Analysis Method Research Articles

Kepuasan Pasien Pengguna Badan Penyelenggara Jaminan Sosial Berbasis Kualitas Pelayanan

This research aims to determine and analyze the quality of service, both simultaneously and partially, on the satisfaction of patients using the Social Security Administering Agency at the Ciawi Bogor Community Health Center. The sample size was 100 respondents taken using simple random sampling technique with accidental sampling method with criteria; 1) patients who have sought treatment at the Ciawi Community Health Center at least once; 2) patients who are mature enough to fill out the questionnaire are 17 years old. The questionnaire was tested using validation, reliability and classical assumption tests. The results of this test are valid, reliable, and can be used for regression data. The analytical method used in this research is a descriptive and verification method with a quantitative approach. The research results show that service quality variables, both simultaneously and partially, have a positive and significant effect on patient satisfaction at the Ciawi Bogor Community Health Center. The driver of patient satisfaction using the Social Security Administering Agency at the Ciawi Bogor Community Health Center is reliability.

Predicting positon solutions of a family of nonlinear Schrödinger equations through deep learning algorithm

We consider a hierarchy of nonlinear Schrödinger equations (NLSEs) and forecast the evolution of positon solutions using a deep learning approach called Physics Informed Neural Networks (PINN). Notably, the PINN algorithm accurately predicts positon solutions not only in the standard NLSE but also in other higher order versions, including cubic, quartic and quintic NLSEs. The PINN approach also effectively handles two coupled NLSEs and two coupled Hirota equations. In addition to the above, we report exact second-order positon solutions of the sextic NLSE and coupled generalized NLSE. These solutions are not available in the existing literature and we construct them through generalized Darboux transformation method. Further, we utilize PINNs to forecast their behaviour as well. To validate PINN's accuracy, we compare the predicted solutions with exact solutions obtained from analytical methods. The results show high fidelity and low mean squared error in the predictions generated by our PINN model.

Optimization for industrial robot joint movement in non-horizontal 3D printing application

When a robot is printing a sequence of non-horizontal goal poses, its joint values often undergo significant variations, resulting in challenges such as singularities or exceeding joint limits. This paper proposes two new methods aimed at optimizing goal poses to solve the problem. The first method, employing an analytical approach, modifies the goal poses to maintain the 4th joint value of a 6-axis industrial robot at zero. This adjustment effectively reduces the motion range of the 5th and 6th axes. The second method utilizes numerical optimization to adjust the goal poses, aiming to minimize the motion range of all joints. Leveraging the analytical method to obtain one good initial value, numerical optimization is subsequently applied to complete the entire path optimization, creating an optimization workflow. It is also possible to use only analytical methods for computational efficiency. The feasibility and effectiveness of these two methods are validated through simulation and real project case.

Advances in Understanding Tungsten Disulfide Dynamics during the Hydrogen-Evolution Reaction: An Initial Step in Elucidating the Mechanism.

Tungsten disulfide (WS2), a promising electrocatalyst made from readily available materials, demonstrates significant effectiveness in the hydrogen-evolution reaction (HER). The study conducts a thorough investigation using various analytical methods such as scanning electron microscopy (SEM), transmission electron microscopy (TEM), energy dispersive spectrometry (EDS), X-ray diffraction (XRD), X-ray absorption spectroscopy (XAS), electron paramagnetic resonance (EPR), and in situ Raman spectroscopy. These techniques have uncovered changes in the WS2 particle structure during HER. Through employing EPR, XAS, and in situ Raman spectroscopy, the research reveals structural and chemical transformations. This includes the formation of novel W species and signs of W-O bond formation. Moreover, significant changes in the morphology of the particles were observed. These findings offer enhanced insights into the mechanisms of WS2 under HER conditions, highlighting its catalytic performance and durability.

Fiber Laser-Generated Silver-109 Nanoparticles for Laser Desorption/Ionization Mass Spectrometry of Illicit Drugs.

