Physicochemical characteristics of the soil around the quarry sites in Ogun State, Nigeria

Quarrying of rock for construction purposes is a significant industry in Nigeria thus enhancing the economy. This study aimed at assessing the physicochemical parameters of the soil around the quarry sites across selected Local Government Areas in Ogun State. Geographic Information System approach was used to map the various quarry locations in Ogun State, from which eight sites were selected namely Isara, Idode, Iwaye, Ogbere, Ilagbe, Adelokun, Baaki Ake and Igodo. A total of 48 soil samples (topsoil and subsoil) were collected for cation exchange capacity (CEC), electrical conductivity (EC) and Particle size distribution using standard procedures. Sampling was done in dry and wet seasons. Data were subjected to descriptive and inferential statistics using SAS package (9.4 version). However Variable Quality Control Package was used to determine the Control Limit for the soil parameters. Sixty quarries were identified. The electrical conductivity of 505.1±0.6 μS/cm of the topsoil at Igodo in the wet season was above the Upper control limit (UCL) of 501.6 μS/cm. The CEC of topsoil and subsoil in all locations (dry and wet seasons) were lower than the control, with the exception of Isara and Baaki Ake in subsoil (wet season). The sand sizes and percentage weights of sizes (topsoil) of Idode and Adelakun were higher than the control (75 – 150 μm with 74.0%) in dry season; while in wet season, Adelokun (180 μm and 62.4%) was higher than the control (75 – 150 μm and 15.1%). In the subsoil during dry season Idode and Baaki Ake were higher than the control (75 – 150 μm and 71.5%); while during wet season Baaki Ake and Adelokun were higher than the control (75 – 150 μm and 19.4%). In conclusion, quarries had polluted the surrounding soils with sandy particle, EC and reduce CEC values

A Brief Comparison of K-means and Agglomerative Hierarchical Clustering Algorithms on Small Datasets

In this work, the agglomerative hierarchical clustering and K-means clustering algorithms are implemented on small datasets. Considering that the selection of the similarity measure is a vital factor in data clustering, two measures are used in this study - cosine similarity measure and Euclidean distance - along with two evaluation metrics - entropy and purity - to assess the clustering quality. The datasets used in this work are taken from UCI machine learning depository. The experimental results indicate that k-means clustering outperformed hierarchical clustering in terms of entropy and purity using cosine similarity measure. However, hierarchical clustering outperformed k-means clustering using Euclidean distance. It is noted that performance of clustering algorithm is highly dependent on the similarity measure. Moreover, as the number of clusters gets reasonably increased, the clustering algorithms’ performance gets higher

Validation of an Arabic Translation of the Functioning Everyday with a Wheelchair Self-Report Tool: A Pilot Study

Background: The Functioning Everyday with a Wheelchair (FEW) tool is a self-report questionnaire developed to measure consumers’ perceptions of how the features of their wheeled mobility and seating device support their ability to carry out their daily functions and routines independently, safely, and efficiently. Method: A committee of three bilingual qualified occupational therapists translated the English version into Arabic, which was verified by back translation. The final versions were administered with 26 bilingual English-Arabic speakers. The subjects were randomly assigned to answer either the English or Arabic version of the FEW questionnaire first, followed by answering the FEW questionnaire in the other language. The scores obtained were tested for agreement using the kappa statistic. Results: We found significant substantial agreement between the scores obtained from the two versions (p \u3c 0.000). The kappa measurement of agreement was 0.755 (95% confidence interval, 0.59–0.92). Conclusion: The Arabic translation of the FEW self-report tool was generated for potential use in research and clinical practice and could be useful to wheelchair professionals who would like to use this tool for Arabic-speaking wheelchair users. Further validation of the developed Arabic version is recommended

Evaluación del rendimiento de bases de datos embebida: un estudio empírico

Introduction: With the rapid deployment of embedded databases across a wide range of embedded devices such as mobile devices, Internet of Things (IoT) devices, etc., the amount of data generated by such devices is also growing increasingly. For this reason, the performance is considered as a crucial criterion in the process of selecting the most suitable embedded database management system to be used to store/retrieve data of these devices. Currently, many embedded databases are available to be utilized in this context. Materials and Methods: In this paper, four popular open-source relational embedded databases; namely, H2, HSQLDB, Apache Derby, and SQLite have been compared experimentally with each other to evaluate their operational performance in terms of creating database tables, retrieving data, inserting data, updating data, deleting data. Results and Discussion: The experimental results of this paper have been illustrated in Table 4. Conclusions: The experimental results and analysis showed that HSQLDB outperformed other databases in most evaluation scenarios