Interpretation Techniques Research Articles

A New Workflow for Assessment of Fluid Components and Pore Volumes From 2D NMR Measurements in Formations With Complex Mineralogy and Pore Structure

It is challenging to reliably identify fluid components and estimate their saturations in formations with complex lithology, complex pore structure, or varying wettability conditions. Common practices for assessing fluid saturations rely on the interpretation of resistivity measurements. These techniques require model calibration, which is time consuming/expensive and can only differentiate conductive and nonconductive fluids. Interpretation of 2D NMR maps provides a viable alternative for identifying fluid components and fluid volumes. However, conventional techniques for the interpretation of 2D NMR rely on cutoffs in the T1-T2 or D-T2 maps. The application of cutoffs is prone to inaccuracies when fluid-component relaxation responses overlap. To address these shortcomings, we introduce a new workflow for identifying/tracking fluid components and estimating their volumes from the interpretation of 2D NMR measurements. We developed a workflow that approximates 2D NMR maps with a superposition of 2D Gaussian distributions. The algorithm automatically determines the optimum number of Gaussian distributions and their corresponding properties (i.e., amplitudes, variances, and means). Next, a clustering technique is implemented to the dataspace containing the Gaussian distribution parameters obtained for the entire logged interval. Each Gaussian is assigned to a cluster corresponding to different pore/fluid components. We then calculate the volumes under the Gaussian distributions corresponding to each cluster at each depth. The volumes associated with each cluster translate directly into the pore volumes corresponding to the different fluid components (e.g., heavy/light hydrocarbon, bound/free water) at each depth. A highlighted contribution of this work is that, in contrast to the alternative petrophysical interpretation techniques for fluid characterization, the introduced workflow does not require calibration efforts, user-defined cutoffs, or proprietary data sets. Furthermore, approximating 2D NMR data with a superposition of Gaussian distributions improves the accuracy of estimated pore volumes of fluid components with overlapping NMR responses. The clustering using the Gaussian distribution parameters as inputs enables depth tracking of different fluid components without making use of user-defined 2D cutoffs. Finally, the multidimensional nature of the introduced clustering provides the unique capability of identifying different fluid components with 2D NMR response located in the same range of coordinates in a T1-T2 map. We successfully verified the reliability and robustness of the new workflow for enhancing petrophysical interpretation in two organic-rich mudrock formations with complex mineralogy and pore structure.

Adverse Effects of Recruiting Non-Expert Officials

This article elaborates and analyzes the meaning of the predictive Hadith about Handing over affairs to non-experts, then criticizes its authenticity and analyzes its content and implementation in the present context. The method used is the tahli>li method and textual, intertextual, and contextual interpretation techniques with a multidisciplinary approach. Before criticizing the authenticity of the Hadith based on the rules of hadith validity, first, perform takhri>j al-hadi>s\ by using the keyword d} uyi'at al-ama>nat. After recognizing the quality of the traditions traced in this study as sahih traditions, a study of ma'a>ni> al-hadith followed. The results show that the Prophetic tradition is of sahih quality. This study shows that the predictive hadith "Handing over affairs to non-experts is a sign of the end of the world," narrated by Ahmad ibn Hanbal, in terms of quantity, is classified as a garīb hadith because the narrator's line from the level of the companion (a'lā) to the fourth level of the narrator is single. Regarding the quality of the sanad and matan, it is classified as a hasan hadith because one of the narrators, Fulaih, based on the conclusion of the assessment of the hadith critics, was not d}a>bit}. However, due to the existence of al-Bukhari's chain of transmission, the Hadith rises to the level of sahih ligairih. Thus, the Hadith has legal authority to be used as an argument (hujjah).

