Qualitative Model Research Articles

Dynamics simulations of hypoxia inducible factor-1 regulatory network in cancer using formal verification techniques

Hypoxia-inducible factor-1 (HIF-1) regulates cell growth, protein translation, metabolic pathways and therefore, has been advocated as a promising biological target for the therapeutic interventions against cancer. In general, hyperactivation of HIF-1 in cancer has been associated with increases in the expression of glucose transporter type-1 (GLUT-1) thus, enhancing glucose consumption and hyperactivating metabolic pathways. The collective behavior of GLUT-1 along with previously known key players AKT, OGT, and VEGF is not fully characterized and lacks clarity of how glucose uptake through this pathway (HIF-1) probes the cancer progression. This study uses a Rene Thomas qualitative modeling framework to comprehend the signaling dynamics of HIF-1 and its interlinked proteins, including VEGF, ERK, AKT, GLUT-1, β-catenin, C-MYC, OGT, and p53 to elucidate the regulatory mechanistic of HIF-1 in cancer. Our dynamic model reveals that continuous activation of p53, β-catenin, and AKT in cyclic conditions, leads to oscillations representing homeostasis or a stable recovery state. Any deviation from this cycle results in a cancerous or pathogenic state. The model shows that overexpression of VEGF activates ERK and GLUT-1, leads to more aggressive tumor growth in a cancerous state. Moreover, it is observed that collective modulation of VEGF, ERK, and β-catenin is required for therapeutic intervention because these genes enhance the expression of GLUT-1 and play a significant role in cancer progression and angiogenesis. Additionally, SimBiology simulation unveils dynamic molecular interactions, emphasizing the need for targeted therapeutics to effectively regulate VEGF and ERK concentrations to modulate cancer cell proliferation.

Experiences of Turkish nurses volunteering in the disaster zone following the 2023 Türkiye-Syria earthquake.

The study aimed to determine the experiences of Turkish nurses who volunteered in the disaster zone following the 2023 Türkiye-Syria earthquake. The earthquake devastated 11 cities in Türkiye, which required a comprehensive humanitarian response. Despite the crucial role of nurses in crisis management, limited research exists on their experiences in earthquake zones. Nurses experience difficulties in disaster areas, such as physical conditions, lack of disaster plans, and inadequate disaster training before being deployed to earthquake areas. This study used a qualitative research model and case study design. Data were collected through in-depth qualitative interviews. Nine participants who had worked in earthquake zones for at least 5 days were included in the study. The discussions were recorded, transcribed verbatim, and analyzed thematically. The purposive sampling method was used, and the Consolidated Criteria for Reporting Qualitative Research guidelines were followed. As a result, 4 themes, namely physical conditions, psychosocial conditions, professional conditions, and organization, and their 14 subthemes were determined. This study revealed that nurses encountered difficulties related to physical conditions in earthquake zones. Moreover, the emotional burden expressed by the nurses highlighted the profound psychological impact of disaster response. This study determines that disaster preparedness and support for nurses should be enhanced to improve the arrangement of health services in future calamities. The results of this study can be used by nurse managers and healthcare policymakers to create nursing training programs that focus on disaster-related competencies.

Fishermen Community Participation in Tiku Awareness Tourism Group on Sustainable Tourism Development.

Implementing sustainable tourism development within tourist villages has emerged as a growing trend in the tourism industry, catering to the contemporary needs of tourists and serving as a valuable economic resource. It is also regarded as a gauge of the successful execution of sustainable tourism within local communities, exemplifying their active involvement in harnessing the tourism potential of their regions. This article seeks to chronicle the execution of sustainable tourism development in coastal areas, focusing on village-based tourism due to its profound impact on local economies and the transformations in people's ways of life. To illustrate this, a case study was conducted in South Tiku Village, Tanjung Mutiara District, Agam Regency. The research employed a descriptive qualitative modeling approach and in-depth interviews involving village stakeholders, the Awareness Tourism Group (Kelompok Sadar Wisata), and the South Tiku Tourism Village community. As a result, it became evident that the residents of Tiku Tourist Village have effectively harnessed their village's potential through sustainable tourism development. This is substantiated by the notable increase in income among the South Tiku Tourist Village community, primarily attributable to tourism-related activities. Furthermore, this transformation has seen former fishermen transition into tourism entrepreneurs, reshaping their livelihoods.

