Analysis Techniques Research Articles

Integration by design: driving mineral system knowledge using multi-modal, collocated, scale-consistent characterisation

Abstract. Recent decades have seen an exponential rise in the application of machine learning in geoscience. However, fundamental differences distinguish geoscience data from most other data types. Geoscience datasets are typically multi-dimensional, and contain 1D (drill holes), 2D (maps or cross-sections), and 3D volumetric and point data (models/voxels). Geoscience data quality is a product of the data's resolution and the precision of the methods used to acquire them. The dimensionality, resolution, and precision of each layer within a geoscience dataset translate into limitations to the spatiality, scale, and uncertainty of resulting interpretations. Historically, geoscience datasets were overlaid cartographically to incorporate subjective, experience-driven knowledge and variances in scale and resolution. These nuances and limitations that underpin the reliability of automated interpretation are well understood by geoscientists but are rarely appropriately transferred to data science. For true integration of geoscience data, such issues cannot be overlooked without consequence. To apply data analytics to complex geoscience data (e.g. hydrothermal mineral systems) effectively, methodologies that characterise the system quantitatively at a common scale, using collocated analyses, should be sought. This paper provides research and exploration insights from an innovative district-wide, scale-integrated geoscience data project, which analysed 1590 samples from 23 mineral deposits and prospects across the Cloncurry district, Queensland, Australia. Nine different analytical techniques were used, including density, magnetic susceptibility, remanent magnetisation, anisotropy of magnetic susceptibility, radiometrics, conductivity, automated mineralogy based on scanning electron microscopy (SEM), geochemistry, and short-wave infrared (SWIR) hyperspectral data with 561 columns of scale-integrated data (+2151 columns of SWIR data). All data were collected on 2.2 cm × 2.5 cm sample cylinders, a scale at which the confidence in the coupling of data from techniques can be high. These data are integrated by design to eliminate the need to downscale coarser measurements via assumptions, inferences, inversions, and interpolations. This scale-consistent approach is critical to the quantitative characterisation of mineral systems and has numerous applications in mineral exploration, such as linking alteration paragenesis with structural controls and petrophysical zonation. The Cloncurry METAL dataset is made freely available via the AuScope Data Repository: https://doi.org/10.60623/82trleue (Austin et al., 2024).

Humanoid Robot-Assisted Learning for Level 1 Autistic Children: Developing Test Plans to Enhance Number Recognition Skills

Autism Spectrum Disorder (ASD) impacts the social, communication, and cognitive development of affected individuals. Level 1 autism, the mildest form of ASD, presents unique challenges in the acquisition of crucial skills such as number recognition, which serves as a foundational aspect of children's cognitive development. Recent studies have shown that humanoid robots can offer effective intervention strategies for children with autism, fostering learning in a structured, engaging, and interactive environment. This study aims to develop and evaluate test plans using humanoid robots to enhance number recognition skills in Level 1 children with autism. Based on the findings, a test plan was designed to address the unique learning needs of Level 1 children with autism in number recognition. Participants were selected based on specific criteria, ensuring a diverse and representative sample. The humanoid robot's capabilities and features were detailed, emphasizing its role in facilitating the test plan. The research methodology outlined the test plan components, implementation procedure, data collection, and analysis techniques. Results demonstrated that the humanoid robot-assisted test plan was effective in enhancing number recognition skills among Level 1 children with autism. The participants exhibited significant improvements in their abilities to identify and manipulate numbers, as well as increased engagement and motivation in learning tasks. The study also revealed that the interactive and supportive nature of the humanoid robot fostered a positive learning environment, which contributed to the observed improvements in number recognition skills. The implications of these findings extend to the broader field of autism intervention, highlighting the potential benefits of incorporating humanoid robots into educational programs for children with autism.

