Search Results (44)

This article formalizes AI-assisted assessment as a discrete-time policy-level design for iterative feedback and evaluates it in a digitally transformed higher-education setting. We integrate an agentic retrieval-augmented generation (RAG) feedback engine—operationalized through planning (rubric-aligned task decomposition), tool use beyond retrieval (tests, static/dynamic analyzers, rubric checker), and self-critique (checklist-based verification)—into a six-iteration dynamic evaluation cycle. Learning trajectories are modeled with three complementary formulations: (i) an interpretable update rule with explicit parameters $\eta$ and $\lambda$ that links next-step gains to feedback quality and the gap-to-target and yields iteration-complexity and stability conditions; (ii) a logistic-convergence model capturing diminishing returns near ceiling; and (iii) a relative-gain regression quantifying the marginal effect of feedback quality on the fraction of the gap closed per iteration. In a Concurrent Programming course ($n=35$), the cohort mean increased from 58.4 to 91.2 (0–100), while dispersion decreased from 9.7 to 5.8 across six iterations; a Greenhouse–Geisser corrected repeated-measures ANOVA indicated significant within-student change. Parameter estimates show that higher-quality, evidence-grounded feedback is associated with larger next-step gains and faster convergence. Beyond performance, we engage the broader pedagogical question of what to value and how to assess in AI-rich settings: we elevate process and provenance—planning artifacts, tool-usage traces, test outcomes, and evidence citations—to first-class assessment signals, and outline defensible formats (trace-based walkthroughs and oral/code defenses) that our controller can instrument. We position this as a design model for feedback policy, complementary to state-estimation approaches such as knowledge tracing. We discuss implications for instrumentation, equity-aware metrics, reproducibility, and epistemically aligned rubrics. Limitations include the observational, single-course design; future work should test causal variants (e.g., stepped-wedge trials) and cross-domain generalization. Full article

Background: This study deploys the Lean Six Sigma DMAIC framework to achieve systemic optimization of the school subscription process in Tunisia’s public transport service, a critical administrative operation affecting efficiency and customer satisfaction across the urban mobility network. Methods: Beyond conventional applications, the research integrates advanced analytical and process engineering tools, including capability indices, measurement system analysis (MSA), variance decomposition, and root-cause prioritization through Pareto–ANOVA integration, supported by a structured control plan aligned with ISO 9001:2015 and ISO 31000:2018 risk-management standards. Results: Quantitative diagnosis revealed severe process instability and nonconformities in information flow, workload balancing, and suboptimal resource allocation that constrained effective capacity utilization. Corrective interventions were modeled and validated through statistical control and real-time performance dashboards to institutionalize improvements and sustain process stability. The implemented actions led to a 37.5% reduction in cycle time, an 80% decrease in process errors, a 38.5% increase in customer satisfaction, and a 38.9% improvement in throughput. Conclusions: This study contributes theoretically by positioning Lean Six Sigma as a data-centric governance framework for stochastic capacity optimization and process redesign in public service systems, and practically by providing a replicable, evidence-based roadmap for operational excellence in governmental organizations within developing economies. Full article

We studied differences in the decomposition rate between Pinus merkusii Jungh. et de Vriese (tusam) leaves, a representative of needle leaf litter, and Diospyros celebica Bakh. (ebony) leaves, a representative of broadleaf litter, in three forest communities (Karst, Lowland, Pine) on the island of Sulawesi, Indonesia, and identified their determinants. Twenty-four 1 m × 1 m quadrats were set up in each forest community to observe the in situ decomposition process. Near each quadrat, 1 m² litter traps were set to monitor litter production. In addition, 30 litter bags containing tusam leaves and 30 litter bags containing ebony leaves were spread in the three forest communities, in both the dry and wet seasons, to observe their decomposition rate during each season. The ANOVA test showed that the one-year in situ Decomposition Rate Constant (k) was significantly highest in the Karst forest (0.0921/year), followed by the Lowland forest (0.0700/year), and the lowest in the Pine forest (0.0277/year). During the dry season, the mean k-value of tusam leaves was significantly faster than ebony leaves in Karst (0.7162/6 months for tusam, 0.3840/6 months for ebony) and Lowland forests (0.3472/6 months for tusam, 0.1017/6 months for ebony), but on the contrary, it is slower in the Pine forest (0.0498/6 months for tusam, 0.0745/6 months for ebony). During the wet season, there was no significant difference between the mean k-value of tusam leaves compared to ebony leaves in the Karst (0.5217/4 months for tusam, 0.4859/4 months for ebony) and Lowland (0.2397/4 months for tusam, 0.2098/4 months for ebony) forests, but in the Pine forest, the mean k-value of ebony leaves was significantly higher than that of tusam leaves (0.0942/4 months for tusam, 0.1650/4 months for ebony). This study explains that the decomposition process of leaf litter is complex, species-specific, and is controlled by a combination of factors. Extrinsic factors play a more critical role than intrinsic factors in determining the k-value. The low rate of decomposition of tusam leaves under its mother tree stands is not caused by intrinsic factors, but rather by extrinsic factors that inhibit the growth of decomposing agents. Full article

