Process Approach Research Articles

Micro‐Engraving UV‐Sensitive Thin‐Film Transistor from Metal–Metal Oxide Nanoparticles with Band‐Gap Engineering

AbstractAs known, n‐type inorganic semiconductor nanoparticles such as zinc oxide nanoparticles have been explored in various sensing applications, which demand high‐density electronic elements placement for rapid operation. Herein, high‐resolution designs of conductive channels of noble metal‐doped zinc oxide nanoparticles is demonstrated using an engraving transfer printing process and silver metal doping approach. Such thin‐film transistors with reduced feature size to 2 µm fabricated exhibited significantly enhanced electron mobility up 3.46 × 10−2 cm2 V−1 s−1 and light sensitivity. Furthermore, the integration of this micropatterning technology and metal doping in thin‐film transistors is utilized for control of current–voltage characteristics under the ultraviolet radiation with high sensitivity. It is suggested that this approach to design of doped inorganic nanoparticle channels paves the way for high‐density thin‐film transistors suitable for optoelectronic circuit, UV photodetectors and neuromorphic computing systems.

Photooxidation Cross-Linked, Glutaraldehyde Cross-Linked, or Enzyme and Hydrostatic Pressure Processed Decellularized Biomaterials for Cardiovascular Repair Do Not Affect Host Response in a Rat Right Ventricular Outflow Flow Tract Reconstruction (RVOT) Model.

Cardiovascular diseases (CVDs) were responsible for approximately 19 million deaths in 2020, marking an increase of 18.7% since 2010. Biological decellularized patches are common therapeutic solutions for CVD such as cardiac and valve defects. The preparation of biomaterials for cardiac patches involves two main processing methods: glutaraldehyde or photooxidation cross-linking (fixation) and noncross-linked (nonfixation) processing. Despite the variety of products available in the market, cardiac patches still suffer from significant limitations, failing to adequately mimic the properties of biological tissue and restore its function. This study assesses the impact of different processing methodologies on the biological and biomechanical outcomes of three commercially available cardiac patches (CorPatch, CardioCel, PhotoFix) and one newly developed decellularized cardiac patch (Adeka) when implanted as right ventricular outflow tract (RVOT) repair material on a rat model. Four different patches for cardiovascular repair were selected based on their processing approaches and included: photooxidation crosslinked (PhotoFix), glutaraldehyde crosslinked (CardioCel), noncross-linked small intestine submucosa (CorPatch) or enzyme, and hydrostatic pressure (Adeka) processed decellularized biomaterials. Structure and function were characterized prior to implantation via thickness mapping, cross-section morphology, 2D surface topography, 3D volume microstructure, biaxial testing, uniaxial tensile testing, ball burst, and suture retention. Their host-biomaterials response was assessed invivo using a relevant model for cardiovascular repair: a rat (RVOT) reconstruction with 8 and 16-week timepoints. Topological analysis showed that the crosslinked cardiac patches had a more homogeneous thickness distribution when compared to the noncrosslinked patches. This agreed with histological evaluation, where cross-linking processed materials better preserved collagen content than noncrosslinked patches who were also more delaminated. Biaxial data demonstrated that all patches, except CorPatch, recapitulated the anisotropic behavior of healthy left ventricle tissue. The Adeka patch in-plane mechanics at 16 weeks was the one who better resembled the mechanics of healthy cardiac tissue. All patches showed appropriate biocompatibility and function at 8- and 16-week timepoints for RVOT patching. This included echocardiographic assessment, biomechanics, macrophage infiltration and polarization, and angiogenesis. Consistently with a more porous laminae structure, explants histology showed higher cell infiltration in non-crosslinked Adeka when compared to the crosslinked PhotoFix. Overall, both invitro and invivo tests indicate that the material processing does not impact the function, biomechanical performance, and the host response of the patches that can be considered as equally effective as materials based cardiac repair solutions.