La Disponibilidad de cuarzo apto para la talla en las Sierras Pampeanas Australes

This paper presents the results of the study on the availability and use of quartz suitable for flintknapping in the Ongamira Valley (Córdoba, Argentina. At the archaeological sites studied—which correspond to late Holocene hunter-gatherer groups (ca. 1900-3600 BP)—more than 90% of the lithic assemblages were quartz. Researchers articulated different analysis techniques (remote sensing, prospecting, XRF, XRD and petrographic analyses) to understand the origin and local availability of this raw material. The results reveal the location and abundance of the primary quartz sources, estimate the material quality, and help us comprehend the variability between sources. Based on the data, the paper proposes a methodological model for discussing quartz use in the mountainous regions of Central Argentina and its integration into interpretations of human mobility circuits.

PENGARUH KOMPETENSI SDM, SISTEM INFORMASI AKUNTANSI, DAN SISTEM PENGENDALIAN INTERNAL TERHADAP KUALITAS LAPORAN KEUANGAN DANA BANTUAN OPERASIONAL SEKOLAH (STUDI KASUS SEKOLAH JENJANG SD DI KECAMATAN PANGKALANBARU)

This study aims to examine the effect of HR Competence, Accounting Information Systems and Internal Control Systems on the Quality of Financial Statements of School Operational Assistance Funds at the Pangkalanbaru District Elementary School level. The population in this study were elementary schools that were willing to conduct research in Pangkalanbaru District with a total sample of 13 elementary schools and a total of 37 respondents. Data collection techniques using Purposive Sampling techniques with Quantitative research methods using Questionnaires. Analysis techniques used are descriptive statistical analysis, instrument test, classical assumption test, multiple linear regression analysis and hypothesis testing. The results of this study indicate that HR Competence has a positive and significant effect on the Quality of BOS Fund Financial Statements, Accounting Information Systems has no effect on the Quality of BOS Fund Financial Statements, and Control Systems has a positive and significant effect on the Quality of BOS Fund Financial Statements.

Selective Methods Promote Protein Solid-State NMR.

Solid-state nuclear magnetic resonance (ssNMR) is indispensable for studying the structures, dynamics, and interactions of insoluble proteins in native or native-like environments. While ssNMR includes numerous nonselective techniques for general analysis, it also provides various selective methods that allow for the extraction of precise details about proteins. This perspective highlights three key aspects of selective methods: selective signals of protein segments, selective recoupling, and site-specific insights into proteins. These methods leverage protein topology, labeling strategies, and the tailored manipulation of spin interactions through radio frequency (RF) pulses, significantly promoting the field of protein ssNMR. With ongoing advancements in higher magnetic fields and faster magic angle spinning (MAS), there remains an ongoing need to enhance the selectivity and efficiency of selective ssNMR methods, facilitating deeper atomic-level insights into complex biological systems.

Data science and automation in the process of theorizing: Machine learning's power of induction in the co-duction cycle.

Recent calls to take up data science either revolve around the superior predictive performance associated with machine learning or the potential of data science techniques for exploratory data analysis. Many believe that these strengths come at the cost of explanatory insights, which form the basis for theorization. In this paper, we show that this trade-off is false. When used as a part of a full research process, including inductive, deductive and abductive steps, machine learning can offer explanatory insights and provide a solid basis for theorization. We present a systematic five-step theory-building and theory-testing cycle that consists of: 1. Element identification (reduction); 2. Exploratory analysis (induction); 3. Hypothesis development (retroduction); 4. Hypothesis testing (deduction); and 5. Theorization (abduction). We demonstrate the usefulness of this approach, which we refer to as co-duction, in a vignette where we study firm growth with real-world observational data.

Absolute Quantification of Phenylbutanoids in Zingiber cassumunar Roxb. Rhizome by Quantitative 1H NMR.

