Validation Stage Research Articles

Using Quizizz as an Interactive Learning Media to Improve Cognitive Values in Elementary School Students

Learning is a complex process that occurs in a person's life. If there is contact between humans and their environment, individually or in groups, then learning will occur. Students use learning media as an intermediary tool to convey messages or information. Creative media can stimulate students' interest in the lesson. This study aims to investigate the application of quizzes in the digital era's learning process and assess their impact on enhancing students' cognitive values. This study uses a research and development method. Research indicates that to support the teaching and learning process effectively, web-based, application-based learning media must meet the standards of validity, practicality, and effectiveness. In this case, Quizizz, the developed learning medium, underwent material validation and technical evaluation stages to ensure that the presented content aligned with the curriculum and learning objectives. This validation also encompasses the suitability of the material's content and the application's user-friendliness. In addition to the validity aspect, the practicality of the media is also an essential factor in this assessment. Based on the observations and user responses, students and teachers responded positively to Quizizz as a learning medium. The ease of access and use of the available features, such as interactive questions and various quiz modes, make learning more engaging and not boring. This shows that Quizizz is practical and can increase students' interest in learning through a more dynamic approach. Finally, the effectiveness of Quizizz as a learning medium is evident in the results of observations and responses from students and teachers during the learning process. Students looked more enthusiastic and motivated to actively participate in the quizzes, which helped them understand the material better. The provided results analysis feature also aids teachers, enabling more accurate assessment of student learning outcomes. Quizizz is a valid, practical, and effective medium for interactive learning.

A 2024 inventory of test methods relevant to thyroid hormone system disruption for human health and environmental regulatory hazard assessment

Thyroid hormone system disruption (THSD) is a growing concern in chemical hazard assessment due to its impact on human and environmental health and the scarce methods available for assessing the THSD potential of chemicals. In particular, the general lack of validated in silico and in vitro methods for assessing THS activity is of high concern. This manuscript provides an inventory of test methods relevant to THSD. Building on the Organisation for Economic Co-operation and Development (OECD) Guidance Document 150 and recent international developments, we highlight progress in in silico and in vitro methods, as well as in vivo assays. The provided inventory categorizes available methods according to the levels of the OECD Conceptual Framework, with an assessment of the validation status of each method. At Level 1, 12 in silico models that have been statistically validated and are directly related to THSD have been identified. At Level 2, 67 in vitro methods have been listed including those assessed in key initiatives such as the European Union Network of Laboratories for the Validation of Alternative Methods (EU-NETVAL) validation study to identify potential thyroid disruptors. At Levels 3-5, THSD-sensitive endpoints are being included in existing fish-based OECD Test Guidelines to complement amphibian assays. In total, the inventory counts 108 entries comprising established methods (e.g., OECD Test Guidelines) as well as citable methods that are under further development and in some cases are ready for validation or in the initial stages of validation. This work aims to support the ongoing development of strategies for regulatory hazard assessment, such as integrated approaches to testing and assessment (IATAs), for endocrine disruptors, addressing critical gaps in the current testing landscape for THSD in both human and environmental health contexts.

Evaluating rainfall erosivity index from CHIRPS and INMET data in southeast region of Goiás (Brazil)

Abstract. Soil erosion is a form of land degradation commonly observed in different regions of the world, especially in tropical regions. In Brazil, soil erosion caused by rainwater (pluvial erosion) is the most common and worrying form of erosion, causing various environmental, economic and social damage. The main aim of this work was to evaluate the Erosivity Index (EI) relationships based on CHIRPS and INMET data in the municipality of Catalão-GO, that is located in one of the world's largest agricultural frontiers, the Brazilian Cerrado. The results found indicated that the CHIRPS data it is an alternative for calculating EI in the context of municipality of Catalão, southeast region of Goiás state, from a previous stage for statistical validation.