The Construction of Women as Agents of Social Change and the Defense of Indonesian Independence in the Novel Burung-Burung Manyar: Literary Criticism of Postcolonial Feminism

This study aims to dismantle the construction of women as agents of social change and defense of Indonesian independence in the novel Burung-Burung Manyar. This research uses qualitative type research which prioritizes data quality compared to the large amount of data. The material object of this research is Y.B. Mangunwijaya. The formal object of this research is the narrative structure in the novel Burung-Burung Manyar, the image of women in the novel, the novel's quotations, and the theory of feminism. The research data is a novel narrative system that shows research reinforcement quotes, the image of women in the novel, feminist theory and various information that is appropriate to the research topic. Data collection techniques are carried out by reading and recording the information needed in the research. The data interpretation technique was carried out by reading a series of narrative systems in the novel Burung-Burung Manyar in order to find the quotations needed to dismantle the construction of women as agents of social change and defense of Indonesian independence and then linked or analyzed using feminist theory. The results of this study are that in terms of creating and maintaining the nation, women have a sizeable contribution or contribution. This contribution is not only in the form of physical contributions but also non-physical ones. In addition, the contributions made by women are in the form of material and non-material contributions. Second, there are two factors behind women's contribution in fighting for and defending independence, namely: (1) the spirit of nationalism, (2) love of the motherland. Keywords: Women, Defense of Independence, Feminism

Design Information Systems for Malnutrition Analysis Apriori Algorithm

Malnutrition drives lasting detriments across individual and community wellbeing, requiring data-informed action. Advanced analytics through information systems present pathways for revelatory pattern detection from multidimensional health data. This paper outlines a system design encompassing preprocessing, modeling, analysis and interpretation techniques for mining malnutrition dataset through Apriori algorithm. The core data mining methodology enables extraction of frequencies, associations and prediction rules linking nutritional status parameters and food intake patterns. Custom algorithms filter results to high-confidence associations via statistical measures before expert evaluation. System testing verifies accurate architecture for surfaced dietary risk factors of malnutrition down to village-level. The systemization and computational augmentation of health insight derivation provides a template for needs-based analytics platforms. By targeting analysis to community data, impactful interventions become possible. The potential of customized information systems with data mining at the core is highlighted alongside domain challenges requiring cross-disciplinary impetus. The data-to-decisions system with embedded Apriori pipelines demonstrates applied informatics transforming malnutrition strategy through unveiling actionable patterns within intricacies of public welfare data.

Study On The Availability Of Public Green Open Space Using Geographic Information System Data On Public Green Open Space In The City Of Tebing Tinggi

Based on Spatial Planning Law No. 26 of 2007 concerning Spatial Planning, Article 29 states that the proportion of public green open space is 20% of the city area. In Tebing Tinggi City, based on the 2013-2033 RTRW of Tebing Tinggi City, it is stated that Public RTH covers an area of ​​approximately 72.49 Ha or 1.89% of the city area, which means it is not in accordance with the mandate of Spatial Planning Law No. 26 2007 concerning Spatial Planning. This research was conducted to assess the availability of existing public green open space in Tebing Tinggi City based on image interpretation techniques, spatial distribution, the area of ​​existing public green open space, the service radius of existing public green open space, and the suitability of existing public green open space land. This type of research uses quantitative research and uses spatial analysis techniques. The results identified as many as 60 public green open spaces in Tebing Tinggi City with an accuracy score of 86.36%, which means that the interpretation is considered correct because the spatial distribution pattern of public green open spaces in Tebing Tinggi City is random because the distribution of public green open spaces is not evenly distributed in all sub-districts in Tebing Tinggi City. From the results of the spatial distribution pattern, digitization was then carried out with the result that the total public open space area was 105.27 Ha, which was dominated by the burial area of ​​59.67 Ha or 57.07%. Then, based on each area of ​​green open space obtained, a topological classification of public green open space is carried out, consisting of 7 typologies of green open space, namely City Forest, City Park, District Park, Village Park, RW Park, RT Park, Cemetery and Green Belt, which is then analyzed. Service radius. As a result, it is known that the City of Tebing Tinggi has not been served optimally by public green open space because there is not yet one type of public green open space typology that is capable of serving the city of Tebing Tinggi based on buffer analysis carried out using ArcGis. The only public green open space whose service radius almost covers the city of Tebing Tinggi is the cemetery whose existing existence is capable of serving 99.19 percent of the area of ​​the city of Tebing Tinggi. Finally, a land suitability analysis was carried out between the existing public green open space and the Tebing Tinggi City RTRW for 2013-2033 which resulted in land suitability of 79.92%. Meanwhile, the remaining 20.08% was designated for areas that were not public green open space.