Hydrothermal reduction of CO2 into formate in a semicontinuous plant with soft wood as reducing agent

There is a growing interest in producing valuable products from renewable materials such as biomass and CO2. One of the current challenges in this field is developing processes that can be scaled up to match the large volumes of CO2 emissions. In this study, a process is developed that consists of the simultaneous conversion of biomass and reduction of CO2 into formic acid in hydrothermal media. Experimental results from a semi-continuous plant, using NaHCO3 as inorganic CO2 source and soft wood as biomass reducing agent, are presented. In addition to formic acid, the other main products are acetic acid and lactic acid. Nuclear Magnetic Resonance analyses revealed that acetic acid and lactic acid are derived exclusively from the oxidation of biomass, while formic acid is the main product originating from the inorganic carbon source. It was determined that at 300ºC, 37% of the total production of formic acid comes from the reduction of inorganic CO2. Experiments with ramp heating with temperature increments of 5ºC/min showed that low reaction temperatures (150, 200ºC) favor the production of acetic acid from biomass, while higher temperatures (300ºC) promote the production of formic acid and lactic acid from both biomass and CO2. According to these results, a staged heating can lead to a fractionation of these products. These results validate the hydrothermal reduction technology for the scalable, semicontinuous conversion of CO2 using lignocellulosic biomass reductants, and offer a new approach for the downstream processing of the effluent, based on a prior fractionation in the reactor.

Semantic abnormalities in schizophrenia and bipolar disorder: A natural language processing approach.

The diagnostic boundaries between schizophrenia and bipolar disorder are controversial due to the ambiguity of psychiatric nosology. From this perspective, it is noteworthy that formal thought disorder has historically been considered pathognomonic of schizophrenia. Given that human thought is partially based on language, we can hypothesize that alterations in language may help differentiate between schizophrenia and bipolar disorder. In this exploratory study, we employed natural language processing techniques to identify differences in language abnormalities between patients with schizophrenia and bipolar disorder. The KoBERT and KoGPT language models were used to determine sentence acceptability, assessing how natural and therefore acceptable a given sentence is to the general population. In addition, semantic word networks were constructed for each group, and network measures were compared. Patients with schizophrenia or bipolar disorder used less acceptable sentences than controls. Post hoc analysis revealed that the schizophrenia group used less acceptable sentences than the bipolar disorder group. Furthermore, the semantic word networks of the three groups were significantly different in the three network measures. Post hoc analysis revealed a significant difference between the schizophrenia and bipolar disorder networks. The bipolar disorder network generally fell between the schizophrenia and control networks, except in terms of the average clustering coefficient. Patients with schizophrenia and bipolar disorder showed significant differences in sentence acceptability as calculated by the language model, as well as in the network metrics estimated by semantic network analysis. Thus, language abnormalities may represent surrogate markers of thought disorders and help differentiate between schizophrenia and bipolar disorder.

Evolving perspectives on the process approach to writing: From stage models to cognitive models

Evolving perspectives on the process approach to writing: From stage models to cognitive models

Computational analysis of virus-host protein-protein interactions using gene ontology and natural language processing

The role of in-silico computational methods in identifying protein-protein interactions (PPIs) between target and host proteins is crucial for developing effective infection treatments. These methods are essential for quickly determining high-quality and accurate PPIs, predicting protein pairs with the highest likelihood of physical interaction from a large pool, and reducing the need for experimental confirmation or prioritizing pairs for experiments. This study proposes using gene ontology and natural language processing (NLP) approaches to extract and quantify features from protein sequences. In the first step, proteins were represented using gene ontology terms, and a set of features was generated. In the second step, NLP techniques treated gene ontology terms as a word dictionary, creating numerical vectors using the bag of words (BoW), count vector, term frequency-inverse document frequency (TF-IDF), and information content methods. In the third step, different machine learning methods, including Decision Tree, Random Forest, Bagging-RepTree, Bagging-RF, BayesNet, Deep Neural Network (DNN), Logistic Regression, Support Vector Machine (SVM), and VotedPerceptron, were employed to predict protein interactions in the datasets. In the fourth step, the Max-Min Parents and Children (MMPC) feature selection algorithm was applied to improve predictions using fewer features. The performance of the developed method was tested on the SARS-CoV-2 protein interaction dataset. The MMPC algorithm reduced the feature count by over 99%, enhancing protein interaction prediction. After feature selection, the DNN method achieved the highest predictive performance, with an AUC of 0.878 and an F-Measure of 0.793. Sequence-based protein encoding methods AAC, APAAC, CKSAAPP, CTriad, DC, and PAAC were applied to proteins in the SARS-CoV-2 interaction dataset and their performance was compared with GO-NLP. The performance of the relevant methods was measured separately and combined. The highest performance was obtained from the combined dataset with an AUC value of 0.888. This study demonstrates that the proposed gene ontology and NLP approach can successfully predict protein-protein interactions for antiviral drug design with significantly fewer features using the MMPC-DNN model.

