Effective Model Research Articles

Perceived effects of teleworking on adaptive performance: a moderated mediation model of leadership effectiveness and negative affect

Purpose Drawing on the conservation of resources theory, this study aims to explore how individuals’ perceptions of telework effects associate with their adaptive performance by shaping their negative emotional experiences, with perceived leadership effectiveness acting as a critical moderator in this relationship. Design/methodology/approach A cross-sectional design was used, with data collected from a final sample of 209 teleworkers via an online survey. Findings The results demonstrated a significant indirect effect of the perceived effects of teleworking on adaptive performance through negative affect. Furthermore, leadership effectiveness moderated the indirect effect, in a way that the indirect effect was only significant for those who had ineffective leaders and moderate effective leaders; therefore, the indirect effect was strengthened for those who had less effective leaders (versus effective leaders). Originality/value This study examines the positive link between teleworking perceptions and adaptive performance through the reduction of negative affect, in line with the conservation of resources theory. Moreover, a reduction in negative emotions among employees during telework is associated with enhanced performance. Notably, the study reveals that leadership effectiveness moderates this connection as playing a crucial role in moderating these effects, particularly when leaders are perceived as less effective, offering insights for organizations considering telework strategies.

Explainable artificial intelligence for predicting red blood cell transfusion in geriatric patients undergoing hip arthroplasty: Machine learning analysis using national health insurance data.

This study uses machine learning and population data to analyze major determinants of blood transfusion among patients with hip arthroplasty. Retrospective cohort data came from Korea National Health Insurance Service claims data for 19,110 patients aged 65 years or more with hip arthroplasty in 2019. The dependent variable was blood transfusion (yes vs no) in 2019 and its 31 predictors were included. Random forest variable importance and Shapley Additive Explanations were used for identifying major predictors and the directions of their associations with blood transfusion. The random forest registered the area under the curve of 73.6%. Based on random forest variable importance, the top-10 predictors were anemia (0.25), tranexamic acid (0.17), age (0.16), socioeconomic status (0.05), spinal anesthesia (0.05), general anesthesia (0.04), sex (female) (0.04), dementia (0.03), iron (0.02), and congestive heart failure (0.02). These predictors were followed by their top-20 counterparts including cardiovascular disease, statin, chronic obstructive pulmonary disease, diabetes mellitus, chronic kidney disease, peripheral vascular disease, liver disease, solid tumor, myocardial infarction and hypertension. In terms of max Shapley Additive Explanations values, these associations were positive, e.g., anemia (0.09), tranexamic acid (0.07), age (0.09), socioeconomic status (0.05), spinal anesthesia (0.05), general anesthesia (0.04), sex (female) (0.02), dementia (0.03), iron (0.04), and congestive heart failure (0.03). For example, the inclusion of anemia, age, tranexamic acid or spinal anesthesia into the random forest will increase the probability of blood transfusion among patients with hip arthroplasty by 9%, 7%, 9% or 5%. Machine learning is an effective prediction model for blood transfusion among patients with hip arthroplasty. The high-risk group with anemia, age and comorbid conditions need to be treated with tranexamic acid, iron and/or other appropriate interventions.

Effects and regulation Mechanisms of Phosphorus Fertilizer on Soil Carbon Cycle

This paper reviews the effects and regulation mechanisms of phosphorus fertilizer on soil carbon cycle, which is an important process for soil fertility, productivity, and climate change and introduces the background, significance, main pathways, mechanisms, influencing factors, research progress and challenges. The paper shows that phosphorus fertilizer can affect soil carbon cycle through various pathways, such as affecting plant productivity and carbon allocation, soil organic matter decomposition and mineralization, soil microbial community and function, and soil aggregate formation and stability. The paper also shows that the effects of phosphorus fertilizer on soil carbon cycle are influenced by many factors, such as the type, amount, application method and time of phosphorus fertilizer, and the soil type, climate condition, vegetation type and management measures. The paper concludes that the effects of phosphorus fertilizer on soil carbon cycle are both positive and negative, direct and indirect, short-term and long-term, local and global, forming a complex impact network. The paper suggests that more research is needed to clarify the mechanisms, interactions and feedbacks of the effects of phosphorus fertilizer on soil carbon cycle, and to develop effective models and indicators to evaluate and predict the effects of phosphorus fertilizer on soil carbon cycle. The paper also suggests that rational application of phosphorus fertilizer and scientific management of soil carbon cycle are important for improving crop phosphorus use efficiency, reducing excessive application of phosphorus fertilizer, protecting water resources and soil quality, and mitigating climate change.

