Essay writing examination is commonly used learning activity in all levels of education and disciplines. It is advantageous in evaluating the student’s learning outcomes because it gives them the chance to exhibit their knowledge and skills freely. For these reasons, a lot of researchers turned their interest in Automated essay scoring (AES) is one of the most remarkable innovations in text mining using Natural Language Processing and Machine learning algorithms. The purpose of this study is to develop an automated essay scoring that uses ontology and Natural Language Processing. Different learning algorithms showed agreeing prediction outcomes but still regression algorithm with the proper features incorporated with it may produce more accurate essay score. This study aims to increase the accuracy, reliability and validity of the AES by implementing the Gradient ridge regression with the domain ontology and other features. Linear regression, linear lasso regression and ridge regression were also used in conjunction with the different features that was extracted. The different features extracted are the domain concepts, average word length, orthography (spelling mistakes), grammar and sentiment score. The first dataset used is the ASAP dataset from Kaggle website is used to train and test different machine learning algorithms that is consist of linear regression, linear lasso regression, ridge regression and gradient boosting regression together with the different features identified. The second dataset used is the one extracted from the student’s essay exam in Human Computer Interaction course. The results show that the Gradient Boosting Regression has the highest variance and kappa scores. However, we can tell that there are similarities when it comes to performances for Linear, Ridge and Lasso regressions due to the dataset used which is ASAP. Furthermore, the results were evaluated using Cohen Weighted Kappa (CWA) score and compared the agreement between the human raters. The CWA result is 0.659 that can be interpreted as Strong level of agreement between the Human Grader and the automated essay score. Therefore, the proposed AES has 64-81% reliability level.

In this paper, we analyze the effect of the representative spectral indices, normalized difference vegetation index (NDVI), normalized difference water index (NDWI) and normalized difference built-up index (NDBI) on classification accuracies of Landsat-8 OLI image. After creating these spectral index images, we propose five methods to select the spectral index images as classification features together with Landsat-8 OLI bands from 1 to 7. From the experiments we observed that when the spectral index image of NDVI or NDWI is used as one of the classification features together with the Landsat-8 OLI bands from 1 to 7, we can obtain higher overall accuracy and kappa coefficient than the method using only Landsat-8 OLI 7 bands. In contrast, the classification method, which selected only NDBI as classification feature together with Landsat-8 OLI 7 bands did not show the improvement in classification accuracies.

Microalgae offer promising and multifaceted solutions to the ongoing issues regarding energy security and climate change. One of the major bottlenecks in utilizing algal biomass is the excessive amount of moisture to be managed after harvest, which translates to costs in the dewatering step. Newer strategies have been developed to be able to convert algal biomass feedstock to biodiesel without the need for extraction and drying, such as in-situ transesterification. This process can be improved by concurrently subjecting the system under subcritical conditions, which could also potentially remove the use of catalysts as well as offer tolerance to free fatty acid content of the feedstock. A definitive screening design of experiment was utilized to provide an acceptable prediction on the effects of key process parameters – temperature, reaction time, and solvent-to-solid ratio to the obtainable fatty acid methyl ester (FAME) yield and process power consumption. The optimum operating condition, which combines the benefits of maximizing the FAME yield and minimizing the process power consumption was found to be at 220 °C, 2 h, and 8 ml methanol per gram of biomass (80 wt% moisture). This produces a FAME yield of 74.6% with respect to the maximum obtainable FAME. Sensitivity analysis discussed the implications regarding the weight of importance between the two responses of interest. The benefits of the proposed process can be observed when compared to its conventional transesterification counterpart in terms of energy savings and reduced environmental impact. Hence, this process offers a feasible alternative to produce biodiesel from microalgae.

A trigeneration system consists of interdependent technologies for power generation, heat generation, and the generation of cooling effect which leads to the overall improved thermodynamic efficiency of the system. However, the optimal design of a trigeneration system also depends on the product price variability of the energy streams which are highly dependent on the price of the raw materials and the product demand. Taking into consideration expected price fluctuations of streams, it is then possible for plant owners and engineers to evaluate the investment risk associated with the design capacity of a trigeneration system. The study proposes the use of a 2k factorial design of experiments together with fuzzy linear programming to conduct a price sensitivity analysis in the optimal design of a trigeneration system. This type of analysis can provide plant owners information on possible configurations for optimal capacity given uncertainty in the price parameters.

The production of fiber-reinforced concrete presents a complex reaction system, posing significant challenges in determining appropriate material proportions to achieve targeted mechanical properties. To address this issue, this study proposes novel Artificial Neural Network (ANN) models for predicting the compressive and flexural strengths of fiber-reinforced concrete using a backpropagation feed-forward algorithm. A wide range of concrete mix designs was prepared and tested using cylindrical samples for compressive strength and beam samples for flexural strength. Polypropylene fibers were incorporated into the mixes, and all specimens were cured for 28 days in a water-saturated lime solution. The results demonstrated that the ANN models produced strength predictions that closely aligned with experimental data, yielding high correlation values of 99.46% and 98.57% for compressive and flexural strengths, respectively. The best-fit models exhibited mean squared errors of 0.0024 (compressive) and 0.44 (flexural). Furthermore, parametric analysis indicated that the proposed models effectively captured the constitutive relationships among the concrete components and successfully represented the dominant mechanical behavior of the tested specimens.

