Comparing the utilities consumptions in Dubai per category and community: MANOVA and cluster analysis approaches

UAE is marked by the increasing demand for water and electricity due to demographic, environmental and economic factors, coupled with the dependence on water desalination process, which is costly, consumes a lot of energy and is non-environmentally friendly. Like most of the authorities in UAE, Dubai Electricity and Water Authority is facing the challenges of balancing supply with demand and responding to consumer requirements, from one side, and addressing the continuously increasing consumption and slowing it down from another side. Therefore, policy makers can benefit from statistical data analysis in order to make informed decisions. This study aims to equip decision makers with useful tools and analysis to address some of their short- and long-term objectives related to production and consumption. The current study focused on three main objectives: (i) analysing the production of the desalination plants in Dubai, (ii) comparing the consumptions of water and electricity based on the four categories residential, commercial, industrial and others, and (iii) segmenting the various communities in Dubai depending on their consumption behavior. The data used for this study is collected from the open government data and SAS Programming is adopted for data analysis. The results of the analysis revealed that the desalinated water production follows an upward trend, yet still not in line with the consumption growth. Furthermore, there are significant differences between the four categories for both water and electricity consumptions. The highest levels of consumptions are associated with the residential and commercial categories. Finally, the cluster analysis technique revealed fifteen clusters of communities depending on the consumption levels.

Analysis of Atmospheric Circulation Patterns in the Babol Roud Watershed

precipitation is a variable whose value, intensity, and type changes in temporal and spatial dimensions. the occurrence of heavy rainfall is an aspect of these changes which is a dangerous factor in the incidence of natural disasters such as flooding. In this study, with the aim of investigating the behavior of precipitation and discharge of Babol Roud watershed, the Talar station was selected as an indicator station. Then, the cycles governing precipitation and discharge parameters of the station were estimated at the annual scale (1976 - 2011) based on the spectral analysis technique. The results of spectral analysis on time series of discharge and precipitation at a 95% certainty level indicate the existence of a common annual 2 cycle in discharge and annual precipitation. The coincidence of a 2 years old significant cycle in the time series of precipitation and discharge means that in 2 years the repetition of annual precipitation events affects annual discharge. The Extraction heavy rainfall in the Babol Roud watershed was examined the study of atmospheric conduct at the sea level pressure (SLP) and the elevation of the atmosphere medium level (500 hpa level) at the time of their occurrence. After grouping these days, based on cluster analysis technique, three patterns were determined as the dominant patterns of heavy rainfall in the Babol Roud Watershed.

Management Practice in Small and Medium-Sized Enterprises: Problems and Solutions from the Perspective of Open Innovation

A large proportion of small and medium-sized enterprises are managed by their owners and founders. The goal of this research was to describe the diversity of management practices in owner-managed SMEs. Understanding this diversity will raise awareness of the challenges SMEs are facing and suggest possible solutions that will help improve their management and sustainability. In this study, 205 owner-managed SMEs with more than nine people employed were analyzed using a company self-assessment based on a tailor-made governance model. Data were analyzed using statistical analysis software in combination with a visual analysis. To group similar companies, the cluster analysis technique was used. The results showed a high diversity in how companies were managed and their performances. This research indicated that statistical analysis itself is not sufficient for exploring this diversity, and other approaches, such as visual analysis, must be used as well.

COVID-19 Emergency Remote Teaching Opinions and Academic Performance of Undergraduate Students: Analysis of 4 Students’ Profiles. A Case Study

Distance learning due to the COVID-19 lockdown, commonly called emergency remote teaching (ERT), substantially changed the methodology of teaching and possibly students’ perceptions of the quality of lectures. Students’ opinions should be collected and analyzed jointly with other data such as academic performance to assess the effect of this pandemic on learning. A 20-question, 4-point Likert scale specific questionnaire was designed and validated twice by a panel of experts. The survey was sent to the 365 industrial engineering undergraduate students enrolled in a chemistry course. Responses (n = 233) and academic data were collected, and four student profiles were identified by using the k-means cluster analysis technique: ‘The Lucky’, ‘The Passive’, ‘The Autonomous Learner’ and ‘The Harmed’. Students experienced the ERT differently according to their profile. Undergraduates who were better autonomous learners excelled in academic performance and were more participative in the survey. In general, students preferred face-to-face classes over distance learning. Undergraduates’ learning has been impaired due to the circumstances. However, contrary to their beliefs, the situation has benefited them with respect to grades when comparing their performance with students from previous years. Discovering what challenges students faced to adapt to the situation is key to giving students tools to grow as autonomous learners and to enable educators to apply tailored teaching techniques to improve the quality of lectures and enhance student satisfaction.

