Process Approach Research Articles

The Classification Method is Used for Sentiment Analysis in My Telkomsel

User reviews significantly impact how mobile apps are perceived and provide developers with valuable insights into improving the functionality and quality of their products. Sentiment analysis of these evaluations helps identify the main issues faced by consumers, such as technical difficulties, costs, and service levels. The main objective of this study is to classify user sentiment into positive and negative categories, focusing on the MyTelkomsel app. With the use of Google Play Scraper, 39,493 reviews on various app versions and user experiences were collected. This data was analyzed using multiple machine learning models, including Support Vector Machines (SVM), Naive Bayes, Random Forest, and Gradient Boosting, alongside the Natural Language Processing (NLP) approach. The results show that 39.2% of the reviews are positive, while 60.8% reflect negative sentiment. Among the models, SVM showed the highest accuracy in sentiment classification with a value of (0.854792), while Naive Bayes (0.775541), Random Forest (0.829725), and Gradient Boosting (0.819344) also performed well in sentiment classification. These findings suggest that developers can leverage the insights gained from this analysis to proactively improve the performance and user experience of the MyTelkomsel app, by addressing technical and service-related issues identified in user reviews.

Penerapan Teknologi Pendidikan: Menghadapi Kendala, Menciptakan Solusi

This research aims to identify obstacles and solutions in the application of educational technology through literature review. Technology has great potential in improving the quality of learning, but its application in the field still faces various obstacles. This research uses a qualitative approach with a literature review method. The sources used include academic journals, scientific articles, government reports, and publications from relevant national and international educational institutions. The data obtained is analyzed thematically by identifying the main categories that emerge from the literature. This literature review reveals four main obstacles in the application of educational technology, namely the lack of adequate infrastructure, limited technological competence among educators, resistance to change, and lack of policy support from the government and educational institutions. To overcome these obstacles, several solutions are recommended, including increasing equitable access to technology infrastructure, providing sustainable technology training for educators, implementing an adaptive approach in the technology integration process, and strengthening policies that support technology adoption in education

Realization of partially transmitted titanium/glass windows using a nanosecond pulsed laser

Titanium has been widely used in aerospace, ocean exploration, and other extreme fields because of its superior physical and chemical properties. In some specific fields, it is necessary to process titanium films to control the optical properties. In this study, we designed and fabricated four kinds of titanium-coated BK7 glass windows by using a weak 532 nm nanosecond pulsed laser and then analyzed their effective transmittance with a digital image processing approach. Finally, the actual effective transmittances of all processed samples were measured by utilizing a well-designed optical system. The results show that the total transmittance of the titanium-coated glass windows is from 5.70% to 48.72%. The processed four types of samples might be applied in different application scenes for the resistance of cosmic radiation. Such special windows we obtained in the study are thought to effectively shield cosmic rays and transmit some visible light at the same time. Our works may open up a new path for the realization of special windows used in some fields such as airline, outer space, scientific research stations at high altitude, etc.

Tightly Coupled SLAM Algorithm Based on Similarity Detection Using LiDAR-IMU Sensor Fusion for Autonomous Navigation

In recent years, the rise of unmanned technology has made Simultaneous Localization and Mapping (SLAM) algorithms a focal point of research in the field of robotics. SLAM algorithms are primarily categorized into visual SLAM and laser SLAM, based on the type of external sensors employed. Laser SLAM algorithms have become essential in robotics and autonomous driving due to their insensitivity to lighting conditions, precise distance measurements, and ease of generating navigation maps. Throughout the development of SLAM technology, numerous effective algorithms have been introduced. However, existing algorithms still encounter challenges, such as localization errors and suboptimal utilization of sensor data. To address these issues, this paper proposes a tightly coupled SLAM algorithm based on similarity detection. The algorithm integrates Inertial Measurement Unit (IMU) and LiDAR odometry modules, employs a tightly coupled processing approach for sensor data, and utilizes curvature feature optimization extraction methods to enhance the accuracy and robustness of inter-frame matching. Additionally, the algorithm incorporates a local keyframe sliding window method and introduces a similarity detection mechanism, which reduces the real-time computational load and improves efficiency. Experimental results demonstrate that the algorithm achieves superior performance, with reduced positioning errors and enhanced global consistency, in tests conducted on the KITTI dataset. The accuracy of the real trajectory data compared to the ground truth is evaluated using metrics such as ATE (absolute trajectory error) and RMSE (root mean square error).

