Averaged Research Articles

Digital Marketing and Its Impact on Organizational Attraction: An Analytical Study of the Opinions of a Sample of Administrative Leaders in the Public Telecommunications and Information Technology Company

The main purpose of this research is to understand the role of digital marketing in the Public Telecommunications and Information Company by exploring its four dimensions (communication channels, social media, digital relations, and digital technologies) in enhancing its organizational attractiveness. The problem of the research, the company is struggling how to cover all consumer needs and preferences, comprehend their behavior, and set specific objectives to improve organizational attraction, whether to increase subscribers or enhance interactions with other companies. Digital marketing requires sustained time and effort for long-term success, which is why this research aims to provide a set of marketing mechanisms to address the problem, potentially through electronic platforms or social media. To achieve objective of the research, the descriptive-analytical approach was used. A questionnaire was the main tool used to collect data and information was distributed to a sample of the company's middle management leaders (119) in the Public Telecommunications and Information Company. the researchers employed a series of descriptive statistical techniques that included the arithmetic mean, standard deviation, coefficient of variation, and simple regression coefficient by using the statistical program (SPSS. V.26). The results confirmed the significant impact of digital marketing on enhancing the leaders' ability to increase organizational attractiveness, enabling them to execute their assigned activities and tasks more effectively. Paper type: Research paper.

Identifying Personality Types of Mayors in Local Government in Bulgaria According to the Myers – Briggs Typology

In this article, we make an attempt to explore and identify the personality types of mayors in Bulgarian local government by applying the Myers-Briggs typology indicator. This article presents part of the results of a more extensive author’s survey of mayors in municipalities and districts across the country. The fieldwork was carried out in the period between June 2022 and September 2023, with responses received from 57 of the mayors. The attention will be directed towards one of the questionnaires applied in this author’s study, which included questions regarding leadership models in Bulgarian municipalities. The methods used to analyze the collected data are descriptive statistics and calculation of an arithmetic mean value. The analysis identifies personality types of Bulgarian mayors and the strengths and weaknesses for each type. The results show that the personality type ‘Executive’ is the most common one among mayors in Bulgarian municipalities. The results provide an opportunity to expand the research and include qualitative methods such as in-depth interviews, focus groups and others, with the aim of examining the specifics of the individual case and linking it to the context of the environment. In addition, in the future, the relationship between personality type, charisma, emotional intelligence and the managerial style of the mayors will be investigated, as a result of which a profile can be built, through which candidates for public positions can be selected. Further research will also look at the relationship between the personality type of the mayor, the financial and economic condition of the municipality and the attracted investments during their mandate term.

Predicting the Future Signalization of Traffic-Actuated Signals Using Extreme Gradient Boosting

Stops, braking, and acceleration maneuvers at traffic signals cause high fuel consumption and emission levels. Green light optimized speed advisory (GLOSA) can help to reduce a proportion of such unnecessary vehicle maneuvers. For this, GLOSA systems require reliable switching time estimations as input. However, estimating the switching times of traffic-actuated signals can be challenging. The signalization of traffic-actuated lights is adjusted to match the current traffic. Based on green-time requests, the status of signals can change with almost no lead time. This paper presents an approach for predicting the signalization of traffic-actuated signals. We exploit the limited nature of the space for switched signal state combinations at intersections, and express combinations of motorized traffic-related signal states as one feature to depict the motorized traffic signalization state (MTSSt) at an intersection. Predicting MTSSt-switches allows later determination of the switching times of individual signals. To conduct the predictions, the machine learning method “extreme gradient boosting” was used. A three-step methodology—data preparation, tuning the prediction procedure, and testing the approach—was applied and evaluated on the historical data of four traffic-actuated signalized intersections. The results showed that within a period of the next 30 s, signal changes were predicted with an overall precision and sensitivity of about 95% and an average mean absolute error of less than 1.1 s. In this period, the sequence of switched signals was predicted accurately to the second, without any deviation, in, on average, over 82% of cases.