Modeling item-level heterogeneous treatment effects: A tutorial with the glmer function from the lme4 package in R.

Recent advancements in education scholarship have introduced Item Response Theory (IRT) models to address treatment heterogeneity at the assessment item level. These models for item-level heterogeneous treatment effects (IL-HTE) enable detailed analyses of treatments that may have varying impacts on individual items within an assessment. This article offers a comprehensive tutorial for applied researchers interested in implementing IL-HTE analysis in R, utilizing the lme4 package. Using empirical data from a second-grade reading comprehension assessment as a running example, this tutorial emphasizes model-building strategies, interpretation techniques, visualization methods, and extensions. By following this tutorial, researchers will gain practical insights into utilizing IL-HTE analysis for enhanced understanding and interpretation of treatment effects at the item level.

Application of machine learning models in the capacity prediction of RCFST columns.

Rectangular concrete-filled steel tubular (RCFST) columns are widely used in structural engineering due to their excellent load-carrying capacity and ductility. However, existing design equations often yield different design results for the same column properties, leading to uncertainty for engineering designers. Furthermore, basic regression analysis fails to precisely forecast the complicated relation between the column properties and its compressive strength. To overcome these challenges, this study suggests two machine learning (ML) models, including the Gaussian process (GPR) and the extreme gradient boosting model (XGBoost). These models employ a range of input variables, such as the geometric and material properties of RCFST columns, to estimate their strength. The models are trained and evaluated based on two datasets consisting of 958 axially loaded RCFST columns and 405 eccentrically loaded RCFST columns. In addition, a unitless output variable, termed the strength index, is introduced to enhance model performance. From evolution metrics, the GPR model emerged as the most accurate and reliable model, with nearly 99% of specimens with less than 20% error. In addition, the prediction results of ML models were compared with the predictions of two existing standard codes and different ML studies. The results indicated that the developed ML models achieved notable enhancement in prediction accuracy. In addition, the Shapley additive interpretation (SHAP) technique is employed for feature analysis. The feature analysis results reveal that the column length and load end-eccentricity parameters negatively impact compressive strength.

Mediasi Menurut Hukum Perdata dan Hukum Islam dalam Sengketa Pengasuhan Anak

The role of a mediator is crucial in facilitating understanding among parties involved in a dispute. Mediators aid the parties in comprehending each other's perspectives regarding the contested issues and subsequently assist them in making objective assessments of the overall situation during negotiation processes. This research specifically aims to identify the legal standing, both in civil law and Islamic law, concerning the disputed issue of child custody with the assistance of a neutral third party (mediator). The research employs a qualitative method, involving the collection, analysis, and comprehensive interpretation of narratives. Legal materials obtained from literature reviews and legal approaches are analyzed using systematic interpretation techniques. The research findings highlight that the mediator's ability significantly influences the success of mediation. If mediation fails to reach an agreement, the mediator submits a written notification to the presiding judge, who then informs the panel judge handling the case. The disputing parties are expected to appear at the scheduled hearing, and the trial process continues as usual.

Unravelling Cenozoic carbonate platform fluid expulsion: Deciphering pockmark morphologies and genesis in the Tanintharyi shelf of the Andaman Sea as promising hydrocarbon reservoirs

