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Electroencephalography (EEG) signals are highly susceptible to noise and artifacts, which can degrade analysis accuracy, particularly in Autism Spectrum Disorder (ASD) studies. Therefore, effective preprocessing is required to improve signal quality prior to further analysis. This study proposes an integrated EEG preprocessing pipeline that combines a Finite Impulse Response (FIR) band-pass filter (0.5–70 Hz) with notch filtering and detrending, followed by temporal denoising using the Stationary Wavelet Transform (SWT) with the Daubechies 4 mother wavelet and spatial filtering based on SPHARA. This dual-domain approach is designed to address both temporal and spatial noise in multichannel EEG signals. Experimental results demonstrate that the proposed FIR combined with SWT and SPHARA pipeline consistently outperforms single-domain preprocessing methods, achieving a maximum Signal-to-Noise Ratio (SNR) of 31.93 dB. The proposed method also produces the lowest Mean Absolute Error (MAE) (16.81 µV) and Standard Deviation (SD) (0.75 µV), indicating high signal stability with minimal amplitude distortion. Root Mean Square Error (RMSE) values remain stable within the range of 29.5–592.3 µV, with a minimum RMSE of 29.5 µV, demonstrating effective noise suppression while preserving signal energy. These results confirm that integrating temporal and spatial preprocessing significantly improves EEG signal quality and supports more reliable EEG analysis for ASD-related studies.

Background: The development of industrial technology in the Industry 4.0 era has encouraged the implementation of intelligent monitoring systems to improve machine reliability and operational efficiency. However, machine fault diagnosis systems based on artificial intelligence often face limitations in terms of interpretability because the models used are complex and difficult to explain. Objective: This study aims to develop a deep learning-based industrial machine fault diagnosis system integrated with an Explainable Artificial Intelligence (XAI) approach to improve diagnostic accuracy while providing interpretable insights for users. Method: The research method involves collecting data from industrial machine sensors consisting of vibration signals, temperature measurements, and acoustic signals, followed by data preprocessing and feature extraction processes. The processed data are then used to train a deep learning-based diagnostic model, after which explainability methods such as SHAP or LIME are applied to analyze the contribution of each feature to the model’s prediction results. Model performance is evaluated using accuracy, precision, recall, and F1-score metrics. Results: The results indicate that the proposed deep learning model achieves better performance compared to conventional machine learning methods such as Support Vector Machine and Random Forest. Furthermore, the explainability analysis reveals that vibration amplitude, increases in machine component temperature, and anomalies in acoustic signals are the main factors influencing machine fault detection. Therefore, the proposed system not only improves the accuracy of machine fault diagnosis but also provides transparency in the decision-making process, thereby supporting the implementation of predictive maintenance in smart manufacturing environments.

The plastic recycling industry generates wastewater that poses a potential threat to aquatic environments if not managed optimally. Performance evaluation of Wastewater Treatment Plants (WWTP) is typically conducted partially by comparing outlet parameters against effluent standards, often failing to depict water quality conditions holistically. Therefore, this study employs the Water Quality Index (WQI) approach using the Canadian Council of Ministers of the Environment (CCME) method to evaluate the performance of PT X's WWTP in East Java. This research utilized secondary data from inlet and outlet wastewater quality tests over 23 months (January 2024–November 2025), covering 10 parameters. Analysis was conducted by calculating removal efficiency and determining factors F1 (scope), F2 (frequency), and F3 (amplitude) as the basis for the WQI-CCME calculation. The results indicate that the WWTP achieved high efficiency (>80%) in reducing dominant parameters such as TSS, TDS, BOD, and COD. However, violations of effluent standards were still observed in certain parameters. The obtained WQI-CCME value was 69,30, categorized as "Fair" with a moderate level of violation. These findings demonstrate that although the WWTP meets regulatory standards, the WQI-CCME approach provides a more comprehensive assessment of performance. Optimization of advanced treatment units is recommended to improve effluent quality and sustainable WWTP performance.                                                                   

