This paper aims to recommend the most suitable golf course for each user by focusing on golf courses and analyzing customer reviews. Furthermore, by examining the recommendation results, the goal is to clarify the characteristics of each golf course from the user’s perspective and contribute to the promotion of each golf course. The procedure of this paper is first to extract user preferences using Word2vec and TF-IDF from reviews. Next, the extracted user preferences are matched with golf course features. Finally, recommendations are made based on the geographical relationship between the user and the golf course. As a result, a high accuracy rate is achieved. Additionally, some keywords that should be used in promotions for each golf course feature have been identified.

The purpose of this study is to address the challenges of high peak-to-average power ratio (PAPR) and nonlinear power amplifier (PA) distortion in multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems based on the IEEE 802.11ac. The research integrates iterative clipping filtering (ICF) for PAPR reduction and the Saleh model predistortion (PD) for PA linearization. Implemented on a software defined radio (SDR) platform using NI-USRP devices, the system is evaluated in real-world line-of-sight (LOS) and non-line-of-sight (NLOS) environments. Results show significant PAPR reduction, from 19.7 dB to 10.3 dB, and improved PA linearity, achieving a 94.80% error vector magnitude (EVM) reduction. Furthermore, the combined approach exhibits lower symbol error rates (SER) and error-free data transmission, particularly under LOS conditions. Compared to conventional methods, the system demonstrates superior execution efficiency with 475–503 ms processing times.

This study optimizes the machining process of Inconel-800 superalloy using nanofluid minimum quantity lubrication (MQL) with multi-wall carbon nanotubes (MWCNTs) and biodegradable coconut oil. A Taguchi design with 27 trials is used to examine the effects of varying nanoparticle concentrations and machining parameters on surface roughness and temperature. The optimized nanofluid MQL system improves surface roughness by 26.22%, reduces surface roughness peak-to-valley by 12.06%, and significantly lowers temperature, demonstrating improved quality and thermal management. A Kriging model predicts outcomes with high accuracy (R2 > 0.9), and multi-objective optimization using Kriging and the non-dominated sorting genetic algorithm II identifies an optimal balance between surface roughness and temperature. Additionally, using coconut oil as the lubricant base in the nanofluid MQL system promotes sustainable machining by reducing reliance on conventional lubricants and environmental impact. These findings validate the effectiveness of advanced optimization techniques combined with nanofluid MQL for superior sustainable machining of superalloys.

The present work proposes a fatigue crack growth rate (FCGR) model for steel pipelines subjected to sinusoidal loading using a modified exponential function. The modification in the exponential function is made for the non-dimensional parameter using the stress intensity range (ΔK) as the crack driving force. The acceptable values of ΔK for FCGR in stage-I ranged between 17.45-20.46 MPa√m, between 20.46-21.41 MPa√m for stage-II, and between 21.41-21.98 MPa√m for stage-III. A new correlation is also developed between the specific growth rate and the non-dimensional number. The modified exponential function predicted the FCGR within the acceptable values for all three stages in the radial direction. It shows the best performance for stage-I of FCGR and the lowest for stage-III. The microstructure envisages shallowed microvoids, while the striations and secondary cracks are mostly perpendicular to the FCG direction.

This study proposes an optimal electric vehicle (EV) charging (OEVC) management methods to minimize electricity costs and energy losses in the distribution system, which arise from the growing demand for EV charging. a multi-objective particle swarm optimization (MOPSO) algorithm is used to solve the OEVC multi-objective optimization (MOO). Additionally, the time-of-use (TOU) tariff is used to coordinate between the distribution system operator and EV users, which can help increase the efficiency of the charging schedule. Monte Carlo Simulation (MCS) is used to model virtual EV user behavior and create EV charging load profiles. The proposed MOPSO-based OEVC approach is verified on the modified IEEE 33-bus distribution test system, using MATLAB software, under both uncontrolled and controlled charging case studies. The simulation results demonstrate that the proposed method optimizes EV charging efficiently, achieving reductions of approximately 7.60% in electricity costs and 28.73% in energy losses compared to the uncontrolled charging case.

