Checking Process Research Articles

Do we need a guideline for all: a qualitative study on the experiences of male athletes following anterior cruciate ligament reconstruction

BackgroundConsidering the low rate of qualitative studies on athletes with anterior cruciate ligament reconstruction (ACLR), aiming to access in-depth data, we thought that the utilization of the qualitative method would allow us to collect the appropriate and sufficient data to yield novel findings and achieve sound conclusions. The study’s aim was to investigate anterior cruciate ligament (ACL) injury experience processes in athletes who had undergone isolated ACLR, reveal the clinically related milestones, and highlight the necessary gaps.MethodsSemi-structured interview techniques, in-depth follow-up questions, and thematic analysis were used to explore the experiences of participants with isolated ACL injuries 1–3 years after surgical treatment. The study was conducted in the Orthopaedics and Traumatology outpatient clinic of the Adana City Training and Research Hospital and included 14 male athletes who had undergone isolated primary ACLR. The study group’s demographic and qualitative data were collected in the first week of September 2023. The member checking process was completed in the third following week. A thematic analysis checklist was used to ensure the reliability of the thematic analysis. The Consolidated Criteria for Reporting of Qualitative Research (COREQ) guidelines were followed.ResultsThe experiences of 14 patients (22.78 ± 3.76 years, all males) were summarized into four themes that emerged from the data analysis process: ‘The Distinctions in the Participants’ Experiences Regarding the Moment of Injury,’ ‘Gathering Information about the ACL Injury,’ ‘Factors That Facilitate The Treatment Process and Reinforce Positive Experiences,’ and ‘Desperate Plight: Main Points of Patients’ Negative Experiences.’ Based on the main themes, there were 14 subthemes.ConclusionsOur study revealed that varying perceptions of ACL injury presented by the participants, which were caused by all stakeholders, including themselves, the professional environment, family members, social network, and the healthcare staff, showed that the physical and psychological impacts of the injury were observed in different severity levels at each stage of the process. We believe that an extensive guide for athletes with ACL injuries that includes all components of well-being and displays the required details for the sports club/coach, family/companion, and physician.

Systolic array-based CNN accelerator soft error approximate fault tolerance design

To satisfy the massive computational requirement of Convolutional Neural Networks, various Domain-Specific Architecture based accelerators have been deployed in large-scale systems. While improving the performance significantly, the high integration of the accelerator makes it much more susceptible to soft-error, which will be propagated and amplified layer by layer during the execution of CNN, finally disturbing the decision of CNN and leading to catastrophic consequences. CNNs have been increasingly deployed in security-critical areas, requiring more attention to reliable execution. Although the classical fault-tolerant approaches are error-effective, the performance/energy overheads introduced are non-negligible, which is the opposite of CNN accelerator design philosophy. In this article, we leverage CNN's intrinsic tolerance for minor errors and the similarity of filters within a layer to explore the Approximate Fault Tolerance opportunities for CNN accelerator fault tolerance overhead reduction. By gathering the filters into several check groups by clustering to perform an inexact check while ensuring that serious errors are mitigated, our approximate fault tolerance design can reduce fault tolerance overhead significantly. Furthermore, we remap the filters to match the checking process and the dataflow of systolic array, which can satisfy the real-time checking demands of CNN. Experimental results exhibit that our approach can reduce 73.39%performance degradation of baseline DMR.

MONITORING THE SAUSAGE PRODUCT USING LANEY DEMERIT CHART BASED ANALYTICAL HIERARCHY PROCESS

Ready-to-eat sausage is a food product that has a limited shelf life. Therefore, regularly monitoring the quality of packaged ready-to-eat sausage products is important to ensure that the products meet the established quality standards. Twelve types of product defects need to be observed in the final checking process to meet the quality standards, namely Wrinkle, Dots, Leaking, Product Stain, Non-standard Form, Poor Print Quality, Vacuum Leaks, Weak Ties, Body Defects, Uneven Length, Broken Node, and Small Stain. This study aims to apply the Laney Demerit Control Chart (LDCC) and Analytical Hierarchy Process-Integrated Statistical Process Control (AHP-ISPC) methods to monitor the quality of packaged ready-to-eat sausage production at XYZ Inc. The data is from the quality testing of ready-to-eat sausage products taken from XYZ Inc. for six months from April 1, 2023, until September 30, 2023. The findings reveal that conventional control charts (u control chart, demerit control chart, and AHP-based demerit control chart) exhibited oversensitivity because it is attributed to the large number of samples produced by the company, prompting the need for a more balanced approach. Implementing the Laney u control chart, Laney demerit control chart, and the AHP-based Laney demerit control charts successfully achieved statistical control in phase I. In contrast, phase II still demonstrated challenges, particularly with the AHP-based Laney Demerit Control Chart detecting the highest number of out-of-control points. This suggests that phase II remains statistically out of control, necessitating further analysis or corrective measures to enhance process stability. Additionally, the process capability analysis indicated that the production process during the specified period lacked capability, as evidenced by a capability index value below one (0.883).