Carpal tunnel syndrome (CTS) is the most common chronic neuropathy of the upper limb, characterized by compression of the median nerve within the carpal tunnel. It typically results from repetitive hand movements or trauma and leads to pain, numbness, and weakness, making it a significant occupational health concern. Increased intracarpal pressure causes venous stasis, edema, and ischemic damage, which slow nerve conduction and are detectable through electrodiagnostic (EDX) studies. Recently, ultrasonographic measurement of the median nerve cross-sectional area (CSA) has been proposed as a noninvasive diagnostic option. This study aimed to evaluate the diagnostic accuracy of ultrasound-based CSA measurement compared with EDX findings in CTS patients. The research was conducted at Al-Imamain Al-Kadhimain Medical City in Baghdad from November 2024 to March 2025 and included 100 patients (200 hands). All individuals underwent both EDX and high-resolution ultrasonography using a 5–13 MHz linear probe, with CSA calculated by the direct tracing method. CTS was confirmed in 102 hands (51%). Affected hands demonstrated significantly prolonged distal motor and sensory latencies, reduced amplitudes, and lower conduction velocities (p < 0.001). Mean CSA was significantly larger in CTS hands (13.75 ± 3.95 mm²) than in non-CTS hands (10.15 ± 3.33 mm², p < 0.001). ROC analysis produced an AUC of 0.776 and an optimal cutoff of 11.5 mm² (72% sensitivity, 76% specificity). CSA also increased with CTS severity. Moderate accuracy was observed when differentiating mild from moderate CTS at a 12.5 mm² cutoff, and moderate from severe CTS. In conclusion, median nerve CSA measurement by ultrasound is a reliable, noninvasive, and rapid tool for diagnosing and grading CTS, complementing EDX assessment.  

A steel truss bridge is a structure constructed from a series of interconnected steel bars. The loads received by this structure are analyzed and transferred to the steel bars that make up the structure. Factors that need to be analyzed in bridge construction include the location and surrounding environmental conditions. After identifying the type of soil and bedrock beneath the surface, the materials used must meet strength or durability test standards before the bridge can be operated. The materials used must comply with the Indonesian National Standard (SNI). In addition to the factors mentioned above, the bridge must undergo a feasibility evaluation. Among the various tests used to evaluate bridge strength, one is vibration testing. The vibration test method, when compared to the load test method, shows higher cost efficiency and does not cause damage to the structure.

An electrostimulator is a medical device designed to deliver controlled electrical stimulation to nerves and muscles, supporting rehabilitation and therapy for patients with neuromuscular disorders. This study focuses on designing and developing a portable electrostimulator that offers three distinct waveform modes: continuous wave, discontinuous wave, and dense-disperse wave, providing versatility for different therapeutic needs. The device is powered and controlled by an Arduino Mega 2560 microcontroller, coupled with a Nextion touchscreen LCD interface that allows users to adjust waveform type, frequency, and stimulation intensity with ease. Waveforms are generated through an NE555 IC circuit, with amplitude adjusted via a potentiometer and subsequently amplified using a step-up transformer to achieve therapeutic voltage levels. Functionality and performance tests were conducted using an oscilloscope, and the device was benchmarked against a commercial KWD-808 electrostimulator. Results demonstrate that the developed electrostimulator reliably produces the intended waveforms, achieving peak voltages up to 32V and frequencies ranging from 33.3 Hz to 66.6 Hz, confirming its effectiveness and feasibility for non-clinical nerve and muscle therapy applications.

This study presents a modal analysis of Pertamina EP Cepu’s closed drain pump 510-P9002, which operates in the condensate–water treatment unit of the Jambaran Tiung Biru field. Field vibration measurements conducted in August 2024 indicated a fundamental frequency of 25 Hz, corresponding to 1×RPM of the driving motor, with maximum amplitudes reaching 13.46 mm/s. Such excessive vibration poses risks of mechanical damage, reduced equipment service life, and potential operational failure. To address this issue, finite element analysis (FEA) was employed to examine the dynamic response of the pump, determine its natural frequencies, and identify possible resonance conditions. A CAD model of the pump–vessel assembly was developed, meshed, and analyzed under actual boundary conditions. The results showed several natural frequencies ranging between 23.16 and 26.65 Hz, which are close to the excitation frequency, suggesting a very high likelihood of resonance. Various structural modifications were then evaluated, including a half casing and two types of full casings. Among these, the full casing B design provided additional stiffness in the motor support area; however, none of the modifications effectively reduced vibration within the internal components. Based on these findings, the study recommends the implementation of a dynamic vibration absorber (DVA) tuned to the excitation frequency, along with the redesign of structural components to shift natural frequencies away from operating excitation. These solutions are expected to improve operational stability, extend equipment lifespan, and enhance overall system reliability. The outcomes of this research provide important insights for managing vibration issues in pump systems operating under similar conditions, particularly in the oil and gas industry where continuous, stable operation is critical.

