Student productivity is influenced by various factors, including academic habits, lifestyle characteristics, and digital distraction behaviors. The increasing use of digital technologies, such as smartphones, social media, and online gaming, has created new challenges for maintaining student focus and academic performance. Therefore, understanding and predicting student productivity levels is important for supporting effective educational management and student success. This study aims to classify student productivity levels using machine learning techniques based on academic, behavioral, and digital distraction variables. The study utilized the Student Productivity & Digital Distraction Dataset obtained from Kaggle, consisting of 20,000 student records. The productivity score was transformed into five productivity categories, namely very low, low, medium, high, and very high productivity. Four machine learning algorithms, including Decision Tree (DT), and K-Nearest Neighbors (KNN), Gradient Boosting (GB), and Random Forest (RF) were evaluated using accuracy, precision, recall, F1-score, and confusion matrix analysis. The results showed that RF achieved the best performance with an accuracy of 81.15%, precision of 81.35%, recall of 81.15%, and F1-score of 81.23%, outperforming GB, DT, and KNN. The findings indicate that ensemble learning methods are more effective in modeling the complex relationships among academic habits, lifestyle factors, digital distraction, and student productivity. Furthermore, the study demonstrates the potential of machine learning as a decision-support tool for educational management, enabling the identification of students with different productivity levels and supporting data-driven interventions to improve academic outcomes.

This study aims to examine the relationship between digital competence and knowledge management (KM) readiness in higher education institutions, addressing the limited integration of these constructs in developing-region contexts and their role in post-pandemic institutional transformation. A quantitative descriptive–inferential design was employed. Data were collected from 250 respondents, including lecturers, administrative staff, and students across 12 public and private universities, using validated instruments adapted from the Knowledge Management Process Questionnaire (KMSP-Q) and the European Digital Competence Framework (DigComp). Pearson correlation and independent samples t-tests were used to analyse relationships and group differences. The findings revealed significant positive associations between key dimensions of digital readiness and perceived KM readiness. Technological support demonstrated the strongest correlation with knowledge sharing (r = 0.62, p < 0.01), while HR training was positively associated with team reflexivity (r = 0.58, p < 0.05). Overall, digital competence was at a moderate level (mean = 58.2), with public universities reporting significantly higher perceived KM readiness than private universities (p < 0.05). The study is limited to a single regional context and a cross-sectional design. Future research may adopt comparative and longitudinal approaches to further examine institutional transformation dynamics. The findings provide actionable insights for higher education leaders to strengthen digital capability, organisational learning, and KM governance to support sustainable digital transformation. This study contributes to the literature by offering an integrated perspective on digital competence and KM readiness in developing-region universities, positioning these constructs as interdependent drivers of knowledge-based transformation and providing transferable insights for global higher education research.

The objective of this study is to develop and validate the Student Social Resilience Scale for Disasters (SSRD) as a reliable instrument for measuring the social resilience of high school students in disaster-prone areas. Despite the growing global focus on the issue of resilience, there remains a paucity of instruments that are specifically capable of capturing the multidimensional nature of adolescent social resilience in the context of education, particularly in disaster-affected areas. The present study involved 800 students from high schools in Indonesia and employed Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) to assess the psychometric quality of the SSRD. The EFA results identified three main dimensions, namely Individual, Relationship, and Contextual, while CFA confirmed the construct validity with excellent model fit indices (CFI = 0.968; TLI = 0.965; RMSEA = 0.055). Reliability analysis demonstrated high internal consistency across all dimensions (Cronbach's Alpha 0.925). Moreover, the second-order confirmatory factor analysis (CFA) supported the existence of a second-order construct in the individual factor. This construct was found to consist of self-efficacy and emotional regulation, problem-solving and adaptability, and motivation and perseverance. These findings confirm that SSRD is a valid and reliable instrument for measuring students' social resilience in disaster-prone contexts. In addition to its contribution to the development of theory through the integration of personal, relational, and contextual dimensions, this instrument also has practical implications for education policymakers and practitioners in the design of more targeted disaster education interventions and psychosocial support.