MG+: Towards Efficient Context Inconsistency Detection by Minimized Link Generation

ABSTRACTSelf‐adaptive applications are becoming increasingly attractive, with the ability to smartly understand their runtime environments (or contexts) and deliver adaptive services, for example, location‐aware navigation or resource‐sensitive suggestions. However, due to inherent noises in the process of sensing and interpreting environmental information, there is a growing demand for guarding the consistency of collected contexts to avoid application misbehaviour and, at the same time, minimize extra costs. Existing work attempted to achieve this by speeding up the kernel constraint checking module inside the consistency guarding process. Most of these efforts were spent on reusing previous checking results or parallelizing the checking process, but they all leave one central step of constraint checking, that is, link generation, untouched. In this step, the checking engine provides reasons to explain the violation of constraints under check. It occupies a substantial part of the total time cost. Focusing on this key link generation step, we proposed MG, which deploys a rigourous analysis to automatically identify and avoid redundancy in the link generation without harming any correctness of the checking results. MG has been proven sound (always guaranteeing correctness) and complete (entirely removing redundancy). Moreover, based on our observation that MG's redundancy elimination also assists another core step of constraint checking to reduce unnecessary computation further, we additionally enhance MG with an escape‐condition optimization to escape unnecessary evaluation of truth values to further improve the efficiency of constraint checking in an aspect other than link generation. We call it MG+ for distinguishing. Our experiments with synthesized and real‐world consistency constraints reported that, compared with existing work, MG eliminates all link redundancy (83% to 0%), and based on it, MG+ further reduces significant truth value calculations (e.g., 49.74% reduction when combined with ECC and Con‐C). Generally, MG brought 14–500 speed‐ups in link generation, and MG+ further made 1.2–1.9 speed‐ups in truth value evaluation. Altogether, MG reduced the total constraint checking time up to 45.4%, and MG+ reduced it up to 61.0%.

Image segmentation on void regional formation in the flip-chip underfilling process by comparing YOLO and mask RCNN

Purpose This paper aims to identify a suitable convolutional neural network (CNN) model to analyse where void(s) are formed in asymmetrical flip-chips with large amounts of the ball-grid array (BGA) during underfilling. Design/methodology/approach A set of void(s)-filled through-scan acoustic microscope (TSAM) images of BGA underfill is collected, labelled and used to train two CNN models (You Look Only Once version 5 (YOLOv5) and Mask RCNN). Otsu's thresholding method is used to calculate the void percentage, and the model's performance in generating the results with its accuracy relative to real-scale images is evaluated. Findings All discoveries were authenticated concerning previous studies on CNN model development to encapsulate the shape of the void detected combined with calculating the percentage. The Mask RCNN is the most suitable model to perform the image segmentation analysis, and it closely matches the void presence in the TSAM image samples up to an accuracy of 94.25% of the entire void region. The model's overall accuracy of RCNN is 96.40%, and it can display the void percentage by 2.65 s on average, faster than the manual checking process by 96.50%. Practical implications The study enabled manufacturers to produce a feasible, automated means to improve their flip-chip underfilling production quality control. Leveraging an optimised CNN model enables an expedited manufacturing process that will reduce lead costs. Originality/value BGA void formation in a flip-chip underfilling process can be captured quantitatively with advanced image segmentation.