Quantitative determination of pharmacologically active constituents in medicinal plants is critical for quality control. Due to the chemical complexity of the crude plant extracts, the presence of interfering compounds is often problematic for the unambiguous quantitation of the designed bioactive compounds. Considering the method of quantification, quantitative NMR spectroscopy (qNMR) has gained substantial popularity as a powerful and effective technique for both qualitative and quantitative analyses of natural products. The aim of this study is to develop a quantitative NMR method for quantifying the bioactive phenylbutanoids in Zingiber cassumunar rhizome crude extract. Quantitative 1H NMR (qHNMR) measurements were performed on a 600 MHz NMR spectrometer using an internal standard for the determination of the absolute quantities of four phenylbutanoids in Z. cassumunar rhizome crude extract. The direct quantification of four characteristic phenylbutanoids, i.e., (E)-1-(3',4'-dimethoxyphenyl)butadiene (DMPBD), (E)-1-(2',4',5'-trimethoxyphenyl)butadiene (TMPBD), (E)-4-(3',4'-dimethoxyphenyl)but-3-en-1-ol, and (E)-4-(3',4'-dimethoxyphenyl)but-3-en-1-yl acetate, in crude extract by qHNMR using an internal standard was achieved, with high specificity and sensitivity. The selected 1H NMR signals could unambiguously be assigned and did not overlap with other resonances, including the highly similar compounds DMPBD and TMPBD. The method is linear in the concentration range of 0.70-14.52 mg/mL, with a limit of quantification of 0.46-0.68 mg/mL. The RSD values of intraday and interday precisions are in the range of 0.23%-0.74% and 0.29%-0.52%, respectively. The average recoveries are 99.54%-100.18%. A rapid, accurate, and precise method using 1H NMR for the simultaneous quantitation of four phenylbutanoids in the crude extract of Z. cassumunar rhizome was developed and validated.

TIA-UNet: Transformer-Enhanced Deep Learning for Adolescent Idiopathic Scoliosis Spinal X-Ray Image Segmentation

Abstract Adolescent Idiopathic Scoliosis (AIS) is a common spinal deformity where precise diagnosis is crucial for developing effective treatment strategies. Traditional manual X-ray image analysis is time-consuming and highly dependent on the operator’s expertise, thus constraining diagnostic efficiency and accuracy.This study aimed to develop an automated thoracolumbar spine segmentation method utilizing deep learning to enhance the efficiency and accuracy of AIS diagnosis. We introduced TIA-UNet, an innovative network architecture that combines Convolutional Neural Networks (CNN) with Transformer models.By integrating the IR Block and DA Block, TIA-UNet was optimized for feature extraction and multi-scale information fusion. The model underwent training and validation using our established Adolescent Scoliosis Medical Dataset (ASMD).TIA-UNet attained a Dice similarity coefficient of 90.02%, a Mean Intersection over Union (MIoU) of 81.96%, and a Hausdorff Distance (HD) of 4.09, significantly surpassing current state-of-the-art methods such as UNet and TransUNet. Moreover, TIA-UNet exhibited superior computational efficiency regarding parameter count, inference time, and floatingpoint operations (FLOPs).As an automated medical image segmentation algorithm, TIA-UNet enhanced segmentation accuracy while maintaining high computational efficiency, demonstrating significant potential for clinical diagnostic applications. This study provides compelling evidence supporting the utilization of deep learning
techniques in medical image analysis.

Enhancing silicon spectral emission in LIBS using Tesla coil discharge

Laser-induced breakdown spectroscopy (LIBS) is a powerful technique for elemental analysis, offering rapid analysis, minimal sample preparation, wide elemental coverage, and portability. To enhance the detection sensitivity of LIBS, increasing the spectral emission intensity is crucial. This paper explores the use of Tesla coil (TC) discharge as an alternative to spark discharge in silicon LIBS. The study examines the influence of TC discharge on both time-integrated and time-resolved spectra, with and without TC discharge; the corresponding electron temperature and density are obtained. The results show that TC discharge significantly amplifies the spectral intensity, improving signal sensitivity in LIBS analysis. Specifically, in the laser energy range from 7.4 to 24.0 mJ, TC discharge increased the average spectral line intensities of Si (II) 385.60 nm and Si (I) 390.55 nm by factors of 8.4 and 5.1, respectively. Additionally, the average electron temperature and density were enhanced by approximately 3.2% and 4.2%, respectively, under TC discharge. The advantages of TC discharge include higher energy deposition, extended discharge duration, reduced electrode erosion, and enhanced safety. This research contributes to advancing LIBS technology and expanding its applications in various fields.

