Initial Set Research Articles

Development and validation of an assessment tool for higher education learning abroad programs: A qualitative Delphi study

BackgroundHigher education learning abroad programs provide many benefits to healthcare students. However, inadequate preparation prior to their international travel, and misdirected motivations for their participation, can jeopardise the benefits and increase the risks. While it is pivotal to objectively evaluate pre-departure student preparation and the impact the programs have on students, existing assessment questionnaires fail to inform these aspects. ObjectivesTo develop an assessment tool (two surveys) to holistically evaluate learning abroad programs for higher education healthcare students. DesignA qualitative Delphi technique. ParticipantsA total of 24 experts who had designed, managed, participated in, hosted, funded, and/or researched Australian learning abroad programs for higher education students. MethodsThe study was conducted over a 4-month period. An initial set of pre-departure and post-program questions was developed through a review of the existing literature and from a post-program survey previously used at an Australian higher education institution. Subsequently, experts provided constructive and practical feedback on the questions to be included in the two surveys. ResultsThe experts reached consensus on the 26 questions to be included in the pre-departure survey and the 16 questions in the post-program survey after three Delphi rounds. Key areas of interest to the experts were mitigating student risk, improving effectiveness of pre-departure briefings, gaining a better insight into student motivations for participation, and understanding the impact the programs have on students. ConclusionsThe development of this valid assessment tool (two surveys) will provide higher education institutions and program leaders with the ability to better evaluate the effectiveness of the pre-departure preparation they provide to students, understand the motivations of students who participate, as well as assessing the impact these programs have on students. The insights gathered can be used to improve future program offerings and maximise the benefits to healthcare students while reducing the risks.

Nuclear data resources and initiatives for nuclear astrophysics

Research into the cosmic synthesis of the elements, the evolution and explosion of stars, the nature of the early Universe, and other important topics in nuclear astrophysics are at the forefront of nuclear science. These studies are motivating laboratory measurements and theoretical calculations that, after significant investments, are pushing the boundaries of what is possible. The latest nuclear results, however, must be specially prepared before they can be used to advance our knowledge of the cosmos. This processing requires a set of resources unique to nuclear astrophysics, and an impressive collection of nuclear reaction and nuclear structure datasets, processing codes, thermonuclear reaction rate libraries, and simulation codes and services have been developed for the field. There are, however, some serious challenges to these efforts that will only worsen in the future, making it important to develop strategies and act now to ensure a sustainable future for this work. After detailing the specific data types needed for nuclear astrophysics and the available data resources, the major challenges in this work and their implications are discussed. A set of initiatives are proposed to meet those challenges along with suggested implementations and possible ways that they may advance our understanding of the Universe and strengthen the field of nuclear astrophysics.

Effect of dry ice as admixture on engineering properties of alkali-activated slag: setting time, strength, and sustainability

Alkali-activated slag (AAS) is a low-carbon binder with the potential to replace cement-based materials. In this study, AAS was prepared by using dry ice as an admixture. The effects of dry ice on the actual engineering properties, such as the initial and final setting times and compressive strength of the AAS, were studied. In addition, the microstructure of the AAS was analyzed with X-ray diffraction, Fourier transform infrared spectroscopy, and scanning electron microscopy. The results of these experiments revealed that the added dry ice increased the setting time of the AAS. The initial and final setting times of the AAS with 2.5 ∼ 7.5% dry ice were prolonged by 59 ∼ 122 and 118 ∼ 173 min, respectively. Furthermore, the addition of dry ice to the AAS improved its mechanical properties. At 28 days, the compressive strengths of the AAS supplemented with dry ice were 4.7 ∼ 11.6% higher than that of the control group.

A comparative evaluation of Elo ratings- and machine learning-based methods for tennis match result prediction

Elo ratings-based methods, including the recently proposed Weighted Elo method, have been found to perform well when forecasting tennis match results, however, whether they can outperform machine learning (ML) has not been established. In this study, a comparative evaluation of the two types of methods is conducted using the Sports Result Prediction CRISP-DM experimental framework. The first full year of mens ATP tennis data (2006), in a dataset containing matches from 2005 to 2020, was set to be the initial training set and 1 year of data was incrementally added to this set to predict 14 test years, from 2007 to 2020. Features were ranked based on their average rank across five feature selection techniques. It was found that, of the five ML models, Alternating Decision Trees (ADTrees) and Logistic Regression achieved higher accuracies than Elo ratings and similar accuracies to predictions derived from betting odds. Furthermore, ADTrees show potential in this domain, with solid performance achieved with an interpretable decision tree that allows for variation in the average betting odds difference threshold.