현상학적 방법을 활용한 사회과 예비교사의 교육실습 경험 연구

This study is done to find out what kind of experiences current teaching practice actually provides for Social Studies student-teachers and what specific roles teaching practice plays by using phenomenological description analysis. To focus on individual experience of each participant, this study analyzed experiences of seven student-teachers who completed their teaching practice by using Giorgi description analysis. The main results of this study are as follows. Firstly, student-teachers evaluated teaching practice was necessary and useful experience while they evaluated teaching practice wasn’t effective enough for them to enhance their teaching professionalism. In particular, student-teachers evaluated they lacked in experiences with self-leadership in designing the lesson and practicing teaching and communicating with students. Secondly, student-teachers evaluated that a mentor teacher’s feedback was not enough both in quality and quantity, that reflection on teaching with fellow student-teachers was meaningful experience but not high quality. Also, a problem was found that the ability which improved through teaching practice was limited to the aspects of simple teaching skills. Thirdly, it seemed that student-teachers’ checking their views on teaching profession was done only superficially. They hardly experienced checking and improving their teaching philosophy, views on teachers, views on learners and views on subjects. They just stayed on the process of checking whether they really wanted to be a teacher or not. To make teaching practice more professional and pedagogically meaningful teacher training course, opportunities to play diverse roles independently should be provided for studentteachers and more specialized mentor system should be built.

Pengembangan Sistem Absensi Online dalam Memonitoring Kehadiran Mahasiswa untuk Mempermudah Proses Perkuliahan

Technological developments are progressing rapidly nowadays. Especially developments in Information Technology science. This can be seen from everyday life, how Indonesian people in general always involve technology in carrying out daily activities. However, it is still rare for people to use technology to help with their work or fulfill work needs. It is not uncommon for attendance activities to become part of the assessment between lecturers and students, which influences the final grades received by students. And there is often fraud committed by students regarding attendance in order to meet the minimum attendance figures for teaching and learning activities. Taking absences between students is a common thing. This can be detrimental to lecturers, other students, and also students who carry out absentee activities. The attendance sheet used for student attendance is often damaged, even though later this attendance sheet will be used as a report regarding the teaching and learning activities carried out in one semester. If the document is damaged, it will affect the document checking process carried out by the university.

Developing a High Velocity Dataset Quality Checking Pipeline

ObjectiveThe volume and frequency of refreshed data within the [organisation] has increased significantly since the beginning of the COVID-19 pandemic. Therefore, a more efficient data quality (DQ) checking process was necessary. Approach Having previously developed an automated DQ checking tool, the focus was on re-engineering the process of DQ task allocation and communication of results. Results 5 analysts were trained in DQ checking. A JIRA workflow tracks the management of data loading. When a dataset is ready for DQ, the Data Manager allocates a ticket to the DQ Lead who then allocates it onto one of the 5 analysts. Via a DQ Slack channel, the analyst is informed and acknowledges receipt of the task. On completion of DQ, the analyst updates the ticket and transfers it to the appropriate workflow stage. Passed DQ tickets are transferred to the Data Manager for data release, whereas failed ones are placed “On Hold”. The DQ Lead triages the issues and liaises with relevant parties for resolution, which may require data amendments. On receipt of amended data, the DQ ticket is transferred back to the queue and the analyst is notified to re-check the data. ConclusionsThe team now complete a high volume of DQ checks efficiently. In 2023, 405 datasets, containing 1716 tables, were quality checked, with the initial DQ taking, on average, 2.6 days. Implications The improved speed of DQ checking ensures projects access the latest available data whilst maintaining the expected DQ levels, integrity and reputation of the TRE.

RANCANG BANGUN SISTEM INFORMASI PENGOLAHAN DATA CUSTOMER DIGITAL MARKETING PADA MB STORE

The purpose of this research is to design and build a data processing information system at the MB Store which is engaged in digital marketing services. The current problems Processing customer data while still using static data so that it still makes it difficult for admins to find and collect customer data information both those that are being processed, currently running and also those who have ordered before. This static data collection also often causes confusion or misunderstanding between customers and service officers in controlling different customer needs, besides that it also often causes confusion between one customer order package and another, who have and have not paid. , still in the process of ordering as well as the customer data information itself. This is caused because the data is entered separately in each document in Microsoft Excel so that the checking process must be carried out manually. So that this also causes the admin officer to have difficulties in re-data, tracking and matching customer data that has been stored. To overcome the problems that occur, research is carried out which aims to provide solutions to the Miss Bolin Store (MB Store), namely by building a Customer Data Processing Information System using Microsoft Visual Studio and MySQL. The model used in the development of this research uses the Waterfall Model with 5 stages, namely software requirements analysis, design, program code generation, testing and maintenance. Collecting data using interview techniques, observation and literature study. This information system is expected to make it easier for admins to find data, collect customer information, help the service process to customers be faster and can reduce the occurrence of mistakes or misunderstandings in the customer order data itself.