Cannabinoids and opioids are the most prominently used drugs in the world, with fentanyl being the main cause of drug overdose-related deaths. Monitoring drug use in groups as well as in individuals is an important forensic concern. Analytical methods, such as mass spectrometry (MS), have been found most useful for the identification of drug abuse on a small and large scale. Pulsed fiber laser 2D galvoscanner laser-generated nanomaterial (PFL 2D GS LGN) was obtained from monoisotopic silver-109. Nanomaterial was used for laser desorption/ionization mass spectrometry of selected illicit drug standards with standard high-resolution reflectron-based time-of-flight MALDI apparatus. Δ9-THC, 11-OH-THC, 11-COOH-THC, fentanyl, codeine, 6-monoacetylmorphine (6-MAM), heroin, tramadol, and methadone were chosen as test compounds. Illicit drugs were tested in a concentration range from 100 μg/mL to 10 pg/mL, equating to 50 μg to 50 fg per measurement spot. For all analyzed compounds, identification and quantification by silver-109-assisted laser desorption/ionization (LDI) MS was possible, with uncommon [M + 109Ag3]+ and [M - H]+ ions present for certain structures. The results of the quantitative analysis of drugs using silver-109 PFL 2D GS LGN for LDI MS are presented. Laser-generated NPs are proven to be useful for the analysis of selected drugs, with exceptionally good results for fentanyl monitoring in a broad range of concentrations.

Energy, Exergy, Exergoeconomic, and Environmental (4E) Analysis of the Existing Gas Turbine Power Plants in BOB - PT. Bumi Siak Pusako Pertamina

The power plants in Indonesia are mostly used to supply energy for the industrial sector, including the upstream oil and gas as well as mining companies. Several companies operating in Riau contribute to the status of the region as the largest oil producer in Indonesia. These companies rely on self-generated electricity for their operations with subsequent impact on the environment. Therefore, this research was conducted to analyze the flow of energy, exergy, exergoeconomic, and environment at the 6 MW power plant operated by BOB - PT Bumi Siak Pusako - Pertamina Hulu. The second law of thermodynamics was used to evaluate energy efficiency as the maximum achievable effort. This was further integrated with economic principles to appraise the useful and wasted costs associated with thermodynamic systems through the concept of exergoeconomics. The results showed that the thermal efficiency of the gas turbine power plant was 42.85% and the exergy efficiency was 33.22% with the largest loss recorded in the combustion chamber to be 3.091 MW in the form of vibration, friction, or expansion of the components. It was also discovered that the exergy efficiency of each component was above 75%, thereby indicating the components of the gas turbine power plant components were in good condition. Moreover, the largest exergy destruction cost was 2349.16 USD/h and the exergy cost was 3,778.05 USD/kWh. The exhaust emission generated by the gas turbine power plant was 0.21 kg/s or equivalent to 0.1425 kg/kWh requiring a forest area of 11.63 ha. The results showed that the analytical method used could be comprehensively developed and applied to other power plants in Indonesia. It could also be used to understand system performance, identify energy losses, optimize energy efficiency, and link economic aspects with energy use.

Reentrant phase behavior in systems with density-induced tunneling

We show that correlations in strongly interacting many-particle systems can create quantum decoherence, leading to a mechanism of dissipation that does not rely on an external source. Using analytical methods, we study a bosonic many body system in two dimensions, with extended interactions between particles. We show that, as expected, the system can be driven out of a coherent state. Surprisingly, when the interaction strength is sufficiently large, the system reenters the superfluid phase even after coherence is lost. The breakdown of quantum coherence is a certainty, but interpreting the process correctly relies on understanding and preserving the nature of the coupling between the constituents of the many particle system. The methods used provide a natural cutoff point at the critical temperature, where superfluidity breaks down.