Minimal pinning control for set stability of Boolean networks

In this paper, set stability of Boolean networks (BNs) is studied based on semi-tensor product of matrices. First, an index-vector is introduced to verify whether a BN can be set stable from an initial set to a destination set. Then, an algorithm is proposed to obtain the minimal set of pinning nodes to achieve set stability, if a BN is not set stable. Moreover, in order to reduce the computational complexity, a hybrid pinning control technique is proposed, where the designed controllers are not entirely dependent on all the nodes of the networks. Further, the issue of synchronization is discussed by using the proposed hybrid pinning control methods. Finally, simulations are presented to illustrate the effectiveness of the obtained results in this paper.

The Mechanical Properties and Water-Reducing and Retarding Mechanism of a Xylonic Cement Admixture.

This study explores the mechanical properties, as well as the water-reducing and setting delay mechanism, of a novel xylonic acid-based water reducer applied to cementitious materials. Four xylonic acid water reducers were synthesized in this study: XACa (PX) from pure xylose, XACa (HS) from hemicellulose hydrolysate, XANa (PX) from pure xylose, and XANa (HS) from hemicellulose hydrolysate. These were generated through the whole-cell catalysis of Gluconobacter oxydans bacteria, using pure xylose and hemicellulose hydrolysate as substrates. The findings indicate that the xylonic acid-based water reducer can attain a water-reducing capability between 14% and 16% when the dosage (expressed as a mass fraction of cement) is roughly 0.2%. In initial and final setting tests, XACa (PX) demonstrated a pronounced retarding influence at admixture levels below 0.15%, reaching its apex at 0.10%. This delayed the initial setting time by 76% and the final setting time by 136% relative to the control group. However, a slight pro-setting effect was noted beyond a 0.2% dosage. In the compressive and flexural tests of concrete, under the same slump, the XA group improved its mechanical properties by 5% to 10% compared to the SodiuM lignosulfonate (SL) group. In the air content and chloride ion migration resistance tests, the XA group reduced the air content by 38% compared to the SL group, but also increased the data of rapid chloride migration (DRCM) by 16%. Characterization studies revealed that the carboxyl and hydroxyl groups in xylonic acid undergo chemisorption with the Si-O bonds on the surface of cement particles. These groups interact with the Si-O bonds on cement particles, contributing to water-reducing effects and delaying the setting process by impeding Ca2+ ion aggregation in the calcium-silicate-hydrate gel. Its significant water-reducing effect, adjustable setting time, and excellent mechanical and durability properties suggest its viability as an alternative to lignosulfonate series water-reducing agents.

Manufacture of alkali-activated cementitious materials using municipal solid waste incineration fly ash (MSWIFA): The effect of the Si/Al molar ratio on fresh and hardened properties

Due to the escalating production of municipal solid waste and the widespread adoption of incineration technology, there is a substantial demand for secure disposal of municipal solid waste incineration fly ash (MSWIFA). In this research, alkali-activated MSWIFA via combination with metakaolin (MK) and cement was used to produce an eco-friendly composite. The influence of Na2O content and modulus (Ms) of alkali activator on the early compressive strength and setting time of the composites was investigated, and 4 wt% Na2O content was found to be the most appropriate alkali content. Then, the influence of the Si/Al ratio (2.35–3.18) on the fresh properties (initial setting time, rheology properties, and hydration heat) and hardened properties (compressive strength and shrinkage performance) of composite materials was studied. The microscopic mechanism of the effect of the Si/Al ratio on material properties was explained by XRD, TG, and SEM-EDX. The results showed that the compressive strength of the composite can reach 16.4 MPa at 28 days, which met the quality standard of MU15 (GB/T 25779–2010) of load-bearing concrete perforated brick. With the increase of the Si/Al ratio, the setting time of the slurry is prolonged and the rheological property has deteriorated. The composites with a lower Si/Al ratio (2.35) have the densest structure and the lowest degree of drying shrinkage, which leads to a higher strength. This study provides a potential solution for MSWIFA solidification in low-carbon construction applications.