Identification of apple watercore based on ConvNeXt and Vis/NIR spectra

This paper proposes a method for discriminating between normal apples and watercore apples using visible/near-infrared spectroscopy technology, which combines Gramian Angular Fields (GAF) encoding technology and the ConvNeXt deep learning network. The existing feature extraction methods for visible/near-infrared spectroscopy data do not perform deeper information mining on the extracted features, which results in the quality of the established model being entirely determined by the extracted features. Additionally, the process of building a visible/near-infrared spectroscopy data classification model is complex and time-consuming, and the accuracy of the established model is not high. To address these issues, the experimental visible/near-infrared spectroscopy data of apples was first transformed into two-dimensional images using Gramian Angular Summation Fields (GASF) and Gramian Angular Difference Fields (GADF) with sizes of 64, 128, 256, and 512. These images were then input into the ConvNeXt network, and the performance of different encoding methods and sizes was compared. The results showed that, under the conditions provided in this paper, the GADF encoding method with a size of 256 achieved the highest classification accuracy of 98.48%. Next, ResNet, EfficientNet, and RegNet deep learning networks were selected to classify the encoded images under the same conditions. The results above indicate that the apple variety discrimination method based on GAF encoding technology and ConvNeXt network combined with visible/near-infrared spectroscopy technology can achieve deep information mining of visible/near-infrared spectroscopy data and provide a relatively simple method for establishing qualitative classification models of visible/near-infrared spectroscopy. This method has a relatively excellent discrimination effect between normal apples and watercore apples.

Electro-chemo-physical analysis for long-term reinforcement corrosion within the reactive system of concrete

This paper presents an electro-chemo-physical model for analyzing long-term chloride-induced reinforcement corrosion in concrete structures. The integration of electrochemical and thermodynamic analyses enables the proposed model to capture the influence of simultaneous reactions within concrete on the corrosion process. The model is validated against experiments, where the necessity of considering the complicated reactions within concrete in long-term corrosion modeling is underscored. Building upon experimental observations and numerical explorations, a potential corrosion acceleration effect resulting from Friedel's salt dissolution in a longer term of corrosion propagation is discovered. Thereafter, a new qualitative model for describing the reinforcement depassivation process in concrete is proposed, along with discussions on governing mechanisms. From a computational perspective, the study also identifies hematite and magnetite as thermodynamically stable rusts under different concentrations of Fe2+ and O2. The proposed model and discoveries are poised to contribute significantly to scientifically robust predictions of deterioration and remaining service life for aging reinforced concrete structures.

Qualitative and quantitative assessment of apple quality using bulk optical properties in combination with machine learning and chemometrics techniques

This study aimed to understand the quantitative relationship between the bulk optical properties (BOP), soluble solids content (SSC), and fruit firmness (FF) of apples, along with the qualitative discrimination of apple cultivar and shelf-life. The absorption coefficient (μa) and reduced scattering coefficient (μs′) of 200 apples from four cultivars during 36-days shelf-life were determined using the single integrating sphere technique in 500-1000 nm. Partial least squares regression (PLSR) and random forest (RF) algorithms were used to establish quantitative prediction models for SSC and FF based on the BOP of apples. The results indicated that the PLSR models based on μɑ and μs′ were optimal for quantitative prediction of SSC (R2p=0.749, RMSEP=0.507) and FF (R2p=0.745, RMSEP=0.571), respectively. RF and linear discriminant analysis (LDA) were used to establish qualitative models for discriminating apple cultivar and shelf-life, demonstrating that the RF model based on μɑ and μɑ+ μs′ had the highest accuracy for the determination of apple cultivar and shelf-life, respectively, with the prediction set reaching 93.2 % and 85.7 %. Overall, RF was better than LDA for qualitative discrimination; however, it was less effective than PLSR for quantitative modeling.

Influence of the Carrier Gas Flow in the CVD Synthesis of 2-Dimensional MoS2 Based on the Spin-Coating of Liquid Molybdenum Precursors.