Enhancing suicidal behavior detection in EHRs: A multi-label NLP framework with transformer models and semantic retrieval-based annotation

Background:Suicide is a leading cause of death worldwide, making early identification of suicidal behaviors crucial for clinicians. Current Natural Language Processing (NLP) approaches for identifying suicidal behaviors in Electronic Health Records (EHRs) rely on keyword searches, rule-based methods, and binary classification, which may not fully capture the complexity and spectrum of suicidal behaviors. This study aims to create a multi-class labeled dataset with annotation guidelines and develop a novel NLP approach for fine-grained, multi-label classification of suicidal behaviors, improving the efficiency of the annotation process and accuracy of the NLP methods. Methods:We develop a multi-class labeling system based on guidelines from FDA, CDC, and WHO, distinguishing between six categories of suicidal behaviors and allowing for multiple labels per data sample. To efficiently create an annotated dataset, we use an MPNet-based semantic retrieval framework to extract relevant sentences from a large EHR dataset, reducing annotation space while capturing diverse expressions. Experts annotate the extracted sentences using the multi-class system. We then formulate the task as a multi-label classification problem and fine-tune transformer-based models on the curated dataset to accurately classify suicidal behaviors in EHRs. Results:Lexical analysis revealed key themes in assessing suicide risk, considering an individual’s history, mental health, substance use, and family background. Fine-tuned transformer-based models effectively identified suicidal behaviors from EHRs, with Bio_ClinicalBERT, BioBERT, and XLNet achieving the F1 scores (0.81), outperforming BERT and RoBERTa. The proposed approach, based on a multi-label classification system, captures the complexity of suicidal behaviors effectively particularly “Suicide Attempt” and “Family History” instances. The proposed approach, using task-specific NLP models and a multi-label classification system, captures the complexity of suicidal behaviors more effectively than traditional binary classification. However, direct comparisons with existing studies are difficult due to varying metrics and label definitions. Conclusion:This study presents a robust NLP framework for detecting suicidal behaviors in EHRs, leveraging task-specific fine-tuning of transformer-based models and a semi-automated pipeline. Despite limitations, the approach demonstrates the potential of advanced NLP techniques in enhancing the identification of suicidal behaviors. Future work should focus on model expansion and integration to further improve patient care and clinical decision-making.

TOA based mobile source localization with unknown start transmission time and sensor position uncertainty in asynchronous networks

ABSTRACT This letter addresses the issue of mobile source localization using time of arrival (TOA) measurements in scenarios where the source and sensors are not synchronized, and the start transmission time is unknown. We propose a non-convex weighted least squares (WLS) minimization problem for estimating the source position, velocity and start transmission time from the distance measurement equation, without the need for Taylor approximation commonly used in traditional processing approaches. To effectively solve the non-convex WLS optimization problem, we use the semi-positive definite relaxation (SDR) technique to transform the original problem into a convex semi-positive definite programming (SDP) problem. Additionally, we further extend the proposed method to scenarios where the positions of sensors are uncertain. Computer simulation results demonstrate that the proposed method surpasses the existing methods, whether the positions of sensors are known or uncertain.

Linear prediction on Cent scale for fundamental frequency analysis

Understanding the fundamental frequency and harmonic content of audio signals is crucial for many applications in music analysis, including music transcription, audio synthesis, and genre identification. This study formulates a signal processing approach combining Linear Prediction (LP) analysis and the Cent scale to accurately characterize the pitch and harmonic structure of the audio signals. Pitch tracking on the LP spectrum in the Cent scale provides more accurate and reliable pitch estimation, especially in the presence of noise or overlapping harmonics. The Cent scale aligns the harmonics of different notes more closely, making it easier to discern the correct pitch.

Lightweight macro-pixel quality enhancement network for light field images compressed by versatile video coding

Previous research demonstrated that filtering Macro-Pixels (MPs) in a decoded Light Field Image (LFI) sequence can effectively enhances the quality of the corresponding Sub-Aperture Images (SAIs). In this paper, we propose a deep-learning-based quality enhancement model following the MP-wise processing approach tailored to LFIs encoded by the Versatile Video Coding (VVC) standard. The proposed novel Res2Net Quality Enhancement Convolutional Neural Network (R2NQE-CNN) architecture is both lightweight and powerful, in which the Res2Net modules are employed to perform LFI filtering for the first time, and are implemented with a novel improved 3D-feature-processing structure. The proposed method incorporates only 205K model parameters and achieves significant Y-BD-rate reductions over VVC of up to 32%, representing a relative improvement of up to 33% compared to the state-of-the-art method, which has more than three times the number of parameters of our proposed model.