Survival Rate of Self-Adhesive Resin Cement on Various Crown Materials: A Scoping Review

Abstract Aim: The use of crowns made of all-ceramic, metal-ceramic, or porcelain fused to metal (PFM) with a self-adhesive resin cement as an indirect restorative process is a method of indirect restorative. The cement is anticipated to improve survival rates due to its many benefits. Thus, this research aimed to ascertain how long self-adhesive resin cement will last when applied to different types of crown materials. Materials and Methods: The study used a scoping review methodology that followed a Population, Concept, and Context framework. The articles included were published from January 1, 2013, to May 24, 2023, full-text, full-access, written in English, original article study design (clinical studies, observational studies, follow-up case reports, and comparative studies), and in vivo method. Boolean operators were applied to search across the databases Science Direct, PubMed, and EBSCOhost with the corresponding keywords “((survival rate) OR (success rate) AND (adhesive resin cement) AND (crown)).” Since Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) was implemented as the study design, it did not include randomization or statistical analysis. However, we added bias-risk information. Results: There were 104 studies in total. After the duplication checking and abstract screening processes, 23 articles were provided. A full-text assessment was done, and seven articles were extracted. Additionally, information about the author, publication year, study design, research methodology, self-adhesive resin cement brand, crown material, research duration, and outcomes was gathered. Conclusions: The results concluded that the self-adhesive resin cement survival rate on various crown materials, both all-ceramic and PFM, within 6 years is excellent. Due to the minimal number of articles that met the inclusion criteria, additional research is required.

COVID-19 information spaces, boundaries, and information sharing: an interview study

Introduction. The goal of this paper is to understand who shares information used by the general public about COVID-19 and how they decided what information to share. Method. Our qualitative work is based on semi-structured interviews conducted from April 2022 through December 2023 with 23 people who have provided COVID- 19 information through paid and volunteer roles. We used the critical incident technique. We also asked participants about their information gathering and credibility checking processes; their role in spreading information; and their typical audience for sharing. Analysis. We transcribed interviews and conducted thematic analysis in MAXQDA software. Results. We conceptualise the information space as consisting of the audience, communities, sharers, and experts. We illustrate three distinct exemplars of sharers. We describe how personal and historical experiences create boundaries around individuals (sharers and audience), which determine what sources of information are trustworthy, and how our participants share the information with their audience. Conclusion. For COVID-19 information, the audience may be identified first or the information service may be formed first. Fact-checkers and science journalists’ job is to report truthful and verified information, and they do not tailor it to a specific community as much as people-centered sharers, such as patient advocates.

Moving automated compliance checking to the operational phase of the building life-cycle: analysis and feasibility study in the UK

Construction regulations ensure a base level of safety of built assets. Formal checking of built assets against regulations happens at defined points in their life-cycle, and often there is limited automation involved. The movement towards digitising and automation elements of physical site inspections paves the way towards a whole life-cycle approach for compliance checking. This paper explores whole life-cycle compliance checking. It does this by extending current checking processes which are common in the design and construction phases into the operational phase of the building life-cycle, proposing that the best way to achieve this is through automated/semi-automated data capture and analysis. To test the feasibility of this concept, this paper will review existing academic literature, analyse existing regulations and conduct proof of concept testing. This paper has found that the concept of whole-lifecycle compliance checking is becoming increasingly important to better monitor and assure high risks elements of the built environment. When applied to the operational phase, it, however, becomes impractical and expensive when relying on solely manual inspections. Furthermore, there is a significant quantity of viable use cases for this approach and that there are viable technologies to leverage them.

PERANCANGAN APLIKASI CEK RADIUS OUTLET PT. IJS BERBASIS WEB MENGGUNAKAN METODE HAVERSINE FORMULA

PT. IJS is a company engaged in marketing cafe and restaurant culinary franchise licenses. Opening a new franchise has rules in the form of a radius between locations that cannot be close together. This company still uses a conventional way of checking the location radius, so it takes longer and is less efficient in the checking process. Based on this problem, the researchers built a check radius outlet application for PT. Web-based IJS calculates the shortest distance using the waterfall model and the Haversine Formula method. The designed application is tested using a black box, comparing the system results with actual conditions using an odometer and user acceptance tests. Furthermore, the black box test results were declared successful, and an accuracy value of 94% was obtained with actual conditions. The user acceptance test received a usability level percentage of 89.93% which is very strong from the user's point of view. Keywords: Black Box, Franchise, Metode Haversine Formula, UAT, Waterfall