Looking at the Modern to Better Understand the Ancient: Is It Possible to Differentiate Mars Pigments from Archaeological Ochres?

This article offers a discussion of the possibility of distinguishing ochres from Mars pigments. The discussion addresses technological, archaeological, and artistic aspects. Natural earth pigments such as ochres, siennas, and umbers have been widely used from the Paleolithic to the present day and still find wide application despite the development of synthetic iron oxide pigment synthesis processes, called Mars pigments. The potential ability of today’s analytical techniques to distinguish between two classes of pigments of the same color with very similar chemical composition—but perhaps sufficient for reliable recognition—is also discussed. The paper begins by addressing the proper use of the terms “ochres” and “Mars pigments” and their accurate identification in artworks. It reviews the literature on the chemical–mineralogical characterization of yellow and red iron pigments and analyzes pigment catalogs to understand how companies distinguish ochres from Mars pigments. An experimental analysis using External Reflection Infrared Spectroscopy (FTIR-ER) compared painting samples made with natural ochres and Mars pigments, confirming the literature findings and suggesting future research directions. Key differences such as hematite in yellow ochres and specific spectral peaks in red ochres support the potential of FTIR-ER spectroscopy as a noninvasive tool for distinguishing pigments, especially for fragile artifacts and archaeological applications.

Optimization of Magnetic Biochar Preparation Process, Based on Methylene Blue Adsorption

The search for low-cost and effective adsorbents for the removal of organic dyes from contaminated water is urgently needed. The substantial amount of waste mushroom cultivation substrates generated in practical production can serve as an ideal material for the preparation of adsorbents. In this study, we investigated the main control parameters affecting the performance of magnetic mushroom substrate biochar and optimized the process of preparing biochar by using the Plackett–Burman and central composite design methods. Various analytical techniques including SEM, EDX, BET, and VSM were used to characterize the biochar. The results indicate that the carbonization temperature had the most significant impact on the yield and adsorption performance of biochar. Under the conditions of a carbonization temperature of 600 °C, a carbonization retention time of 1 h, and an impregnation ratio of 0.1, the yield and methylene blue adsorption value of magnetic biochar were 42.54% and 2297.04 μg/g, respectively, with a specific surface area of 37.17 m2/g. This biochar effectively removed methylene blue from the solution, demonstrating a high economic efficiency for wastewater treatment and pollution control. Furthermore, the adsorption–desorption cycle studies revealed its excellent stability and reusability. Additionally, based on the response surface methodology, a three-dimensional surface model of the adsorption performance of magnetic biochar under different carbonization conditions was established, providing a theoretical basis for the preparation of magnetic biochar from agricultural wastes.

Farm to fork applications: how vibrational spectroscopy can be used along the whole value chain?

Vibrational spectroscopy is a nondestructive analysis technique that depends on the periodic variations in dipole moments and polarizabilities resulting from the molecular vibrations of molecules/atoms. These methods have important advantages over conventional analytical techniques, including (a) their simplicity in terms of implementation and operation, (b) their adaptability to on-line and on-farm applications, (c) making measurement in a few minutes, and (d) the absence of dangerous solvents throughout sample preparation or measurement. Food safety is a concept that requires the assurance that food is free from any physical, chemical, or biological hazards at all stages, from farm to fork. Continuous monitoring should be provided in order to guarantee the safety of the food. Regarding their advantages, vibrational spectroscopic methods, such as Fourier-transform infrared (FTIR), near-infrared (NIR), and Raman spectroscopy, are considered reliable and rapid techniques to track food safety- and food authenticity-related issues throughout the food chain. Furthermore, coupling spectral data with chemometric approaches also enables the discrimination of samples with different kinds of food safety-related hazards. This review deals with the recent application of vibrational spectroscopic techniques to monitor various hazards related to various foods, including crops, fruits, vegetables, milk, dairy products, meat, seafood, and poultry, throughout harvesting, transportation, processing, distribution, and storage.