Pockmarks, intriguing seafloor geological and geomorphological features, are commonly observed in marginal basins with hydrocarbon potential. As a Cenozoic marginal sea, the Andaman Sea is known for its significant petroleum reserves, and exploring its back-arc continental margin has revealed favourable conditions for petroleum occurrences. This study focuses on the Tanintharyi passive continental margin in the Andaman Sea, employing extensive stratigraphic and morphological analyses based on 2D and 3D seismic data interpretation techniques. Specifically, sub-seafloor characteristics of the Tanintharyi shelf region were investigated, focusing on comprehensively understanding pockmark morphologies, including their generation, evolution, migration, preservation, and the complete process leading to seabed leakage. This study revealed the potential of the Oligocene/Early Miocene carbonate platform in the Tanintharyi shelf region as a significant hydrocarbon reservoir for the upwelling buoyant fluids from the deep East Andaman Basin. Besides reservoir function, this carbonate platform serves as a passageway for the migration of fluids from the deep-sea area to the shallow-sea area, thereby playing a pivotal role in supporting fluid expulsion mechanisms in shaping a pockmark train adjacent to truncated sedimentary formations surrounding a geomorphological high on the contemporary seafloor. Additionally, the study examines the influence of changes in sedimentary facies and the tectonic setting of the Andaman Sea on pockmark evolutions, with a specific emphasis on the role of the uppermost shallow marine shale beds in developing sub-seafloor overpressure systems due to their impermeable seal rock properties. The article presents substantial evidence for the initiation of pockmark fields during the Middle Miocene period, followed by their transformation into pockmark trains on the present-day seafloor, attributed to the triggering effect of sub-seafloor overpressure systems due to changes in sedimentary dynamics in the Andaman Sea.

Predicting the fundraising performance of environmental crowdfunding projects: An interpretable machine learning approach

Crowdfunding has become a pivotal fundraising method for environmental organizations. However, the fundraising performance of environmental crowdfunding projects remains subpar, prompting the need for improvements. Effectively addressing this challenge entails the precise prediction of each project's fundraising performance and a comprehensive understanding of the intricate correlations between various features and fundraising success. In response to these imperatives, this study introduces an interpretable framework meticulously designed for predicting the fundraising performance of environmental crowdfunding projects. This comprehensive framework integrates ten theoretically significant features to form the predictive model's feature set. It adopts a diverse array of eight algorithms for training and harnesses SHAP values and ALE plots for insightful post-hoc interpretation, thereby providing valuable insights into the nuanced roles played by these features. Validated on a dataset comprising 3,101 environmental crowdfunding projects from Tencent Charity, the proposed framework outperforms state-of-the-art methods, demonstrating an improvement of 5.9% in predictive performance. Furthermore, the post-hoc interpretation techniques accurately depict the roles of the features. This study carries substantial practical implications for environmental crowdfunding project creators to optimize project design, for crowdfunding platform administrators to enhance platform performance, and for local governments to improve regional environmental governance.

Weakly supervised segmentation models as explainable radiological classifiers for lung tumour detection on CT images

PurposeInterpretability is essential for reliable convolutional neural network (CNN) image classifiers in radiological applications. We describe a weakly supervised segmentation model that learns to delineate the target object, trained with only image-level labels (“image contains object” or “image does not contain object”), presenting a different approach towards explainable object detectors for radiological imaging tasks.MethodsA weakly supervised Unet architecture (WSUnet) was trained to learn lung tumour segmentation from image-level labelled data. WSUnet generates voxel probability maps with a Unet and then constructs an image-level prediction by global max-pooling, thereby facilitating image-level training. WSUnet’s voxel-level predictions were compared to traditional model interpretation techniques (class activation mapping, integrated gradients and occlusion sensitivity) in CT data from three institutions (training/validation: n = 412; testing: n = 142). Methods were compared using voxel-level discrimination metrics and clinical value was assessed with a clinician preference survey on data from external institutions.ResultsDespite the absence of voxel-level labels in training, WSUnet’s voxel-level predictions localised tumours precisely in both validation (precision: 0.77, 95% CI: [0.76–0.80]; dice: 0.43, 95% CI: [0.39–0.46]), and external testing (precision: 0.78, 95% CI: [0.76–0.81]; dice: 0.33, 95% CI: [0.32–0.35]). WSUnet’s voxel-level discrimination outperformed the best comparator in validation (area under precision recall curve (AUPR): 0.55, 95% CI: [0.49–0.56] vs. 0.23, 95% CI: [0.21–0.25]) and testing (AUPR: 0.40, 95% CI: [0.38–0.41] vs. 0.36, 95% CI: [0.34–0.37]). Clinicians preferred WSUnet predictions in most instances (clinician preference rate: 0.72 95% CI: [0.68–0.77]).ConclusionWeakly supervised segmentation is a viable approach by which explainable object detection models may be developed for medical imaging.Critical relevance statementWSUnet learns to segment images at voxel level, training only with image-level labels. A Unet backbone first generates a voxel-level probability map and then extracts the maximum voxel prediction as the image-level prediction. Thus, training uses only image-level annotations, reducing human workload. WSUnet’s voxel-level predictions provide a causally verifiable explanation for its image-level prediction, improving interpretability.Key points• Explainability and interpretability are essential for reliable medical image classifiers.• This study applies weakly supervised segmentation to generate explainable image classifiers.• The weakly supervised Unet inherently explains its image-level predictions at voxel level.Graphical