Diagnostic Approaches of Palatogingival Groove: A Systematic Review

Palatogingival Grooves (PGG) constitute developmental anatomical irregularities primarily situated along the palatal aspects of the roots of maxillary incisors. Literature is sparse on diagnostic approaches of PGG. Our study aimed to alleviate clinicians the process of recognizing anomaly. Systematic electronic searches of scientific literature without publication date or language restriction were undertaken in 2023.11.03 research was conducted in PubMed, Willey Online Library, Science Direct and Medline Ovid. Using specific keywords 54 articles with 67 cases were included in our study according to inclusion and exclusion criteria. Pre-made Cochrane data extraction templates were used for frequency evaluation, nonparametric Spearman’s, Kendall rank correlation coefficients, Mann-Whitney U Test and Odds ratio was assessed by statistic to evaluate significant correlations. Most quantitively expressed symptoms of PGG included increased tooth mobility, appearance of purulent discharge, sinus tract and periodontal pocket with an average mean of 9,14 ± 2,30 mm along with painful vertical per-cussion. However, statistical assessment showed inverse correlation between in-creased mobility and painful percussion. Furthermore, quantitively, deeper perio-dontal pockets were accompanied by more prominent gingival alterations, however only one opposing significant correlation was noted between the depth of per-iodontal pocket and existence of additional root. Frequency evaluation revealed that IPR was the main diagnostic tool used in majority of cases. Deeper periodontal pocket was assessed by conjunction of two or three tools. CBCT was used most with deeper periodontal pocket averaging 9,75 ± 1,02 mm. In conclusion, quantita-tive data suggest that periodontal pockets averaging 9,14 ± 2,30 mm, I or II-degree mobility, painful percussion and purulent discharge are the main PGG characteristics, with sparce significant correlations between them. IPR was noted as the main roentgenological modality as should be used firstly when suspecting PGG, yet both quantitative and statistical data suggest that CBCT is ought to be used to alleviate the assessment.

Estimation of Lifetime Performance Index for Generalized Inverse Lindley Distribution Under Adaptive Progressive Type-II Censored Lifetime Test

The lifetime performance index (LPI) is an important metric for evaluating product quality, and research on the statistical inference of the LPI is of great significance. This paper discusses both the classical and Bayesian estimations of the LPI under an adaptive progressive type-II censored lifetime test, assuming that the product’s lifetime follows a generalized inverse Lindley distribution. At first, the maximum likelihood estimator of the LPI is derived, and the Newton–Raphson iterative method is adopted to solve the numerical solution due to the log-likelihood equations having no analytical solutions. If the exact distribution of the LPI is not available, then the asymptotic confidence interval and bootstrap confidence interval of the LPI are constructed. For the Bayesian estimation, the Bayesian estimators of the LPI are derived under three different loss functions. Due to the complex multiple integrals involved in these estimators, the MCMC method is used to draw samples and further construct the HPD credible interval of the LPI. Finally, Monte Carlo simulations are used to observe the performance of these estimators in terms of the average bias and mean squared error, and two practical examples are used to illustrate the application of the proposed estimation method.

Modeling and economic analysis of block absorber (RadFrac) performance with stage variations for post-combustion CO2 capture

This study presents a comprehensive modeling and economic evaluation of a Monoethanolamine (MEA)-based post-combustion carbon capture (PCC) absorber unit. The simulation provides detailed insights into the absorber's internal profiles, including temperature gradients and the percentage of CO2 captured, with a particular focus on the effects of varying the number of stages and absorber diameters. Validation against experimental data demonstrated close alignment between simulated and observed values, with an average Root Mean Square Deviation (RMSD) of 0.018 and 4.0129, respectively, confirming the reliability of the simulation in capturing the complex dynamics of the absorber.The economic analysis, conducted using the Aspen Process Economic Analyzer (APEA), simplified the complex relationship between absorber segmentation, capital and operational costs, and absorber diameter. The study revealed that increasing the absorber diameter and the number of stages leads to a significant rise in Total Capital Cost (TCC), Total Operating Cost (TOC), Equipment Cost, and Total Installed Cost (TIC), with the highest costs observed at a stage number of 90 and a diameter of 1.0 m. However, the analysis identified an optimal configuration at an absorber diameter of 0.45 m with either 10 or 20 stages. This setup effectively balances cost and CO2 capture efficiency, offering a more economical solution compared to configurations with larger diameters and higher stage numbers, which substantially increase expenses. The findings highlight the critical trade-offs between operational efficiency and capital expenditure, stressing on the crucial role of absorber diameter in determining the economic feasibility of the absorber block in PCC systems.