Reliable precipitation inputs are essential for hydrological modeling, yet global precipitation products often exhibit substantial discrepancies that introduce significant uncertainties into streamflow simulations and projections. In this study, we assessed the relative contribution of precipitation dataset uncertainty to discharge simulations and projections, in comparison with uncertainties from model structure, model parameters, and climate projections, in the Liujiang catchment, southwest China. Three widely used satellite-based products (CHIRPS, PERSIANN, and IMERG) and one reanalysis dataset (ERA5) were combined with three hydrological models of varying structural complexity to simulate streamflow. Using an ANOVA-based variance decomposition framework, we quantified the contributions of different uncertainty sources under both historical and future climate conditions. Results showed that precipitation input uncertainty dominates discharge simulations during the calibration period, contributing over 60% of total variance particularly at high flows, while interactions among precipitation, model structure, and parameters govern low-flow simulations. Under future climate scenarios, climate projection uncertainty overwhelmingly dominates discharge predictions with 50–80% of uncertainty contribution, yet precipitation products still contribute significantly across time scales. The compensation of precipitation biases by hydrological models can cause parameter values to deviate from their true physical meaning. This deviation may further amplify the differences in discharge projections driven by different precipitation products under future climate conditions and increase the overall uncertainty of streamflow projections. Overall, this study introduced an integrated approach to simultaneously assess precipitation uncertainty across flow regimes and future climate scenarios. These results emphasized the necessity of using ensemble approaches that incorporate multiple precipitation products in hydrological forecasting and impact studies, particularly in data-scarce regions reliant on global datasets. Full article

In this study, we evaluate a phosphorus-based fire retardant (HR Prof) on Norway spruce using Simultaneous Thermal Analysis (STA: TG/DTG/DSC), Gas Chromatography–Mass Spectrometry (GC–MS), and bench-scale mass-loss measurements. Relative to the untreated reference, HR Prof re-routes decomposition toward earlier dehydration and transient char, simplifies the evolved gas mixture in the 150–250 °C range, and reduces burning intensity during 600 s of radiant exposure. Across 150/200/250 °C, identified components fell from 20/24/51 (reference) to 5/9/9 (HR Prof); no phosphorus-containing volatiles were detected in this window. Mass-loss tests showed a lower average burning rate (0.107 vs. 0.156%·s⁻¹) and a smaller cumulative loss at 600 s (64.2 ± 9.5% vs. 93.7 ± 2.1%; one-way ANOVA, p < 0.05 for percentage loss). STA was conducted in air; the transient char formed at an intermediate temperature is oxidized near ~600 °C, explaining the low final residue despite earlier charring. A count-based Poisson model corroborated the significant reduction in volatile component richness for HR Prof (p < 0.001). The cross-method correspondences—earlier condensed-phase dehydration/char → leaner volatile pool → lower and flatter burning-rate profiles—support a condensed-phase-dominated protection mechanism within the conditions studied. Full article

A cobalt vanadate (CoV₂O₆) photocatalyst was successfully synthesized and characterized for the degradation of organic dyes under visible light. Structural analysis revealed a monoclinic crystalline phase with a band gap energy of 2.13 eV, indicating strong visible light absorption. X-ray photoelectron spectroscopy (XPS) confirmed the presence of cobalt (Co), vanadium (V), and oxygen (O) in the material composition. Morphological investigations using SEM and TEM showed highly irregular particles with no defined geometric shape. Photocatalytic activity was evaluated using Rhodamine B (RhB) and Methyl Orange (MO) as model pollutants. Degradation efficiencies of 80% and 50% were achieved for RhB and MO, respectively, highlighting a selective performance towards the cationic dye. Radical scavenging experiments indicated that hydroxyl radicals and photogenerated holes were the dominant reactive species in RhB decomposition. The photocatalytic process was further optimized using response surface methodology (RSM), and the ANOVA analysis confirmed the significance of the quadratic model (p < 0.05). These findings demonstrate the potential of CoV₂O₆ as an efficient and selective photocatalyst for treating dye-contaminated wastewater. Full article