Background: Non-communicable diseases (NCDs), including hypertension, diabetes mellitus, and chronic obstructive pulmonary disease (COPD), remain significant public health challenges in Indonesia. Despite advances in clinical management, patients lived experiences, particularly regarding disease-related knowledge and self-care practices, have received limited scholarly attention. Methods: A qualitative phenomenological approach was employed using focus group discussions and in-depth interviews with 19 participants, including 12 patients with hypertension, diabetes mellitus, and COPD; five primary health workers from community health center; and two district health office staff. The research was conducted between February and June 2024. Data were analyzed using descriptive phenomenological methods. Results: Three interrelated themes emerged. First, participants described delayed symptom recognition accompanied by limited initial understanding of their disease. Second, deeply entrenched lifestyle-related risk factors, including smoking, high salt and sugar intake, and physical inactivity. Third, inconsistent self-management practices following diagnosis. Although some participants attempted to adhere to medical advice and adopt healthier behaviors, these efforts were frequently constrained by inadequate health literacy, low motivation, and barriers to healthcare access. Persistent misconceptions regarding disease etiology were evident, reflected in culturally embedded terms such as “sweet urine” for diabetes and “lung pain” for COPD. Family support and religious beliefs were found to exert a dual influence, functioning as both facilitators and barriers to treatment adherence. Conclusion: Improving chronic disease outcomes in Indonesia requires the integration of culturally responsive health education, patient-centered communication, continuous follow-up, and health literacy interventions that are aligned with the sociocultural context of affected communities.

Knowledge management (KM) is increasingly important for Indonesian universities, which face rapid technological change and heightened demands for transparency, quality, and competitiveness. This study developed and validated a measurement instrument to identify key barriers and enablers of KM implementation in Indonesian higher education. A 22-item scale was developed for the local context and organized into six aspects: trust in individuals, trust in management, reward system, organizational process, IT, and technical support. The scale was produced in two versions: one for lecturers/education staff and one for students. Content validity was evaluated using the Content Validity Ratio (CVR) and the Content Validity Index (CVI) with expert panels (eight raters), followed by a pilot administration to 60 respondents (30 lecturers/staff and 30 students). The scale demonstrated strong content validity (mean I-CVI 0.87–0.91), acceptable item discrimination (lecturer/staff: 0.471–0.834; student: 0.250–0.785), and high internal consistency (Cronbach’s alpha: 0.952 for lecturer/staff; 0.923 for students). These results indicate that the instrument is robust for diagnosing KM enablers and barriers in Indonesian universities and can support targeted policies and interventions. Future studies should expand validation across diverse institutions nationwide.

Importance:The World Health Organization (WHO) Safe Childbirth Checklist (SCC) has been adapted and implemented in at least 35 countries. Consistently, the SCC has shown increased adherence to practices, but there are mixed results regarding its association with health outcomes in different settings. Objective:To examine the association of SCC implementation with mortality, accounting for variations in evidence-based practices (EBP) adherence. Design, Setting, and Participants:In this meta-analysis, data were pooled from 3 cluster randomized trials of the SCC (January 1, 2014, to December 31, 2017). Intention to treat (ITT) and a complier average causal effect analysis (CACE) on EBPs and perinatal mortality were estimated via a generalized linear model. The primary facilities were in Uttar Pradesh, India; basic emergency obstetric facilities were in Aceh, Indonesia; and primary and secondary health centers were in Khyber Pakhtunkhwa, Pakistan. Interventions:In India, the 8-month SCC intervention involved facility engagement, a launch event, and 8 months of tapered coaching. In Indonesia, the 6-month SCC intervention included 11 coaching visits. In Pakistan, the 12-month SCC intervention included light touch external monitoring, skills training, and supplies assessment. Main Outcomes and Measures:Primary outcomes were stillbirth and perinatal and early neonatal mortality. Secondary outcomes were adherence to 15 EBPs, facility supply availability, and safety culture perceptions. Results:Pooled data included 169 511 births, supply assessments from 163 facilities, and 6298 observed deliveries for EBPs and health workers' perceptions on safety culture. Mortality did not differ in the full sample; however, during months when EBP observations were conducted, stillbirth rates in the intervention facilities were lower by 9.8 per 1000 births (95% CI, -18.5 to -1.1; P = .03; q = .05) in the ITT analysis and 14.5 per 1000 births (95% CI, -27.2 to -1.7; P = .03; q = .05) in the CACE analysis compared with control facilities. EBP adherence was higher by 3.6 practices (95% CI, 3.3 to 4.1; P < .001; q = .001) in the ITT analysis and 6.0 practices (95% CI, 5.3 to 6.8; P < .001; q = .001) in the CACE analysis in intervention facilities. Conclusions and Relevance:In this meta-analysis, SCC use in lower-middle-income settings was associated with increased EBP adherence and lower rates of stillbirths when EBPs were directly observed. Further research is needed to identify additional factors to optimize SCC's potential impact on maternal and newborn safety outcomes.