Household energy consumption forecasting based on adaptive signal decomposition enhanced iTransformer network

With the presence of volatility and noise in the energy consumption data, existing energy consumption forecasting methods have difficulties in achieving satisfactory forecasting results from multiple perspectives, including accuracy, robustness, generalization, and efficiency. This study introduces a novel energy consumption forecasting framework that combines the adaptive signal decomposition method and iTransformer (ASSA-iTransformer) to tackle the above-mentioned difficulties. Each subsequence obtained by ASSA decomposition was fed into the iTransformer framework, and deep-level time series information was extracted. Compared to state-of-the-art methods across various evaluation dimensions, ASSA-iTransformer has improved the average mean absolute error (MAE) by 23.7%, root mean square error (RMSE) by 27.6%, and mean absolute percentage error (MAPE) by 16.4%. Moreover, the R2 values on five real-world household energy consumption datasets are close to 1, demonstrating overall outstanding performance.

The Effect of Inclusive Leadership on Organizational Agility: An analytical study in the General Company of Battery Industry

Purpose: Explaining the impact of comprehensive leadership in achieving organizational agility that contributes to achieving the desired goals and retaining competent cadres. Theoretical framework: Achieving the primary goal, which is by defining the effect of inclusive leadership with its dimensions on organizational agility. The research investigated the extent to which comprehensive leadership contributes to enhancing organizational agility through employees’ ownership of the two concepts. Design/methodology/approach: The descriptive-analytical method was adopted, and the data were analyzed to reach the results using SPSS V.26 software. The company registration department, a subsidiary of the General Company for Battery Industries, was chosen to represent the study population. Data was collected from 111 employees, including managers and employees, using a a questionnaire. It was analyzed using statistical methods such as arithmetic mean, standard deviation, and simple linear regression. Findings: The higher level of managers adopting inclusive leadership, and organizational flexibility. Research, practical & social implications: Promoting a culture within the organization that encourages proactiveness and interest in innovation. This can be achieved by encouraging the manager and employees to share new ideas and suggestions. Originality/value: Describing contemporary administrative concepts that play a major role in improving the level of public services provided to citizens.

Design, Implementation, and Analysis of an Assessment and Accreditation Model to Evaluate a Digital Competence Framework for Health Professionals: Mixed Methods Study.

Although digital health is essential for improving health care, its adoption remains slow due to the lack of literacy in this area. Therefore, it is crucial for health professionals to acquire digital skills and for a digital competence assessment and accreditation model to be implemented to make advances in this field. This study had two objectives: (1) to create a specific map of digital competences for health professionals and (2) to define and test a digital competence assessment and accreditation model for health professionals. We took an iterative mixed methods approach, which included a review of the gray literature and consultation with local experts. We used the arithmetic mean and SD in descriptive statistics, P values in hypothesis testing and subgroup comparisons, the greatest lower bound in test diagnosis, and the discrimination index in study instrument analysis. The assessment model designed in accordance with the competence content defined in the map of digital competences and based on scenarios had excellent internal consistency overall (greatest lower bound=0.91). Although most study participants (110/122, 90.2%) reported an intermediate self-perceived digital competence level, we found that the vast majority would not attain a level-2 Accreditation of Competence in Information and Communication Technologies. Knowing the digital competence level of health professionals based on a defined competence framework should enable such professionals to be trained and updated to meet real needs in their specific professional contexts and, consequently, take full advantage of the potential of digital technologies. These results have informed the Health Plan for Catalonia 2021-2025, thus laying the foundations for creating and offering specific training to assess and certify the digital competence of such professionals.

Research on Submersible Position Prediction Based on Kalman Filter Algorithm

The global situation of the world's submersible technology is experiencing a boom, with countries competing to develop advanced submersible equipment to explore the deep sea, undersea resources, and uncharted territories. With the wide range of applications of submersibles in various fields, the risks of communication loss and mechanical failures faced during their missions require the establishment of accurate predictive models of submersible positions to ensure safe and efficient rescue. In this study, a model containing equations of motion is developed from the motion characteristics of a submersible on the seafloor. Uncertainties such as sea currents, topography, and seawater density are considered, and a random walk model is used to simulate these effects to ensure that the model is closer to reality. Subsequently, combined with the Kalman filter algorithm, a time-dependent position prediction model is constructed in this study. The model can predict the position of the submersible in real-time, and after error analysis, it shows that the average variance and root mean square error are at a low level, which proves that its prediction is accurate and reliable, and provides strong technical support for the safe rescue of the submersible.