Can TDABC (Time-Driven Activity-Based Costing) help the NOVO hospital's medical biology laboratory to value the appropriate prescription? - Role of the biologist in reducing healthcare costs

The relevant and correct prescribing of medical biology is a major public health issue. Correct prescribing is a legal obligation under article L6211-8 of the French Public Health Code and is an integral part of the biologist's daily work, already specified in the 2012 (section 4.7) and 2022 (section 5.3.3) versions of the NF EN ISO 15189 standard. COFRAC document SH REF 02 v08 specifies the requirements for consultancy services. The adaptation of the prescriptions is a revision of the contract with the prescriber, which makes it possible to optimize patient care and ensure the satisfaction of the laboratory users. Although essential, accurate prescribing is time consuming. "Chronophage" is the term that has been used for almost a decade. At present, the work of biologists in this area is not valued. In fact, there is no evaluation system to highlight this regulation control activity. To date, no work has been published to estimate this time, this chronophagy, and to evaluate its impact. Cost measurement using Time-Driven Activity-Based Costing (TDABC), a variant of Activity-Based Costing (ABC), is based on a process approach. The main contribution of TDABC is that it uses a single cost driver: time. Serum immunofixation is a test used to confirm and monitor plasma cell dyscrasias, the archetype of which is multiple myeloma. The learned societies and the IMWG provide explicit diagnostic criteria, but the guidelines do not address the frequency of follow-up. In particular, the frequency of repeat and follow-up serum immunofixation remains unaddressed from an evidence-based medicine perspective. This work has made it possible to highlight the savings made between 1st January and 31th August 2023 thanks to the involvement of the biologist and to highlight his essential role in the process of controlling the overall expenditure (reagents, human resources, time and money) in the specialized biochemistry sector (bench: proteins) and to strengthen the role of the biologist within the institution. The various players in the healthcare sector - prescribing physicians, biologists, hospital administrators and, last but not least, patients - all have an essential role to play in maximizing value for patients and in the healthcare economy.

Towards boundary crossing: primary and secondary school teachers teaching creative writing and its redrafting

Purpose There is little research into how teachers think about and teach creative writing and its redrafting and how this might differ depending upon the age of the pupils being taught. This paper aims to compare the creative writing conceptualisations and practices of primary school teachers (5–11-year olds) and secondary school teachers (11–18-year-olds) in England through a qualitative survey. This comparison enables to think about the influence of policy on creative writing in primary and secondary schools as well as what professional development could look like for these teachers to improve the teaching of creative writing. Design/methodology/approach A qualitative survey exploring the creative writing and redrafting pedagogies and conceptualisations was responded to by primary school teachers (n = 18) and secondary school teachers (n = 19). Taking an ecological view of creative writing and teacher identity, the authors undertake a comparative analysis of the survey data using the 5A’s theory of creativity (Glaveanu, 2013) and a view of professional identity existing within “landscapes of practice” (Wenger-Trayner, Wenger-Trayner, 2015). This enables to illuminate how and why creative writing is contextually afforded, or otherwise, in primary and secondary landscapes of practice. Findings This analysis demonstrates how the redrafting of creative writing is marginalised in both landscapes of practice and how redrafting is largely conceptualised as a technical rather than critical or creative action. The authors show how teachers, particularly in primary school, aim for their pupils to produce “products” rather than engaging in the “process” of creative writing. This analysis also shows that whilst creative writing is overall more marginalised in the secondary school landscape, it is often taught through process approaches. In both landscapes of practice, the re-drafting of creative writing is largely taught through product approaches. Research limitations/implications This research is potentially skewed by the fact that we recruited our participants through networks relating the teaching of English, including creative writing. What is worrying about this limitation, however, is that the picture of creative writing in schools in England probably leans more to a product approach than the picture this research has uncovered. Practical implications Professional development for teachers in both landscapes is needed in relation to pedagogical actions for creative writing and its redrafting. Some of the key differences we have outlined in conceptualisations and practices between primary and secondary schools landscapes, notably the overuse of product-based teaching actions in primary landscapes, and some of the differences we have outlined within discrete landscapes of practice, notably how some primary school teachers feel more confident to challenge the product-based approach, with one conceptualising redrafting as “creative”, indicate that professional development should involve teachers working across schools. Social implications Policy needs to be reformed to move away from the technicist view of creative writing held in both landscapes of practice. Linked to this, the way creative writing is assessed as a product in secondary schools needs to change – the re-introduction of portfolio-based coursework (Bishop, 1990) would provide the affordance of redrafting as an action central to creative writing processes. Originality/value To the best of the authors’ knowledge, this is a rare piece of research which compares primary and secondary school teachers’ approaches to teaching creative writing. It shows that primary school teachers can be formulaic in the way they teach creative writing, using product approaches. However, in secondary schools the picture is different: teachers, particularly those, who are writers themselves, give students more agency in redrafting and shaping their writing. This indicates how professional development should involve primary and secondary school teachers in dialogue with one another to cross boundaries of practice.