Indonesia continues to face a high maternal mortality rate despite improvements in maternal health service coverage. Antenatal care (ANC) performance is commonly evaluated using service coverage indicators, such as first antenatal visit (K1) and fourth antenatal visit (K4). However, these indicators do not fully capture the quality of care delivered during ANC contacts. The aim of this study was to assess ANC implementation based on the Indonesian Ministry of Health 10T Standard (10T), develop quality-adjusted performance indicators integrating coverage and service quality, and identify factors associated with midwife performance in primary health services. A cross-sectional study was conducted among village midwives in Banda Aceh, Indonesia. ANC quality was assessed using 37 indicators derived from the 10T Standard, while midwife performance was evaluated using conventional coverage indicators and newly modified quality-adjusted indicators, namely modified K1 (K1mod) and modified K4 (K4mod). Individual, organizational, and psychological determinants of performance were analyzed using structural equation modeling (SEM). The results indicated that conventional ANC coverage was high, with mean K1 and K4 values of 99.61% and 91.51%, respectively. However, after adjustment for 10T implementation, performance declined substantially to 84.89% for K1mod and 77.94% for K4mod, indicating that coverage-based indicators overestimated actual performance. Implementation of the 10T Standard varied across components: medical identity recording (95.4±5.7%), medical examination (90.6±8.1%), and intervention (87.6±10.7%) were relatively well implemented, whereas some components, such as counseling or health education and nutritional assessment, were less consistently performed. Key gaps were observed in nutritional assessment. SEM showed acceptable model fit (RMSEA=0.038; GFI=0.971; AGFI=0.939; TLI=0.964; CFI=0.981). Organizational factors had the strongest direct effect on midwife performance (β=0.361, p<0.001) and ANC quality (β=0.310, p<0.001), followed by individual and psychological factors. ANC quality also had a significant direct effect on midwife performance (β=0.388, p<0.001) and mediated the effects of individual, organizational, and psychological factors. These findings indicate that high ANC coverage does not necessarily reflect high-quality service delivery. Quality-adjusted indicators provide a more comprehensive measure of midwife performance, and strengthening organizational support, particularly resources, leadership, and incentives, is essential to improve ANC quality and performance in primary health services.

Underwater fish detection is a key task in marine monitoring and aquaculture, but its performance is often degraded by low illumination, turbidity, scattering, and color distortion in underwater environments. This paper presents an underwater fish detection framework that integrates CLAHEbased image enhancement with YOLOv3 object detection and demonstrates its deployment on an edge AI platform using a Streamlit-based interface. The DeepFish dataset (6,517 images with 15,463 annotated fish) was used for training and evaluation, while a local Indonesian dataset from Sabang, Aceh (3,111 annotated images) was employed to evaluate crossdomain generalization. Several CLAHE variants were quantitatively compared using UIQM, UCIQE, and LOE, with Blending CLAHE combined with Percentile Stretching selected as the optimal pre-processing method. Detection performance was evaluated under multiple scenarios, including training and testing on original and enhanced images, direct cross-domain testing, and fine-tuning on local data. Experimental results show that image enhancement improves detection accuracy on DeepFish (mAP@0.5 from 96.15% to 97.05%), while fine-tuning is necessary to mitigate domain shift and achieve reliable performance on local waters. The proposed end-to-end system was successfully deployed on a Jetson Orin Nano and operates entirely offline, providing real-time detection results through a web-based interface.