Cross-scale static/dynamic shear damage evolution mechanism of 2D C/SiC ceramic matrix composite with transient hysteresis effect

The intricate woven structure of two-dimensional woven carbon fiber-reinforced silicon carbide (2D C/SiC) poses challenges to the investigation of damage failure mechanisms and the prediction of material performance, thereby limiting its further application in aerospace engineering. In this work, static/dynamic short beam shear tests with different densities of 2D C/SiC were carried out. It was observed that high-density specimens with fewer microporous defects exhibited better strength characteristics than those with lower density. Specifically, the shear strength of 2D C/SiC with a density of 2.07 g/cm³ increased by 18.6% compared to that of the specimen with a density of 1.94 g/cm³. The transient hysteresis strengthening effect of materials at high strain rates was discovered through cross scale research methods. It was found that deformation coordination ability of 2D C/SiC under dynamic loading was weaker than that under quasi-static conditions. Moreover, the deformation hysteresis in temporal and spatial distributions induced significant fiber plucking, further accelerating dislocation movement within the internal lattice and thereby enhancing strength of the material. The failure path of materials often follows the direction of pore defects, and stress concentration leads to cracking behavior of fiber bundles. Furthermore, microcracks within the matrix, caused by residual stress, resulted in localized regions of reduced strength. This induced the matrix cracking failure with further crack expansion within the material structure under external loads. To provide technical references for practical engineering applications and design, a realistic and effective finite element prediction model was developed for 2D C/SiC composites based on their structural characteristics.

Artificial intelligence algorithms for predicting post-operative ileus after laparoscopic surgery

ObjectiveBy constructing a predictive model using machine learning and deep learning technologies, we aim to understand the risk factors for postoperative intestinal obstruction in laparoscopic colorectal cancer patients, and establish an effective artificial intelligence-based predictive model to guide individualized prevention and treatment, thus improving patient outcomes. MethodsWe constructed a model of the artificial intelligence algorithm in Python. Subjects were randomly assigned to either a training set for variable identification and model construction, or a test set for testing model performance, at a ratio of 7:3. The model was trained with ten algorithms. We used the AUC values of the ROC curves, as well as accuracy, precision, recall rate and F1 scores. ResultsThe results of feature engineering composited with the GBDT algorithm showed that opioid use, anesthesia duration, and body weight were the top three factors in the development of POI. We used ten machine learning and deep learning algorithms to validate the model, and the results were as follows: the three algorithms with best accuracy were XGB (0.807), Decision Tree (0.807) and Neural DecisionTree (0.807); the two algorithms with best precision were XGB (0.500) and Decision Tree (0.500); the two algorithms with best recall rate were adab (0.243) and Decision Tree (0.135); the two algorithms with highest F1 score were adab (0.290) and Decision Tree (0.213); and the three algorithms with best AUC were Gradient Boosting (0.678), XGB (0.638) and LinearSVC (0.633). ConclusionThis study shows that XGB and Decision Tree are the two best algorithms for predicting the risk of developing ileus after laparoscopic colon cancer surgery. It provides new insight and approaches to the field of postoperative intestinal obstruction in colorectal cancer through the application of machine learning techniques, thereby improving our understanding of the disease and offering strong support for clinical decision-making.

Prediction of stress levels in sleep patterns based on random forest

The prevalence of stress in contemporary society has emerged as a significant concern, exerting a profound influence on our daily lives. The objective of this study is to predict stress levels in sleep patterns through the utilization of a machine learning algorithm known as random forest. The significance of stress detection has increased due to its potential to induce various issues such as insomnia and depression. The examination of stress can assist individuals in mitigating the adverse effects associated with prolonged exposure to stress. The study commences with the preprocessing phase, followed by an exploratory data analysis, subsequent dataset splitting, identification of significant features, and concludes with model training. The utilization of the random forest model can enhance the comprehension of the association between sleeping characteristics and levels of stress. Furthermore, it produces a f1-score of 98 percent, indicating a strong predictive capability for determining stress levels in sleep patterns. The proposed method can effectively predict stress levels during sleep mode. This study can provide an effective model for society to prevent people's psychological problems in advance.