A cellular automata modelling approach for grain growth topological evolution process of ternary cathode materials combined with deep neural networks

Ternary cathode materials are pivotal in high-performance battery technologies, with grain size influencing their electrochemical performance. However, the absence of real-time grain size inspection during material preparation poses challenges in maintaining the consistent quality of ternary cathode materials. To address this, this paper proposes a novel method employing cell automata to model the topological evolution of grain growth in these materials, integrated with deep neural networks (DNN). The grain growth process is divided into two stages: heating and constant temperature. In the heating stage, varying heating rates and plateau temperatures serve as DNN inputs, yielding the primary grain distribution for the cell automata model in the constant temperature stage. Based on cell automata, the grain growth model links the grain growth rate to the grain size distribution in the constant temperature stage. A surface energy constraint rule, based on local curvature and grain boundary surface tension, governs growth rates. The grain boundary growth ratio is also used to create a grain ID transition variable, dictating grain ID conversion in the model. This approach accurately simulates the dynamic evolution of polycrystalline grain size and morphology. Simulation results show that this method effectively models grain growth in ternary cathode materials, offering insights for optimising the sintering process and improving material quality.

A 3D modeling of the effect of layup orientation on the thickness uniformity of cured composite L-shaped laminates

This paper presents a three-dimensional modeling approach for the curing process of polymer-matrix composites (PMCs). The presented model could fully characterize the heat transfer, exothermic reactions in resin crosslinking, material time-varying properties, resin flow, and fiber compaction during the curing process. The predicted non-uniform distribution of thickness of the L-shaped composite laminate samples coincides well with experimental results. It is observed that the three-dimensional model, compared to the two-dimensional model, better captures the solidification in complex structural regions, such as the R-corner and the thickness variation region. By comparing the residual stress, fiber volume fraction, and non-uniform thickness of the laminate samples with different layup orientations, the experimental and computational results both show that the corner region of [90]20 laminate is thinner than other regions, with a higher fiber volume fraction. This is attributed to the mismatch of reactive stresses between the tool side and bag side, primarily compressive radial stress. In contrast, the corner region of the [0]20 laminate is thicker than other regions, with a lower fiber volume fraction. This difference is mainly due to the mismatch of radial stresses, hindrance caused by compressive hoop stresses in the compaction process, and potential wrinkling of hoop fibers due to interlaminar shear constraints.

Highly Mixed Index Facet Engineering Induces Defect Formation and Converts the Wave-Transmissive Mott Insulator NiO into Electromagnetic Wave Absorbent.

Mott insulator possesses the property of converting into semiconductor under supernormal conditions and achieving the Mott insulator-semiconductor transition (IST) holds great scientific value. Nevertheless, current IST methodologies possess certain limitations because they are not capable of being implemented under conventional conditions, thereby limiting their practical applications. Herein, a highly mixed index facets (HMIF) strategy is proposed to construct homogeneous interfaces with gradient work function (WF) in Mott insulator NiO, accompanied by numerous oxygen vacancies. These vacancies provide additional defect energy levels and inhomogeneous charge distributions, resulting in a 180 fold enhancement of conductivity, realizing the IST process, and inducing the defect polarization. In addition, HMIF configuration induces electron transport along the index facets with gradient WF, ultimately leading to accumulation on the specific facet. This accumulation allows this facet can be considered as a dipole with its adjacent facets and makes NiO to attenuate electromagnetic waves (EMW) through dipole polarization. Therefore, NiO with exposed HMIF possesses improved EMW absorption properties (80-fold higher than that of commercial NiO), realizing the transition from EMW-transmissive to EMW-absorbing materials. This research presents an approach for the IST process, discovers the polarization behavior that occurred on specific index facet, and extends its potential application in EMW absorption.