Does the development of strategic readiness represent a response to the relationship of strategic competencies and the healthcare information system? Evidence from private hospitals in Jordan

This study introduces a model designed to improve the strategic readiness of private hospitals in Amman by incorporating strategic competencies as an independent variable and using a healthcare information system as a mediator. Targeting private hospitals with over 140 beds, the research included a population of 3263 employees across various managerial levels. Data collection methods involved interviews and electronic questionnaires, resulting in a sample size of 344. Statistical analyses comprised exploratory and confirmatory factor analysis, structural equation modeling, and hypothesis testing with SMART PLS 3.3.3 software. The results indicated medium levels of both strategic competencies and healthcare information systems, while strategic readiness was found to be low. Nevertheless, the proposed model showed a direct positive effect of strategic competencies on strategic readiness, with the healthcare information system acting as a significant partial mediator. Evaluation metrics included the arithmetic mean, standard deviation, and path analysis. This model surpasses traditional methods by effectively linking strategic competencies and information systems to enhance strategic readiness, providing a strong framework for improving hospital responses to crises and dynamic changes. The study suggests focusing on enhancing and developing strategic competencies and integrating a comprehensive healthcare information system to optimize hospital operations and increase readiness.

A Study On Importance Of Recycling And The Environmental Health Effects Of Waste Management

The study was conducted to analyze the health effects of waste management on environment. For this purpose, a descriptive type of research was conducted with a sample size of 100 respondents which was chosen based on convenient sampling technique. Structured questionnaire was used to collect the data. The data was tabulated and analyzed using percentage analysis and other statistical tools like chi square test, weighted average mean and Friedman rank test. The findings of the study revealed that all the respondents were aware of waste management and the major factor that encourages recycling is that shorter distance for disposal. There is a relationship between containers used for keeping wastes before its disposal and Household disease because of improper waste management.

Construction of global IGS-3D electron density (Ne) model by deep learning

In this study, we construct a global IGS-3D Ne model that generates global 3-D electron density (Ne) from International Global Navigation Satellite Systems (GNSS) Service (IGS) total electron content (TEC) data through deep learning. As a first step towards this, we make a model to generate a vertical electron density profile from a TEC value using Multi-Layer Perceptron (MLP). In this process, we use the vertical electron density profiles and the corresponding TEC values of the IRI-2016 model from 2001 to 2008 for training, 2009 and 2014 for validation, and 2010 to 2013 for a test. The next step is to generate global IGS electron density profiles using the global IGS TECs as input data for the model, which is called the global IGS-3D Ne model. We evaluate the IGS-3D Ne model by comparing the electron density profiles from the incoherent scatter radars (ISRs) at three stations with the IGS-3D Ne model from 2010 to 2013. The evaluation shows that the electron density profiles from the IGS-3D Ne model are closer to the ISR data than those of the IRI model, especially at high latitudes. The IGS-3D Ne model shows that the averaged root mean square error (RMSE) values between IGS and ISR electron density profiles are 0.37 log(m−3), 0.22 log(m−3), and 0.34 log(m−3) for all test datasets at Jicamarca, Millstone Hill, and EISCAT stations, respectively. These results suggest that our method has sufficient potential to enhance the ability to predict global electron density profiles.

Analysis of seasonal rainfall variability with innovative graphical methods of Konya Closed Basin, Türkiye

This study looked into the variability of seasonal rainfall in the semi-arid Konya Closed Basin (KCB), a water-limited region with high exploitation demand for water resources due to agricultural activities. In the study, seasonal precipitation data of eleven stations in the basin for the period 1971–2020 were used and analyzed with four trend methods (Şen-ITA, CWTSD, IPTA, and TPSC). Visual and statistical findings from the analyses showed generally increasing trends in winter and summer precipitation and decreasing trends in spring precipitation in the basin. In autumn precipitation, according to Şen-ITA, there is an increasing trend in seven stations and a decreasing trend in four stations, while according to CWTSD, there is an increasing trend in six stations and a decreasing trend in five stations. Based on the arithmetic mean IPTA graphs, Winter-Spring transitions in all stations occur from a rising trend region to a falling trend region, but Spring-Summer transitions take the reverse direction. In the standard deviation IPTA graphs, similar transitions in the trend regions are predominant. To the mean and standard deviation TPSC graphs, most of the stations generally have similar behavior. Spring-Summer transition arrows are dominant in Zone III, while Summer-Autumn and Autumn-Winter transitions are dominant in Zone I. Although Winter-Spring transitions are variable, they are more intense in Zone I and III. CWTSD and Şen-ITA methods are also consistent with the results obtained from IPTA graphs, both visually and numerically.

Competing and Noncompeting Risk Models for Predicting Kidney Allograft Failure.