Top-Down Proteomics Identifies Plasma Proteoform Signatures of Liver Cirrhosis Progression

Cirrhosis, advanced liver disease, affects 2-5 million Americans. While most patients have compensated cirrhosis and may be fairly asymptomatic, many decompensate and experience life-threatening complications such as gastrointestinal bleeding, confusion (hepatic encephalopathy), and ascites, reducing life expectancy from 12 to less than 2 years. Among patients with compensated cirrhosis, identifying patients at high risk of decompensation is critical to optimize care and reduce morbidity and mortality. Therefore, it is important to preferentially direct them towards specialty care which cannot be provided to all patients with cirrhosis. We used discovery Top-down Proteomics (TDP) to identify differentially expressed proteoforms (DEPs) in the plasma of patients with progressive stages of liver cirrhosis with the ultimate goal to identify candidate biomarkers of disease progression. In this pilot study, we identified 209 DEPs across three stages of cirrhosis (compensated, compensated with portal hypertension, and decompensated), of which 115 derived from proteins enriched in the liver at a transcriptional level and discriminated the three stages of cirrhosis. Enrichment analyses demonstrated DEPs are involved in several metabolic and immunological processes known to be impacted by cirrhosis progression. We have preliminarily defined the plasma proteoform signatures of cirrhosis patients, setting the stage for ongoing discovery and validation of biomarkers for early diagnosis, risk stratification, and disease monitoring.

Ensemble deep learning and anomaly detection framework for automatic audio classification: Insights into deer vocalizations

Audio recordings have emerged as a pivotal tool in field observations, enriching environmental monitoring in both the spatial and temporal dimensions. However, the richness and complexity of these recordings pose significant challenges, primarily when extracting specific sound clips from long recordings owing to the presence of ambient noise and other irrelevant sounds. Traditional methods, such as manual extraction or a sliding window over audio segments, hinder practical bioacoustic applications. Therefore, we propose a framework that begins with a robust segmentation method for extracting sound clips that potentially contain deer vocalizations. This segmentation method relies on acoustic anomaly detection and can markedly improve computational efficiency, facilitating deployment in environments with limited resources. Subsequently, the isolated clips were classified into deer and non-deer categories using machine learning models. Our investigation assessed three state-of-the-art deep learning models, ResNet50, MobileNetV2, and EfficientNet-B2, considering various hyperparameter configurations to optimize the performance. We utilized 3842 clips from two sites, Oze National Park and Taki, for training and testing. The outcomes demonstrated that all models exhibited comparable performances, with median accuracies of 98.3 % and 92.9 % during the validation and testing stages, respectively. However, no single model outperformed the others across all the evaluation metrics. For instance, ResNet50 in different configurations led to the best accuracy, F1 score, precision, and specificity, whereas MobileNetV2 had the best recall. Therefore, we adopted a consensus-based ensemble scoring system in which an audio clip was classified as a deer call when at least two of three models concurred in their classification to enhance the reliability of our classifications. Our findings demonstrated that the Ensemble approach significantly enhanced the classification performance, achieving an accuracy of 99.2 % in the test stage. The proposed approach was successfully deployed during the deer rutting seasons in Oze and Taki in 2019 and 2021, respectively. We gained invaluable insights into deer behavior by analyzing deer calls' frequency, timing, and duration. Additionally, the spatial distribution of deer calls in Taki enabled us to detect a breach in the city's protective fencing and an association between the spatial patterns of deer calls and crop damage in the two fields. We aimed to draw a comprehensive picture of deer activity, which has significant implications for both conservation efforts and understanding animal behavior in various habitats. The insights gathered from this research contribute to the scientific understanding of deer behavior and serve as a foundation for future studies and conservation initiatives. By incorporating advanced machine learning models into environmental monitoring, we have paved the way for more data-driven approaches in wildlife research.

Aplicación de redes neuronales artificiales a la modelación de lluvia-escorrentía en la cuenca del río Chancay Lambayeque

Between the months of December to April, regions of northern Peru, including Lambayeque, are affected by maximum extreme events, wreaking havoc on homes, flooding crop fields, collapsing hydraulic works, and the most irreparable loss of human lives. In this line, the objective of this research was to apply Artificial Neural Networks to rain-runoff modeling in a basin in northern Peru, namely, the Chancay Lambayeque river basin belonging to the Pacific slope. For this purpose, records of precipitation and flows of 30 years (hydrological normal) were collected from 12 hydrometeorological stations belonging to the basin and neighboring it. Thus, applying a model of Long and Short Term Memory Networks (LSTM) we proceeded to model the rain, seeking to follow the behavior of the flows observed in the Racarrumi hydrometric station, with 80 % of the information the model was trained and with 20 % it was validated. In short, it was obtained that in the modeling validation stage, the Nash coefficient was 0.93, corresponding to the qualifier "very good".