This article explores the transmission of identity politics within families and its impact on the attitudes and political choices of first-time voters in Aceh. The significance of this study is emphasized by the electoral periods in Aceh and Indonesia, which are frequently influenced by strong identity politics. The study addresses two main questions: How does family identity influence the political understanding of first-time voters, and how does the transmission of political identity occur within families? Employing a qualitative case study approach, the research collected data through interviews with first-time voters from UIN Ar-Raniry and Universitas Malikussaleh. Findings reveal that in the context of Aceh, political identity, entrenched within family dynamics through political affiliations, religious values, and personal experiences, significantly shapes the political perspectives of first-time voters. Predominantly, the paternal figure acts as the primary agent of socialization, disseminating political orientations through family discussions and daily interactions, often without allowing for critical evaluation. This research confirms that families not only serve as institutions influencing and transmitting political views to first-time voters in Aceh but also restrict their ability to conduct independent political assessments due to the reinforcement of traditional and religious values in this transmission.

Since disasters can happen at any time, having strong disaster resilience is crucial. Thus, it is critical to consider how local wisdom will endure. This research question is about how to integrate local wisdom and STEM-based disaster resilience for disaster mitigation. Therefore, this research aims to reveal the importance of this integration. Data collection was carried out by distributing questionnaires to teachers in Aceh province. A total of 144 teachers participated in this research. The results of this research are that local wisdom values must be integrated into the learning process to improve post-disaster recovery through STEM. In this way, it is hoped that local wisdom can be applied to learning at school through STEM. Therefore, stakeholders, society, and government must pay attention to this situation to create disaster-resistant students and communities.

In the context of increasing climate-related disasters, transformative resilience encompassing adaptation, innovation, and sustainability has emerged as a critical concept for strengthening community-level resilience. This systematic literature review (following PRISMA guidelines) synthesizes insights from 39 peer-reviewed studies (2016–2025) to assess how transformative resilience is conceptualized and measured. It aims to inform evidence-based strategies for strengthening community-level resilience to climate change. Findings indicate that adaptation strategies dominate the literature, often focusing on socio-ecological resilience and adaptive capacity indicators. However, recent studies increasingly advocate integrated frameworks that combine adaptation with innovation and sustainability to achieve transformative outcomes. Commonly used indicators include socio-ecological resilience metrics, social capital, and adaptive capacity, although their definitions and measurements vary widely across studies. Key knowledge gaps were identified: notably, standardized measurement tools for transformative resilience are lacking, and insufficient attention has been paid to governance and institutional transformation in resilience efforts. These gaps underscore the need for future research to develop standardized indicators and holistic frameworks that address multi-level governance and institutional change. The findings have implications for both research and policy, suggesting that evidence-based, cross-sectoral strategies are required to bolster community-level resilience in the face of climate change.

The Sendai Framework for Disaster Risk Reduction 2015–2030 articulates the need for a clear understanding of responsibilities across public and private stakeholders, including academia. This study aimed to clarify how university faculty members in Aceh Province, Indonesia, devastated by the 2004 Aceh Tsunami, perceive the role of universities in suggesting policy recommendations for disaster risk reduction (DRR) and sustainable development, and their perspective on the relationship between those roles and the main functions of universities. A questionnaire survey was conducted with 400 respondents in Aceh Province from July 2023 to November 2023. The authors examined descriptive statistics, followed by Kruskal–Wallis tests and structural equation modeling (SEM). The authors found that most university faculty members were optimistic about the role of universities in providing policy recommendations for DRR. Furthermore, interest in DRR activities and relevant past experiences may influence their perception of these roles. The SEM analysis showed that faculty members perceive universities as key contributors to DRR policy recommendations. Based on the above, the authors posit that universities must adopt measures that empower faculty members to gain interest and experience in DRR activities. Steady progress in the main functions of universities is essential for articulating DRR policy recommendations.

Disasters in Aceh caused significant losses from 2014 to 2023, impacting various regions differently. This study utilizes disaster occurrence and mortality data for six disaster types (floods, landslides, extreme weather, drought, forest and land fires, and earthquakes) collected for each regency in Aceh, sourced from Badan Penanggulangan Bencana Aceh (BPBA). The impact of these disasters is profound, often resulting in significant mortality and extensive damage to property and livelihoods. The time trend of disaster occurrences was analyzed using Annual Percent Change (APC) estimated using weighted linear regression models. Mortality trends were assessed using statistical tests for distribution normality and variance. Results show that floods were the most frequent disaster, primarily affecting coastal regions, while landslides were most deadly in central areas of Aceh. APC results revealed upward trends in disaster frequency, notably in Aceh Tengah and Aceh Tenggara, while certain regions exhibited more stable disaster patterns. Mortality rates were highest in the northwestern and southern regions, with significant losses due to floods and landslides, while forest fires had zero mortality across all regions. These findings underscore the need for targeted disaster risk reduction strategies that account for regional differences in vulnerability and impact. Policymakers should prioritize context-specific interventions to mitigate future disaster risks in Aceh.