Estimation of Fracture Size and Probability Density Function by Setting Scanlines in Rectangular Sampling Window

Abstract Rock masses are very important materials in geotechnical engineering. In engineering rock mass, fracture is the relatively weak part of mechanical strength in rock mass and is the most important factor controlling the deformation, damage, and permeability of rock mass. Therefore, investigating fractures is very important for characterizing rock mass. This paper proposed a new approach by using uniformly equidistant orthogonal scanlines. Within the study context, the solution formula of fracture size is derived by establishing the space intersection model of arbitrary fracture and scanline, rectangular window, and a rectangular box with a rectangular window. Then, fractures were randomly generated in a certain size cube and compared with the traditional Kulatilake trace length integral evaluation method. The study results have shown that the proposed method is more reasonable and accurate. Then, this method was applied to an adit of Songta Hydropower Station. Finally, a new fracture diameter probability density estimation method was proposed, the fracture diameter of the normal distribution was verified, and the parameters of the probability density function obtained by the scanlines method were in agreement with the initial set parameters. In summary, the proposed scanlines method can well estimate the mean value of the fracture diameter and the probability density function of the fracture size.

Abstract 15070: Predicting Steam-Pop Incidence Using Ex-Vivo Bipolar Ablation Model of Porcine Ventricular Myocardium

Introduction: Bipolar ablation has been reported to be able to create deep myocardial ablation lesion as compared to unipolar ablation, however, the incidence of steam-pop can lead to undesirable periprocedural complication. Hypothesis: Steam-pop occurrence during bipolar ablation can be predicted by the settings of bipolar ablation and the myocardial tissue characteristics such as tissue thickness and initial impedance prior to RF. Methods: Using porcine cardiac tissues ex-vivo ablation experiments were conducted. We adopted the commercially available radiofrequency (RF) ablation catheters. Each ablation catheter was placed on both sides of the myocardial slice at an angle of 45 degrees under targeted contact force of 10g. We recorded the occurrence of steam-pop under the multiple settings of tissue thickness, RF power and ablation duration. We created a generalized linear model (GLM) to predict steam-pop based on the bipolar ablation settings and the myocardial tissue characteristics. Results: In total 194 bipolar applications were analyzed. Steam-pops were observed in 11 out of 194 applications (5.7%). In the applications with steam-pop, the RF energy was higher (median 40W [q1-q3: 30-50] vs 30W [20-40], P=0.017), the initial impedance was lower (88 [86.75 - 91.5] vs 93 Ohm [89.75 - 95.75], P=0.0042) and the tissue thickness was thinner (9.4 [8.43 - 9.89] vs 12.9 mm [10.06 - 15.47], P<0.0001) as compared to the applications without steam-pop. Using tissue thickness, RF energy and initial impedance, the constructed GLM achieved sensitivity of 100%, specificity of 93% and the area under the receiver-operator-characteristics curve of 0.969. Conclusion: Our data showed that the steam-pop occurrence during bipolar ablation may be predicted based on the initial RF settings and the tissue characteristics in the ex-vivo porcine myocardial model. Further studies are necessary to validate the feasibility of predicting steam-pops using the RF and tissue characteristics in the beating heart and human heart, which may enable us to titrate the RF settings and lead to safer bipolar ablation.

Abstract 12114: Advancing Heart Failure Knowledge and Therapies Through a Multicenter Registry and Innovative Platform for Extended Outpatient Hemodynamic Monitoring: The Hemodynamic Frontiers in Heart Failure (HF 2 )

Background: Hemodynamic Frontiers in Heart Failure (HF 2 ) is a multicenter research consortium of institutions with active remote pulmonary artery pressure monitoring programs that aims to promote research in this field. The consortium recently created a registry of patients with pulmonary artery hemodynamic (HD) monitors (HDM) to facilitate the research mission. Goals/Aims: HF 2 registry aims to collect demographic, clinical, laboratory, echocardiographic and HD data from HDM patients to create a test bed to advance scientific knowledge about ambulatory HD monitoring and quickly evaluate new therapies. Methods: HF 2 includes patients aged >18 who would have been implanted with a HDM as per FDA indications of NYHA Class III HF diagnosis with a prior hospitalization or patients with NYHA Class II or BNP elevation without hospitalization. HF 2 registry data warehouse rests through University of Kansas Medical Center (KUMC) and was approved by their institutional review board (IRB) board followed by local IRBs at participating institutions with required Data use agreements. Institutions report data into the electronic registry database using REDCap, housed at KUMC. Results: This initial data set includes 254 patients implanted from 2019 until May 2023. See Table 1 for initial demographic, comorbidity, laboratory, echocardiographic, and medication data. See Table 2 for HD measurements at time of implant. Conclusion: A real-world registry/test bed of patients with HDM can evaluate long-term outcomes in such patients, provide data in unique patient groups, and provide an opportunity to evaluate HD effects of new HF therapies in rapid turnaround cross over trials.