Runners have a high probability of sports injuries due to improper landing patterns. This study aimed to investigate the effects of three different foot strike patterns on lower limb muscle activation in healthy young male university students without specialized sports training experience. Methods: Sixteen healthy male college students (age: 21 ± 1 years) participated in this study. They performed running with three different foot strike patterns: forefoot strike (FFS), midfoot strike (MFS), and rearfoot strike (RFS) at controlled speeds of 1.4–1.6 m/s. EMG signals from six lower limb muscles (vastus lateralis, vastus medialis, rectus femoris, tibialis anterior, lateral gastrocnemius, and medial gastrocnemius) during the stance phase were collected using a wireless EMG system (1000 Hz). Ensemble Empirical Mode Decomposition (EEMD) was employed to analyze the time–frequency characteristics of lower limb EMG signals and ankle joint co-activation patterns to investigate the corresponding neuromuscular control mechanisms. Statistical analyses were performed using repeated-measures ANOVA, and significance was set at p < 0.05. Results: The timing of maximum energy in lower limb muscles during the stance phase occurred earlier in RFS compared to FFS and MFS. At initial ground contact, the low-frequency component energy (below 60 Hz) of the medial gastrocnemius was significantly higher in MFS and RFS compared to FFS, while FFS exhibited significantly higher high-frequency component energy (61–200 Hz). The co-activation of ankle dorsiflexors and plantar flexors (TA/GM) was also significantly higher in MFS and RFS compared to FFS. During the 100 ms before foot contact, the low-frequency component energy (below 60 Hz) of the lateral gastrocnemius was significantly higher in MFS compared to FFS, and the degree of TA/GM co-activation was significantly higher in both MFS and RFS compared to FFS. Conclusions: The maximum frequency in lower limb muscles appeared earliest during the mid-stance phase in the rearfoot strike (RFS) pattern. Moreover, during the pre-activation and early stance phases, frequency differences were observed only in the medial gastrocnemius, with RFS showing significantly higher low-frequency power. Full article

This study aims to compare decision tree and Analysis of Variance (ANOVA) techniques as feature selection methods, combined with Wavelet Packet Decomposition (WPD) for feature extraction, to enhance the diagnosis of faults in belt conveyor idlers. Belt conveyors are widely used in mining for efficient transport, but idlers composed of rollers are frequently subject to failure, making continuous monitoring essential to ensure reliability. Automated diagnostic solutions using vibration signals and machine learning rely on signal processing for feature extraction, often requiring dimensionality reduction or feature selection to improve classification accuracy. Due to the limitations of traditional techniques such as Principal Component Analysis (PCA) in handling temporal variations, Decision Tree and ANOVA emerge as effective alternatives for feature selection. This framework applied to each feature selection method, and Support Vector Machine (SVM) was used as a classification technique. The diagnostic performance of each method, including the case without feature selection, was evaluated. The results showed a higher diagnostic accuracy performance for the approaches that applied the features from the decision tree and from ANOVA. The improvement in the diagnosis of roller failures with feature selection was corroborated with the hit rates of failure mode, severity level, and location of a defective roller above 93.5%. Full article

Voice biometric systems play a critical role in numerous security applications, including electronic device authentication, banking transaction verification, and confidential communications. Despite their widespread utility, these systems are increasingly targeted by sophisticated spoofing attacks that leverage advanced artificial intelligence techniques to generate realistic synthetic speech. Addressing the vulnerabilities inherent to voice-based authentication systems has thus become both urgent and essential. This study proposes a novel experimental analysis that extensively explores various dimensionality reduction strategies in conjunction with supervised machine learning models to effectively identify spoofed voice signals. Our framework involves extracting multicepstral features followed by the application of diverse dimensionality reduction methods, such as Principal Component Analysis (PCA), Truncated Singular Value Decomposition (SVD), statistical feature selection (ANOVA F-value, Mutual Information), Recursive Feature Elimination (RFE), regularization-based LASSO selection, Random Forest feature importance, and Permutation Importance techniques. Empirical evaluation using the ASVSpoof 2017 v2.0 dataset measures the classification performance with the Equal Error Rate (EER) metric, achieving values of approximately 10%. Our comparative analysis demonstrates significant performance gains when dimensionality reduction methods are applied, underscoring their value in enhancing the security and effectiveness of voice biometric verification systems against emerging spoofing threats. Full article