Pengaruh Kualitas Audit, dan Opinion Shopping Terhadap Penerimaan Opini Audit Going Concern (Studi Empiris pada Perusahaan Manufaktur yang Terdaftar di Bursa Efek Indonesia (BEI) Periode 2017-2020)

This study aims to examine the effect of audit quality and opinion shopping on acceptance of going concern audit opinions in manufacturing companies listed on the Indonesia Stock Exchange (IDX) for the 2017-2020 period. The research sample consisted of 15 companies. The method used was purposive sampling, the number of observations was 193 manufacturing companies listed on the IDX for the 2017-2020 period. Annual report data obtained by indo-exchange file(IDX). In this research, the analytical method used is logistic statistics with SPSS Version 21 for windows software. The results of this study indicate that audit quality and opinion shopping have no partial effect on acceptance of going-concern audit opinions. The results of the omnibus test (f test) show that audit quality and opinion shopping simultaneously influence acceptance of going-concern audit opinions.

Time and habitat structure shape insect acoustic activity in the Amazon.

Insects are the most diverse animal taxon on Earth and play a key role in ecosystem functioning. However, they are often neglected by ecological surveys owing to the difficulties involved in monitoring this small and hyper-diverse taxon. With technological advances in biomonitoring and analytical methods, these shortcomings may finally be addressed. Here, we performed passive acoustic monitoring at 141 sites (eight habitats) to investigate insect acoustic activity in the Viruá National Park, Brazil. We first describe the frequency range occupied by three soniferous insect groups (cicadas, crickets and katydids) to calculate the acoustic evenness index (AEI). Then, we assess how AEI varies spatially and temporally among habitat types, and finally we investigate the relationship between vegetation structure variables and AEI for each insect category. Overall, crickets occupied lower and narrower frequency bands than cicadas and katydids. AEI values varied among insect categories and across space and time. The highest acoustic activity occurred before sunrise and the lowest acoustic activity was recorded in pastures. Canopy cover was positively associated with cricket acoustic activity but not with katydids. Our findings contribute to a better understanding of the role of time, habitat and vegetation structure in shaping insect activity within diverse Amazonian ecosystems. This article is part of the theme issue 'Towards a toolkit for global insect biodiversity monitoring'.

Quality and safety of new types of dairy products based on cow’s and mare’s milk with vegetable additives

The modern market of dairy products is quite developed, but the issue of compliance with the quality and safety of their production remains relevant. The purpose of this paper was to study the organoleptic, physicochemical and safety indicators of combined dairy products based on mare’s and cow’s milk using herbal supplements. In the course of the research, analytical and statistical methods of information processing were used; experimental studies of raw materials and the finished combined product, namely the determination of acidity, solids, fat and protein; energy and biological values of the product were calculated. Technological processes of combined dairy products based on cow’s and mare’s milk using a vegetable origin filler were studied. Of the three prototypes of the proposed dairy products, it was determined to recommend samples No. 2 and 3. High quality indicators are observed in them, in particular, a rich taste with a pronounced taste of additional components, a pleasant smell and appearance. Also, the finished product in samples No. 2 and 3 have high safety indicators. Pathogenic and harmful microorganisms and substances were not found, acceptable microorganisms are within the maximum permissible limits. According to the research results, new dairy products are recommended using vegetable raw materials with different fat, protein, energy value, caloric content and lactose content. In the process of writing this paper, indicators of nutritional, energy value and safety of new types of dairy products based on cow and mare’s milk with vegetable additives were determined. Taking into account the research results and the materials of previous scientific papers, authors consider it promising to develop and sell dairy products based on cow’s and mare’s milk using a filler of plant origin. The advantages of the resulting product include: consumer interest in such products, high quality indicators, product safety, the possibility of using products in functional and specialized nutrition.Graphical

Advanced UPLC-MS/MS Method for the Quantification of SIPI6398 in Rat Plasma and Its Pharmacokinetic Characterization.