A trend arises among industrial and government sectors to transition from the conventional economic system to the new Circular Economy. Its benefit of material security, resource efficiency, and economic growth has attracted government institutions and business sectors to adopt the new trend. However, its challenge falls on the real complexities of economic systems. Adoption of the Circular Economy requires careful consideration of possible challenges. Previous works have aimed to identify the drivers of Circular Economy through surveys based on the frequency of data. The results provided useful information for the decision making of the transition. However, it is also limiting as it does not address a plausible chain-like effect of the drivers which can aid stakeholders determine which course of action is necessary for an efficient transition. Hence, this study is focused in determining these causal drivers by using the DEMATEL approach. DEMATEL is a methodology that identifies the cause and effect relationship between drivers, of which, it can then determine the top causal driver. The study uses a case study in the Philippines to illustrate the capability of the methodology of determining the causality between drivers of Circular Economy. The results of the case study were able to identify ‘economic attractiveness’, with a net cause/effect value of 1.22, and ‘consumer demand’, with a net cause/effect value of 0.87, as the main causal driver while ‘company culture’, with a net cause/effect value of -1.22, as the main effect. The result implies that the improvement in the circular business models and increase in customer awareness are the top priority for the transition. The application of this work is intended to provide researchers an alternative approach in identifying the critical causal drivers of Circular Economy.

Electric vehicle offers a cleaner and sustainable alternative to transportation as it eliminates direct carbon dioxide emission through the conventional internal combustion engine. With the increase in the global population and economic development, the demand for transportation and the adoption of electric vehicles is unprecedented. However, the adoption of electric vehicle on a national-scale requires long-term planning of infrastructure development, and energy generation and distribution. The study focuses on the development of a systematic mathematical programming approach in the optimal planning of the energy mix of the additional power generation capacity arising from the adoption of the electric vehicle in a developing country. The study considers the 2030 horizon which includes, the cost of power generation and distribution per energy mix, and the forecasted commissioning and decommissioning of energy plants. The study proposes a fuzzy mixed-integer non-linear programming model in the optimal planning of the energy mix for the adoption of EV while minimizing carbon footprint, minimizing the total capital cost, and minimizing the electricity cost. A case study in the adoption of electric vehicle in the Philippines will be utilized to demonstrate the capability of the model. In addition, a comparison of the electricity cost of the business as usual (BAU) scenario and this study has been evaluated. The results show that the various renewable energy technologies for power generation are selected initially from 2019 to 2022 and 2029 to 2030, while the fossil-fuel based power plants were utilized from 2023 to 2028. The results revealed the electricity cost from the study is relatively lower than the BAU scenario. The results of the model are intended to aid and guide policymakers in the potential adoption of electric vehicles, especially in the energy planning sector.

o monitor the initiation of concrete cracking beyond the service life of the structure, a novel prediction model of time to cracking of concrete cover using artificial neural network (ANN) was developed in this study. Crack mitigation prevents corrosion and crack development to occur in a more rapid phase that is an essential component in performance-based durability design of reinforced concrete structures. Data available in various literatures were used in the development of the ANN model which is a function of compressive strength, tensile strength, concrete cover, rebar diameter, and current density. The neural network model was able to provide reasonable results in time predictions of cracking of concrete protective cover due to formations of corrosion products. The performance of ANN model was also compared to various analytical and empirical models and was found to provide better prediction results. Even with limitations in the available training data, the ANN model performed well in simulating cracking of concrete due to reinforcement corrosion.

Analyzing cardiovascular activity of patients using ECG clinical paper printouts requires prior knowledge and practice. This research used spatial-oriented image processing methods for analyzing ECG readings by retrieving only the essential features, and not all ECG data, to assist physicians in diagnosis. Different values such as Atrial (rate/min) and Ventricular (rate/min), QRS interval (sec), QT interval (sec), QTc (sec), and PR interval (sec) were successfully extracted with indication as to whether the values are within the accepted normal values, given the patient’s gender and age. Performance of the system was tested based on accuracy, RMSE and normalized RMSE. The methodology achieved average accuracy as high as 95.424 % while the PR interval feature extraction achieved a relatively low average accuracy of 87.196%.

Charlson Ong’s award-winning novel An Embarrassment of Riches (2000) creatively narrates the history of the Chinese diaspora as an overdetermined event in the Southeast Asian Region. The novel explores the politics of belonging, displacement, identity, and territory through a/n (re)imagined nation built primarily on Philippine historical and cultural contingencies. This essay seeks to explore the junctures and vicissitudes of the Chinese diaspora in the region through the following questions: How is hybridity manifested by selected characters and other cultural elements in the narrative? Second, how do the perceptions of homeland, marked by ambivalence and contradiction, operate in the narrative? And third, how does the memory of the characters’ homeland or the lack of it contribute to identity formation? Furthermore, an analysis of the novel’s form has been conducted, possibly asserting the notion that the genre is also a hybridized entity. This paper illustrates the complexity of the Chinese diaspora in the region and its bearing to identity formation.