This work explores a novel integration of experimental conversion of waste cooking oil (WCO) into biodiesel with advanced machine learning modeling to optimize transesterification outcomes. A reusable CaO catalyst derived from egg shells was employed, delivering a more affordable and sustainable option compared to typical homogeneous catalysts. A total of 16 experimental runs were conducted to investigate the effects of catalyst concentration (CC), reaction temperature (RT), and methanol-to-oil molar ratio (MOR) on biodiesel yield. Four boosted ML algorithms XGBoost, AdaBoost, Gradient Boosting Machine (GBM), and CatBoost were applied to model the process, with hyperparameter tuning via grid search and validation through k-fold cross-validation (k = 5) and residual plots to ensure reliability and mitigate overfitting. CatBoost emerged as the best-performing model (R² = 0.955, RMSE = 0.83, MSE = 0.68, MAE = 0.52), predicting a maximum biodiesel yield of 95% at 3% CC, 80 °C RT, and a 6:1 MOR. Feature importance and partial dependence plots identified MOR and CC as the most influential parameters. Engine performance tests further validated the practical viability of CaO-based biodiesel, showing 26% lower CO emissions and 13% lower smoke emissions compared to diesel, resulting in a marginal 2.83% decline in brake thermal efficiency alongside a 4.31% rise in fuel consumption. This interdisciplinary approach combining green catalyst development with interpretable machine learning demonstrates a promising pathway for cleaner energy applications and data-driven optimization in biodiesel research.

A wearable device-based fetal movement detection system for pregnant women is proposed to resolve the problems of low accuracy of fetal movement detection by fetal heart monitor, difficulties of fetal movement monitoring by pregnant women in person, and inability to monitor for long periods of time by ultrasonic Doppler imaging device. The overall software design flow of the system is proposed after determining the overall structure of the system based on symmetric sensor. The application circuit of the three-axis acceleration sensor MC3672 and its supporting sensor data collection program are designed, and the application circuit of the main control chip NRF52840 with Cortex-M4 core is analyzed. The function of data collection and algorithm recognition result transfer to a smartphone is realized through the fetal movement recognition and algorithm design and Bluetooth communication design. Finally, the system test scheme is introduced, which involves performing functional tests on four healthy pregnant volunteers and analyzing the results. The experimental results show that the average recognition rate and correct rate of this system to recognize fetal movement is 89.74% when using the real fetal movement actively perceived by pregnant women as the standard, achieving a domestic and wearable design of fetal movement monitoring device for pregnant women that can be used to analyze and predict the fetal health condition.

Water pollution from the industrial dyes is a serious hazard to ecosystems, and addressing this issue is a significant challenge. To address these issues, we are fabricated BaDy_xFe_12−xO₁₉ (x = 0.02 to 0.06) by sol-gel auto-ignition (SGA) technique. Several characterizations were used to scrutinize the structural, optical, photocatalytic, and magnetic traits of the produced samples. The X-ray diffraction (XRD) of the sample revels the hexagonal crystal structure. The field emission scanning electron microscopy (FESEM) of both samples reveal the existence of agglomerated grains showing hexagonal shapes. X-ray photoelectron spectroscopy (XPS) analyses confirm the oxidation state of every element present in the synthesized nanomaterials. The specific surface area was found to be 1.069 m²/g for BDF1 and 1.466 m²/g for BDF3. The band gap of the BDF1, BDF2, and BDF3 samples are found 2.16, 2.12, and 1.99 eV. The photocatalytic efficacy of the catalysts was examined by removal of the CR in natural light. A notable degradation efficiency of 89.29% are achieved by the BDF3 catalyst within 90 minutes under natural sunlight irradiation. The results demonstrate a straightforward and efficient approach for producing photocatalytic materials that are highly effective for the elimination of dye pollutants in wastewater treatment.

Phase change materials (PCMs) are increasingly essential in thermal energy storage (TES) systems (TES) because of their excellent energy storage density per unit volume, particularly in low‐ and medium‐temperature applications. This investigation focuses on the improvement of advanced composite phase change materials (CPCMs) by integrating 20 wt% carbon quantum dots (CQDs) into paraffin wax (PW) to increase thermal conductivity. The CPCMs were synthesized using the ultra‐sonication technique. The microstructural morphology of the PCMs and CPCMs was characterized using scanning electron microscopy (SEM). Thermal conductivity measurements were conducted using a Heat Flow Meter, while latent heat and melting temperature were determined through differential scanning calorimetry (DSC). The findings revealed an important enhancement in thermal conductivity with the addition of CQDs mixed with PCM matrix. Specifically, the addition of 20 wt% CQDs into PW increased the thermal conductivity to 0.452 W/m K and enhanced the latent heat by 10.4%. Furthermore, the CPCMs exhibited a larger and higher melting temperature range compared to conventional PCMs, highlighting their improved thermal performance.

3D printing has brought significant changes to manufacturing sectors, making it possible to produce intricate, multi-layered designs with greater ease. This study focuses on optimizing the compressive strength (CS) of functionally graded multi-material (PLA/Almond Shell Reinforced PLA) which is fabricated with the aid of the FFF process, a widely used additive manufacturing technique. Six different machine learning models (ML) were utilized to estimate CS using key process parameters, namely print speed (PS), layer height (LH), and printing temperature (PT). Among six ML models, Polynomial Regression (PR) performed best, with an R² of 0.88 and the lowest error metrics (MAE = 1.38, RMSE = 1.9, MSE = 3.6). SHAP analysis indicated that PS is the most influential parameter, followed by LH. PR predicted optimal parameters (PS = 19 mm/s, LH = 0.1 mm, PT = 216°C) and yielded a predicted CS of 36 MPa, which was experimentally validated as 34.8 MPa with a low error of 3.44%. Also, the PR outperformed the traditional Taguchi method, which predicted a CS of 33.74 MPa, showing a 7.5% improvement and lower error. This demonstrates that PR-based ML optimization offers better accuracy and improved mechanical performance, making these FGMs suitable for various consumer applications.

Hard turning is a high-precision machining approach widely adopted in manufacturing for finishing hardened alloy steels that exhibit superior hardness and excellent wear resistance. The residual stresses induced during the hard turning process significantly impact the performance and reliability of the machined component. This study presents a comprehensive finite element analysis to predict residual stress distribution and thermal behavior during dry hard turning of AISI 52100 steel under varying cutting conditions. The Power Law material model, incorporating a strain hardening function, was employed to simulate the material’s behavior at high strain rates, accounting for strain rate sensitivity and thermal softening due to elevated temperatures during machining. The model further includes a Coulomb friction approach to capture the interactions between the tool, chip, and workpiece. The cutting speed was found to have the most significant impact on surface tensile stresses. The subsurface residual stresses were greatly affected by the feed rate. The elevated feed rates resulted in increased compressive residual stresses being induced in the machined component. The developed FEM model demonstrated its effectiveness as an essential tool for pre-processing residual stress predictions, which in turn helps in the design and manufacture of reliable, high-quality, components. The thermal performance of coated carbide tools; more specifically, the performance of titanium nitride (TiN), titanium carbonitride (TiCN), and aluminum oxide (Al₂O₃) coating layers were examined. Tools coated with multilayer structures incorporating Al₂O₃ as the top layer demonstrated superior thermal barrier performance, leading to a notable reduction in both heat generation and maximum cutting temperatures. The cutting temperature data recorded using embedded thermocouple technique with infrared thermometers showed a good agreement with the FEM results. This validation confirms the AdvantEdge’s simulation precision and enhances understanding of machining dynamics, contributing to robust component design with superior surface integrity.