Bridge infrastructure is a vital component of transportation systems that is vulnerable to structural damage caused by dynamic loads, environmental factors, and aging. Early crack detection is crucial to prevent structural failures that may lead to catastrophic consequences. This study aims to develop a non-destructive detection method based on acoustic sensors to identify cracks in bridge structures with higher sensitivity and accuracy compared to conventional visual inspections. The research was conducted through laboratory experiments and field tests using acoustic sensors, data acquisition devices, and signal analysis software. The procedure included sensor installation on a bridge model, simulation of artificial cracks with varying sizes and positions, recording of acoustic wave signals, and data analysis using frequency spectrum, amplitude, and waveform pattern approaches. The results show significant differences between normal and cracked conditions in the frequency spectrum, where cracks produced amplitude anomalies at specific frequencies. Amplitude analysis revealed a positive correlation between crack size and acoustic signal intensity, while waveform pattern analysis demonstrated the influence of crack position on distortion levels. Cracks located at the center generated the highest distortion, followed by joints and edges. These findings confirm that acoustic sensors, particularly fiber-optic-based ones, offer advantages such as high sensitivity, reliability under complex environmental conditions, and the ability to detect subsurface cracks. The implications of this research highlight the potential development of an acoustic sensor-based structural health monitoring system integrated with real-time analysis software, thereby supporting preventive maintenance, extending infrastructure lifespan, and enhancing transportation safety.

Early detection and prediction of preeclampsia are crucial to prevent severe complications and ensure timely interventions, Specific ECG patterns, including PR segment, Q wave duration and amplitude, ST segment, U wave, and sinus rhythm were under study for their potential indicators of preeclampsia. This study aims to investigate the predictive role of these ECG patterns in preeclamptic pregnant women in the third trimester of pregnancy. Fourty (40) consenting pregnant women were recruited from St. Philomina Catholic Hospital, Edo State, Nigeria. These subjects consisted of  twenty (20) normotensive  and twenty (20) preeclamptic pregnant women in their  third trimester of pregnancy. After the subjects were  identified and recruited into the study, they were taken to the laboratory where their vital signs was taken and their ECG patterns recorded with ECG machine. Data obtained from this study were analysed using Graph Pad Prism 9. Results generated were expressed as mean ± SEM and a P-value of ≤ 0.05 were considered statistically significant. results from this present study show no significant differences were observed in the P-R segment, R-R interval, Q wave duration, Q wave amplitude The study underscores the multifactorial nature of cardiovascular changes in preeclampsia and highlights the potential of ECG parameters in aiding early detection, risk stratification, and management of the condition, despite  parameters showing no significant differences. However, PR Segment, Q Wave duration and amplitude, ST Segment , U wave and Sinus rhythm cannot be used to predict preeclampsia  

The ultimate goal of predicting preeclampsia that can enhance early detection and risk stratification in pregnant women, by leveraging the diagnostic potential of ECG patterns, we hope to improve maternal and fetal outcomes and contribute to the development of personalized care strategies for preeclamptic patients. Current diagnostic methods for preeclampsia rely primarily on routine blood pressure monitoring and proteinuria assessment, which have limited sensitivity and specificity. The aim of this study is to investigate the predictive role of P wave duration, amplitudes and morphology in preeclamptic pregnant women during the third trimester. Fourty (40) consenting pregnant women were recruited from St. Philomina Catholic Hospital, Edo State, Nigeria. These subjects consisted of  twenty (20) normotensive  and twenty (20) preeclamptic pregnant women in their  third trimester of pregnancy. After the subjects were  identified and recruited into the study, they were taken to the laboratory where their vital signs was taken and their ECG patterns recorded with ECG machine. Data obtained from this study were analysed using Graph Pad Prism 9. Results generated were expressed as mean ± SEM and a P-value of ≤ 0.05 were considered statistically significant. Results from this present study show statistically significant increases in P wave duration, amplitude and abnormal M pattern among preeclamptic compare to normotensive pregnant women, consistent with prior research, abnormal M pattern in P wave morphology is linked to atrial pathology in preeclampsia. The study underscores the multifactorial nature of cardiovascular changes in preeclampsia and highlights the potential of ECG parameters in aiding early detection, risk stratification, and management of the condition.