SIPI6398 is a novel anti-schizophrenia agent with a new mechanism of action and demonstrates better target selectivity and safety compared to its competitors. However, few in vivo studies on the pharmacokinetics and bioavailability of SIPI6398 have been performed. A rapid and simple ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) approach was developed for accurate quantification of SIPI6398 in rat plasma. A simple protein precipitation of acetonitrile-methanol (9 : 1, v/v) was used to treat plasma. Chromatography was performed on a UPLC HSS T3 column (50 mm × 2.1 mm, 1.8 μm) at a flow rate of 0.4 ml/min. The mobile phase consisted of acetonitrile-water (with 0.1% formic acid) and gradient elution was used, and the elution time was 4 minutes. Quantitative analysis was performed using electrospray ionization (ESI) in positive ion detection mode with multiple reaction monitoring (MRM) mode. To evaluate the pharmacokinetics and bioavailability, SIPI6398 was administered to rats in two different ways: oral (4 mg/kg) and intravenous (2 mg/kg) administration. The calibration curve for the UPLC-MS/MS approach shows excellent linearity in the range of 1-2000 ng/mL with an r value above 0.99. The precision, accuracy, recovery, matrix effect, and stability results all meet the criteria established for biological analytical methods. The UPLC-MS/MS method was successfully applied it to pharmacokinetics study of SIPI6398. The bioavailability of SIPI6398 was calculated to be 13.2%. These studies have the potential to contribute towards a more comprehensive comprehension of the pharmacokinetics and bioavailability of SIPI6398.

In Situ Monitoring of Allylamine and 1,1,3,3-Tetramethyldisiloxane Reaction to Form 2,2,7,7-Tetramethyl-1-oxa-3-aza-2,7-disilacycloheptane Using Raman Spectroscopy

The cyclic siloxazane, i.e., 2,2,7,7-tetramethyl-1-oxa-3-aza-2,7-disilacycloheptane (1), is a useful industrial intermediate that is challenging to produce. One method to synthesize 1 is through concurrent dehydrocoupling and hydrosilylation reactions between 1 and allylamine. This chemistry is highly exothermic and also generates a significant amount of H2. Raman spectroscopy was demonstrated to be an effective analytical method to track both reactions. It enables a safe reaction scale-up by monitoring the consumption of allyl groups and the silicon hydride (SiH) groups. Both functional groups could be readily quantified by using a solvent as an internal standard. It is found that the observed SiH signal can be attributed to an intermediate species, which would undergo a relatively slower hydrosilylation reaction. Interesting kinetics behavior is observed that leads to a mechanistic understanding of the catalyst's role in catalyzing the two elementary steps. The ability to track these moieties is critical to the development of a safe process that avoids accumulation of SiH intermediates.

Stain-Free Approach to Determine and Monitor Cell Heath Using Supervised and Unsupervised Image-Based Deep Learning

Cell-based medicinal products (CBMPs) are a growing class of therapeutics that promise new treatments for complex and rare diseases. Given the inherent complexity of the whole human cells comprising CBMPs, there is a need for robust and fast analytical methods for characterization, process monitoring, and quality control (QC) testing during their manufacture. Existing techniques to evaluate and monitor cell quality typically constitute labor-intensive, expensive, and highly specific staining assays. In this work, we combine image-based deep learning with flow imaging microscopy (FIM) to predict cell health metrics using cellular morphology “fingerprints” extracted from images of unstained Jurkat cells (immortalized human T-lymphocyte cells). A supervised (i.e., algorithm trained with human-generated labels for images) fingerprinting algorithm, trained on images of unstained healthy and dead cells, provides a robust stain-free, non-invasive, and non-destructive method for determining cell viability. Results from the stain-free method are in good agreement with traditional stain-based cytometric viability measurements. Additionally, when trained with images of healthy cells, dead cells and cells undergoing chemically induced apoptosis, the supervised fingerprinting algorithm is able to distinguish between the three cell states, and the results are independent of specific treatments or signaling pathways. We then show that an unsupervised variational autoencoder (VAE) algorithm trained on the same images, but without human-generated labels, is able to distinguish between samples of healthy, dead and apoptotic cells along with cellular debris based on learned morphological features and without human input. With this, we demonstrate that VAEs are a powerful exploratory technique that can be used as a process monitoring analytical tool.