Feasibility of Biopreneurship Project-Based Science Module for Students in the Bagek Kembar Ecotourism Area

The biopreneurship project-based science module is teaching material designed to combine biological concepts with entrepreneurship through project-based learning. This research aims to develop a biopreneurship-based science module valid for increasing students' scientific literacy, entrepreneurial motivation, and environmental literacy in the Bagek Kembar Ecotourism Area. This research is development research using the ADDIE model, namely Analyze, Design, Development, Implementation, and Evaluation. The development and validation stages of the module were carried out at the University of Mataram and SMA Negeri 1 Sekotong. The research instrument was a validation questionnaire given to science experts and teachers. The validity of the Science Module is based on the Aiken V Index. The results of the biopreneurship project-based science module validity show that the Aiken V Index for the size and appearance of the module cover, module design, module content, and language used are 0.95, 0.95, 0.93, and 0.94, respectively. Included in the valid category. This research concludes that the biopreneurship project-based science module is suitable for use in schools to increase students' scientific literacy, entrepreneurial motivation, and environmental literacy in Bagek Kembar ecotourism.

Development of English Board Learning Media to Increase Learning Motivation in English Subjects in Grade 3 of Elementary Madrasah

Students are less motivated to follow the English learning process. The methods and approaches used by teachers are less interesting to students, still centered on the teacher. This study developed English board learning media, and to increase student enthusiasm and make it easier for teachers to transfer knowledge to students. English Board is expected to be fun, interesting, and motivating, and not make students bored when participating in English learning activities. The "English Board" learning media can increase student enthusiasm in learning and provide a pleasant learning experience for students. English Board media uses a research and development model. This study was only conducted in five stages, namely: 1) potential and problem stage, 2) data collection stage, 3) product design stage, 4) product validation stage, and 5) product revision stage. The results of the media validity obtained an average percentage of 89.77%, this result is included in the category of very good or very suitable for use in implementing English learning activities.

Development, Content Validity and Usability of a Self-Assessment Instrument for the Lifestyle of Breast Cancer Survivors in Brazil

Background/Objectives: Breast cancer is the most common cancer among women globally, and it negatively impacts diet and quality of life, increasing the risk of recurrence. Adhering to World Cancer Research Fund (WCRF) and American Institute for Cancer Research (AICR) lifestyle guidelines, such as healthy eating habits and nutritional status, can help in primary and secondary cancer prevention. However, no questionnaire was found for self-assessment of these guidelines for the Brazilian population. The aim of this study is to carry out content validity, pilot, and usability testing of the self-administered digital instrument “PrevCancer” assessing adherence to the WCRF/AICR recommendations in Brazilian female breast cancer survivors. Methods: We conducted a psychometric study that involved the development of an instrument based on WCRF/AICR recommendations. Assessment of content validity involved the Content Validity Index (CVI) based on expert assessments (n = 7). The pilot study involved the System Usability Scale (SUS) after applying the developed instrument (n = 65) and anthropometric assessment for convergent validity by female participants (n = 55). The final usability test consisted of evaluating the satisfaction with the instrument of women with breast cancer (n = 14). Results: The “PrevCancer” instrument demonstrated good content (CVI = 1.0) as well as good usability and acceptability in the pilot study (mean SUS score = 88.1). The convergent validity stage demonstrated positive associations between the PrevCancer parameters and anthropometric parameters (p < 0.001). In the final usability study (mean SUS score = 90.3), participants’ receptivity to the instrument was excellent. Conclusions: The PrevCancer instrument had valid content and great usability by the target population, proving to be a useful tool for future cancer research.