Formulation development and scale-up of dutasteride liquisolid tablets

Introduction Liquisolid (LS) technology is particularly advantageous for poorly water-soluble drugs administered in very low doses because of the improved dissolution rate and superior content uniformity. However, there is a lack of research papers describing the application of this concept on an industrial scale. Thus, we present trials conducted to develop tablets containing 0.5 mg of water-insoluble dutasteride (DTS) according to the LS approach. Methods We divided the study into two stages: developing a placebo formulation and producing LS tablets containing DTS on a pilot scale. We tested all the manufactured tablets for mass uniformity, resistance to crushing, disintegration time, dissolution, stability, and presence of impurities. Results We demonstrated that a standard high-shear granulator mixer with a spraying system is effective for LS formulation development and transfer to the pilot scale. We were able to compress the system into tablets with the desired assay, content uniformity, dissolution, and mechanical strength. Conclusion Multiple operations can be performed on one piece of equipment – that is, pre-mixing a carrier, wetting of the carrier with a solution of an active ingredient in a nonvolatile liquid, mixing of the resulted mass with a coating agent, as well as additional excipients. Preparation of powder blends ready for tableting in line with the one-pot process approach is especially advantageous for the safety of staff engaged in the manufacturing of highly potent drug products.

Guideless Artificial Life Model for Reproduction, Development, and Interactions.

Reproduction, development, and individual interactions are vital yet complex natural processes. Tierra (an ALife model proposed by Thomas Ray) and cellular automata, which can manage these aspects in a complex manner, are significantly limited in their ability to express morphology and behavior. In contrast, the virtual creatures proposed by Karl Sims have a considerably higher degree of freedom in terms of morphology and behavior. However, they also exhibit a limited capacity for processes like reproduction, development, and individual interactions. In addition, they employ genetic algorithms, which can result in a loss of biological diversity, as their implementation necessitates predefining a fitness function. Contrarily, the evolution of natural life is determined by mutation and natural selection, rather than by a human-defined fitness function. This study carefully extracts the characteristics of these models to propose a new Artificial Life model that can simulate reproduction, development, and individual interactions while exhibiting a high expressive power for morphology and behavior. The model is based on the concept of incorporating Tierra and cellular automata mechanisms into a cell that moves freely in 3-D space. In this model, no predefined fitness function or form that qualifies as a living creature exists. In other words, this approach can be rephrased as searching for persistent patterns, which is similar to the approach of Conway's Game of Life. The primary objective of this study was to conduct a proof-of-concept demonstration to showcase the capabilities of this model. Guideless simulation by the proposed model using mutation and natural selection resulted in the formation of two types of creatures-dumbbell shaped and reticulated. These creatures exhibit intriguing features, exploiting the degrees of freedom inherent to the proposed model. Particularly noteworthy is their unique method of reproduction, which bears a striking resemblance to that of real organisms. These results reinforce the potential of this approach in modeling intricate processes observed in actual organisms and its ability to generate virtual creatures with intriguing ecologies.