Caracterización fenotípica y simbiótica de las bacterias que nodulan Genista saharae en la región árida de Argelia

Twenty bacterial strains had been isolated from root nodules of Genista saharae that grew wild in Biskra and El Oued city (Northeastern Algerian Sahara). This study focused on obtaining isolates of legume nodule bacteria (LNB) from the plant G. saharae and evaluated their effectiveness in forming a symbiotic relationship with the legume species Vigna unguiculata through cross-inoculation. Additionally, the study aimed to identify the successful cross-inoculation group of LNB strains based on their phenotypic characteristics. The growth capacity of isolates under varying salinity conditions [NaCl] and pH levels was investigated using a spectrophotometer (96-microplate reader). The API 20NE and API 20E systems were used to identify the biochemical characteristics of the isolates. In addition, the rhizospheric soil samples from the two study sites were analyzed using standard analytical techniques of soil. All isolates established effective symbioses with Vigna unguiculata, were Gram-negative rods, and were fast-growing. The optimal growth temperature was between 28 °C and 37 °C; some isolates were thermophiles and specifically withstood extreme heat between 45-50 °C. Furthermore, they demonstrated a wide tolerance range to pH (5–10) with salt tolerance ranging from 100 mM to 500 mM. Biochemical results revealed that the isolates assimilated various sources of carbon and nitrogen and displayed numerous enzyme activities. Physicochemical analysis revealed that all the soils were deficient in nutrients and had an alkaline pH. This study enabled us to identify the effective stress-tolerant strains, which could be used in the future to inoculate plants for environmental applications. Keywords: Rhizobia, morpho-physiological and biochemical characterization.

The molecular composition of birch tar and its infrared spectrum

Birch tar was the first substance made by humans that is otherwise not available in nature. The oldest artefacts date to ~ 200 thousand years ago in Europe but birch tar becomes more frequent around 45 thousand years ago and even more so from the Mesolithic onwards. Their study has important implication for our understanding of evolutionary processes such as cognitive capacity and cultural evolution. What remains unknown is the overall molecular composition of birch tar. Several approaches based on gas chromatography-mass spectrometry have shown the potential of studying solvent soluble molecules to understand the botanical origin of different tars, their manufacturing techniques and their post-depositional alteration mechanisms. However, birch tar’s soluble fraction cannot shed light on its mechanical properties. In this study, we conduct gas chromatography-mass spectrometry and spectroscopic analysis of seven bulk birch tar samples, including infrared band assignment and structural refinement of organic molecules in the insoluble fraction. We find that the overall proportion that cannot be analysed by gas chromatography accounts for 85–90% in birch tar. This phase consists of polymerised molecules with a structure analogous to asphaltenes but containing a significantly greater amount of oxygen-related functional groups. Our findings have implications for understanding the viscosity, adhesiveness and stiffness of birch tar and they call for caution in applying analytical techniques that only target soluble molecules in birch tar.

Multiplatform metabolomic interlaboratory study of a whole human stool candidate reference material from omnivore and vegan donors.