Prognostic value of myocardial redox signalling: a machine learning multi-omics approach

Abstract Background Oxidative stress has been proposed to be implicated in cardiovascular pathology. Studies have linked myocardial oxidative stress with the development of atrial fibrillation, but its role in the prediction of long-term outcomes and mortality is yet to be elucidated. Purpose To apply discovery network transcriptomics and genomics to atrial tissue (AT) obtained from patients undergoing cardiac surgery, to phenotype myocardial redox-state and identify potential therapeutic targets. Methods We included 1,032 patients undergoing cardiac surgery. Arm 1 included 258 patients in whom atrial tissue (right atrial appendage) was used for ex-vivo quantification of NOX-derived superoxide by lucigenin-enhanced chemiluminescence. Arm 2 included 414 patients in whom exploratory, unsupervised gene network analysis in human atrial tissue RNA sequencing data was performed to identify atrial tissue redox-sensitive signatures (blue signature). Arm 3 included 881 patients genotyped with the genome-wide SNP array UK Biobank Axiom Array and used for machine learning construction of GenRedOx, a SNP model built to predict the blue transcriptomic signature. Associations with future incidence of major adverse myocardial events (MAME: cardiovascular death and/or heart failure) was assessed across all arms using cox regression models (adjusted for age, sex, hypertension, dyslipidaemia, diabetes mellitus, body mass index, and plasma TNFa). Results Over a median follow-up of 5.27 years [IQR: 4.29-6.83] high atrial tissue (AT) NOX-derived superoxide was independently associated with MAME risk (Adj. HR[95%CI]: 11.7 [2.18 , 62.82], p=0.004, a). Unsupervised transcriptomic analysis in Arm 2 revealed 15 coexpressed gene ‘modules’ or 'signatures' (b). The blue signature was significantly associated with AT superoxide production with "immune response-regulating and activating signalling" being amongst the top enriched pathways. Patients with high eigengene values for the blue signature showed a 5.5-fold higher adjusted risk of MAME (c). In Arm 3, extreme gradient boosting of SNP data, trained using the blue transcriptomic signature as ground truth, revealed GenRedOx (d), a genomic artificial intelligence score, which was found to be prognostic of MAME in the validation stage (e). Homozygosity for the minor allele of SNP rs723897 in the Chemokine-Like Factor Super Family 7 (CMTM7) gene had the highest gain in the model. Conclusion We present for the first time two novel human myocardial transcriptomic and genomic signatures that reflect changes in redox state and identify long-term cardiovascular risk. Targeting pathways in the myocardium related with immune response signalling may lead to the development of novel therapeutics in cardiovascular disease.

Identification of genetic risk variants for Type-2 Diabetes mellitus in Pakistani Pashtun population: A case-control association study

Background and Objective: Pakistan, a South Asian developing country, is experiencing a rapid increase in number of diabetes cases. High prevalence ratio of diabetes in Pakistani population and lack of genetic research studies prompted us to design this study. This present study investigated Pakistani Pashtun population for (known and novel SNPs) and its possible correlation with Type-2 Diabetes Mellitus (T2DM). Methods: This two stage (discovery & validation stage), case-control association study included one thousand individuals (Patients with T2DM=500 & controls=500) from eight districts of Khyber Pakhtunkhwa Pakistan. The study duration/period was from March 2018 to January 2020. In the first stage (the discovery stage) the target population was screened for known and novel T2DM-associated genetic markers. In the validation stage, identified variants were confirmed for T2DM association using MassARRAY genotyping and association analysis. Results: Exome sequencing detected eleven known and four novel/new genetic markers in the study population. Novel variants were preferred over the known for follow-up analysis/validation. Among the identified variants strong associations were confirmed for the following variants; rs1781133/ANKRD65 (OR=2.10, 95%Cl=1.06–3.08, P=0.003) rs2274791/TTLL10 (OR=1.97, 95%Cl=1.36-2.62, P=0.025), rs71628928/RNF223 (OR=1.82, 95%Cl=0.97-1.92, P=0.041), and rs609805/SCNN1D (OR=2.21, 95%Cl=1.92-3.09, P=0.001) with T2DM; other reported variants showed no noticeable association (having P>0.05) with T2DM. Conclusion: This study reports new genetic risk variants for T2DM in Pashtun population providing valuable insights into the genetic basis of T2DM in this group. doi: https://doi.org/10.12669/pjms.40.10.10292 How to cite this: Jan A, Mothana RA, Kaimori JY, Muhammad T, Khan M, Ali SS, et al. Identification of genetic risk variants for Type-2 Diabetes mellitus in Pakistani Pashtun population: A case-control association study. Pak J Med Sci. 2024;40(10):2336-2343. doi: https://doi.org/10.12669/pjms.40.10.10292 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Application of meta-heuristic hybrid models in estimating the average air temperature of Caspian sea coast of Iran