Weather derivatives are financial instruments influenced by temperature fluctuations, impacting industries such as agriculture, tourism, and energy. Accurate temperature modeling is essential for improving risk assessment and hedging strategies. This study evaluates the effectiveness of two forecasting hybrid approaches: the Fourier Ornstein-Uhlenbeck (OU) process, a widely used stochastic model, and the Fourier-Elman Recurrent Neural Network (ERNN), a hybrid neural network-based model. Daily temperature data from Chiang Mai, Thailand, spanning January 2005 to December 2021, were analyzed. The predictive performance of each model was assessed using root mean square error (RMSE). The results indicate the Fourier ERNN model (RMSE = 0.106) significantly outperforms the Fourier OU process (RMSE = 2.299), demonstrating superior accuracy in capturing both seasonal and stochastic variations in temperature dynamics. Thus, deep learning-based hybrid models provide a more effective framework for temperature forecasting. The proposed approach has potential applications in climate risk management, weather derivative pricing, and decision-making in climate-sensitive sectors.

This study aims to enhance miner safety through real-time monitoring and emergency responses. To achieve this, a multi-functional mining helmet (MFMH) is designed with location tracking via a global system for mobile communications (GSM) and global positioning system (GPS), hazardous gas detection, lighting, and temperature regulation, along with vibration-based alerts for emergency notification. The helmet is tested in simulated mining environments to assess its performance. The system successfully detected hazardous gases at concentrations of 41.23 ppm, triggered automatic lighting when luminosity dropped below 35 lux, and maintained internal temperatures between 26 ℃ and 27 ℃, demonstrating its effectiveness in safety.

This study evaluates the compressive strength of a cubic lattice scaffold made from Titanium alloy (Ti-6Al-4V) for mandibular bone implants. Scaffold designs with pore sizes ranging from 800 µm to 1000 µm were analyzed using finite element analysis under compressive forces of up to 800 N. Pore sizes of 800 µm and 850 µm achieved a safety factor greater than 1.4, indicating their suitability for both dynamic and static loading. Planned production with bound metal deposition, maintaining a density below 35%, emphasizes material efficiency and cost-effectiveness. Results indicate that 800 µm and 850 µm pore sizes offer optimal strength and safety, suggesting effective mandibular implant integration. Further research on cyclic load testing and osseointegration is recommended.

Geopolymers are widely recognized as an eco-friendly alternative material. However, the impact of pressing stresses and low NaOH concentrations on their properties remains underexplored. This research aims to investigate the effects of pressing stresses on unit weight, porosity, water absorption, and compressive strength of high-calcium fly ash geopolymer paste with low NaOH concentrations. The low NaOH concentrations of 0.5, 1.0, and 2.0 M, pressing stresses of 10, 20, and 30 MPa, and liquid-to-binder ratios of 0.10, 0.12, 0.14, 0.16, 0.18, and 0.20 by weight are used. The specimens of geopolymer paste are oven-dried at 60°C for 24 hours before evaluation. The testing results show that the compressive strength of casted geopolymer paste is between 2 to 15 MPa, with higher compressive strength associated with lower porosity. The water absorption rate is between 11% and 21% by weight, which has a higher water absorption rate as the porosity increases.

This study aims to formulate a calculation for earthquake shaking intensity (rs_mSIS) based on the response spectrum (RS) using the Japan Meteorological Agency-seismic intensity scale. The research investigates the relationship between the response spectrum parameters—period and maximum acceleration—and the earthquake source types, including megathrust, Benioff, and shallow crust/background sources. Artificial ground motions are generated and analyzed using Matlab to calculate shaking intensity values, which are then used to develop the rs_mSIS formula. The formulation is validated against actual response spectrum data from 15 Indonesian cities and demonstrated high accuracy, with the Wariyatno coefficient applicable across all models. This approach provides a standardized method to assess seismic intensity, offering enhanced reliability for building design in earthquake-prone areas and serving as a valuable tool for engineers and urban planners to improve earthquake resilience in diverse seismic environments.