Prognostic models are becoming increasingly relevant in clinical trials as potential surrogate endpoints, and for patient management as clinical decision support tools. However, the impact of competing risks on model performance remains poorly investigated. We aimed to carefully assess the performance of competing risk and noncompeting risk models in the context of kidney transplantation, where allograft failure and death with a functioning graft are two competing outcomes. We included 11,046 kidney transplant recipients enrolled in 10 countries. We developed prediction models for long-term kidney graft failure prediction, without accounting (i.e., censoring) and accounting for the competing risk of death with a functioning graft, using Cox, Fine-Gray, and cause-specific Cox regression models. To this aim, we followed a detailed and transparent analytical framework for competing and noncompeting risk modelling, and carefully assessed the models' development, stability, discrimination, calibration, overall fit, clinical utility, and generalizability in external validation cohorts and subpopulations. More than 15 metrics were used to provide an exhaustive assessment of model performance. Among 11,046 recipients in the derivation and validation cohorts, 1,497 (14%) lost their graft and 1,003 (9%) died with a functioning graft after a median follow-up post-risk evaluation of 4.7 years (IQR 2.7-7.0). The cumulative incidence of graft loss was similarly estimated by Kaplan-Meier and Aalen-Johansen methods (17% versus 16% in the derivation cohort). Cox and competing risk models showed similar and stable risk estimates for predicting long-term graft failure (average mean absolute prediction error of 0.0140, 0.0138 and 0.0135 for Cox, Fine-Gray, and cause-specific Cox models, respectively). Discrimination and overall fit were comparable in the validation cohorts, with concordance index ranging from 0.76 to 0.87. Across various subpopulations and clinical scenarios, the models performed well and similarly, although in some high-risk groups (such as donors over 65 years old), the findings suggest a trend towards moderately improved calibration when using a competing risk approach. Competing and noncompeting risk models performed similarly in predicting long-term kidney graft failure.

Assimilation of Satellite-Derived Reservoir Storage Data to Improve Global Hydrodynamic Modeling

In this study, we explore the potential of assimilating satellite-derived reservoir storage data into the global-scale hydrodynamic model CaMa-Flood, focusing on the Yangtze River basin. We evaluated three data assimilation (DA) methods: direct assimilation (DIR), anomaly based assimilation (ANO), and normalized assimilation (NOM). Our results show that the DIR method achieved the most significant improvements in reservoir storage and downstream discharge simulations. DIR reduced the average relative root mean square error (rRMSE) of reservoir storage estimates by 80.5%, and increased discharge correlation (ΔCC) by 78.6% in the 14 validated discharge stations. ANO, while effective in certain cases, led to mixed results, with 56.4% of the 39 assimilated dams showing improved storage estimates and a modest 7.8% reduction in average RMSE. NOM had minimal impact, with negligible changes in RMSE or discharge correlation (ΔCC). The direct assimilation method (DIR) consistently outperformed the others, improving both reservoir storage and downstream discharge estimates. However, the magnitude of improvement varied across locations, highlighting the need for the further refinement of DA techniques and input data, especially for regions with complex reservoir operations. Our findings enhance reservoir representation in global hydrodynamic models and improve the predictability of river dynamics and water resource management.

Estimating the GPS time-varying differential code bias between C1 and P1 observations to provide a model product

Abstract Based on the time-varying characteristics of GPS satellite differential code biases (DCB) between C1 and P1 observations, a method for directly estimating its model coefficients is proposed to streamline processing and enhance product accuracy. Evaluation over three days compares estimated DCB (C1–P1) values and assesses real-time service (RTS) capabilities in Single Point Positioning (SPP). Three-day DCB (C1-P1) estimates match single-epoch time-varying DCB (C1-P1) series fitting accuracy. SPP results show significant improvement over Orbit Determination in Europe (CODE), reducing average root mean square error in three dimensions by 12 cm and by 4 cm compared to Chinese Academy of Sciences (CAS). Real-time DCB (C1-P1) service maintains stable and accurate performance, comparable to fitted values of single-epoch time-varying series. Real-time SPP application shows the method enhances positioning accuracy compared to CODE by up to 35%, confirming the feasibility and effectiveness of the proposed model estimation method.

Symmetries and symmetry-generated averages of elastic constants up to the sixth order of nonlinearity for all crystal classes, isotropy and transverse isotropy.