The rise of industrial societies leads to higher greenhouse gas emissions, profoundly affecting the climate in coastal regions. Consequently, air temperature readings from standard meteorological stations are key indicators of the Earth's environmental condition. Therefore, accurate estimation of daily temperature in each region is one of the important prerequisites for agricultural planning as well as water resources management and drought prevention, which can be done in different ways such as experimental, semi-experimental and intelligent models. In this research, WSVR, AIG-SVR, GWO-SVR and BAT-SVR hybrid models were investigated and evaluated in order to estimate the average daily air temperature on the shores of the Caspian Sea located in the north of Iran. For modeling, weather station data from Babolsar meteorological station located in Mazandaran province were used. During the water year from 2012 to 2022, daily parameters including relative humidity, maximum temperature, minimum temperature, wind speed, and evaporation were selected as network inputs, with the average daily air temperature as the network output. To assess and compare model performances, several criteria were employed including correlation coefficient, root mean square error (RMSE), mean absolute error (MAE), Nash-Sutcliffe efficiency (NSE), and percentage bias. Comparative analysis revealed that the WSVR model surpassed other models, demonstrating the highest correlation coefficient (0.992), lowest RMSE (0.096), and lowest MAE (0.042). The highest Nash Sutcliffe criterion (0.996) and bias percentage (0.001) were prioritized in the validation stage.

Development of a Guided Inquiry-Scientific Laboratory (GISL) Based Health Chemistry Practicum Module

Chemistry practicum learning requires a comprehensive approach, which improves understanding of concepts and develops various essential skills needed in the professional and academic world. This research aims to develop a Guided Inquiry-Scientific Laboratory-based Health Chemistry module and conduct empirical feasibility tests. This module is designed with a guided inquiry approach integrated with scientific methods in laboratory activities. The practicum module was developed for second-grade vocational school students in health expertise with material covering colloids, hydrocarbons, polymers, biomolecules, and chemical separation. This development research uses the Borg and Gall model, which consists of planning, organizing, implementing, assessing, and analyzing data. This research was only carried out to the validation stage by material experts, media experts, peer reviewers, and reviewers (five Health Vocational School teachers). The assessment carried out by the reviewer includes eight assessment aspects with 30 assessment indicators. Data was collected through questionnaires, observations, and interviews. Data analysis was carried out descriptively, quantitatively, and qualitatively, guided by ideal assessment criteria to determine the quality of the practicum module. The research results show that the Guided Inquiry-Scientific Laboratory (GISL) based Health Chemistry practicum module that has been developed has a score of 132.52 with an ideal percentage of 88.3467% in the very good (SB) category. This indicates that this module is suitable for use in health chemistry practicum learning, and it is hoped that this research can be continued at the next stage.

Psychotic symptoms associated increased CpG methylation of metabotropic glutamate receptor 8 gene in Chinese Han males with schizophrenia and methamphetamine induced psychotic disorder: a longitudinal study

Methamphetamine use can produce psychotic symptoms almost indistinguishable from schizophrenia (SCZ). Variation in DNA methylation may be closely implicated in the etiology and longitudinal development of psychiatric disorders. However, the relationship between psychotic symptoms, functional disability, and DNA methylation is still unclear. This study consists of three periods: discovery, validation, and follow-up. In the discovery stage, we employed genome-wide DNA methylation profiling (Illumina 450K) in peripheral blood mononuclear cells to test whether DNA methylation associates with psychotic symptoms and function state in representative SCZ and methamphetamine-induced psychotic disorder (MIP) patients. Then, we found seven differentially methylated regions/genes (DMRs, in UBA6, APOL3, KIF17, MLLT3, GRM8, CSNK1E, SETDB1) overlapping with genetic variants reported in previous studies of psychosis. In the validation stage, we compared the above-mentioned seven genes by MethLight qPCR method in Chinese Han males (N = 109 SCZ patients, N = 99 methamphetamine use disorder with MIP patients, N = 150 methamphetamine use disorder without MIP patients, N = 282 normal controls, age range: 18–50 years). GRM8 showed robustly altered methylation, which has passed rigorous filtration in subsequent validation, suggesting a remarkable contribution to SCZ and MIP. In addition, hypermethylation of GRM8 showed a significant association with the total scores of the Positive Negative Syndrome Scale and WHO disability assessment schedule II in both baseline and follow-up periods. Our findings suggest that increased CpG methylation in the promoter of GRM8 is a potential candidate epigenetic biomarker of psychotic symptoms in transdiagnostic samples of SCZ and MIP.