Ultrafast field-driven valley polarization of transition metal dichalcogenide quantum dots

We study theoretically the electron dynamics of transition metal dichalcogenide (TMDC) quantum dots (QDs) in the field of an ultrashort and ultrafast circularly polarized optical pulse. The QDs have the shape of a disk and their electron systems are described within an effective model with infinite mass boundary conditions. Similar to TMDC monolayers, a circularly polarized pulse generates ultrafast valley polarization of such QDs. The dependence of the valley polarization on the size of the dot is sensitive to the dot material and, for different materials, show both monotonic increase with the dot radius and nonmonotonic behavior with a local maximum at a finite dot radius.

Classical-trajectory model for ionizing proton-ammonia molecule collisions: the role of multiple ionization

We use an independent electron model with semi-classical approximation to electron dynamics to investigate differential cross sections for electron emission in fast collisions of protons with ammonia molecules. An effective potential model for the electronic orbitals is introduced, and utilized in the context of the classical-trajectory Monte Carlo (CTMC) approach for single-electron dynamics. Cross sections differential in electron emission angle and energy are compared with experimental data. Compared to previous scattering-theory based quantum-mechanical results the time-dependent semi-classical CTMC approach provides results of similar quality for intermediate and high ionized electron energies. We find some discrepancies in the total cross sections for q-fold ionization between the present model and independent-atom-model calculations. The double ionization cross sections are considerably larger than recent experimental data which are derived from coincidence counting of charged fragments. The calculated triple ionization cross sections exceed the experimental coincidence data for q = 3 by several orders of magnitude at intermediate energies.

Bridging the Cybersecurity Gap: A Comprehensive Analysis of Threats to Power Systems, Water Storage, and Gas Network Industrial Control and Automation Systems

This research addresses the dearth of real-world data required for effective neural network model building, delving into the crucial field of industrial control and automation system (ICS) cybersecurity. Cyberattacks against ICS are first identified and then generated in an effort to raise awareness of vulnerabilities and improve security. This research aims to fill a need in the existing literature by examining the effectiveness of a novel approach to ICS cybersecurity that draws on data from real industrial settings. Real-world data from a variety of commercial sectors is used in this study to produce a complete dataset. These sectors include power systems, freshwater tanks, and gas pipelines, which together provide a wide range of commercial scenarios where anomaly detection and attack classification approaches are critical. The generated data are shown to considerably improve the models’ precision. An amazing 71% accuracy rate is achieved in power system models, and incorporating generated data reliably increases network speed. Using generated data, the machine learning system achieves an impressive 99% accuracy in a number of trials. In addition, the system shows about 90% accuracy in most studies when applied to the setting of gas pipelines. In conclusion, this article stresses the need to improve cybersecurity in vital industrial sectors by addressing the dearth of real-world ICS data. To better understand and defend against cyberattacks on industrial machinery and automation systems, it demonstrates how generative data can improve the precision and dependability of neural network models.

Numerical investigation of effective nonlinear coefficient model for coupled third harmonic generation

In this paper, the optimal solution of effective nonlinear coefficient of quasi-phase-matching (QPM) crystals for coupled third harmonic generation (CTHG) was numerically investigated. The effective nonlinear coefficient of CTHG was converted to an Ising model for optimizing domain distributions of aperiodically poled lithium niobate (APPLN) crystals with lengths as 0.5 mm and 1 mm, and fundamental wavelengths ranging from 1000 nm to 6000 nm. A method for reconstructing crystal domain poling weight curve of coupled nonlinear processes was also proposed, which demonstrated the optimal conversion ratio between two coupled nonlinear processes at each place along the crystal. In addition, by applying the semidefinite programming, the upper bound on the effective nonlinear coefficients deff for different fundamental wavelengths were calculated. The research can be extended to any coupled dual χ(2) process and will help us to understand better the dynamics of coupled nonlinear interactions based on QPM crystals.

Predictability comparison of sizing parameters for postoperative vault after implantable Collamer lens implantation.