Comparison of stearic acid and oleic acid for shrinkage-mitigating of alkali-activated slag composites

The large drying shrinkage is one of the most serious factors limiting the practical application of alkali-activated slag concrete (AASC). In this study, based on the successful shrinkage reduction of waste cooking oil for alkali-activated slag paste (AASP), the influence of stearic acid (saturated fatty acids) and oleic acid (unsaturated fatty acids) on fresh properties (setting time and fluidity), physical characteristics (density and water absorption), mechanical strengths (flexural strength and compressive strength), and durability (drying shrinkage, self-shrinkage, and carbonation resistance) are systematically compared. The experimental results reveal that admixing stearic acid and oleic acid can effectively inhibit the drying shrinkage and self-shrinkage of the AASP. Specifically, adding 1 wt% stearic acid (oleic acid) can reduce the drying shrinkage of the AASP by 20% (25%) and self-shrinkage by 32% (55%), respectively. Remarkably, the combined usage of the two additives can maximize the performance of the AASP, such as obtaining satisfactory mechanical strengths, extending the initial setting time, and improving the carbonation resistance. The developed AASP can be regarded as a low-cost and environmentally-friendly product, which can promote practical applications of AASC.

Audio Feature Space Analysis for Emotion Recognition from Spoken Sentences

An analysis of low-level feature space for emotion recognition from the speech is presented. The main goal was to determine how the statistical properties computed from contours of low-level features influence the emotion recognition from speech signals. We have conducted several experiments to reduce and tune our initial feature set and to configure the classification stage. In the process of analysis of the audio feature space, we have employed the univariate feature selection using the chi-squared test. Then, in the first stage of classification, a default set of parameters was selected for every classifier. For the classifier that obtained the best results with the default settings, the hyperparameter tuning using cross-validation was exploited. In the result, we compared the classification results for two different languages to find out the difference between emotional states expressed in spoken sentences. The results show that from an initial feature set containing 3198 attributes we have obtained the dimensionality reduction about 80% using feature selection algorithm. The most dominant attributes selected at this stage based on the mel and bark frequency scales filterbanks with its variability described mainly by variance, median absolute deviation and standard and average deviations. Finally, the classification accuracy using tuned SVM classifier was equal to 72.5% and 88.27% for emotional spoken sentences in Polish and German languages, respectively.

Laboratory tests to understand tephra sliding behaviour on roofs

Following explosive eruptions, loading from tephra fall deposits can lead to roof collapse. However, the load may be reduced significantly by tephra sliding on pitched roofs. We present small-scale laboratory tests to investigate tephra sliding behaviour on metal, fibre cement sheet and tile roofing. We tested 10–30 cm thicknesses for dry and wet deposits of pumice, scoria and basaltic ash. We found that tephra did not slide on roof pitches ≤ 15° for coarse-grained deposits and ≤ 12° for dry ash. Thin deposits of wet ash were stable at pitches ≤ 30°. In addition, tephra was mainly shed on pitches ≥ 32° for metal roofs and ≥ 35° for fibre cement and tiles. Using these results, we have produced an initial set of sliding coefficients for tephra for simply pitched roofs that can be used to help prioritise roofs for clearing during an eruption and assist in designing roofs to withstand tephra fall.

Effect of Fluorohydroxyapatite on Biological and Physical Properties of MTA Angelus.