A democracia representativa no Brasil: uma análise da atuação dos deputados federais frente aos índices de desenvolvimento de seus estados e a região geográfica que ocupam

Purpose – This study seeks to verify whether the performance of Brazilian Congressional Representatives is related to MHDI and GDP per capita indices and the regions they represent. Methodology – Regarding the performance of the parliamentarians, the data used were those made available by the Transparência Brasil portal and analyzed using the multivariate exploratory non-hierarchical cluster analysis technique K-means. Federative Units were divided into clusters according to the similarities they presented with respect to the variables that made up the analysis. Findings – After the analyses, we were able to determine that there is no relation of equivalence between the performance of representatives in Congress and the observed research development indices (MHDI and GDP per capita) with states of different regions and quite distinct conditions of development making up the same group of analysis. Originality/value – The researched data, or even the identification of transparency actions demonstrates the wide variety of analysis possibilities that are available in terms of discussions of the performance of political agents and their respective returns for the population. We would suggest that future studies can use other data or other reports displayed in the transparency portals of various spheres of government. Other research possibilities can be developed based on qualitative analyses of the effective representativity of the projects proposed by these representatives of the people.

A Typological Classification for Assessing Farm Sustainability in the Italian Bovine Dairy Sector

Italy is among the most important countries in Europe for milk production. The new European policies encourage a transition towards sustainability and are leading European dairy farms to follow new trajectories to increase their economic efficiency, reduce their environmental impact, and ensure social sustainability. Few studies have attempted to classify dairy farms by analyzing the relationships between the structural profiles of farms and the social, environmental, and economic dimensions of sustainability. This work intends to pursue this aim through an exploratory analysis in the Italian production context. The cluster analysis technique made it possible to identify three types of dairy farms, which were characterized on the basis of indicators that represented the three dimensions of sustainability (environmental, social, and economic sustainability) and the emerging structural relationships based on the structural characteristics of the dairy farms. The classification made it possible to describe the state of the art of the Italian dairy sector in terms of sustainability and to understand how different types of farms can respond to the new European trajectories.

3D Reservoir Rock Type Model Based on Cluster Analysis Technique for Rumaila Formation in the Ahdeb Oil Field, Central Ira

This study presents is a classification of reservoir properties (porosity and shale volume) into rock types for carbonate Rumaila reservoir in Central Iraq (Ahdeb Field). The Cluster analysis method is used to identify rock types and to recognize well log clusters of similar characteristics. For most subsurface research, the determination of the rock type (lithofacies and petrofacies) is not adequate enough because of a lack of cores and cuttings. An interactive petrophysics software program was used to get the results of a cluster analysis technique, in order to determine the rock typing (log facies) in Rumaila formation units in the Ahdeb oil field. Initially, petrophysical parameters such as porosity, shale volume and quantity of various reservoir minerals were determined using the probabilistic evaluation process. In the second stage, the multi-resolution graphic clustering method was employed to separate the sequential electrofacies which resulted in the identification of four electrofacies with different geological reservoir properties. The vertical variations of the rock type for Rumaila formation are based on four log facial groups. These log groups are categorized according to porosity and shale volume of formation based on responses to well logs after division of Rumaila formation into four units (Ru-1, Ru-2, Ru-3, and Ru-4).A 3D rock type model for Rumaila Formation was performed using Petrel software in order to illustrate the horizontal distribution of rock type along the Ahdeb field and showing the best characterized of reservoir rock type in any unit of Rumaila Formation. Cluster analysis technique classified porosity and shale volume, which were calculated for Rumaila Formation using well logs, into four similar characteristics rock types: rocktype-1, rock type-2, rock type-3 and rock type-4. A 3D Petrel model of rock type shows that rock type-2 has better reservoir quality than other rock types in Rumaila Formation which is characterized by high porosity and low shale volume. The model clarifies the distribution of rock type-2 in the Ahdeb field at units Ru-1 and Ru-3 of Rumaila Formation