Atomically thin molybdenum disulfide (MoS2) is a two-dimensional semiconductor with versatile applications. The recent adoption of liquid molybdenum precursors in chemical vapor deposition has contributed significantly to the reproducible wafer-scale synthesis of MoS2 monolayer and few-layer films. In this work, we study the effects of the carrier gas flow rate on the properties of two-dimensional molybdenum disulfide grown by liquid-precursor-intermediate chemical vapor deposition on SiO2/Si substrates. We characterized the samples using Optical Microscopy, Scanning Electron Microscopy, Raman spectroscopy, and Photoluminescence spectroscopy. We analyzed samples grown with different nitrogen carrier flows, ranging from 150 to 300 sccm, and discussed the effect of carrier gas flows on their properties. We found a correlation between MoS2 flake lateral size, shape, and number of layers, and we present a qualitative growth model based on changes in sulfur provision caused by different carrier flows. We show how the use of liquid precursors can allow for the synthesis of homogeneous, single-layer flakes up to 100 µm in lateral size by optimizing the gas flow rate. These results are essential for gaining a deeper understanding of the growth process of MoS2.

Modern management model for KAREB Bojonegoro cooperative: Integration of ABM, lean six sigma, and RBV and TPB approaches

In global business competition, ABM helps cooperatives such as KAREB Bojonegoro Cooperative achieve cost efficiency without sacrificing quality. KAREB Cooperative's expenditure is very high, reaching IDR158.82 billion in five years, with the main costs on fuel, labor, and maintenance. Optimizing expenditure and cost management is important to reduce waste, increase efficiency, and profitability. Various studies have shown that Lean Six Sigma and ABM methods increase efficiency, reduce waste, and improve financial performance in various industries with proper implementation and intensive training. However, external factors, recent data, and regional variations, with deficiencies in data and methodology, and challenges in lean sigma implementation have not contributed simultaneously. This study offers the Integration of ABM, RBV, and TPB in Lean Sigma at KAREB Bojonegoro Cooperative because it is suspected that the condition of tobacco processing is not optimal and this approach needs to improve sustainable production efficiency. This study will analyze the implementation of ABM and Lean Sigma for cost control and waste reduction in tobacco drying at Kareb Cooperative with the contribution of Resource-Based View (RBV) and Theory of Planned Behavior (TPB) to improve process quality and optimal resource monitoring. This study uses a qualitative model with a case study method to assess the implementation of Activity-Based Management (ABM) in production cost efficiency, linking it with Lean Six Sigma, Resource-Based View (RBV), and Theory of Planned Behavior (TPB). Key informants from Kareb Cooperative Bojonegoro, including the Head of Production, Accounting, and HR, provided insights into the impact of ABM and internal factors on efficiency. The research instruments included in-depth interviews, observation, and documentation, while the research procedure followed a systematic flow to ensure the validity and relevance of the data. This study states that the implementation of ABM at Kareb Cooperative Bojonegoro allocates costs based on actual activities, improving efficiency and cost control. Costs include procurement of materials (Rp24,375,300,000), production (Rp9,216,862,000), and distribution (Rp6,000,000,000). Lean Sigma focuses on reducing waste such as overproduction, defects, and waiting by improving processes and training. The ability is quite large in identifying production inputs and related costs such as fuel (Rp14,375,300,000), raw materials (Rp10,000,000,000), and production and distribution processes. Waste management such as overproduction and waiting, as well as the implementation of ABM and Lean Sigma, aims to increase efficiency and reduce costs. Evaluation and training are carried out to optimize processes and reduce waste. Setting costs based on activities, while Lean Sigma uses the Just-In-Time (JIT) system. Variable costs are optimized, production is controlled to reduce defects,and distribution and storage are improved for cost efficiency. The keys to success are open communication, management support, and intensive training. The implementation of ABM and Lean Sigma in Kareb Bojonegoro Cooperative effectively reduces costs and waste through training, JIT, and process control. These findings are expected to increase productivity, reduce costs, and improve the competitiveness of cooperatives and provide a model for implementing modern management.

Optical control of multiple resistance levels in graphene for memristic applications

Neuromorphic computing has emphasized the need for memristors with non-volatile, multiple conductance levels. This paper demonstrates the potential of hexagonal boron nitride (hBN)/graphene heterostructures to act as memristors with multiple resistance states that can be optically tuned using visible light. The number of resistance levels in graphene can be controlled by modulating doping levels, achieved by varying the electric field strength or adjusting the duration of optical illumination. Our measurements show that this photodoping of graphene results from the optical excitation of charge carriers from the nitrogen-vacancy levels of hBN to its conduction band, with these carriers then being transferred to graphene by the gate-induced electric field. We develop a qualitative model to describe our observations. Additionally, utilizing our device architecture, we propose a memristive crossbar array for vector-matrix multiplications.