Spiritual Meaning Making and Trauma Recovery: Evaluation of a Psycho-social Intervention for Earthquake Survivors in Turkey.

This study sets out to develop a psycho-social model for assessing the effectiveness of spiritually oriented logotherapy for earthquake victims with PTSD following the 6 February 2023, Kahramanmaraş earthquake in Turkey. The findings of this study will undoubtedly contribute to the development of post-disaster spiritual support services. We developed and implemented a six-week "Psycho-Social Training Programme through Spiritually Oriented Meaning" in the cities of Malatya, Elazığ and Kahramanmaraş, which were significantly affected by the earthquake. A randomised controlled trial (RCT) design was used, with participants randomly assigned to either the intervention group or the control group. Pre-test and post-test assessments were conducted to evaluate the programme's impact. The findings were clear: the programme significantly reduced PTSD symptoms (p < .05), proving the effectiveness of a meaning-oriented approach in the survivors' healing process. Additionally, the programme made a positive contribution to spiritual support services and provided a model to meet post-disaster psychological needs. This programme demonstrated its potential as an effective method for addressing PTSD and enhancing spiritual recovery in earthquake survivors.

Comparative analysis of deep learning and traditional airborne electromagnetic data processing: A case study

State-of-the-art airborne electromagnetic (AEM) systems conduct large-scale surveys and produce thousands of line kilometers of data. These large volumes of AEM data are generally subjected to manual inspection for identifying and culling data corrupted by couplings from various sources, such as fences and power lines, as well as the assessment for late-time noise. Although effective, this traditional AEM data processing is subjective and time consuming. This study evaluates the efficacy of a recently proposed unsupervised deep-learning method for the automated processing of AEM data on a large-scale survey conducted in New Zealand. The automated system processes 6471 line kilometers of data in approximately 15 min — a task that could extend more than 5–16 days if done manually by a trained expert. Although manual processing is not the ultimate benchmark, both processing schemes have an agreement of approximately 88% for the low-moment data and approximately 96% for the high-moment data. The inversion results for manual and automated system processing also indicate comparable data fits and resistivity models. The automated system demonstrates strong generalization capabilities; however, it faces challenges when data affected by various sources, such as anthropogenic activities, mimic credible 1D responses. This underscores the need for manual inspection in contexts wherein it is necessary to distinguish between true anomalies and noise. This study not only validates the automated system’s applicability across diverse geologic settings but also emphasizes the importance of balancing automated and manual approaches for optimal AEM data processing.

How to Teach a Sociology Student Good Data Analiysis Using SPSS?

The paper presents the author’s thoughts and explores the process of teaching sociology students to analyze data using SPSS and the course book by N. Voronina, published in 2024. It also provides a brief overview of the available domestic learning materials in SPSS-based data analysis and argues that the genre of course books written by a new generation of researchers and teachers is of relevance. N. Voronina’s course book shows an understanding of the specifics of sociology students, as students who still have signs of avoiding information that is heavy in formulas and mathematical terminology, together with an understanding of how important is gaining analytical skills for professional activities. The author argues that this contradiction can be removed by bringing the research methodology to the forefront, while the analysis methods themselves should come second. The author’s high level of empathy towards students who study analysis and the use of 232 pictures embracing all the nuances of the applied processes can contribute to effective learning and application of the methods under consideration. The course book outlines all data processing and analysis approaches that may be required to develop the basic skills of a sociology research analyst. Deep and substantial description of regression analysis, decision tree method (CHAID), and discriminant analysis techniques are redundant in this context. Therewith, the author disregards the fact that the most common sociology research method is crosstabs, and its brief overview in the course book is insufficient.

Modeling Product Degradation with Heterogeneity: A General Random-Effects Wiener Process Approach

Degradation of many products in practical applications is often subject to unit-to-unit heterogeneity. Such heterogeneity can be attributed to heterogeneous quality of the raw materials and the fluctuation of the manufacturing process, as well as the heterogeneous usage conditions and environments. The heterogeneity leads to the scattering of the degradation rates and diffusion intensities in the population. To model this phenomenon, this study proposes a general random-effects Wiener process model that accounts for the unit-to-unit heterogeneity in the degradation drift and the volatility simultaneously. In particular, the drift of the Wiener process is characterized by a normal distribution and the diffusion parameter is characterized by an independent inverse Gaussian distribution. The proposed model is flexible for characterization of heterogeneous degradation, and permits an analytically tractable model inference. An EM algorithm incorporating the variational Bayesian method is developed to estimate the model parameters, and a parametric bootstrap approach is proposed to construct confidence intervals. The performance of the proposed model and the estimation algorithm is validated by Monte Carlo simulations. The degradation data of an infrared LED device and the wearing data of the magnetic head of a hard disk driver are studied based on the proposed model. With comprehensive comparative studies, the good performance of the proposed model in fitting the real degradation data is validated.

