources of deviations from the general trends, which we find to be explainable based on knowledge of the city structure and places of attractions. We show examples how some of the outliers can be related to external factors such as specific social events.

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