Coastal hazards create impact damage on vital assets in lowland coastal zones. While education, experience, and information exposure affect individual preparedness, the combined impact of coastal hazards on community readiness needs further exploration. This study aims to explore factors influencing coastal community preparedness, by examining relationships between preparedness and information exposure variables, and by assessing the impact of knowledge, experience, and information exposure using Structural Equation Modelling (SEM). This study involves 932 respondents in the Indonesian coastal cities of Banda Aceh, Mataram, and Ambon. The SEM analysis confirmed a well-fitting model with the following statistics: chi 2 = 17.961, RMSEA = 0.017, GFI = 0.996, CFI = 0.998, AGFI = 0.986, TLI = 0.996, and Normed Chi-Square = 1.283. These results indicate strong alignment between the proposed model and the observed data. The study found positive relationships between preparedness variables and their respective indicators. Similarly, exposure variables related to information sources on coastal hazards and early warnings also showed positive associations with their indicators. Education, experience, and exposure to information were identified as significant factors influencing community preparedness, explaining 89 % of the variability in preparedness variables. These findings underscore the importance of these factors in enhancing community resilience to coastal hazards.

In Aceh province (Indonesia), the city of Banda Aceh and the surrounding areas face a persistent and significant tsunami threat due to their location in a disaster-prone zone. On December 26, 2004, the region was devastated by a catastrophic tsunami. Twenty years later, while awareness of tsunami risks has grown, community preparedness for evacuation remains inconsistent. This study aims to assess community evacuation strategies, and identify key determinants of evacuation preparedness in tsunami-prone areas, focusing on factors such as education, age, evacuation training, local wisdom, and floor type as socioeconomic indicators. Using a cross-sectional survey design, the study gathered data from 287 respondents through a questionnaire survey, and a Focus Group Discussion (FGD), with 25 participants representing the government, NGOs, community leader, and academics, as well as field observations in disaster-prone zones. The data were analysed using ordinal logistic regression to identify key predictors of preparedness. The findings confirm previous research highlighting the complex nature of disaster preparedness, and the crucial role of community-based initiatives. This study underscores the need for equitable access to resources, inclusive training programmes, and the integration of local wisdom into formal disaster preparedness frameworks, in order to enhance community resilience. Finally, the study emphasises the importance of tailored preparedness strategies, including virtual evacuation tools, in order to improve tsunami evacuation effectiveness and boost community readiness in vulnerable regions.

Abstract Background The lack of accurate and affordable monitoring of glycated hemoglobin (HbA1c) is a common issue among patients with diabetes in low- and middle-income countries. We aimed to test a tablet- and smartphone-based point-of-care (TSB POC) device against a local laboratory-based measure of HbA1c for monitoring diabetes under real-world conditions. Methods For this cross-sectional clinical method applicability study, capillary and venous blood was collected in duplicate and analyzed at local primary health care centers. For a heterogeneity test, the tests were performed by an expert, and by a team of local nurses. The study was conducted in a multicenter design in rural and urban Aceh, Indonesia in 2019, and included a total of 533 adults. We mainly used Bland-Altman plots to assess the number of readings within the 95%-limits of agreement (LoA) and Deming regressions. Results The results show a mean difference between capillary HbA1c on the test device and the reference method of −0.54 [CI0.95 = −1.6933; 0.6048] with 5.21% of measurements outside the LoA and a Pearson’s r = 0.91 in the Deming Regression. There is no significant difference in test concordance between local nurses and the expert (4.23% versus 5.13% results outside the LoA [CI0.95 = −0.0331; 0.0511]). Conclusions TSB POC for analysis of HbA1c is an acceptable alternative for accessible monitoring of diabetes patients under these conditions. This method could provide access to high-quality diagnostic decisions through regular and cost-effective HbA1c monitoring directly in healthcare facilities, thus providing better access to essential health services.

