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.

The generalized gradient approximation (GGA) to density functional theory (DFT) calculations indicate that the highly localized states derived from the defects of nitrogen doped carbon nanotube with divacancy (4ND-CNxNT) contribute to strong Sc and Ti bindings, which prevent metal aggregation. Comparison of the H2 adsorption capability of Sc over Ti-decorated 4ND-CNxNT shows that Ti cannot be used for reversible H2 storage due to its inherent high adsorption energy. The Sc/4ND-CNxNT possesses favorable adsorption and consecutive adsorption energy at the local-density approximation (LDA) and GGA level. Molecular dynamics (MD) study confirmed that the interaction between molecular hydrogen and 4ND-CNxNT decorated with scandium is indeed favorable. Simulations indicate that the total amount of adsorption is directly related to the operating temperature and pressure. The number of absorbed hydrogen molecules almost logarithmically increases as the pressure increases at a given temperature. The total excess adsorption of hydrogen on the (Sc/4ND)10-CNxNT arrays at 300 K is within the range set by the department of energy (DOE) with a value of at least 5.85 wt%.

One of the most useful innovations in concrete technology is Self Compacting Concrete that has the ability to flow efficiently and maintain material homogeneity. The rapid change in the behavior of concrete due to accelerating admixtures can significantly affect the workability properties of the mixture and reduce its ability to flow efficiently. To describe the influence of superplasticizers blended with accelerant on the rheological properties of SCC, several mixtures were tested for Slump Flow, L-Box, and Screen Stability tests. Artificial neural network was used to obtain a model describing the constitutive relationships between the material components and workability parameters of SCC and was optimized using Genetic Algorithm. Results showed that ANN was able to establish the relationship of rheology to the concrete material components and GA derived the optimum proportion for best rheological performance. Most of the design samples of SCC with blended superplasticizer and sodium lignosulfate accelerant were not able to perform well in the flowing ability due to inefficiency of the fresh SCC to flow. The increasing dosage of accelerant however rendered strong stability between the concrete particles allowing the SCC samples to resist segregation and maintain material homogeneity.

This paper gives an analysis of the transducer power gain and stability of a multistage, narrow-band amplifier employing nonunilateral electron devices. The amplifier is assumed to consist of n identical stages with the input and output terminations. The individual amplifier stage consists of a general active two-port device, such as the transistor, characterized by its four short-circuit admittance parameters, plus a two-terminal interstage network and an ideal coupling transformer. Both the individual amplifier stage and the over-all cascade of n amplifier stages, considered as a composite active two-port, are also characterized by their short-circuit admittance (Y) parameters. Relations between these Y parameters of the individual amplifier stage and the Y parameters of the over-all iterative amplifier have been derived. The transducer gain of the amplifier as a function of the interstage and the terminating network parameters has been studied. The transducer gain is optimized with respect to the external passive terminations and is expressed in terms of a design parameter\gamma, which is directly related to the terminating conductances of the amplifier. It is shown that for an amplifier employing inherently stable active devices, there is a value of\gammawhich gives maximum transducer power gain; for an amplifier employing potentially unstable active devices, the optimum transducer power gain of the amplifier will, in general, be a monotonically decreasing function of\gamma. In any case any prescribed value of\gammadetermines the maximum gain obtainable from the amplifier. The amplifier's margin toward instability is prescribed through prescribing a number\rho_{l}. For\rho_{l}greater than unity, the amplifier will be stable. Control of\rho_{l}is effected through appropriate choice of the design parameter\gamma. A relation relating\rho_{l}, and\gammahas been derived. Some fundamental considerations in the design of multistage, narrow-band amplifiers employing general active two-port devices are given. Results of experimental two-and three-stage transistor amplifiers are presented which show excellent agreement between the theoretical and the experimental results.