A Study on the Roads Flatness Using International Roughness Index in New Taipei City

The quality of roads is an indicator of urban progress. The development of tourism and economy contributes to the increasing demands for transportation and, thus, aggravated burdens and vulnerability to damage of these roads, and the result is compromised transportation quality and safety. The Road Leveling Project is aimed to road updates and improvement of pavement quality. New Taipei City was selected as the subject for this study. International roughness index (IRI) was selected for field survey and statistical comparison. The outcome indicated that the IRI spread between 3.5 and 6.5 m/km before road leveling with an average of 4.71 m/km; the index fell between 2.5 and 4.5 m/km after road leveling with an average of 3.12m/km, suggesting that the IRI of the tested road sections showed a declining trend. For multi-lane road sections tested, the improvement was greater on the outer lanes than on the inner lanes. This proves that the implementation of the Road Leveling Project has made significant improvement in terms of pavement flatness. Suggestions are proposed in this study for the subsequent management and improvement polices of the Road Leveling Project, hoping that the pavement quality improvement continues to contribute to the extension of road service life and ride comfort.

Simple spectrophotometric methods for the determination of amlodipine and atorvastatin in bulk and tablets

Background Simultaneous spectrophotometric determination of samples containing more than one analyte presents analytical challenge; the choice of an analytical procedure is strictly related to the extent of overlapping between the individual absorption peaks of these components; if the absorption peaks are satisfactorily resolved, the determination is not problematic, but if the individual component signals are partly or totally overlapped, then powerful techniques are needed. Combined amlodipine and atorvastatin are typical example where special techniques are needed to resolve bands overlapping. Results Application of multiwavelength regression and absorbance factor methods to the analysis of atorvastatin and amlodipine combination proved to be satisfactorily capable of accurate and precise determination of the two analytes. The two methods recoveries were very close to the expected analytes concentrations, and the precision of the methods was < 2% relative standard deviation. Statistical comparison indicated that there is no significant difference between the assay results obtained by the two method as the calculated t values 0.91 and 1.13 for amlodipine and atorvastatin, respectively, were less than the tabulated t value 2.23 at 95% confidence level. Conclusion The proposed methods are accurate, precise, simple and inexpensive. They can be applied successfully to the analysis of the two drugs in combined dosage form.

Benchmarking Deep Learning Methods for Aspect Level Sentiment Classification

With the advancements in processing units and easy availability of cloud-based GPU servers, many deep learning-based methods have been proposed for Aspect Level Sentiment Classification (ALSC) literature. With this increase in the number of deep learning methods proposed in ALSC literature, it has become difficult to ascertain the performance difference of one method over the other. To this end, our study provides a statistical comparison of the performance of 35 recent deep learning methods with respect to three performance metrics-Accuracy, Macro F1 score, and Time. The methods are evaluated for eight benchmark datasets. In this study, the statistical comparison is based on Friedman, Nemenyi, and Wilcoxon tests. As per the results of statistical tests, the top-ranking methods could not significantly outperform several other methods in terms of Accuracy and Macro F1 score and performed poorly on-time metric. However, the time taken by any method is crucial to analyze the overall performance. Thus, this study aids the selection of the Deep Learning method, which maximizes the accuracy and Macro F1 score and takes minimal time. Our study also establishes a framework for validating the performance of new and alternate methods in ALSC that can be helpful for researchers and practitioners working in this area.

Forest Fire in East Nusa Tenggara during 2015-2019: Comparison to Forest Fire in Kalimantan and Sumatera

East Nusa Tenggara is one of the provinces in Indonesia that has big forest fires following some provinces in Kalimantan and Sumatra. However, forest fires in East Nusa Tenggara have less attention in forest fires discussion in Indonesia. This study aims to analyze forest fires in East Nusa Tenggara and their impact on reducing visibility and increasing carbon monoxide (CO) from 2015 to 2019. In this study, hotspot, forest fire area, Oceanic Niño Index, visibility, and CO total column data were used to analyze the forest fires using a statistical comparison method in East Nusa Tenggara, Kalimantan, and Sumatra. The result shows that the number of hotspots in East Nusa Tenggara less than in Kalimantan and Sumatra for the same forest fire area. The forest fires in East Nusa Tenggara do not harm the atmospheric environment significantly. East Nusa Tenggara dominantly consists of savanna areas with no peatland, hence, the forest biomass burning produces less smoke and CO. Furthermore, the forest fire in East Nusa Tenggara has not an impact on decreasing visibility and increasing CO total column, in contrast, visibility in Sumatra and Kalimantan has fallen to 6 km from the annual average, and CO total column rise three times of normal condition during peak fire.

Female violence victims` anxiety and styles of communication

The article presents an analysis of the types of anxiety specific to women victims of violence. To highlight the peculiarities of anxiety, a statistical comparison is made with the results obtained by another group of women, who were not subjected to violence. Likewise, the communication skills characteristic of victimized women and their specific communication styles are presented.

A Simple Application for Computing Reference Evapotranspiration with Various Levels of Data Availability—ETo Tool

Reference evapotranspiration (ETo) estimations may be used to improve the efficiency of irrigated agriculture. However, its computation can be complex and could require numerous weather data that are not always available for many locations. Different methods are available to estimate ETo when limited data are available, and the assessment of the most accurate one can be difficult and time consuming. There are some standalone softwares available for computing ETo but none of them allow for the comparison of different methods for the same or different datasets simultaneously. This paper aims to present an application for estimating ETo using several methods that require different levels of data availability, namely FAO-56 Penman–Monteith (PM), the Original and the three modified Hargreaves–Samani (HS and MHS1, MHS2 and MHS3), Trajkovic (TR) and the single temperature procedure (MaxTET). Also, it facilitates the comparison of the accuracy estimation of two selected methods. From an example case, for where the application was used to compute ETo for three different locations, results show that the application can easily and successfully estimate ETo using the proposed methods, allowing for statistical comparison of those estimations. HS proves to be the most accurate method for the studied locations; however, the accuracy of all methods tends to be lower for costal locations than for more continental sites. With this application, users can select the best ETo estimation methods for a specific location and use it for irrigation purposes.