The use of ECG patterns as predictors of preeclampsia offers a promising approach, as it is a widely available and cost-effective tool. Specific ECG patterns, including angle of deviation, QRS Complex (Right Ventricular Hypertrophy (RVH) , Left Ventricular Hypertrophy (LVH)), and T wave amplitudes as a potential tool for predicting preeclampsia. The aim of this study is to investigate the predictive role of angle of deviation, QRS Complex (Right Ventricular Hypertrophy (RVH) , Left Ventricular Hypertrophy (LVH)), and T wave amplitudes in preeclamptic pregnant women during the third trimester. Fourty (40) consenting pregnant women were recruited from St. Philomina Catholic Hospital, Edo State, Nigeria. These subjects consisted of  twenty (20) normotensive  and twenty (20) preeclamptic pregnant women in their  third trimester of pregnancy. After the subjects were  identified and recruited into the study, they were taken to the laboratory where their vital signs was taken and their ECG patterns recorded with ECG machine. Data obtained from this study were analysed using Graph Pad Prism 9. Results generated were expressed as mean ± SEM and a P-value of ≤ 0.05 were considered statistically significant. Results from this present study show no significant differences were observed in QRS complex angles related to right ventricular hypertrophy (RVH) between normotensive and preeclamptic pregnant women. Notably, there was a significant increase in QRS complex related to left ventricular hypertrophy (LVH) in preeclamptic pregnant women, indicating left ventricular remodeling's importance. Moreover, there was a significant increase in T wave amplitude, this suggests underlying myocardial electrical remodeling or dysfunction in preeclampsia, emphasizing the need for cardiovascular monitoring. The study underscores the multifactorial nature of cardiovascular changes in preeclampsia and highlights the potential of ECG parameters in aiding early detection.

A pressure vessel is a closed container that stores pressurized liquids or gases. Low-carbon steel is used as a material for pressure vessels that operate at low to moderate temperatures. One of the important aspects of manufacturing pressure vessels is the welding process. The welded area tends to be the weakest point due to exposure to high heat, which can lead to greater residual stress and potentially cause cracking. This study aims to evaluate the effect of variation in ample angle and current strength on mechanical properties, especially tensile strength, and hardness, to determine the optimal amplitude angle and current strength. The research method applied is an experiment by conducting SMAW welding on SS400 steel using E7016 electrodes, with a single V ample angle variation of 55°, 60°, 65°, and strong welding current of 110 A, 120 A, and 130 A, then a tensile test and hardness test are carried out. The results show that the tensile strength of the raw material is 481.2  with a hardness of 191.95 HVN. For the variation of the amputation angle, the highest tensile strength value was recorded at an angle of 55°, which was 567.471 N/mm2 , while the lowest was at an angle of 65°, which was 559.997 N/mm2 . The highest hardness value at a 65° angle reached 328.422 HVN, while the lowest at a 55° angle was 312.878 HVN. In terms of current strength variation, the highest tensile strength is obtained at 130 A which is 568.421 N/mm2 and the lowest at 110 A is 552.339 N/mm2 . The highest hardness value is found at a current strength of 110 A, which is 343.411 HVN, and the lowest at a current strength of 130 A, which is 295.122 HVN. The optimal parameters were found at a yield angle of 55° with a tensile strength of 567.471 N/mm2 and a hardness of 312.8 HVN and a current strength of 130 A with a tensile strength of 568.421 N/mm2 and a hardness of 295.1 HVN.

Vibration due to misalignment of the propeller shaft is detected using the VA-12 Vibration Analyzer sensor which is equipped with a Piezoelectric Accelerometer PV-571. Vibration measurements are carried out in alignment and misalignment conditions. The VA-12 Vibration Analyzer detection tool displays the results of the Vibration signal. Must be protected against the vibrations generated. Vibration signal analysis is done by looking at or observing the signal amplitude and signal frequency with two conditions of alignment and misalignment. The VA-12 Vibration Analyzer is used to analyze vibration signals and then convert them to Fast Fourier Transform (FFT) and Wavelet Transform (WT). In this analysis, a comparison of vibrations is carried out during alignment and misalignment conditions.