Development of a canva-based heyzine flipbooks e-module as an alternative learning resources on pancasila education subjects

The purpose of this research is that the learning materials used by teachers in the learning of Pancasila Education are judged to be boring, less interesting, difficult, confusing and cause students' knowledge to be less than maximum. The purpose of this study is to develop Canva-based Heyzine Flipbooks e-module learning media on smartphones/laptops as a Pancasila Education learning medium as well as find out the feasibility and assessment of Canva-based Heyzine Flipbooks e-module as an alternative learning resource on Pancasila Education subject. This study was conducted at SMA Negeri 1 Percut Sei Tuan. The subjects of the study were class XI-Matlanfor students at Percut Sei Tuan Secondary School 1, and this study used the research and development method developed by Willis. The development model used was R2D2.Questionnaires, interviews, observation and documentation were used as data collection techniques in this study. Data analysis technique This study used both qualitative and quantitative methods of analysis. The qualitative data of this study were obtained from validators, subject matter experts and the media at the validation stage. Quantitative constitutes data describing the results of product development in the form of e-module-based learning media Heyzine Flipbooks based on Canva. The obtained data were then analyzed using the formula of validity percentage(%) = tse/tsh x 100. The results of the study stated that the heyzine flipbooks on E-module learning were perceived as very useful and very interesting to use in the field because of the research data obtained from media experts, subject matter experts,PPKn teachers and student responses were declared very viable categories with the acquisition of subject matter expert validation of 98%, media expert validation of 98% and teacher evaluation of 96%. The very feasible category was then reinforced with a student response of 93% with the very interesting category. This proves that learning media is highly recommended to be applied in SMA Negeri 1 Percut Sei Tuan

The human volatilome meets cancer diagnostics: past, present, and future of noninvasive applications.

Cancer is a significant public health problem, causing dozens of millions of deaths annually. New cancer screening programs are urgently needed for early cancer detection, as this approach can improve treatment outcomes and increase patient survival. The search for affordable, noninvasive, and highly accurate cancer detection methods revealed a valuable source of tumor-derived metabolites in the human metabolome through the exploration of volatile organic compounds (VOCs) in noninvasive biofluids. This review discusses volatilomics-based approaches for cancer detection using noninvasive biomatrices (breath, saliva, skin secretions, urine, feces, and earwax). We presented the historical background, the latest approaches, and the required stages for clinical validation of volatilomics-based methods, which are still lacking in terms of making noninvasive methods available and widespread to the population. Furthermore, insights into the usefulness and challenges of volatilomics in clinical implementation steps for each biofluid are highlighted. We outline the methodologies for using noninvasive biomatrices with up-and-coming clinical applications in cancer diagnostics. Several challenges and advantages associated with the use of each biomatrix are discussed, aiming at encouraging the scientific community to strengthen efforts toward the necessary steps to speed up the clinical translation of volatile-based cancer detection methods, as well as discussing in favor of (i) hybrid applications (i.e., using more than one biomatrix) to describe metabolite modulations that can be "cancer volatile fingerprints" and (ii) in multi-omics approaches integrating genomics, transcriptomics, and proteomics into the volatilomic data, which might be a breakthrough for diagnostic purposes, onco-pathway assessment, and biomarker validations.

Continuous Process Verification 4.0 application in upstream: adaptiveness implementation managed by AI in the hypoxic bioprocess of the Pichia pastoris cell factory.