Carbon Dioxide Sensing Based on Off-Axis Integrated Cavity Absorption Spectroscopy Combined with the Informer and Multilayer Perceptron Models.

Off-axis integrated cavity output spectroscopy (OA-ICOS) allows the laser to be reflected multiple times inside the cavity, increasing the effective absorption path length and thus improving sensitivity. However, OA-ICOS systems are affected by various types of noise, and traditional filtering methods offer low processing efficiency and perform limited feature extraction. Deep learning models enable us to extract important features from large-scale, complex spectral data and analyze them efficiently and accurately. We propose a carbon dioxide (CO2) sensor operating in the near-infrared spectral region (1.602 μm) based on OA-ICOS and deep learning models. A radiofrequency (RF) noise source is employed to reduce the cavity-mode noise in OA-ICOS and thus improve the signal-to-noise ratio (SNR). A time-series-based neural network, known as the informer, is employed for filtering CO2 spectral time series. After filtering, spectral features are directly extracted from the filtered spectral data and CO2 concentrations are predicted using a multilayer perceptron (MLP) model. Our results showed that the SNR attained using informer filtering approximately double those obtained using traditional filtering methods (Savitzky-Golay filtering, Kalman filtering, and wavelet threshold). The linear correlation coefficient (R2) between measured concentrations and standard concentrations was increased from 79.74% (obtained by using the absorption-peak-fitting method) to 98.52% (obtained by using the proposed MLP model). Moreover, the detection limit of the CO2 sensor using the MLP model reached 1.38 ppm at 224.4 s, a 3.79-fold improvement compared to that obtained by using the absorption-peak-fitting method. Our results demonstrate the feasibility of integrating deep learning methods in the field of spectroscopy-based sensing and provide a promising approach for spectral data processing.

Microstructure, microhardness and wear properties investigation in friction stir processing of stir cast Mg/B4C metal matrix composite

Abstract Friction Stir Processing (FSP) has emerged as an excellent processing approach to tailor the microstructural and other properties of cast alloys and composite materials. In the current investigation, a stir-cast magnesium metal matrix composite (MMMC) reinforced with 12 wt% B4C particles was processed using single-pass FSP. FSP was carried out with a simple cylindrical tool pin using different tool rotational speeds of 800, 1000, 1200 rpm and transverse speed of 60 mm min−1. Severe deformation occurred during FSP which helped to refine the distribution of the B4C particles and grain refinement of as-cast composites. Microstructural examination showed an appreciable grain refinement from 98 to 5 μm because of dynamic recrystallization during FSP. The maximum microhardness of the friction stir processed (FSPed) composites increased from 73 Hv to 105.4 Hv. Pin-on-disk wear tests were conducted under dry sliding conditions in the load range of 1 kg to 5 kg at a 1 m s−1 sliding velocity. The wear results indicated an enhancement of wear properties of FSPed composites in comparison to as-cast magnesium metal matrix composites. The improved hardness and wear resistance results were pertaining to the remarkable refinement in grain size coupled with refined B4C particulates and uniform distribution. The best results for hardness and wear resistance of the FSPed composite were obtained for a sample processed at a tool rotation speed of 1000 rpm. SEM/EDS of the worn samples was carried out. The wear mechanism was found as slight abrasion and oxidation. After single pass FSP, the surface of Mg/12 wt% B4C cast composite showed remarkable changes in worn surface morphology.

Longterm efficacy of a patient focused intervention in patients with asplenia- a three year follow-up of the PrePPS trial.