CNN-LandCoverNet: An Effective Framework of Land Cover Classification Using Hybrid Metaheuristic-Aided Ensemble-Based Convolutional Neural Network

Land cover (LC) categorization is considered a necessary task of intelligent interpretation technology for remote sensing imagery that is intended to categorize every pixel to perform the predefined LC classification. Land Use and Land Cover (LULC) information has the ability to provide various insights in order to overcome environmental and socioeconomic impacts such as disaster risk, climate change, poverty, and food insecurity. Therefore, image categorization tasks are involved in conventional works, where the classical visual interpretation techniques completely depend upon professional knowledge as well as a professional’s classification experience, which is more susceptible to subjective awareness, inefficient, and time consuming. By overcoming this issue, the latest deep-structured approach is suggested to perform the LC image classification. Initially, the land images are gathered. Further, the collected images are employed for patch splitting, where the images are split into multiple patches. After splitting, the patches are fed to the Ensemble-based Convolutional Neural Network (ECNN), which is constructed with a Fully Convolutional Network (FCN), U-Net, DeepLabv3, and Mask Region-based Convolutional Neural Network (Mask R-CNN) for performing segmentation. Here, the hyperparameters are optimally tuned with the Hybrid Billiards-inspired Water Wave Algorithm (HB-WWA) by integrating the Billiards-inspired Optimization Algorithm (BOA) and Water Wave Algorithm (WWA). Finally, the classification is carried out with a fuzzy classifier. Thus, the performance is validated and measured through diverse metrics. Consequently, the developed work has demonstrated enhanced classification accuracy when tested on other existing algorithms.

Prediction of equivalent sand-grain size and identification of drag-relevant scales of roughness – a data-driven approach

Despite decades of research, a universal method for prediction of roughness-induced skin friction in a turbulent flow over an arbitrary rough surface is still elusive. The purpose of the present work is to examine two possibilities; first, predicting equivalent sand-grain roughness size $k_s$ based on the roughness height probability density function and power spectrum (PS) leveraging machine learning as a regression tool; and second, extracting information about relevance of different roughness scales to skin-friction drag by interpreting the output of the trained data-driven model. The model is an ensemble neural network (ENN) consisting of 50 deep neural networks. The data for the training of the model are obtained from direct numerical simulations (DNS) of turbulent flow in plane channels over 85 irregular multi-scale roughness samples at friction Reynolds number $Re_\tau =800$ . The 85 roughness samples are selected from a repository of 4200 samples, covering a wide parameter space, through an active learning (AL) framework. The selection is made in several iterations, based on the informativeness of samples in the repository, quantified by the variance of ENN predictions. This AL framework aims to maximize the generalizability of the predictions with a certain amount of data. This is examined using three different testing data sets with different types of roughness, including 21 surfaces from the literature. The model yields overall mean error 5 %–10 % on different testing data sets. Subsequently, a data interpretation technique, known as layer-wise relevance propagation, is applied to measure the contributions of different roughness wavelengths to the predicted $k_s$ . High-pass filtering is then applied to the roughness PS to exclude the wavenumbers identified as drag-irrelevant. The filtered rough surfaces are investigated using DNS, and it is demonstrated that despite significant impact of filtering on the roughness topographical appearance and statistics, the skin-friction coefficient of the original roughness is preserved successfully.