Integration of Consensus‐Based Pharmacophore Mapping and Molecular Docking in Sequential Virtual Screening: Toward the Discovery of Novel PI3Kγ Inhibitors

AbstractNumerous research studies have demonstrated the significant correlation of phosphatidylinositol‐3 kinase gamma (PI3Kγ) with the onset and progression of various human diseases, highlighting PI3Kγ as a promising therapeutic target. However, PI3Kγ demonstrates considerable similarity with other isoforms in the PI3K family, presenting significant challenges in the creation of PI3Kγ inhibitors. This study presents an ensemble‐based virtual screening approach to discover novel inhibitors targeting PI3Kγ. Eganelisib (IPI‐549) is the sole selective PI3Kγ inhibitor that has progressed to clinical trials, making it a significant model for the advancement of novel PI3Kγ inhibitors. Initially, common feature pharmacophore and receptor–ligand pharmacophore models were independently developed using IPI‐549 and its potent derivatives, in conjunction with the crystal complex of PI3Kγ/IPI‐549. Both qualitative pharmacophore models proved highly effective at distinguishing between active and inactive compounds. Then, four widely utilized docking programs were chosen for assessment, where the Glide_SP mode demonstrated superior predictive accuracy in sampling ligand conformations during binding, effectively distinguishing between PI3Kγ inhibitors and noninhibitors. Finally, a virtual screening protocol was conducted to screen the ChEMBL database, utilizing similarity search, consensus‐based pharmacophore mapping, and sequential molecular docking. This process resulted in the identification of multiple molecules exhibiting notable promise as potent PI3Kγ inhibitors.

ПРИМЕНЕНИЕ ЭТНОГРАФИЧЕСКОЙ ПАРАДИГМЫ ПРИ РАЗРАБОТКЕ МАТЕРИАЛОВ КОЛИЧЕСТВЕННЫХ ИССЛЕДОВАНИЙ

In this work, the author made an attempt to adapt the methods of collecting, storing and processing information when conducting a qualitative research (ethnography) to conduct and formulate a quantitative research. The relevance of the work is due to the need for continuous improvement of research methods, taking into account the possibility of convergence of research methods from various scientific fields. The aim of the work is to pose questions to the content of reporting materials that are deeply worked out in the course of qualitative research and the presentation of the results of such, but at the same time receive insufficient attention (in the author's opinion) of researchers when working in the framework of quantitative research. The main task was to determine common points in the development of ethnography (as a finished work) and the development of reporting material on abstract quantitative research. The model proposed by the authors R. M. Emerson, R. I. Fretz and L. L. Shaw is considered as the basic initial qualitative model. This model covers the entire process of ethnographic research: both field and desk components. The author of this work has studied a large array of materials in the field of qualitative research, in particular, qualitative methods of data analysis. The result of the work can be considered recommendations for filling the reporting materials of quantitative research, which are given in the text of the article with explanations on their meaning and the benefits that they can bring to authors and readers.

Hybrid Machine Learning Approach to Zero-Inflated Data Improves Accuracy of Dengue Prediction.

Spatiotemporal dengue forecasting using machine learning (ML) can contribute to the development of prevention and control strategies for impending dengue outbreaks. However, training data for dengue incidence may be inflated with frequent zero values because of the rarity of cases, which lowers the prediction accuracy. This study aimed to understand the influence of spatiotemporal resolutions of data on the accuracy of dengue incidence prediction using ML models, to understand how the influence of spatiotemporal resolution differs between quantitative and qualitative predictions of dengue incidence, and to improve the accuracy of dengue incidence prediction with zero-inflated data. We predicted dengue incidence at six spatiotemporal resolutions and compared their prediction accuracy. Six ML algorithms were compared: generalized additive models, random forests, conditional inference forest, artificial neural networks, support vector machines and regression, and extreme gradient boosting. Data from 2009 to 2012 were used for training, and data from 2013 were used for model validation with quantitative and qualitative dengue variables. To address the inaccuracy in the quantitative prediction of dengue incidence due to zero-inflated data at fine spatiotemporal scales, we developed a hybrid approach in which the second-stage quantitative prediction is performed only when/where the first-stage qualitative model predicts the occurrence of dengue cases. At higher resolutions, the dengue incidence data were zero-inflated, which was insufficient for quantitative pattern extraction of relationships between dengue incidence and environmental variables by ML. Qualitative models, used as binary variables, eased the effect of data distribution. Our novel hybrid approach of combining qualitative and quantitative predictions demonstrated high potential for predicting zero-inflated or rare phenomena, such as dengue. Our research contributes valuable insights to the field of spatiotemporal dengue prediction and provides an alternative solution to enhance prediction accuracy in zero-inflated data where hurdle or zero-inflated models cannot be applied.