Monitoring Ti3C2Tx MXene Degradation Pathways Using Raman Spectroscopy.

Extending applications of Ti3C2Tx MXene in nanocomposites and across fields of electronics, energy storage, energy conversion, and sensor technologies necessitates simple and efficient analytical methods. Raman spectroscopy is a critical tool for assessing MXene composites; however, high laser powers and temperatures can lead to the materials' deterioration during the analysis. Therefore, an in-depth understanding of MXene photothermal degradation and changes in its oxidation state is required, but no systematic studies have been reported. The primary aim of this study was to investigate the degradation of the MXene lattice through Raman spectroscopic analysis. Distinct spectral markers were related to structural alterations within the Ti3C2Tx material after subjecting it to thermal- and laser-induced degradation. During the degradation processes, spectral markers were revealed for several specific steps: a decrease in the number of interlayer water molecules, a decrease in the number of -OH groups, formation of C-C bonds, oxidation of the lattice, and formation of TiO2 nanoparticles (first anatase, followed by rutile). By tracking of position shifts and intensity changes for Ti3C2Tx, the spectral markers that signify the initiation of each step were found. This spectroscopic approach enhances our understanding of the degradation pathways of MXene, and facilitating enhanced and dependable integration of these materials into devices for diverse applications, from energy storage to sensors.

Analisis Perbandingan Kinerja Perusahaan Sebelum dan Sesudah Employee Stock Option Program (ESOP) (Studi Empiris pada Perusahaan Manufaktur yang Listing di Bursa Efek Indonesia Tahun 2017-2020)

Implementation of the Employee Stock Option Program in this study aims to compare the company's performance both before and after the implementation of the Employee Stock Option Program for manufacturing companies listed on the IDX in 2017-2020. This research is a manufacturing issuer with as many as 193 companies. Purposive Sampling used in the sampling technique, so that a sample of 11 companies. Available analytical methods include descriptive statistical data analysis, normality test which also uses other tests (paired sample t test), and hypothesis testing with the Paired Sample Test and Wilcoxon test. The results of the study concluded that there were no differences in ROA, ROE, NPM, and TATO before and after the implementation of ESOP in manufacturing companies listed on the IDX for the 2017-2020 period.

Ultrasensitive detection of chlortetracycline in animal-origin food using molecularly imprinted electrochemical sensor based on SnS2/ZnCo-MOF and AuNPs

The chlortetracycline (CTC) residue in food poses a threat to human health. Therefore, developing sensitive, convenient and selective analytical methods for CTC detection is crucial. This study innovatively uses tin disulfide/bimetallic organic framework (SnS2/ZnCo-MOF) nanocomposites in conjunction with gold nanoparticles (AuNPs) to co-modify a glassy carbon electrode (GCE). Further, a molecularly imprinted polymer (MIP)-based electrochemical sensing platform Au-MIP/SnS2/ZnCo-MOF/Au/GCE (AZG) was fabricated for selective CTC detection. SnS2/ZnCo-MOF enhanced the stability and surface area of the AZG sensor. The presence of AuNPs facilitated electron transport between the probe and the electrode across the insulating MIP layer. The fixation of AuNPs and MIP via electropolymerization enhanced the selective recognition of this sensor and amplified its output signal. The AZG sensor demonstrated a wide linear detection range (0.1–100 μM), low detection limit (0.072 nM), and high sensitivity (0.830 μA μM−1). It has been used for detecting CTC in animal-origin food with good recovery (96.08%–104.60%).