This study aimed to assess the effect of addition of fluorohydroxyapatite (FHI) on biological and physical properties of mineral trioxide aggregate (MTA) Angelus. In this in vitro, experimental study, nano-FHI powder was first synthesized, and the morphology and chemical structure of particles were evaluated by scanning electron microscopy (SEM), Fourier-transform infrared spectroscopy (FTIR), and X-ray diffraction (XRD). Three groups were evaluated in this study: MTA Angelus, MTA modified with 10% FHA, and MTA modified with 15% FHA. After mixing, the materials were applied to ring molds (10 mm diameter, 1 mm height), and the setting time of the three groups was evaluated according to ISO6876 and ASTMC266-03 with a Gillmore needle. The pH was measured using a pH meter at 24 and 48 hours and 7 days after mixing. The cytotoxicity of the materials was assessed in freshly mixed form and after 1 and 7 days using the methyl thiazolyl tetrazolium (MTT) assay according to ISO10993-5. Data were analyzed by one-way and repeated measures ANOVA and Tukey's test (alpha = 0.05). The addition of FHA to MTA significantly decreased the initial setting time (P < 0.05) and had no significant effect on cell viability (compared with pure MTA Angelus) at 1 and 7 days. However, modified MTA groups in freshly mixed form showed significantly lower cell viability (P < 0.05). The pH remained alkaline at all time points. Addition of 15% FHA to MTA Angelus decreased its setting time with no adverse effect on cell viability (except for fresh form) or pH.

Experimental study on application performance of foamed concrete prepared based on a colloidal NanoSiO2-stabilized foam

In this study, colloidal nanoSiO2 (CNS) was added into the foaming agent at 0, 0.5, 1, 1.6, and 2 wt% of the binder mass to enhance the stability of foaming agent and prefabricated foam, further the stability of foamed slurry and hardened foamed concrete. First, the stability of CNS on foaming agent and prefabricated foam was investigated by examining surface tension, surface viscoelastic modulus E, foam parameters, surface shear viscosity, and morphology. Prefabricated foam containing CNS was then used to prepare foamed concrete with a dry density of 700 kg/m3. The rheological characteristics, volume change, setting time, hydration properties, and gas content for fresh foamed slurry and the pore characteristics, compressive strength, and thermal conductivity of hardened foamed concrete were characterized. The microstructural crystalline structures of the foamed slurry and hardened foamed concrete were also investigated. Results of this study indicated that nanoSiO2 nanoparticles in colloidal state improved the stability of foam, especially when the CNS dosage was ≥ 1 wt%, because CNS caused increases in the surface viscoelasticity modulus E of the foaming agent and the surface shear viscosity of the prefabricated foam. Moreover, foamed slurry with a shorter hydration induction period, more stable volume, a shorter initial setting time, and higher viscosity was obtained when the CNS dosage was ≥ 1 wt%. Hardened foamed concrete with higher early compressive strength and superior thermal insulation properties was correspondingly obtained when fresh slurry with higher stability was used. However, the effect of CNS will decrease on the compressive strength of the hardened foamed concrete after 14 days of hydration. Finally, mechanisms were proposed regarding the stabilization effects of CNS on the prefabricated foams, foamed slurry, and hardened foamed concrete.

Predicting high-risk periods for weight regain following initial weight loss.

The aim of this study was to develop a predictive algorithm of "high-risk" periods for weight regain after weight loss. Longitudinal mixed-effects models and random forest regression were used to select predictors and develop an algorithm to predict weight regain on a week-to-week basis, using weekly questionnaire and self-monitoring data (including daily e-scale data) collected over 40 weeks from 46 adults who lost ≥5% of baseline weight during an initial 12-week intervention (Study 1). The algorithm was evaluated in 22 adults who completed the same Study 1 intervention but lost <5% of baseline weight and in 30 adults recruited for a separate 30-week study (Study 2). The final algorithm retained the frequency of self-monitoring caloric intake and weight plus self-report ratings of hunger and the importance of weight-management goals compared with competing life demands. In the initial training data set, the algorithm predicted weight regain the following week with a sensitivity of 75.6% and a specificity of 45.8%; performance was similar (sensitivity: 81%-82%, specificity: 30%-33%) in testing data sets. Weight regain can be predicted on a proximal, week-to-week level. Future work should investigate the clinical utility of adaptive interventions for weight-loss maintenance and develop more sophisticated predictive models of weight regain.