This study aimed to reassess the long-term impact of a Health Action Process Approach (HAPA)-informed intervention on guideline adherence among asplenic patients and their physicians, three years post-intervention. This follow-up study was conducted within the framework of the interventional PrePSS (Prevention of Postsplenectomy Sepsis Score) study. Patients aged 18 or older with anatomical asplenia were in enrolled in a prospective controlled, two-armed historical control group design. The intervention group received tailored educational materials, medical alert cards, and a telephonic HAPA-based intervention. A telephonic follow-up assessment was conducted three years post-intervention, evaluating adherence to preventive measures using the PrePSS-score (range 0-10) and propensity-score based overlap weighting. Out of 106 patients who had received the intervention, 79 (75%) completed the three-year follow-up. The PrePSS-scores further increased compared to six months post-intervention (median of 8.08 points vs 7.60 points), with notable improvements in vaccination coverage and availability of emergency antibiotics. Only four participants reported severe infections requiring hospitalization, none of which were typical of post-splenectomy sepsis. 47/79 (59%) of participants contracted SARS-CoV2 but clinical courses were mild with only one patient needing hospital and ICU treatment. Patients who had received our novel telephonic intervention exhibited significant and sustained improvement in adherence to guideline-based preventive measures at three years post intervention. HAPA-based interventions may yield more sustained effects than traditional nudging strategies.

Prospects of Internal Control in Chain Organizations

The article studies specific features of internal control in chain organizations and demonstrates topicality of the subject in business, including tourism sphere. The author analyses Russian and overseas experience of internal control used in key fields of today’s economic system, in particular, in finance and accounting. Different concepts of internal control are investigated, a great number of theoretical and methodological methods of internal control are provided and various systems, forms and types of internal control are studied. In order to extend the current ideas of internal control the article advanced the concept based on the notion saying that within the frames of one business sometimes it is necessary to discuss not a single system of internal control, but a complex package of such systems. The author came to the conclusion that such types of control components analyzed in the research as planning, cyber control, rewards and compensations, administrative and cultural control, in case systematic and process approaches are used in organization of the system of internal control could help attain the set goals and provide reliability of business, including tourism sector, and its efficiency.

An Automated Approach for Domain-Specific Knowledge Graph Generation─Graph Measures and Characterization.

In 2020, nearly 3 million scientific and engineering papers were published worldwide (White, K. Publications Output: U.S. Trends And International Comparisons). The vastness of the literature that already exists, the increasing rate of appearance of new publications, and the timely translation of artificial intelligence methods into scientific and engineering communities have ushered in the development of automated methods for mining and extracting information from technical documents. However, domain-specific approaches for extracting knowledge graph representations from semantic information remain limited. In this paper, we develop a natural language processing (NLP) approach to extract knowledge graphs resulting in a semantically structured network (SSN) that can be queried. After a detailed exposition of the modeling method, the approach is demonstrated specifically for the synthetic chemistry of organic molecules from the text of approximately 100,000 full-length patents. In this paper, we focus specifically on characterizing the knowledge graph to develop insights into the linguistic patterns and trends within the data and to establish objective graph characteristics that may enable comparisons among other text-based knowledge graphs across domains. Graph characterization is performed for network motif structures, assortativity, and eigenvector centrality. The structural information provided by the measures reveals language tendencies commonly employed by authors in the text discourse for chemical reactions. These include observations of the prevalence of descriptions of specific compound names, that common solvents and drying agents cut across large numbers of chemical synthesis approaches, and that power-law trends clearly emerge in the limit of larger corpora. The findings provide important quantitative characterizations of knowledge graphs for use in validation in large data settings.

Gaussian Process Approach to Constructing Transferable Force Fields for Thiolate-Protected Gold Nanoclusters.

Gold nanoparticles can exhibit unique physical and chemical properties, such as plasmon resonances or photoluminescence. These nanoparticles have many atoms, which leads to high computational costs for density functional theory (DFT) calculations. In this work, we used the FLARE++ (fast learning of atomistic rare events) code and incorporated an active learning algorithm to construct force fields for gold thiolate-protected nanoclusters. We started training the force field using Au20(SCH3)16 as the initial structure and then applied the trained force field to perform molecular dynamics (MD) simulations. We then validated the machine learning force field using different types of gold nanoclusters as testing models. The test results were integrated into the existing database and retrained again. The final force fields show success in predicting energies for nanoclusters not only in the training database but also outside the database. These tests revealed that the force field has achieved quantum mechanical level accuracy in some key performance metrics.