This study aims to assess the accuracy of three parameters (white-to-white distance [WTW], angle-to-angle [ATA], and sulcus-to-sulcus [STS]) in predicting postoperative vault and to formulate an optimized predictive model. In this retrospective study, a cohort of 465 patients (comprising 769 eyes) who underwent the implantation of the V4c implantable Collamer lens with a central port (ICL) for myopia correction was examined. Least absolute shrinkage and selection operator (LASSO) regression and classification models were used to predict postoperative vault. The influences of WTW, ATA, and STS on predicting the postoperative vault and ICL size were analyzed and compared. The dataset was randomly divided into training (80%) and test (20%) sets, with no significant differences observed between them. The screened variables included only seven variables which conferred the largest signal in the model, namely, lens thickness (LT, estimated coefficients for logistic least absolute shrinkage of -0.20), STS (-0.04), size (0.08), flat K (-0.006), anterior chamber depth (0.15), spherical error (-0.006), and cylindrical error (-0.0008). The optimal prediction model depended on STS (R2=0.419, RMSE=0.139), whereas the least effective prediction model relied on WTW (R2=0.395, RMSE=0.142). In the classified prediction models of the vault, classification prediction of the vault based on STS exhibited superior accuracy compared to ATA or WTW. This study compared the capabilities of WTW, ATA, and STS in predicting postoperative vault, demonstrating that STS exhibits a stronger correlation than the other two parameters.

Digital Referendum - Effective Modeling of Public Administration in Georgia

State management in its content is a complex and at the same time dynamic process. The second half of the 20th century can be considered the initial period of management transformation and establishment of governance structures.
 Governance based on democratic principles is based on a number of fundamental principles, the correct implementation of which is the most important prerequisite for the establishment of democratic institutions. One such important democratic process is the referendum.
 In Europe, the practice of referendum is most established in Switzerland, which is governed by a system of direct democracy. Accordingly, in order to solve problematic issues, citizens in Switzerland take part in both regional and national referendums, which are undoubtedly democratic in content and respond to the fundamental principles of modern public administration.
 In Georgia, where there is little experience in holding a referendum, the implementation of a digital referendum is the latest challenge, which will be an important step in the way of the country's European integration. The involvement of citizens in the solution of important issues for the state allows the so-called possibility of communication between society and government. It eliminates "black holes" and ensures the reduction of conflicts between them. Of course, all the positive results of the referendum depend on how fairly and objectively it will be conducted.
 Thus, one of the important challenges of the digitalization of the public administration of Georgia is the digital referendum, the implementation and development of which is an effective step on the way of the state development of Georgia and as a result ensures the acceleration of the country's institutional and European integration processes.
 In the era of digital governance, it is necessary to develop new approaches to state intervention in all spheres of public life and public service delivery systems in order to optimally respond to the needs of citizens. Analysis and comparison methods are mainly used in the work, proposals are given in the direction of improving the provision of public services in connection with the electronic referendum.

Autonomous Vessel Design for Efficient Marine Debris Collection: A MATLAB Simulink and Arduino-Based Approach

The growing threat that marine pollution poses to human health and biodiversity requires innovative responses. This study aims to create autonomous, unmanned vessels for collecting marine debris to prevent the negative effects of marine litter, especially plastics and industrial waste. The study uses MATLAB Simulink for dynamic modeling, simulating how the ship interacts with different debris and maritime settings. The effectiveness of advanced control systems that combine feedforward and feedback controls is evaluated regarding debris collection and pathfinding. The methodology of this study involves the design and simulation of the vessel’s hydrodynamic behavior, control systems, and debris interaction mechanisms. A scaled-down prototype has been built, integrating elements like a double-propeller hull, Arduino-driven control mechanisms, and a debris-gathering system. The prototype’s efficiency in gathering debris and mobility is demonstrated by real-world testing conducted in controlled situations. According to the results, the integration of modern control systems has been found to greatly improve the vessel’s debris collection efficiency. How the prototype functions in different situations demonstrates its ability to serve as a scalable solution for marine pollution. The result of this study emphasizes the possibility of automated, unmanned marine debris collection vessels as an environmentally friendly way to reduce marine pollution. The effective prototype testing, modeling, and simulation bring up new avenues for research and development in this area, which benefits the preservation of marine ecosystems.