Investigation of the permafrost zone by geophysical techniques and determination of primary categorisations of permafrost conditions in rocks

The investigation of the permafrost zone situated in the Yamal Peninsula pertains to gasand hydrate-saturated intervals. The avoidance of gas discharge frequently met during well drilling is of utmost importance. The allocation of permafrost rocks governs the quest for novel approaches to analyze the geocryological situation of the locale, together with ascertaining the principal regularities of the zone structure, ascertained via geological and geophysical investigations, analysis of well drilling data, and seismic assessments.The investigation of the permafrost zone plays a crucial role in well drilling to prevent various types of complications. Moreover, the interpretation of geophysical survey data in combination with drilling and seismic data allows us to construct a model of the zone's lateral spread.Carrying out geophysical methods helps to prevent, to some extent, and also prevent possible emergency situations. A fairly large fund of geophysical material has been reviewed, and a complex of studies in the permafrost zone of the Yamal Peninsula in many fields has been analyzed.Following these studies, this article presents the primary geophysical survey material interpretation techniques with highlighted criteria for identifying different permafrost conditions in rocks.

SEMIOTIKA DALAM TARI : STUDI KASUS TARI DOLALAK

Dolalak Dance is a dance themed around the joy of Dutch soldiers. Dolalak Dance serves as a sign that displays specific meanings perceived by the community as a sign that interprets symbolic meanings. The Dolalak Budi Santoso group also offers symbolic meanings in their performance. The purpose of this research is to understand the semiotics of the Dolalak Dance performance in Kaliharjo Village, Purworejo Regency. This research utilizes a descriptive qualitative approach, semiotic approach, and etic-emic approach. The main focus of this research is to explore the symbolic meanings of a performance through a semiotic approach. With a semiotic approach, the Dolalak performance is placed as a sign and understood as a signifier by the community. Data validity is ensured through triangulation technique. Data analysis involves description and interpretation techniques. The results of this research show that the semiotics of Dolalak Dance can be seen through symbolic meanings in the form of presentational symbols and discursive symbols within the elements of the performance. Dolalak Dance presents presentational symbols through visual and auditory elements depicting the joy and agility of Dutch soldiers. The discursive symbols of Dolalak Dance are revealed through non-iconic elements of the performance, representing values of goodness and life. In conclusion, the semiotics of Dolalak Dance manifest in the symbolic meanings that arise from the perception of individuals or the community towards the form of the Dolalak Dance performance

SUPPLEMENTARY INCOME AND ENHANCING CIVIL SERVANT PERFORMANCE: A Study of The Government of Wajo Regency

This study aims to describe and analyze the factors in the implementation of TPP granting policies so that TPP granting in Wajo District does not improve the performance of civil servants. This study uses a qualitative method with a phenomenological study. Data collection was carried out by interviews and literature studies. Data were analyzed by using descriptive analysis and interpretation techniques. The results of the study showed that the factors caused the TPP grant in Wajo District did not improve the performance of civil servants, namely the lack of alignment between the performance targets of civil servants and the performance targets of their superiors and local government; the lack of objectivity in evaluating the performance of civil servants by superiors; the complexity of inputting performance through the e-Kinerja application; Civil servants are more oriented towards fulfilling working hours and tend to ignore performance achievements; Civil servants are not satisfied with the amount of TPP; and the monitoring and evaluation function of the TPP awarding policy has not been optimal. This study suggests that local governments carry out performance management in a more planned and structured manner.

What makes accidents severe! explainable analytics framework with parameter optimization

Most analytics models are built on complex internal learning processes and calculations, which might be unintuitive, opaque, and incomprehensible to humans. Analytics-based decisions must be transparent and intuitive to foster greater human acceptability and confidence in analytics. Explainable analytics models are transparent models in which the primary factors and weights that lead to a prediction can be explained. Typical AI models are non-transparent or opaque models, in which even the designers cannot explain how their models arrive at a specific decision. These transparent models help decision-makers understand their judgments and build trust in analytics. This study introduces an innovative, comprehensive model that fuses descriptive, predictive, and prescriptive analytics, offering a fresh perspective on car accident severity. Our methodological contribution lies in the application of advanced techniques to address data-related challenges, optimize feature selection, develop predictive models, and fine-tune parameters. Importantly, we also incorporate model-agnostic interpretation techniques, further enhancing the transparency and interpretability of our model, and separate explanations from models (i.e., model-agnostic interpretation techniques). Our findings should provide novel insights for a domain expert to understand accident severity. The explainable analytics approach suggested in this study supplements non-transparent machine learning prediction models, particularly optimized ensemble models. Our model's end product is a comprehensible representation of crash severity factors. To obtain a more trustworthy assessment of accident severity, this model may be supplemented with insurance data, medical data such as blood work and pulse rate, and previous medical history.