Rapid Detection of Adulteration in Minced Lamb Meat Using Vis-NIR Reflectance Spectroscopy

In view of the phenomenon that adulterated lamb with other animal-derived meats in the market could not be quickly identified, this study used visible near-infrared spectroscopy combined with chemometric methods to quickly identify and quantify lamb rolls adulterated with chicken, duck, and pork. The spectra of the visible–near-infrared band (350–1000 nm) and near-infrared band (1000–1700 nm) of 360 lamb samples, which were mixed with chicken, duck, pork, and 10% lamb oil separately in different increasing proportions, were collected. It was found that the qualitative models of heterogeneous meat (adulterated with chicken, duck, and pork) in lamb were constructed by the combination of first derivative and multiplicative scatter correction (MSC); the accuracy of the validation set reached 100%; the meantime accuracy of the cross-validation set reached 100% (pure lamb), 98.3% (adulterated with chicken), 98.7% (adulterated with duck), and 97.3% (adulterated with pork). Furthermore, the correlation coefficient (R2c) of the adulterated chicken, pork, and duck quantitative prediction models reached 0.972 (chicken), 0.981 (pork), and 0.985 (duck). In summary, the use of Vis NIR can identify lamb meat mixed with chicken, duck, and pork and can quantitatively predict the content of adulterated meat.

Rapid Identification of Medicinal Polygonatum Species and Predictive of Polysaccharides Using ATR-FTIR Spectroscopy Combined With Multivariate Analysis.

Medicinal Polygonatum species is a widely used traditional Chinese medicine with high nutritional value, known for its anti-fatigue properties, enhancement of immunity, delays aging, improves sleep, and other health benefits. However, the efficacy of different species varies, making the quality control of medicinal Polygonatum species increasingly important. Polysaccharides are important in medicinal Polygonatum species because of their potential functional properties, such as antioxidation, hypoglycemia, protection of intestinal health, and minimal toxicological effects on human health, as well as high polysaccharide levels. This study developed a qualitative medicinal Polygonatum species model and a polysaccharides predictive model based on attenuated total reflection Fourier transform infrared spectroscopy (ATR-FTIR) combined with a multivariate analysis approach. ATR-FTIR spectral information of 334 medicinal Polygonatum species samples was collected and the spectral information of different modes was analyzed. The ATR-FTIR spectral differences of three medicinal Polygonatum species were studied by multivariate analysis combined with four spectral preprocessing and three variable selection methods. For the prediction of polysaccharides in Polygonatum kingianum Collett & Hemsl. (PK), we initially determined the actual content of 110 PK polysaccharide samples using the anthrone-sulfuric acid method, then established partial least squares regression (PLSR) and kernel PLSR models in conjunction with attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy. In the visualization analysis, the orthogonal partial least squares-discriminant analysis (OPLS-DA) model based on second-order derivative (SD) preprocessing was most suitable for medicinal Polygonatum species species binary classification, spectral differences between Polygonatum cyrtonema Hua (PC) and other species are evident; in the hard modeling, SD preprocessing improves the accuracy of non-deep learning models for the classification of three medicinal Polygonatum species. In contrast, residual neural network (ResNet) models were the best choice for species identification without preprocessing and variable selection. In addition, the partial least squares regression (PLSR) model and Kernel-PLSR model can quickly predict PK polysaccharides content, among them, the Kernel-PLSR model with SD pretreatment has the best prediction performance, residual prediction deviation (RPD) = 7.2870, Rp = 0.9905. In this study, we employed ATR-FTIR spectroscopy and various treatments to discern different medicinal Polygonatum species. We also evaluated the effects of preprocessing methods and variable selection on the prediction of PK polysaccharides by PLSR and Kernel-PLSR models. Among them, the ResNet model can achieve 100% correct classification of medicinal Polygonatum species without complex spectral preprocessing. Furthermore, the Kernel-PLSR model based on SD-ATR-FTIR spectra had the best performance in polysaccharides prediction. In summary, by integrating ATR-FTIR spectroscopy with multivariate analysis, this research accomplished the classification of medicinal Polygonatum species and the prediction of polysaccharides. The methodology offers the benefits of speed, environmental sustainability, and precision, highlighting its significant potential for practical applications. In future research, on the one hand, it can be further investigated using a portable infrared spectrometer, and on the other hand, infrared spectroscopy can also be applied to the prediction of other chemical components of medicinal Polygonatum species.