Potential Application of The Zero Waste Fashion Method to Optimize Fabric Usage

The fashion industry is one of the largest economic sectors globally, having a significant impact on the environment and society. Comparison between zero waste fashion pattern techniques and conventional pattern techniques is the main focus in efforts to achieve sustainability in the fashion industry. The zero waste pattern cutting can reduce the amount of waste originating from the clothing industry which is formed during the clothing manufacturing process. This method can also be more environmentally friendly than conventional fashion patterns. There are two approaches to the design process from conventional patterns, namely construction patterns and draping patterns. Likewise, the zero waste fashion design process approach is 'from design to pattern' and from 'pattern to design'. All these approaches are compared to understand the design process, its potentioal, comparison, as well as its application and impact in the fashion industry. In this context, the zero waste fashion pattern is a technique for making clothing patterns which aims to produce clothing with less than 15% textile waste. This study applies qualitative techniques, namely literature studies and simulations which aim to understand and evaluate the differences between zero waste fashion patterns and conventional patterns in the fashion industry. The results of this research in achieving sustainability in the fashion industry, can be proven that the Zero-Waste pattern method has emerged as a more sustainable alternative. Being able to have a positive impact on the environment and the potential for more significant social development with increasing understanding of zero-waste fashion patterns, it is hoped that the fashion industry can experience a positive transformation towards more sustainable practices as a whole.

Unlocking barriers and strategies of halal certification for micro and small enterprises in Indonesia: Analytic network process approach

This study aims to identify and prioritize the barriers and strategies to improve halal certification accessibility for micro and small enterprises (MSEs) in Indonesia. The paper utilized the analytic network process (ANP) to model interrelationships among barriers and strategies. Data were collected through structured interviews and surveys involving representatives from halal certification bodies (e.g., BPJPH), government agencies (e.g., DKUM Depok), and experts from academia specializing in halal certification and MSE development. The findings highlight three major barriers: financial constraints, regulatory complexities, and limited awareness among MSE owners. Financial constraints include high certification fees and costs associated with compliance. Regulatory complexities stem from the intricate procedures and bureaucratic inefficiencies in the certification process, which deter small businesses from participating. Additionally, limited awareness and understanding of halal certification’s importance and procedures exacerbate the problem, particularly among MSEs in rural or underdeveloped regions. Financial support, regulatory reforms, and educational programs emerged as the most critical strategies for overcoming these barriers. Specifically, reducing certification fees and simplifying regulatory procedures were deemed essential to increase the uptake of halal certification among MSEs. By integrating actionable insights, this study provides a roadmap for policymakers to foster a supportive ecosystem for MSEs in the halal economy. The recommendations emphasize a balanced approach that combines financial assistance, regulatory reforms, and educational initiatives to address the multifaceted barriers. This analysis contributes to the broader discourse on enhancing MSE participation in the halal market, offering implications for both local and international stakeholders seeking to optimize the halal certification process. Acknowledgment The authors would like to express their sincere gratitude to the Ministry of Education, Culture, Research, and Technology of the Republic of Indonesia (Kemendikbudristek RI) for providing financial support for this investigation. This study would not have been possible without their generous funding and commitment to advancing research in Indonesia. We also extend our appreciation to all the respondents and participants who contributed valuable insights and data for this study, as well as to the institutions involved for their support and collaboration.

Discourse Analysis of International Scientific Organizations in the 2022 Russia–Ukraine Conflict: A Natural Language Processing Approach

The scientific community has not stayed outside the Russia–Ukraine conflict. This study analyzes the attitudes and roles of international scientific organizations in the conflict, based on 923 official statements, through a combination of discourse analysis and Natural Language Processing (NLP) techniques, including sentiment analysis and topic modeling. The findings reveal that 527 organizations issued statements, with 47% explicitly “supporting Ukraine and condemning Russia”, and 13% maintaining a neutral stance. These statements reflect diverse concerns, including the conflict’s immediate humanitarian impact, disruption to scientific collaboration, and broader political and social implications. This research contributes to understanding how international scientific organizations navigate conflict contexts by systematically uncovering their attitudes, focus areas, and actions. Through a thematic analysis, the study demonstrates how these organizations articulate their positions, advocate for specific measures, and leverage their influence to address issues such as economic support, scientific collaboration, and healthcare assistance. By identifying these behaviors, the study clarifies the strategic roles scientific organizations play in shaping discourse and mediating international relations, offering key insights into their impact during geopolitical crises.