Effectiveness of the Cooperative Learning Model in Physics Lessons at Vocational High School Level in Jambi, Indonesia

The low physics learning outcomes of vocational school students in Jambi is a problem that needs to be solved. One solution to improve physics learning outcomes is to use an effective learning model, such as the cooperative learning model. This research used a quasi-experimental design with a pretest-posttest design with control group. The research sample was 60 class X students who were divided into two groups, namely the experimental group who studied with the cooperative learning model and the control group who studied with the conventional learning model. The research instrument used was a physics learning outcomes test. The research results showed that there were significant differences between student learning outcomes in the experimental group and the control group. The learning outcomes of students in the experimental group who studied with the cooperative learning model were higher than the learning outcomes of students in the control group who studied with the conventional learning model. The cooperative learning model is effective in improving the physics learning outcomes of vocational school students in Jambi.

Klasifikasi Gambar Burung Konservasi di Wilayah Papua Barat Menggunakan Transfer Learning

Birds play an important role in maintaining the ecosystem. However, human activities such as poaching have caused some bird species to become endangered. This is rooted in the lack of public understanding of conservation bird species. The purpose of this research is to build an effective machine learning model that classifies conservation and non-conservation birds based on images, so that it is expected to improve the public's understanding of conservation and non-conservation bird species. In this study, Big Transfer (BiT), DenseNet121, and VGG16 are used as the basis for building the model. The dataset used consists of ten species that have been annotated into two classes, namely conservation and non-conservation. The model achieved the best performance with the highest accuracy obtained by the Big Transfer (BiT) model at 96.53%. The built DenseNet121 and VGG16 models have lower accuracy of 92.36% and 81.94%.

Mobility, response and transport in non-equilibrium coarse-grained models

We investigate two different types of non-Markovian coarse-grained models extracted from a linear, non-equilibrium microscopic system, featuring a tagged particle coupled to underdamped oscillators. The first model is obtained by analytically ‘integrating out’ the oscillators and the second is based on a derivation using projection operator techniques. We observe that these two models behave very differently when the tagged particle is exposed to external harmonic potentials or pulling forces. Most importantly, we find that the analytic model has a well defined friction kernel and can be used to extract work, consistent with the microscopic system, while the projection model corresponds to an effective equilibrium model, which cannot be used to extract work. We apply the analysis to two popular non-equilibrium systems, time-delay feedback control and the active Ornstein–Uhlenbeck process. Finally, we highlight that our study could have important consequences for dynamic coarse-graining of non-equilibrium systems going far beyond the linear systems investigated in this manuscript.

An effective and robust lattice Boltzmann model guided by atlas for hippocampal subregions segmentation.

Given the varying vulnerability of the rostral and caudal regions of the hippocampus to neuropathology in the Alzheimer's disease (AD) continuum, accurately assessing structural changes in these subregions is crucial for early AD detection. The development of reliable and robust automatic segmentation methods for hippocampal subregions (HS) is of utmostimportance. Our aim is to propose and validate a HS segmentation model that is both training-free and highly generalizable. This method should exhibit comparable accuracy and efficiency to state-of-the-art techniques. The segmented HS can serve as a biomarker for studying the progression ofAD. We utilized the functional magnetic resonance imaging of the Brain's Integrated Registration and Segmentation Tool (FIRST) to segment the entire hippocampus. By intersecting the segmentation results with the Brainnetome (BN) atlas, we obtained coarse segmentation of the four HS regions. This coarse segmentation was then employed as a shape prior term in the lattice Boltzmann (LB) model, as well as for initializing contours. Additionally, image gradients and local gray levels were integrated into the external force terms of the LB model to refine the coarse segmentation results. We assessed the segmentation accuracy of the model using the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset and evaluated the potential of the segmentation results as AD biomarkers on both the ADNI and Xuanwudatasets. The median Dice similarity coefficients (DSC) for the left caudal, right caudal, left rostral, and right rostral hippocampus were 0.87, 0.88, 0.88, and 0.89, respectively. The proportion of segmentation results with a DSC exceeding 0.8 was 77%, 78%, 77%, and 94% for the respective regions. In terms of volume, the correlation coefficients between the segmentation results of the four HS regions and the gold standard were 0.95, 0.93, 0.96, and 0.96, respectively. Regarding asymmetry, the correlation coefficient between the segmentation result's right caudal minus left caudal and the corresponding gold standard was 0.91, while for right rostral minus left rostral, it was 0.93. Over time, we observed a decline in the volumes of the four HS regions and the total hippocampal volume of mild cognitive impairment (MCI) converters. Analysis of inter-group differences revealed that, except for the right rostral region in the ADNI dataset, the p-values for the four HS regions in the normal controls (NC), MCI, and AD groups from both datasets were all below 0.05. The right caudal hippocampal volume demonstrated correlation coefficients of 0.47 and 0.43 with the mini-mental state examination (MMSE) and Montreal cognitive assessment (MoCA), respectively. Similarly, the left rostral hippocampal volume showed correlation coefficients of 0.50 and 0.58 with MMSE and MoCA,respectively. Our framework allows for direct application to different brain magnetic resonance (MR) datasets without the need for training. It eliminates the requirement for complex image preprocessing steps while achieving segmentation accuracy comparable to deep learning (DL) methods even with small sample sizes. Compared to traditional active contour models (ACM) and atlas-based methods, our approach exhibits significant speed advantages. The segmented HS regions hold promise as potential biomarkers for studying the progression ofAD.