Revolutionizing Automated Cheque Processing: How Advanced Machine Learning Surpasses Traditional Methods

Revolutionizing Automated Cheque Processing: How Advanced Machine Learning Surpasses Traditional Methods

Capsule Attention Based Detection of Contraband in X-Ray Images

Aiming at the problem of low detection accuracy caused by the different postures, different sizes, complex backgrounds and overlapping occlusions of contraband in the security checking process, a Matrix capsule network based on attention mechanism (MCAM) is designed by introducing attention mechanism into the capsule network. Firstly, a multi-feature-extraction (MFE) module is designed to solve the difficulty of detecting contraband with different sizes and complex backgrounds; then the Conv2d with Attention for ConvCap (CACC) is constructed in the convolutional layer of the capsule, and the weight information is computed in the channel dimensions of the feature map to give the contraband regions higher coefficients to enhance the contraband detection ability when the poses are different and the occlusion is severe; finally, a new capsule detection layer is designed by utilizing the pose matrix of the capsule to give full play to the detection ability of the matrix capsule network. The mAPs of the proposed model on SIXray, SIXray10, and SIXray100 are 85.10%, 64.05%, and 53.67%, respectively, which are 42.79%, 19.73%, and 9.41% higher than those of the original network, higher than those of the current mainstream detection networks, and the number of parameters of the network and the amount of computation are also lower.

Quality Text Book Assessment System

The Quality Textbook Assessment System (QTAS) is a sophisticated methodology aimed at rigorously evaluating the quality and appropriateness of textbooks utilized within educational contexts. With the proliferation of educational materials and the pivotal role textbooks play in shaping students' learning experiences, there arises a critical need for a systematic approach to assess their efficacy and alignment with educational standards. QTAS addresses this need by providing educators, curriculum developers, and policymakers with a structured framework for textbook evaluation. The primary objectives of QTAS are multifaceted. Firstly, it seeks to ensure the accuracy and reliability of information presented in textbooks across various subjects and grade levels. Through meticulous fact- checking and content validation processes, QTAS identifies and rectifies inaccuracies, misconceptions, and biases that may compromise the integrity of educational materials. Additionally, QTAS evaluates the clarity and coherence of textbook content, assessing its ability to facilitate comprehension and knowledge retention among students. Moreover, QTAS places a strong emphasis on the alignment of textbooks with established educational standards and learning objectives. By scrutinizing the curriculum relevance and instructional coherence of textbooks, QTAS helps educators select materials that seamlessly integrate with existing teaching practices and educational frameworks. Furthermore, QTAS evaluates the inclusivity and diversity of textbook content, ensuring representation and recognition of diverse perspectives, cultures, and identities. Incorporating both qualitative and quantitative assessment methods, QTAS employs a combination of expert reviews, stakeholder feedback, and statistical analyses to evaluate textbooks comprehensively. This holistic approach enables QTAS to generate nuanced insights into the strengths and weaknesses of educational materials, guiding informed decision-making regarding textbook adoption, adaptation, or supplementation..

대학(원)생의 대화형 인공지능 챗봇 서비스 활용 경험 사례 연구

Objectives The purpose of this study is to present how university (graduate) students use ChatGPT, an interactive artificial intelligence chatbot service, and what their perceptions are about it. Methods In order to achieve research objectives, We interviewed six college (graduate) students who had experience voluntarily using ChatGPT starting in early 2023, immediately after the launch of the ChatGPT service. Afterwards, we derived research results through the process of interview content analysis, member checking, and revision. Results First, university (graduate) students' satisfaction with their first experience of using ChatGPT influenced the frequency and reliability of subsequent ChatGPT use. Second, the purpose of using ChatGPT was information/ knowledge search, acquisition, confirmation, translation, coding and code modification, writing, foreign language learning, and brainstorming. Third, the method of use changed depending on the purpose of using ChatGPT. When brainstorming was needed, simple questions were asked about topics or keywords, and when in-depth information or knowledge was needed, the field, concept, and type of desired answer were specifically presented. When students checked their understanding or needed specific information, they first organized their thoughts and then asked questions. Additionally, they asked questions as if they were talking to a person, verified the information and answers provided by ChatGPT on their own rather than using them as is, and searched for related literature when necessary. Fourth, students recognized that the main characteristic of ChatGPT is that it provides easy and convenient access to various knowledge and information. Fifth, students mentioned that ChatGPT helps learning by saving time and acquiring ideas, but it has limitations such as acquiring incorrect knowledge and information, need for additional confirmation, and low learning effectiveness. Conclusions University (graduate) students are actively experiencing various areas of use of ChatGPT and are receiving meaningful help in learning using it. In the future, it will be necessary to clearly guide students on the strengths and limitations and discover and share various use cases so that they can use ChatGPT effectively.