Human metabolomics has made significant strides in understanding metabolic changes and their implications for human health, with promising applications in diagnostics and treatment, particularly regarding the gut microbiome. However, progress is hampered by issues with data comparability and reproducibility across studies, limiting the translation of these discoveries into practical applications. This study aims to evaluate the fit-for-purpose of a suite of human stool samples as potential candidate reference materials (RMs) and assess the state of the field regarding harmonizing gut metabolomics measurements. An interlaboratory study was conducted with 18 participating institutions. The study allowed for the use of preferred analytical techniques, including liquid chromatography-mass spectrometry (LC-MS), gas chromatography-mass spectrometry (GC-MS), and nuclear magnetic resonance (NMR). Different laboratories used various methods and analytical platforms to identify the metabolites present in human stool RM samples. The study found a 40% to 70% recurrence in the reported top 20 most abundant metabolites across the four materials. In the full annotation list, the percentage of metabolites reported multiple times after nomenclature standardization was 36% (LC-MS), 58% (GC-MS) and 76% (NMR). Out of 9,300 unique metabolites, only 37 were reported across all three measurement techniques. This collaborative exercise emphasized the broad chemical survey possible with multi-technique approaches. Community engagement is essential for the evaluation and characterization of common materials designed to facilitate comparability and ensure data quality underscoring the value of determining current practices, challenges, and progress of a field through interlaboratory studies.

Insights and caveats from mining local and global temporal motifs in cryptocurrency transaction networks

Distributed ledger technologies have opened up a wealth of fine-grained transaction data from cryptocurrencies like Bitcoin and Ethereum. This allows research into problems like anomaly detection, anti-money laundering, pattern mining and activity clustering (where data from traditional currencies is rarely available). The formalism of temporal networks offers a natural way of representing this data and offers access to a wealth of metrics and models. However, the large scale of the data presents a challenge using standard graph analysis techniques. We use temporal motifs to analyse two Bitcoin datasets and one NFT dataset, using sequences of three transactions and up to three users. We show that the commonly used technique of simply counting temporal motifs over all users and all time can give misleading conclusions. Here we also study the motifs contributed by each user and discover that the motif distribution is heavy-tailed and that the key players have diverse motif signatures. We study the motifs that occur in different time periods and find events and anomalous activity that cannot be seen just by a count on the whole dataset. Studying motif completion time reveals dynamics driven by human behaviour as well as algorithmic behaviour.

Learning curve for the combined trans-oral and chest approach to endoscopic selective neck dissection: a cumulative sum (CUSUM) analysis.

The combined trans-oral and chest approach is a novel technique for endoscopic selective neck dissection (SND), with satisfactory oncological safety and cosmetic results. However, this hybrid approach is difficult, and the learning curve remains unclear. In this study, we aimed to evaluate the short-term outcomes for this hybrid approach, and to define the associated learning curve. Clinical data and surgical outcomes of 58 PTC patients who underwent endoscopic SND via combined trans-oral and chest approach between September 2020 and April 2023 were retrospectively reviewed. The cumulative sum (CUSUM) sequential analysis technique is used to detect change, and has been applied for the evaluation of learning curves. The study population included 58 patients (40 females, 18 males), with mean age of 38.9 ± 12.7years and average body mass index of 22.6 ± 4.8kg/m2. In all cases, the same surgical team performed endoscopic SND via combined trans-oral and chest approach. The numbers of positive/retrieved lymph nodes were 4.3 ± 3.2/27.8 ± 8.4 in the lateral compartment and 4.0 ± 3.5/8.4 ± 5.1 in the central compartment. The learning curve was evaluated in terms of an initial phase (20 cases) and a mature phase (38 cases). Operative time was longer during the initial phase, compared to the mature phase (375.2 vs. 274.6min, p = 0.002). The frequency of operative complications was higher during the initial phase, compared to the mature phase (85% vs. 55.3%, p = 0.048). After the experience acquired using endoscopic SND via combined trans-oral and chest approach to treat PTC in 20 patients, the number of complications decreased significantly. Compared to data collected for the initial phase, data collected for the mature phase showed trends toward decreased operating time.