Efforts to Improve Fine Motoric Skills of Early Childhood through Coffee Dregs at Tkit Ar-Rahmah, Bener Meriah Regency

This study aims to determine how efforts to improve the fine motoric abilities of early childhood through coffee grounds at Tkit Ar-Rahmah, Bener Meriah Regency. using the method of class action research (PTK) descriptive qualitative model. The subject of this research is class B children totaling 24 children, of which there are 15 girls and 14 boys at the age of 5-6 years. The results of the achievement of action in cycle 1 showed that the percentage of children's fine motor skills was 37.5% undeveloped (BB), children who began to develop (MB) 41.67%, and children developing as expected (BSH) 20.83%. Whereas in cycle II there was a very good increase, namely with a percentage of 16.67% of children who developed as expected (BSH) and 83.33% of children who developed very well (BSB). Thus it can be concluded that there is an increase in children's fine motor skills through coffee grounds collage media in TKIT Ar-Rahmah, Bener Meriah district.

The Role of Culture Mentabeq in Shaping the Character of Class 1 Students at SDN 2 Selengen

This study aims to determine the role of mentabeq culture in shaping the character of students at SDN 2 Selengen. This study is a qualitative study with a qualitative descriptive analysis model. The data and data sources in this study are primary and secondary. Primary data is data that is directly obtained from the source, namely the principal, class teachers, and students, while secondary data is supporting data that is useful for supporting primary data. Data collection techniques in this study are observation, interviews, and documentation, and the analysis techniques use data reduction, data classification, and drawing conclusions. In this study it was found that mentabeq culture can have a major influence on the formation of student character such as, good, polite, ethical, and obedient to the norms that apply both at school and in the community, because this culture is able to foster a sense of mutual respect between each other..

QUALIMETRIC MODEL OF A HIGH-TECH INFORMATION AND MEASURING SYSTEM FOR THE PREPARATION AND CONDUCT OF TESTS

The issue of the report concerns the selection of information and testing equipment or a system that will be used for planning and conducting tests with minimal error and high efficiency. The complexity of consideration as a whole, when choosing the components of the information and measurement system and determining the qualitative measurement model determines the need to divide it into several stages and consider them separately. The stages of building a qualitative model of a high-tech information and measurement system for preparing and conducting tests are divided into: modeling stage; design stage; stage of implementation; interpretation stage. At the stage of determining the limitations, it should be taken into account in which division the tests are carried out, namely: by purpose; by the level of testing; by stages of product development; by testing finished products; according to the conditions and venue; by the duration of the tests; by type of action; by the result of the action; according to the characteristics of the object. The relevance of the work is the consideration of the stages of building a qualitative model of the information and measurement system in the difficult conditions of the military and political situation in Ukraine. The purpose of the work is to develop the technological aspects of qualitative modeling, as well as the stages of qualitative model examination. Additional results of the work can be – to obtain initial data (parameters); – to break down important indicators (parameters) according to their importance using an expert method; – to assess state and foreign requirements for the system for further work; – to assess and choose a direction based on the price indicator (factor) and duration of work or other indicators; – provide recommendations for making a decision regarding the construction of a qualitative model of the properties of the information and measurement system;–allow to improve (apply) the process of conducting tests of high-tech weapons and military equipment; – allow to use qualitative model databases to reduce preparation time to conducting tests of high-tech weapons and military equipment;–will allow to expand the directions for finding new features and providing recommendations in the further process of tests. The article builds a qualitative model (hereafter KM) of the formation (creation) of KM stages of the information and measurement system (hereafter IMS) at the preparatory stage of OVT tests. Stages of KM examination have been developed based on the current purpose of the tests, the required quantity and quality of recorded and measured indicators, the complexity of measurements and their analysis. Formulated KM efficiency indicators are as follows: minimal error; cost of equipment; equipment resource; others. The stages of selection of IBS components have been developed and the KM of assessment at the preparatory stage of tests has been determined. A model of quantitative quality measurement has been developed, taking into account possible errors and the provided, structural, technological aspects and stages of qualitative modeling. The most expedient tool for a systematic approach to decision-making during the construction of the IBS CM has been determined. The results of a similar review of literary sources of domestic and foreign scientists are presented. However, the insufficient development of the scientific basis for creating KM efficiency indicators is noted. The normative and real vectors of the creation of the CM are determined, which are laid down as important indicators of the effectiveness of the CM, by changing which it is possible to change the direction of the selection of the creation of the CM of the IAS.