Implementation of Creating a Ledger and Trial Balance in PT Syafa Jaya Electric

On moment This every company need accountancy For take notes finance on every transaction . Transactions that have been made carried out in the books later processed For made report finance company . Order in making report finance , First of all, the recording process transaction in accordance with accountancy . Then the checking process return notes accounting , If happen error so can done journal reverser or journal adjustment For repair error existing records _ done . Next making report per month based on balance sheet existing lanes , for example report made _ is report profit and loss , report balance sheet , report internal company taxes , and etc . After made report so journal the must closed so as not to happen recording repeat return . Making process report finance This only Wrong One example from Lots Example of manufacturing process report finance . Balance Sheet the balance is not balanced show exists error entry journal , which is often caused by a number of reason like error in calculation balance account , arrangement _ credit and debit accounts , current . Objective writing This is For know understanding balance sheet balance , function balance sheet balance , type And component balance balance , method used _ in make balance sheet balance , as well method make balance sheet balance . Type study This use method study qualitative , with analysis descriptive And say library approach. Source study obtained from material teachings , book reading , journal And other . Based on results study obtained that There is four function general balance sheet balance, that is function preparation, bookkeeping, correction , and supervisin

Demonstrating a new evaluation method on ReLU based Neural Networks for classification problems

Deep neural networks, which have proven to be effective methods to solve complex problems, can even be applied in decision systems controlling critical processes. However, in such applications the outcomes of the neural network must be checked if it we have a clear understanding regarding the operation of network at given input intervals. The most straightforward, though often computationally expensive approach for this checking process evaluates the network at discrete input points and estimates the expected outputs at the given interval. The present research aims to develop a novel approach that can identify in the case of specific input intervals whether the operation process and the output of a neural network can be considered as known or unknown. During the presented case study, we investigated the ReLU (Rectified Linear Unit) and Sigmoid activation functions, using the double moon and the Banknote Authentication classification problems for demonstration.Our method can be applied to identify certain input intervals where the given neural network cannot support critical decisions. The evaluation is performed based on a nonlinear system of equations and inequalities built on arbitrary continuous activation functions. To define the critical intervals of the input variables (i.e. where the decision-making system should not be relied on), those input variable combinations are identified which result in a non-expected output value. This inverse logic is intended to identify intervals of the input variables where the response of the system and the correct decision are not identical. The presented demonstration examples supported our assumption that the number of the neurons and the dimension of the decision space have a significant impact on the complexity of the evaluation process.

A Numerical Method for Unstable Propagation of Damage in Fiber-Reinforced Plastics with an Implicit Static FE Solver

Finite element analyses of the propagation of damage such as fiber compressive failure and delamination have greatly contributed to the understanding of failure mechanisms of fiber-reinforced plastics owing to extensive studies on methodologies using Continuum Damage Mechanics and Fracture Mechanics. Problems without the need for consideration of inertia, such as Double-Cantilever Beam tests, are usually solved by implicit FE solvers, and explicit FE solvers are appropriate for phenomena that progress with very high velocity such as impact problems. However, quasi-static problems with unstable damage propagation observed in experiments such as Open-Hole Compression tests are still not easy to solve for both types of solvers. We propose a method to enable the static FE solver to solve problems with unstable propagation of damage. In the present method, an additional process of convergence checks on the averaged energy release rate of damaged elements is incorporated in a conventional Newton–Raphson scheme. The feasibility of the present method was validated by two numerical examples consisting of analyses of Open-Hole Compression tests and Double-Cantilever Beam tests. The results of the analyses of OHC tests showed that the present method was applicable to problems with unstable damage propagation. In addition, the results from the analyses of DCB tests with the present method indicated that mesh density and loading history are not significantly influential to the solution.