PENGARUH LEVERAGE, UKURAN PERUSAHAAN, DAN KOMISARIS INDEPENDEN TERHADAP PENGHINDARAN PAJAK PADA PERUSAHAAN SUB SEKTOR PERDAGANGAN BESAR DAN ECERAN YANG TERDAFTAR DI BURSA EFEK INDONESIA (BEI) TAHUN 2019-2023

This study aims to determine the effect of leverage, company size, and independent commissioners as independent variables on tax avoidance as the dependent variable. The object of this research is all wholesale and retail trade subsector companies listed on the Indonesia Stock Exchange in 2019-2023. The number of samples obtained was 17 companies using purposive sampling technique. This study uses secondary data in the form of financial statement data of wholesale and retail trade subsector companies listed on the Indonesia Stock Exchange. The data analysis technique uses multiple regression analysis methods processed with SPSS. The results showed (1) the leverage variable has no effect on tax avoidance with a significance value of 0.125 > from 0.05. (2) the firm size variable affects tax avoidance with a significance value of 0.003 < of 0.05. (3) the independent commissioner variable has no effect on tax avoidance with a significance value of 0.724 > from 0.05.

A hybrid GRA-PCA-TOPSIS Technique Optimization of EDD Process Parameter on Drilling of AL8011/B4C/aloevera composites

A class of cutting-edge materials called hybrid aluminum metal matrix composites has been produced for applications such as the automobile, aerospace, and electronics industry and where weight and strengths are crucial factors. In the present research work, AA8011 composite reinforced with boron carbide particles and green aloevera has been fabricated using an economical stir casting process. In this 1% to 3% of boron carbide and 1% to 2% aloevera were used to fabricate the three different combinations of composites. The electrical discharge drilling process variables such as discharge current, pulse-on time, pulse-off time, and dielectric fluid pressure are investigated using the tubular copper electrode on drilling of hybrid composites. Performance aspects including drilling rate, roundness error, taper angle, and electrode wear rate have been analyzed, and the effect of process variables with the response surface methodology–central composite experimental design. The process parameters during the drilling of composites were optimized using a novel multi-criteria decision-making technique called the hybrid grey relational analysis–pulse component analysis–technique for order of preference by similarity to ideal solution method in order to maximize the drilling rate while concurrently minimizing other aspects. The optimum conditions were reached using the hybrid optimization method and a 1.5 mm tubular copper electrode with an input current of 9 Ampere , pulse on time 25 [Formula: see text]s, pulse off time 12 [Formula: see text]s, and pressure 60 kg/cm2. In comparison to the initial settings, the optimal condition shows a 25% faster drilling rate, with a 23% better roundness value, 14% lesser taper angle, and 7% lesser electrode wear rate.

ACTIVITY BASED COSTING METHOD SEBAGAI ALTERNATIF PENENTUAN HARGA POKOK TARIF JASA RAWAT INAP PADA RSUD DEPATI HAMZAH KOTA PANGKALPINANG

The techniques used in data collection were interviews, literature study, documentation, and internet search. The data analysis technique used was qualitative with descriptive comparative apporach. The research results indicate that: (1) the calculation of inpatient rates using the mehod applied by RSUD Depati Hamzah Kota Pangkalpinang is carried out by charging facility fees per days of treatment, service fees per length of treatment, and reduction per day if the doctor does not visit, while the calculation of inpatient rates using the Activity Based Costing Method is carried out by charging activities that are the patient’s responsibility, such as doctor fees, laundry, consumption, telephone, electricity and water, security, administration, and cleanliness; (2) the inpatiient rates used by RSUD Depati Hamzah Kota Pangkalpinang are higher for VIP Class compared to the Activity Based Costing Method, while for Classes I-III, the hospital rates are lower; (3) the Activity Based Costing Method cannot yet be applied at RSUD Depati Hamza Kota Pangkalpinag due to the lack of consideration related to the continuity and development of services, community purchasing power, principles of justice and propriety, and healthy competition.