Algebraic expressions for averaging linear and nonlinear stiffness tensors from general anisotropy to different effective symmetries (11 Laue classes elastically representing all 32 crystal classes, and two non-crystalline symmetries: isotropic and cylindrical) have been derived by automatic symbolic computations of the arithmetic mean over the set of rotational transforms determining a given symmetry. This approach generalizes the Voigt average to nonlinear constants and desired approximate symmetries other than isotropic, which can be useful for a description of textured polycrystals and rocks preserving some symmetry aspects. Low-symmetry averages have been used to derive averages of higher symmetry to speed up computations. Relationships between the elastic constants of each symmetry have been deduced from their corresponding averages by resolving the rank-deficient system of linear equations. Isotropy has also been considered in terms of generalized Lamé constants. The results are published in the form of appendices in the supporting information for this article and have been deposited in the Mendeley database.

Comparing the Accuracy of Conventional Gypsum and 3D-Printed Dental Casts Using Three-Dimensional Analysis.

Dental impressions and casts play a critical role in dental care, facilitating diagnoses and the fabrication of prostheses. Traditional methods of fabrication involve elastomeric materials that are more prone to errors and patient discomfort. Digital advancements offer promising alternatives, yet their accuracy and applicability to military dentistry remain under-explored. This study evaluates the accuracy of digital casts produced with material available in the Military Health System compared to conventional methods. Using a digital (n = 10) and analog (n = 10) methodology casts were fabricated from a reference cast (n = 1). The reference and cast samples were scanned with a reference scanner to generate stereolithography files. These files were used to generate full arch, single crown, fixed dental prosthesis, and inlay digital casts which were then compared using a three-dimensional (3D) comparison software to evaluate accuracy. Root mean square values were obtained, giving a quantitative evaluation of the deviation of each sample from the reference cast. Statistical analysis consisted of a Shapiro-Wilk and Levene test to account for homogeneity of variances in each group. An ANOVA and Tukey post-hoc test were used to determine differences in accuracy among the full arch and a two-way ANOVA and Tukey post-hoc test evaluated differences in trueness among the casts of the individual preparations. Analog full arch casts had an average root mean square of 106 ±19.18 µm when examining trueness and 12 ±2.58 µm for precision. Digital full arch casts had an average root mean square of 51.9 ±5.39 µm when examining trueness and 4.2 ±1.57 µm for precision. Overall digital casts surpassed analog counterparts in accuracy. Fixed dental prostheses were found to be the only group, which showed no statistically significant difference between digital and analog. These findings validate the potential of digital workflows in enhancing the speed and accuracy of dental care in the Military Health System, while underscoring the need for further exploration and refinement in specific clinical contexts.

Facilitating Students’ Pronunciations on Plosive Consonant Sounds Through Sephonics Software

This research was about facilitating students’ pronunciations on plosive consonant sounds through Sephonics software. It was done at 3rd semester students of ELT Department of Walisongo State Institute for Islamic Studies in the academic year of 2011/2012. The background of this research was the difficulty in learning pronunciation. This research was aimed to responding the following questions; (1) How was the implementation of using sephonics software for teaching pronunciation (plosive consonant sounds) at third semester students of ELT Department, State Islamic Institute of Walisongo, Semarang? (2) Was there any improvement of students’ pronunciation of plosive consonant sounds after being taught using sephonics software? The participant of this research was the 3rd semester students of ELT Department of Education Faculty, Walisongo State Institute for Islamic Studies, Semarang that consisted of 28 students. It was a classroom action research that had two cycles. They were pre-cycle, cycle 1, and cycle 2. The use of sephonics software was used in cycle 1 and cycle 2. The methods of data collection were test and observation in all cycles. The test form was reading aloud that asked students to pronounce some words that contained plosive consonant sounds. Then, the result of test scores was analyzed by using mean to determine whether there was an improvement in each cycle. This study showed that: (1) the first thing that the teacher needed to do to use this software as a teaching aid was that he or she must conduct the class in a language laboratory. If it was not possible, the teacher could conduct the class in a regular classroom by providing several computers or laptops. Then, the students must be grouped based on the number of the computers. The second thing was that this was CALL learning based; the teacher might explain how to use the software in order that the students could practice by themselves outside of class schedule. (2) After collecting data, the result was found that the average (mean) of pre cycle test was 69. Then, it had very significant improvement in cycle 1 and cycle 2. It became 98.7 from cycle 1 test and 99.6 from cycle 2 test. It meant that the research was successful to improve the students’ pronunciation abilities. In conclusion, Sephonics software was successful to facilitate students’ pronunciations on plosive consonant sounds.