Design and Process Considerations for Preparation of Modified Release Ivermectin and Praziquantel Tablets by Wet Granulation.

Dosage forms containing Ivermectin (IVER) and Praziquantel (PRAZ) are important combination drug products in animal health. Understanding the relationship between products with differing in vitro release characteristics and bioequivalence could facilitate generics. The goal of this study was to create granulations for each active ingredient, with similar release mechanisms, but substantially different in vitro release rates, and then compressing these granulations into tablets with differing release rates. Four granulation formulations were created: fast and modified release for PRAZ and IVER, respectively. The manufacturing process used high shear wet granulation and fluid bed drying, milling and sieving. Solid components, including the granulating agent, were blended in a high shear granulator and then water or a hydroalcoholic solution was added to activate the binder and initiate granule formation. Drying in a fluid bed with inlet air temperature set for 70°C and inlet air volume adjusted as required to maintain fluidization. Milling was performed in a cone mill and classification of final product was done using a vibratory sieve shaker with 18, 20, 40, and 60 mesh sieves. Formulations and processing approaches were successfully developed to produce a collection of PRAZ and IVER granules with differing particle size distributions and in vitro release characteristics. Differences in drug content in the classified granulations were observed and attributed to the low surface energy of PRAZ and the different approaches used to incorporate the active ingredients. The granulations were compressed via compaction simulator and the results show the monolithic tablets had four different release profiles.

Using inquiry-based learning approach in the teaching process of divisibility rules

Abstract The aim of this study is to reveal the impact of Inquiry-Based Learning (IBL) on students’ ability to come up with the rules of divisibility themselves and on their academic achievement in the 8th grade Mathematics course as well as to obtain students’ views on the learning and teaching process, through the use of a mixed-methods sequential explanatory design. The quantitative part of the study was composed of a one-group pre-test–post-test design, while the qualitative part involved a case study design. The sample group consisted of 20 students studying in the 8th grade of a secondary school in the 2020/21 academic year. At the end of the procedure, interviews were held with four students. The relevant data analysis indicated that the students’ level of readiness regarding the divisibility rules was not at a sufficient level, and that the difference between the pre-test and post-test scores was statistically significant with a high positive effect size on the students’ academic achievement. The results based on student views revealed that the rules forgotten due to memorization turned out to become permanent with the use of the IBL activities for learning the divisibility rules, that the students understood the subject matter better, that they came up with the divisibility rules themselves during the implementation of the activities, and that they enjoyed such a learning process.

Implementasi Pendekatan Saintifik (K13) pada Pembelajaran Fikih di Yayasan Perguruan Islam Azizi

This study aims to describe the implementation of the scientific approach (K13) in fiqh learning at Yayasan Perguruan Islam Azizi. The research used a qualitative method with field research and data collection techniques through observation, interviews, and documentation. Data analysis techniques included data reduction, data presentation, and verification or conclusion drawing. The data validity technique adopted the interactive model (Miles and Huberman). The results indicate that Yayasan Perguruan Islam Azizi still uses the 2013 curriculum (K13). Based on observations and interviews, the fiqh teachers at Yayasan Perguruan Islam Azizi have implemented the scientific approach in the learning process. This approach involves applying scientific steps such as observing, questioning, gathering information, reasoning, and communicating. It is expected to enhance students' understanding and skills in fiqh material and build critical and analytical thinking abilities. Thus, the implementation of the scientific approach in fiqh learning holds significant potential for continuous application to improve the quality of Islamic education.