The health care management by nurses from the hospital infection control service in light of the Nursing Role Effectiveness Model

The objective was to reflect on the key concepts of the Middle Range Theory (TMA) Nursing Role Effectiveness Model (NREM) and its applicability in the management practices of Nurses working in the Hospital Infection Control Service. This is a theoretical-reflective study, which starts from the philosophical and epistemological conceptions of the TMA - NREM in order to reflect its applicability in the managerial actions of nurses who work in the Hospital Infection Control Service. For this reflection, the authors were based on the main concepts of the theory: structure (organization, nurse and patient), processes (independent, dependent and interdependent actions) and results (care sensitive to the nurse's actions). Through reflection, it can be seen that the key concepts of the aforementioned theory apply and at the same time support the theoretical reflection on the managerial actions of nurses who work in the Hospital Infection Control Service, contributing to elucidate how the interrelationship between the variables of this medium-range theory can favor better results than the reduction of infections in hospital units. It was possible to conclude that the managerial actions of nurses who work in the Hospital Infection Control Service are interrelated, capable of generating sensitive nursing care for patients hospitalized in hospital units.

Comparative Evaluation of Imbalanced Data Management Techniques for Solving Classification Problems on Imbalanced Datasets

Dealing with imbalanced data is crucial and challenging when developing effective machine-learning models for data classification purposes. It significantly impacts the classification model's performance without proper data management, leading to suboptimal results. Many methods for managing imbalanced data have been studied and developed to improve data balance. In this paper, we conduct a comparative study to assess the influence of a ranking technique on the evaluation of the effectiveness of 66 traditional methods for addressing imbalanced data. The three classification models, i.e., Decision Tree, Random Forest, and XGBoost, act as classification models. The experimental settings have been divided into two segments. The first part evaluates the performance of various imbalanced dataset handling methods, while the second part compares the performance of the top 4 oversampling methods. The study encompasses 50 separate datasets: 20 retrieved from the UCI repository and 30 sourced from the OpenML repository. The evaluation is based on F-Measure and statistical methods, including the Kruskal-Wallis test and Borda Count, to rank the data imbalance handling capabilities of the 66 methods. The SMOTE technique is the benchmark for comparison due to its popularity in handling imbalanced data. Based on the experimental results, the MCT, Polynom-fit-SMOTE, and CBSO methods were identified as the top three performers, demonstrating superior effectiveness in managing imbalanced datasets. This research could be beneficial and serve as a practical guide for practitioners to apply suitable techniques for data management.

Pengaruh Model Discovery Learning Berbasis IoT Terhadap Kemampuan Berpikir Kritis Siswa: Meta -analysis

The industrial revolution 4.0 has had a significant impact on the field of education. The role of technology has a positive impact on students' critical thinking abilities in learning. The discovery learning model is an effective learning model associated with Internet of Things (IoT) technology. However, many studies regarding discovery learning models have not found an effect size for Internet-based discovery learning on students' critical thinking skills. The aim of this research is to determine the effect of the IoT-based discovery learning model on students' critical thinking abilities. This type of research is a type of quantitative research with a meta-analysis approach. Inclusion eligibility criteria are research published in 2020-2024, research must be related to IoT-based discovery learning models, research data comes from reputable international journals and proceedings, research has complete data for effect size analysis and sample size (N) > 25 students. Data analysis techniques with the help of JSAP software. The results of the research concluded that the mean effect size value was 1,154, so the IoT-based discovery learning model had a positive effect on students' critical thinking abilities in the high category. These findings provide important information for teachers regarding the application of IoT-based discovery learning models to develop students' critical thinking skills in schools