Influence of selected ingredients and process variables on quality parameters of saturated steam-assisted plant-based meat analogue

Purpose Plant-based meat analogues (PBMAs) hold significant promise as a sustainable solution to meet future protein demands, replicating the taste and nutritional value of meat. However, the present reliance on extrusion technology in PBMA production limits the exploration of more accessible and affordable methods. The current investigation aims to meet the market demand for a scalable and cost-effective processing approach by exploring saturated steam-assisted technology that could broaden the production volume of PBMAs, thereby supplementing protein security and planet sustainability. Design/methodology/approach A one-factor-at-a-time (OFAT) approach is employed to evaluate the effect of ingredients and process conditions on the governing quality attributes (texture, colour and sensory). Findings Among the ingredients, monosodium glutamate (MSG) and nutritional yeast (NY) significantly enhanced the hardness and chewiness of saturated steam-assisted plant-based meat analogues (ssPBMAs) followed by potato protein isolate (PPI), defatted soy flour (DSF) and salt. The addition of PPI and DSF led to a decrease in lightness (L* value) and an increase in the browning index (BI). Sensory evaluations revealed that higher concentrations of DSF imparted a noticeable beany flavour (&gt;20%), whereas PPI (30%) improved the overall sensory appeal. Increased levels of NY (10%) and MSG (5%) enhanced the umami flavour, enhancing consumer preference. Higher thermal exposure time (TTi) (45 min) and temperature (TTe) (120 °C) during processing resulted in softer products with reduced L* values. These findings establish a foundation for selecting and optimizing the ingredients and processing parameters in ssPBMA production. Originality/value The novelty of the current study includes process behaviour of selected ingredients such as PPI, NY, MSG, DSF, salt and adopted process conditions, namely, dough processing time (DPT), protein network development time (PNDT), TTi and TTe on the quality of ssPBMAs.

Application Of Reminiscence Therapy As An Improving Effort Cognitive Function Of Older Age

Cognitive decline in the elderly can affect their physical abilities and quality of life. Cognitive decline is often characterized by impaired memory and communication, resulting in dependence on others. This study aims to apply reminiscence therapy as an effort to improve cognitive function in the elderly. The case study was conducted using the gerontic nursing care process approach. The research subjects consisted of two elderly over 60 years old with cognitive impairment, who were evaluated using the Mini Mental State Examination (MMSE). Reminiscence therapy was implemented in 3 sessions for 2 weeks, each lasting 90 minutes. After the application of reminiscence therapy, both subjects' MMSE scores improved. The first subject showed the score increased from 15 to 21, while the second subject from 16 to 23, which showed improvement in the category of cognitive impairment. The application of reminiscence therapy is able to stimulate long-term memory and improve cognitive function in the elderly. These results are in line with previous research which shows that this therapy is effective in improving cognitive abilities and quality of life of the elderly. Reminiscence therapy is an important intervention to improve memory and cognitive function in the elderly with cognitive impairment. The application of this therapy can make a significant contribution to the care of the elderly and improve their quality of life.

On a New Point Process Approach to Reliability Improvement Modeling for Repairable Systems

ABSTRACTIn this paper, we are the first to consider the combination of the minimal repair with the defined better than minimal repair. With a given probability, each failure of a repairable system is minimally repaired and with complementary probability it is better than minimally repaired. The latter can be interpreted in terms of a reliability growth model when a defect of a system is eliminated on each failure. It turns out that the better than minimal repair can be even better than a perfect one if a perfect repair is understood as a replacement of the whole system or stochastically equivalent operation. We provide stochastic description of the failure/repair process by introducing and describing the corresponding bivariate point process via the concept of stochastic intensity. Distributions for the number of failures for the pooled and marginal processes are derived along with their expected values. The latter can describe the process of reliability growth in applications. Some meaningful special cases are discussed.