The present work aims to determine the levels of contaminant oils in biodiesel obtained from the residual oil of the industrial processing of Nile tilapia via Quasi-Isothermal Thermogravimetry (TGA-qISO) and Differential Scanning Calorimetry (DSC). For this purpose, mixtures of tilapia oil (OT) and biodiesel (BD) were prepared in the mass proportions of OT/BD (5:95 m/m), OT/BD (10:90 m/m), OT/BD (15:85 m/m), OT/BD (20:80 m/m), OT/BD (25:75 m/m) and OT/BD (30:70 m/m). These mixtures were used to construct the calibration curve of the TGA-qISO and DSC techniques. To evaluate the efficiency of these techniques, three samples were prepared at concentrations of 7.01 OT%, 16.66 OT% and 27.05 OT%. The data obtained show that the biodiesel/oil mixtures presented two stages of mass loss, the first between 100 and 200 °C, which was attributed to the decomposition of the biodiesel, and from 250 °C, to the decomposition of the oil. In the DSC curves of the mixtures, it was observed that as the concentration of tilapia oil in the mixtures increases, there is a decrease in the intensity of the peaks and a shift to a higher temperature range. Statistical tools show that the TGA-qISO measurements presented analytical curves with a correlation coefficient (r) of 0.9999, while in the DSC analyses, r of −0.9727 and −0.9903 were obtained. Analysis of variance (ANOVA) confirmed that there is no significant difference between the measurements performed by TGA-qISO and DSC. This result shows that both techniques can be used to determine the oil adulteration in biodiesel samples. Full article

Accurate forecasting of COVID-19 case numbers is critical for timely and effective public health interventions. However, epidemiological data’s irregular and noisy nature often undermines the predictive performance. This study examines the influence of four smoothing techniques—the rolling mean, the exponentially weighted moving average, a Kalman filter, and seasonal–trend decomposition using Loess (STL)—on the forecasting accuracy of four models: LSTM, the Temporal Fusion Transformer (TFT), XGBoost, and LightGBM. Weekly case data from Ukraine, Bulgaria, Slovenia, and Greece were used to assess the models’ performance over short- (3-month) and medium-term (6-month) horizons. The results demonstrate that smoothing enhanced the models’ stability, particularly for neural architectures, and the model selection emerged as the primary driver of predictive accuracy. The LSTM and TFT models, when paired with STL or the rolling mean, outperformed the others in their short-term forecasts, while XGBoost exhibited greater robustness over longer horizons in selected countries. An ANOVA confirmed the statistically significant influence of the model type on the MAPE (p = 0.008), whereas the smoothing method alone showed no significant effect. These findings offer practical guidance for designing context-specific forecasting pipelines adapted to epidemic dynamics and variations in data quality. Full article

Computational thinking skills among high school students have become a global concern, especially in the context of the ever-evolving digital education era. However, the attention given by teachers to this skill during mathematics instruction has not been a priority. This study aims to evaluate and explore the impact of project-based learning (PBL) integrated with flipped classroom on high school students’ computational thinking skills in mathematics. The research design employed a mixed-method approach with a quasi-experimental, nonequivalent pre-test post-test control group design. The experimental group (46 students) and control group (45 students) were selected through simple random sampling from 12th-grade science students. Data were collected through tests, questionnaires, and in-depth interviews, using instruments such as computational thinking skills assessment questions, questionnaires, and interview protocols. Quantitative data analysis was performed using SPSS Version 26 for t-tests and ANOVA, while qualitative analysis was conducted using ATLAS.ti with an abductive-inductive and thematic approach. The findings indicate that PBL integrated with flipped classrooms significantly improved students’ decomposition, pattern recognition, and abstraction skills. The implementation of PBL, integrated with a flipped classroom, created an interactive learning environment, fostering active engagement and enhancing students’ understanding and skills in solving mathematical concepts. Although there was an improvement in algorithmic thinking skills, some students still faced difficulties in developing systematic solutions. The results of this study suggest that further research could explore other methodologies, such as grounded theory and case studies integrated with e-learning, and emphasize visual analysis methods, such as using photo elicitation to explore thinking skills. Full article