Dynamics of lithium plasma in laser-induced breakdown spectroscopy

Laser induced background spectroscopy (LIBS) is a very useful instrument for material surface characterization. Its usage for plasma facing tokamak components is envisaged in future machines. The calibration-free LIBS is also actively tested on existing tokamaks including Li covered surfaces. However, application of the most frequently used calibration-free Saha-Boltzmann technique for interpretation of LIBS results assumes local thermodynamic equilibrium (LTE) and plasma transparency for observed spectral lines. To investigate validity of this approach for Li plasma, we developed a hybrid model using 3DLINE, FEOS and THERMOS codes describing Li plasma formation, expansion, and radiation from the plasma plume. Using the model we reproduced quite well experimental measurements of Li lines radiation dynamics. Calculations show that transparency conditions can be very easily violated for Li LIBS plasmas, which absorbs up to 97% of 671 nm (Li I) line radiation. Other two easily observed in experiments lines, 548 nm (Li II) and 610 nm (Li I), remain relatively less affected by radiation absorption. We also notice that sharper focusing of the laser beam onto the surface makes the plasma more transparent. We believe that these conclusions are important to take into account while using LIBS for characterization of Li coated surfaces.

The significance of morphometric analysis of Shimsha River, Karnataka, India to understand the hydrological and morphological characteristics

AbstractMorphometric analysis is crucial for advancing in various fields, including geomorphology, hydrology, environmental sciences, and geological studies. The objective of the present study is to understand the hydrological and morphological characteristics of the Shimsha River, which flows through the fluvial deltaic plain in the semiarid environment of Karnataka. Remote sensing and geographic information system have been used as mapping and efficient tools for drainage basin morphometry. The emphasis is laid on geological, geomorphological, hydrological, and fluvial features under linear, aerial, shape, relief, geometric, morphotectonic, and social morphometric parameters. The basin area is about 8741 km2, with a dendritic to subdendritic pattern. The sinuosity index is 1.6, which indicates meandering in nature. Areal parameters suggest that the drainage basin has a moderate surface permeability, homogenous lithology, run‐off, erodibility, low relief conditions, infiltration rate, and a well‐developed drainage network. Shape parameter indicates that the basin is subelongated with flatted peak flow and longer duration. Relief factor indicated low relief and intermediate stage of the incision rate. The river has an asymmetry factor is 38.65 and it shows that the channel has shifted southwest. The river shows a perfectly graded profile, with a considerable amount of incision and deposition along its profile showing tectonically unstable, based on the spatial distribution of the potential groundwater zone of about (5260 km2), other waterbody masks covered (285 km2). Standard visual interpretation techniques were applied to recognize and interpret the land use and land cover patterns. A basin rich in clay‐rich soil may lead to a massive flood. Essentially, physiographical and structural conditions controlled the stream order of the basin. The increase in the stream length ratio from lower to a higher order. That indicates a mature geomorphic stage of the study area. Rainfall and local lithology impact the development of segments of the stream.

Peran Financial Technology Dalam Meningkatkan Literasi Keuangan Pada Generasi Z

Technological progress is a new driver of economic growth, if it is related to the financial sector, financial technology has been able to become a new instrument with the hope of increasing financial growth. This research aims to determine the role of financial technology in increasing financial literacy in generation Z . This research uses a qualitative descriptive research method, research that includes an arrangement of interpretative techniques with understanding, not the frequency, of phenomena that often or rarely occur naturally in the social world. The research results show that all respondents know the term financial technology (fintech) and the fintech applications commonly used by generation z are Gopay, OVO, Shopee Pay, Dana. Regarding financial literacy, respondents are in the less literate category, only having limited knowledge about financial institutions, financial products and services. Special education is needed so that literacy regarding fintech and financial literacy continues to increase.