A Simulated-Annealing-Quasi-Oppositional-Teaching-Learning-Based Optimization Algorithm for Distributed Generation Allocation

Conventional evolutionary optimization techniques often struggle with finding global optima, getting stuck in local optima instead, and can be sensitive to initial conditions and parameter settings. Efficient Distributed Generation (DG) allocation in distribution systems hinges on streamlined optimization algorithms that handle complex energy operations, support real-time decisions, adapt to dynamics, and improve system performance, considering cost and power quality. This paper proposes the Simulated-Annealing-Quasi-Oppositional-Teaching-Learning-Based Optimization Algorithm to efficiently allocate DGs within a distribution test system. The study focuses on wind turbines, photovoltaic units, and fuel cells as prominent DG due to their growing usage trends. The optimization goals include minimizing voltage losses, reducing costs, and mitigating greenhouse gas emissions in the distribution system. The proposed algorithm is implemented and evaluated on the IEEE 70-bus test system, with a comparative analysis conducted against other evolutionary methods such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Honey Bee Mating Optimization (HBMO), and Teaching-Learning-Based Optimization (TLBO) algorithms. Results indicate that the proposed algorithm is effective in allocating the DGs. Statistical testing confirms significant results (probability &lt; 0.1), indicating superior optimization capabilities for this specific problem. Crucially, the proposed algorithm excels in both accuracy and computational speed compared to other methods studied.

Bringing physics into the coarse‐grid selection: Approximate diffusion distance/effective resistance measures for network analysis and algebraic multigrid for graph Laplacians and systems of elliptic partial differential equations

AbstractIn a recent paper, the author examined a correlation affinity measure for selecting the coarse degrees of freedom (CDOFs) or coarse nodes (C nodes) in systems of elliptic partial differential equations (PDEs). This measure was applied to a set of relaxed vectors, which exposed the near‐nullspace components of the PDE operator. Selecting the CDOFs using this affinity measure and constructing the interpolation operators using a least‐squares procedure, an algebraic multigrid (AMG) method was developed. However, there are several noted issues with this AMG solver. First, to capture strong anisotropies, a large number of test vectors may be needed; and second, the solver's performance can be sensitive to the initial set of random test vectors. Both issues reflect the sensitive statistical nature of the measure. In this article, we derive several other statistical measures that ameliorate these issues and lead to better AMG performance. These measures are related to a Markov process, which the PDE itself may model. Specifically, the measures are based on the diffusion distance/effective resistance for such process, and hence, these measures incorporate physics into the CDOF selection. Moreover, because the diffusion distance/effective resistance can be used to analyze graph networks, these measures also provide a very economical scheme for analyzing large‐scale networks. In this article, the derivations of these measures are given, and numerical experiments for analyzing networks and for AMG performance on weighted‐graph Laplacians and systems of elliptic boundary‐value problems are presented.

Cost Percentage of Waste Management to Overall Costs in Hospitality Industry

For any business start up this a key part that how to manage finances and what are the initial set up costs and legal documents and procedures for that start up. Every hotel generates a big volume of waste annually so during the Hotel business start up planning it’s a very important factor that how the hotel will manage the waste generated from hotel and guests and what will be initial set up cost for waste management system and what are the monthly or annual cost to run the waste management system ideally. Hotel waste management is an essential part of running a successful hotel. Waste is not only expensive to have collected, but also represents a waste of valuable resources and supplies which were paid for in the first place. Hospitality businesses can take some simple actions to minimize their costs by preventing waste and correctly segregating what goes into bins.As staff manages waste every day, they are best placed to identify improved waste management measures so make sure to engage with them and train them for optimize the waste management system.

Characteristics of Fly Ash as a Constituent Material for Geopolymer

Fly ash waste from the Asam-Asam Power Plant in South Kalimantan continues to increase. Various attempts have been made to utilize fly ash waste, among others. as a basic material for making geopolymers. Geopolymer is environmentally friendly because it replaces all cement with high Silica (Si) and Alumina (Al) wastes such as fly ash. This study aims to characterize fly ash as a raw material of geopolymer. Fly ash was filtered using 200 mesh and then heated in an oven at 105°C for 24 hours. The prepared fly ash was then tested for physical properties and compound analysis using XRF, identification of chemical bonds and functional groups using FTIR, morphology, and elements using SEM EDX. Initial and final setting time was investigated using the Vicat apparatus to determine e whether cement is undergoing proper hydration. The results indicated that the fly ash was classified to class F. That was based on the amount of SiO2 + Al2O3 + Fe2O3 of 85.91%, and the CaO content was less than 10%. Moreover, fly ash’s initial and final setting time was 1072.5 and 3180 minutes, respectively. Fly ash in this research can be used as a raw material for geopolymers.