The experimental approach developed in this research demonstrated how the cloud, the Internet of Things (IoT), edge computing, and Artificial Intelligence (AI), considered key technologies in Industry 4.0, provide the expected horizon for adaptive vision in Continued Process Verification (CPV), the final stage of Process Validation (PV). Pichia pastoris producing Candida rugosa lipase 1 under the regulation of the constitutive GAP promoter was selected as an experimental bioprocess. The bioprocess worked under hypoxic conditions in carbon-limited fed-batch cultures through a physiological control based on the respiratory quotient (RQ). In this novel bioprocess, a digital twin (DT) was built and successfully tested. The implementation of online sensors worked as a bridge between the microorganism and AI models, to provide predictions from the edge and the cloud. AI models emulated the metabolism of Pichia based on critical process parameters and actionable factors to achieve the expected quality attributes. This innovative AI-aided Adaptive-Proportional Control strategy (AI-APC) improved the reproducibility comparing to a Manual-Heuristic Control strategy (MHC), showing better performance than the Boolean-Logic-Controller (BLC) tested. The accuracy, indicated by the Mean Relative Error (MRE), was for the AI-APC lower than 4%, better than the obtained for MHC (10%) and BLC (5%). Moreover, in terms of precision, the same trend was observed when comparing the Root Mean Square Deviation (RMSD) values, becoming lower as the complexity of the controller increases. The successful automatic real time control of the bioprocess orchestrated by AI models proved the 4.0 capabilities brought by the adaptive concept and its validity in biopharmaceutical upstream operations.

Utilization of Computer-Based Testing Technology to Optimize Cognitive-Based Assessment Literacy in the Learning of Islamic Elementary School Students

The focus of 21st-century learning is on developing students' critical thinking, real-world application of knowledge, mastery of information technology, communication, and collaboration skills. Therefore, it is important to integrate information technology into the learning process and assessment of learning outcomes. This research aims to explore how Computer-Based Testing (CBT) can enhance the quality of cognitive assessment procedures based on literacy and improve learning evaluations for students at Islamic Elementary School. This study adopted a qualitative case study design and was conducted at Islamic Elementary School Al Wathoniyah 10 East Jakarta in the second semester of the 2023/2024 academic year. The researcher used interviews, observations, and documentation as data collection methods. The findings of this research show that literacy-based cognitive assessment using CBT can optimize the quality of learning evaluation. Teachers and students are accustomed to using information and communication technology devices in learning activities. There are five stages of the procedure in literacy-based cognitive assessment using CBT to optimize the quality of learning evaluation, including the planning stage, item question development stage, validation stage, implementation stage, and evaluation stage. The investigative conducting procedure truly optimizes the quality of learning evaluation. Islamic Elementary School Al Wathoniyah 10 utilizes CBT, which has literacy-based characteristics. CBT used in learning evaluation is equipped with meaningful stimuli and various forms of questions. The questions in CBT are made as similar as possible to the national assessment questions. The educational report results in the 2023 national assessment, especially in the cognitive domain, have shown improvement. The literacy and numeracy skills of students at Islamic Elementary School Al Wathoniyah 10 have achieved good and moderate results. Both have obtained top rankings nationally.

Development of Scientific Argumentation Test Instruments for Students on the Classification of Living Creatures in Junior High School

Development of Scientific Argumentation Test Instruments for Students on the Classification of Living Creatures in Junior High School. Encouraging students to think and act like scientists is an important basis of the scientific approach to learning. Both students and scientists need arguments. This study uses a research and development (R&D) approach concerning the ADDIE model. The validation stage has been carried out by experts—linguists, material experts, and media experts—as well as one-on-one trials, small groups, and teacher practice. It can be concluded that the results of the expert validation as a whole.

PENGEMBANGAN MEDIA PEMBELAJARAN VIDEO PIKTOGRAM (DIGA) PADA MATERI PIKTOGRAM UNTUK MENINGKATKAN HASIL BELAJAR SISWA KELAS IV SDN SUKORAME 2 KOTA KEDIRI

The aim of this developmental research is to determine the validity, student responses, and improvement in learning outcomes through the use of animated video learning media on pictogram material among fourth-grade students at SDN Sukorame 2 Kota Kediri. The research method employed is Research and Development (R&D) using the ADDIE model, which consists of five stages: Analysis, Design, Development, Implementation, and Evaluation. The results of this study indicate that (1) the validation stage received an accumulated score of 87%, categorized as very good; (2) student responses received an accumulated score of 96%, also categorized as very good; and (3) the improvement on student learning outcomes using the animated video learning media was 55%. Therefore, the development of the animated video learning media on pictogram material is considered valid, practical, and effective.