Attribute Recognition of Buried Pipes Based on Multi-Trace Phase Features Using K-means Clustering for GPR Data Interpretation

Accurate location and depth determination of underground pipes, especially the attribute recognition, are of great importance yet remake a challenging issue in municipal environments. Single-trace phase difference analysis remains a bottleneck due to its inherent and strong randomness in object identification. This paper developed a multi-trace phase difference analysis framework for ground-penetrating radar (GPR) data based on K-means cluster analysis technique and the theory of region of interest (ROI), which could serve as a new criterion for successful pipe attribute recognition. After improving signal-to-noise ratio of GPR data by using the preprocessing techniques, the connected components algorithms (CCA) based on image segmentation and morphological operation is performed to delineate the ROI. The K-means cluster analysis technique is further employed to efficiently extract the multi-trace phase statistical features for comprehensively evaluating the attributes of ROI. We verify this proposed framework by simulated GPR signals, laboratory data and field datasets. Results demonstrate that the proposed method can not only facilitate the attribute recognition of pipes, but also reduce the interpretation ambiguity of the pipe material even in the field site environment. Specifically, if the phase difference of pipe turns out to be even multiples of π, the target can be automatically identified as metallic-category pipes, whereas odd multiples of π, point to non-metallic-category pipes with a lower permittivity than that of the background. This criterion presents promising applicability in subsurface pipeline identification and attributes recognition, especially in constructing a more appropriate initial model of GPR full waveform inversion for survey in pipes.

Understanding Consumers’ Convenience Orientation. An Exploratory Study of Fresh-Cut Fruit in Italy

In Western society, the fresh-cut fruit market is experiencing significant growth, especially in Italy, where, in 2019, the fresh-cut fruit sales volume increased by 35% compared with the previous year. This study aims to understand Italian consumers’ demand for fresh-cut fruits and to explore whether this trend is also affected by the prevalence of healthy lifestyles. Health orientation seems, in fact, to be a growing trend in the food sector. Research has recognized that consumers’ orientation towards products that are ready to be consumed is not only related to saving time. Sociodemographic factors and psychometric variables, including values and lifestyles, play important roles in understanding consumer demand for convenience products. For this purpose, the food-related lifestyles (FRLs) tool was used to profile consumers. The FRLs tool is a useful instrument that describes different ways in which people use food to achieve their values in life. Data were collected by using an online survey carried out with Italian consumers of fresh-cut fruits. By using a cluster analysis technique, four Italian fresh-cut fruit consumer target groups were identified. The largest target group was represented by uninvolved consumers, who are not inclined to cook or plan meals and who are very influenced by the advertising of food products in their buying decisions. An interesting target group, which may represent a challenge for food enterprises in the sector, was health-oriented consumers, who attach great importance to organic certification and to product information. This target group was also characterized by older consumers with higher net monthly household incomes than other target groups.

THE CONSUMER PROFILE AND CAR INSURANCE MARKET OF CUIABÁ, BRASIL

The present study evaluates the car insurance market of Cuiaba-MT, analyses the consumer profile and their predisposition to contract the protection service. It is a precursory study and able to generate important contributions to understand individual preferences because not all people adhere to this type of service. This raises questions to identify the factors that induce the vehicle owners to opt or not for the car insurance contract. The database considers an on-site survey, using a structured questionnaire applied to the car owners at Cuiabá, evaluated by the Cluster Analysis technique. The main results show a market with distinct particularities between genders; however, the income factor is considered a decisive element in the service contracting. Some secondary factors include the knowledge level, the lack of adequate technical and professional support, as well as Relationship Marketing, which also have an influence on the dynamics and in the growth of this market in Cuiabá.