The Use of Participatory Modelling Methods in Agri-Environmental Research – A Systematic Mapping Dataset

Participatory modelling (PM) is a transdisciplinary research approach that involves stakeholder in a modelling process to develop or improve qualitative or quantitative models. To better understand the potential uses of PM in the emerging field of agroecology living lab research, I conducted a systematic search of the peer-reviewed literature and describe 78 participatory agri-environmental case studies in this dataset. Bibliographic data are included and each case study is described in terms of main goal(s), PM method(s) used, involved stakeholders and their contributions and the level of stakeholder participation in the project. I also extracted key metadata (if available), such as publication type and year, study location (country), funding source, project duration and outcomes beyond publications. This dataset adds value by revealing clusters and associations of methods and goals, by showing the dominating role of researchers in this type of participatory research, by pointing out the absence of PM in certain areas of agricultural production, such as hydroponic farming or viticulture and by providing a comprehensive foundation for the related research article. It also contained data not used in the related research article that could provide a basis for future research, such as linking methods, goals and stakeholder involvement with other metadata or comparing this agri-environmental research and other areas.

Near-infrared spectroscopy combined with support vector machine for the identification of Tartary buckwheat (Fagopyrum tataricum (L.) Gaertn) adulteration using wavelength selection algorithms

The frequent occurrence of adulterating Tartary buckwheat powder with crop flours in the market necessitates an urgent need for a simple analysis method to ensure the quality of Tartary buckwheat. This study employed near-infrared spectroscopy (NIRS) for the collection of spectral data from Tartary buckwheat samples adulterated with whole wheat, oat, soybean, barley, and sorghum flours. The competitive adaptive reweighted sampling (CARS) and successive projection algorithm (SPA) were deployed to identify informative wavelengths. By integrating support vector machine (SVM) and partial least squares discriminant analysis (PLS-DA), we constructed qualitative models to discern Tartary buckwheat adulteration. The PLS-DA model exhibited prediction accuracies between 89.78 % and 94.22 %, while the mean-centering (MC)-PLS-DA model showcased impressive predictive accuracy of 93.33 %. Notably, the feature-based Autoscales-CARS-CV-SVM model achieved more excellent identification accuracy. These findings exhibit the excellent potential of chemometrics as a powerful tool for detecting food product adulteration.

Meningkatkan Hasil Belajar PAKAT dengan Model PBL Fase D Kelas 9 SMPN 4 Tebing Tinggi

The low learning outcomes in Catholic religion for 9th-grade students are a problem, and the root of the problem is teachers' propensity to employ a traditional teaching style called lecturing. The researcher tries to apply the PBL approach to enhance learning results. Both qualitative and quantitative descriptive research models are used in this study. SMPN 4 Tebing Tinggi is the research site. The study's findings suggest that applying the PBL approach can enhance students' learning objectives. The fact that learning results improved from cycle 1 to cycle 2 with data showing that in the cognitive aspect, the competent category is at 22.2% and the proficient category at 77.8%. In the affective aspect, the competent category is at 11.1%, and the proficient category is at 88.9%. A similar trend is observed in the psychomotor aspect, with the competent category at 11.1% and the proficient category at 88.9%. The study concludes that the use of the PBL model in learning can enhance the learning outcomes of 9th-grade students.