Aceh Province is highly vulnerable to various hazards, necessitating effective disaster risk reduction strategies. This study aims to develop an instrument to evaluate disaster risk reduction efforts in Aceh Province and to assess progress toward global disaster resilience targets. The data includes secondary disaster-related records from 2005 to 2024 and primary data from the instrument validation process, demonstrating excellent validity results based on the Content Validity Ratio (CVR) and Content Validity Index (CVI). The findings highlight significant improvements in key areas, including reductions in disaster mortality, affected populations, economic losses, damage to critical infrastructure, and strengthened early warning systems. However, challenges persist in implementing local disaster risk reduction strategies and enhancing international cooperation. This study offers practical insights for policymakers and contributes to strengthening disaster resilience and advancing disaster risk management research in sub-national contexts.

Dental health is an important part of overall health. Many individuals still face challenges in maintaining their oral health. In Indonesia, the prevalence of dental diseases including dental caries and gingivitis remains relatively high, particularly in underserved areas like Banda Aceh City. This study evaluates the impact of the family dental nursing care home visit model on dental behavior and health status in the Baiturrahman District of Banda Aceh City. The research divided participants into intervention and control groups using a quasi-experimental design with pre- and post-test control groups. The findings demonstrated the effectiveness of the home visit model conducted in the Baiturrahman sub-district. Significant improvements were noted in various aspects of oral health. The knowledge of oral health among participants increased by an average of 5.639 ± 3.204 points. Attitudes toward dental care improved as well with an increase of 5.115 ± 4.673 points. Practical dental hygiene behaviors also saw a notable enhancement with a rise of 5.902 ± 2.942 points. Additionally, there was a significant reduction in the oral hygiene index by 2.4672 ± 0.9919 and the plaque index decreased by 21.492 ± 12.793. The prevalence of gingivitis dropped by 1.2541 ± 0.7133 indicating better gum health alongside an overall improvement in dental caries status. These results suggest that family dental nursing care through home visits serves as an effective and constructive model for enhancing dental health behaviors.

Chlorogenic acid (CGA) is a key bioactive component in coffee beans, essential for quality and health benefits. However, traditional quantification techniques like HPLC are accurate but time-consuming and resource-intensive. This study examines the potential of Near-Infrared Spectroscopy (NIRS) combined with advanced machine learning (ML) algorithms as a quick and non-destructive alternative for CGA quantification. This study used NIRS spectra (1000–2500 nm) and HPLC reference CGA values from 74 intact Arabica and Robusta green coffee bean samples. Spectral preprocessing was applied using Standard Normal Variate (SNV) and Multiplicative Scatter Correction (MSC) to enhance data quality. To advance previous chemometric work, six ML models and ensemble approaches (KNN, SVR, RFR, GBR, XGBoost, and NGBoost) were systematically assessed using R2, RMSE, and RPD. The NGBoost model trained on MSC-corrected data achieved the best single-model performance (R2 = 0.93, RPD = 3.67), demonstrating superior predictive accuracy compared to all other models, including high-performing ensembles like XGBoost + GBR (R2 = 0.88, RPD = 2.88). MSC consistently outperformed SNV, while significant overfitting was observed in the Random Forest model. Ultimately, the optimized combination of NGBoost and MSC provides a reliable and highly accurate non-destructive solution for CGA quantification, facilitating faster and more effective quality control in the coffee industry.

Accurate lithology prediction is critical for subsurface modeling in oil and gas exploration. However, while machine learning (ML) techniques have been applied to automate lithology classification, class imbalance and noisy attributes are a challenge. This study proposes a novel integration of SMOTE, Evolutionary Algorithm, AdaBoost and K-Nearest Neighbor (KNN) to boost the performance of lithology classification. To this end, SMOTE is employed to tackle the problem of imbalanced dataset while Evolutionary Algorithm (EA) is implemented to feature selection to alleviate the influence of noisy attributes. Also, AdaBoost increases the model robustness by repeating learning process focusing on misclassified samples. The proposed method is tested on the publicly available FORCE 2020 Well Log and Lithofacies Dataset, which contains measurements from multiple wells and comprises eleven lithology classes. It is found that the hybrid approach achieves better performance than the standalone KNN method, attaining 96.75% accuracy and an 89.67% F1-Score. The results of the study reveal that incorporating data balancing, feature selection, and boosting techniques enhances the lithology classification, solving the problems typical of well-log data.