Intracanal medicaments are an important adjunct to the effective disinfection of the root canal system. However, conventional intracanal medicaments do not provide adequate protection against Enterococcus faecalis, which is the organism of interest in many cases of root canal failures. This study aimed to evaluate the influence of biosynthesized calcium oxide nanoparticles (CaO NPs) on the antibacterial activity, pH, microleakage and cytotoxicity of intracanal medicaments. CaO NPs were biosynthesized by the direct thermal decomposition of eggshells (EGS) and the reduction of calcium nitrate with papaya leaf extract (PLE). These nanoparticles were mixed with a proprietary calcium hydroxide powder in 10% and 25% (w/w) concentrations and blended in analytical-grade coconut oil to formulate the experimental medicaments. These were then evaluated for antibacterial activity, pH, microleakage and cytotoxicity at 1 day, 7 days and 15 days. A proprietary calcium hydroxide paste formulation (MX) was used as the control. Means and standard deviations were calculated and analyzed using repeated-measures ANOVA for pH and three-way ANOVA for the antibacterial effect, microleakage and cytotoxicity, followed by LSD post hoc analysis. Significant antibacterial activity was noted against Enterococcus faecalis at all times, with zones of inhibition (ZOI) up to 19.60 ± 2.30 mm. pH levels up to 13.13 ± 0.35 were observed for the experimental groups. Microleakage remained comparable to the control, while cytotoxicity was not observed in any of the groups at any time. Intracanal medicaments formulated with 10% and 25% (w/w) of biosynthesized CaO NPs could be promising candidates for the disinfection of the root canal system compared to conventional counterparts. Full article

Human decomposition processes generate pulses of nutrients, such as carbon (C) and nitrogen (N) in the form of ammonium and nitrate (NO₃⁻), which are released into the surrounding environment. The little exploration related to the potential of cadaveric leachate to influence the physical chemistry and biology of the soil makes it difficult to obtain data and scientific evidence, and subsequently the predominant objective of the current study was to determine soil contamination through the analysis of parameters of physical chemistry that included organic matter (OM), NO₃⁻, texture, humidity, and pH. Soil samples were taken at different depths in two temporary trials (the dry and rainy seasons) in central Ecuador. The Kruskal–Wallace and ANOVA statistical analyses determined significant differences in relation to the sampling sections and by categories, while there were no significant differences in the inter-season analysis; therefore, the study was based on the data obtained in the dry season. The results indicate a tendency towards contamination in cemeteries categorized as critical, that is, moderate, light, and not suitable due to the high values of OM and humidity measured. On the contrary, the soils that corresponded to the cemeteries classified as suitable yielded low values of the analyzed parameters, which corroborates their capacity for the present and future location of cemeteries. Monitoring and managing soil health is crucial to ensure sustainable environmental practices and protect public health; nonetheless, additional research is suggested to confirm the findings of the current study. Full article

Yield and stability are desirable characteristics that crops need to have high agronomic value; sugarcane stands out globally due to its diverse range of products and by-products. However, genotype-environment (G × E) interactions can affect the overall performance of a crop. The objective of this study is to identify genotypes with the highest yield and stability, as well as to understand their independent and interactive effects. A collection of 10 sugarcane varieties was evaluated, including Colombian, Dominican, Ecuadorian lines, and a group of clones planted across five different locations from 2018 to 2020. A two-way ANOVA along with the GGE biplot technique were used to analyze yield and stability. The ANOVA model shows highly significant effects in all cases (p < 0.001) except for the genotype by year and sector interaction (G × Y × S); however, the decomposition by sectors reveals a significant triple interaction in sector 04 (p < 0.05). The GGE biplot model accounted for up to 74.77% of the total variance explained in its PC1 and PC2 components. It also highlighted the group of clones as having the highest yield and environmental instability, and the Ecuadorian varieties EC-07 and EC-08 as having the best yield-stability relationship. We conclude that the combined results of the ANOVA and GGE biplot models provide a more synergistic and effective evaluation of sugarcane varieties, offering theoretical and practical bases for decision-making in the selection of specific varieties. Full article