Advances in the individual authentication of cocoa beans: Vis/NIR spectroscopy as a tool to distinguish fermented from unfermented beans and classify genotypes in the eastern Amazonia

Reliable analytical methods to authenticate high-quality and economically valuable cocoa beans are highly desirable, and NIR spectroscopy stands out as a rapid and non-destructive alternative. This study employs NIR for the authentication and differentiation of 19 whole Forastero cocoa beans from Eastern Amazonia (Pará, Brazil), based on their fermentation status and genetic profiles. Partial Least Squares Discriminant Analysis (PLS-DA) models in wavelength ranges of 400–700 nm, 1400–1600 nm, and 1900–2500 nm, demonstrated high sensitivity and specificity in discriminating fermented from unfermented beans, regardless of genotype. Various compounds, including proteins, lipids, carbohydrates, anthocyanins, and theobromine, provide crucial insights into the spectral regions essential for distinction. The variable importance in projection (VIP) score value greater than 1 was used to select relevant variables, and Linear Discriminant Analysis (LDA) was performed in both the visible (472 nm and 636 nm) and infrared ranges (2096 nm and 2278 nm), demonstrating that absorbances at two specific wavelengths are sufficient for discrimination. The t-distributed stochastic neighbor embedding (t-SNE) indicated a segregation trend of the genotypes based on classification by major genetic groups in unfermented beans, suggesting that the biochemical characteristics shared by them are more prominent before fermentation. The PLS-DA models based on complete vis/NIR spectra showed comparable results in discriminating the 19 cocoa genotypes in both fermented (0.14% prediction error) and unfermented beans (0.16% prediction error). The model's classification errors can be attributed to shared genetic ancestry among the samples, primarily in unfermented beans. This research corroborates the effectiveness of vis/NIR spectroscopy as a straightforward tool for whole cocoa bean authentication, providing rapid insights into genetic diversity regardless of their fermentation state.

Numerical ellipsometry: Artificial intelligence for rapid analysis of indium tin oxide films on silicon

Ellipsometry is a well-known material analytical method widely used to measure thickness and optical properties of thin films and surfaces across a wide range of industrial and research applications including critical dimensions in chipmaking. The method employs the fact that light undergoes a change in polarization state upon reflection from or transmission through a material. The desired properties of the surface structure are related to measurements by the electromagnetic models expressed by Maxwell’s equations as well as models of material properties. The work here demonstrates the use of artificial intelligence in the form of a multilayer perceptron artificial neural network to apply the electromagnetic model. The reflecting surface examined here is composed of indium tin oxide (ITO) films approximately 400 nm in thickness deposited on silicon substrates. Solutions are provided by 299 artificial neural networks, one per wavelength from 210 to 1700 nm across which ITO exhibits transparent as well as absorbing characteristics. Thus, it serves as a proxy for a wide range of other materials. To train the network, simulated measurements are computed at two thicknesses which differ randomly by 1–6 nm and at three different incidence angles of 55°, 65°, and 75°. Following training, results are obtained in less than one second on a conventional desktop computer.

Advances in SARMs anti-doping analysis.

Selective androgen receptor modulators (SARMs) are performance-enhancing drugs (PEDs) that stimulate anabolism, increase muscle mass and strength and promote recovery from exercise. The use of SARMs in sports is considered doping and is strictly prohibited by the World Anti-Doping Agency (WADA) and the International Federation of Horseracing Authorities (IFHA). To monitor the abuse of SARMs in sports, it is essential to develop advanced, selective and sensitive analytical methods that provide reliable results. This review evaluates the advances in this area, with a focus on the identification of target analytes related to SARMs, such as SARMs, their metabolites or markers. The aim is to identify targets that could extend the detection windows of SARMs, provide scientific support for results management and/or offer an indirect biomarker-based approach to doping control. This review also aims to evaluate the current liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) methods developed for the monitoring of SARMs in different biological matrices, including traditional matrices such as urine and serum/plasma samples, as well as alternative matrices such as dried blood spots, hair and nail samples.

Is the past recoverable from the data? Pseudoproxy modelling of uncertainties in palaeoecological data

Is the past recoverable from the data? Pseudoproxy modelling of uncertainties in palaeoecological data