Oriental Institute of Technology

Energy storage systems are becoming increasingly important as more renewable energy systems are integrated into the electrical (or power utility) grid. Low-cost and reliable energy storage is paramount if renewable energy systems are to be increasingly integrated into the power grid. Lead-acid batteries are widely used as energy storage for stationary renewable energy systems and agriculture due to their low cost, especially compared to lithium-ion batteries (LIB). However, Lithium-Ion Battery (LIB) are increasingly used in modern engineering systems from smart phones to electric vehicles. Both lead-acid battery and LIB technology suffers from system degradation and a relatively short lifetime, largely due to its charging/discharging cycles. In the present study, we use Machine Learning methodology to estimate the battery degradation in an energy storage system. It uses two types of datasets: discharge condition and lead acid battery data. In the initial analysis, the Support Vector Regression (SVR) method with the RBF kernel showed poor results, with a low accuracy value of 0.0127 and RMSE 5377. On the other hand, the Long Short-Term Memory (LSTM) method demonstrated better estimation results with an RMSE value of 0.0688, which is relatively close to 0. The use of the LSTM model algorithm for estimating battery RUL with a small error value is a promising development that has the potential to significantly improve battery performance and reduce maintenance costs. Continued research and development in this area will be important for advancing the state of the art in battery management and improving the reliability and performance of battery systems. While the specifics of battery capacity requirements and usage patterns may vary depending on the application, the findings from the previous section on battery aging and RUL estimation are likely to be relevant for a wide range of energy storage systems, including those based on lead-acid batteries. The lead acid battery in the Solar Power Plant of The State Electricity Company shows a faster capacity degradation model compared to the type of NASA measurement data. However, the results shown do not fully describe the conditions for using lead acid batteries in a Solar Power Plant due to the limited dataset owned by the Solar Power Plant, namely the limited quality and quantity of data. The initial State of Health (SOH) and predicted SOH values from the previous section may provide useful insights into battery aging and Remaining Useful Lifetime (RUL) estimation for batteries in certain applications; however, they should be interpreted with caution and may not be directly comparable to the SOH values observed in solar power plant batteries or other real-world applications. It is important to consider the specific operating conditions and battery chemistries involved when interpreting SOH values and making decisions about battery maintenance, replacement, or optimization.

The importance of stress-induced warpage/distortion in silicon semiconductor chips/wafers cannot be understated – especially in light of the most recent trends in the semiconductor industry for 3D Hybrid Integration/Packaging to enable the extreme high density integrated circuits for AI (Artificial Intelligence) and HPC (High Performance Computing) chips. Silicon is very brittle – microcracks and other damages (scratches, dents, pits, cleaves) can easily be found on surfaces of silicon semiconductor wafers and dies. Given residual stress in the wafers/dies, they may grow and propagate into large scale damage and distortions which will lower production yield, make both die-to-wafer (D2W) and wafer-to-wafer (W2W) alignment very difficult in manufacturing and eventually affect performance of the chips during operations. Controlling the stress in silicon wafers/dies is thus key. Die-level warpage and distortion control (especially in the context of Hybrid Bonding Integration/HBI schemes in semiconductor advanced packaging) has direct impact on bonding quality and placement accuracy, and thus immediately affects production yield, yet there is no system commercially available now in the semiconductor industry that offers the capability to track them closely at the die-level, during the various manufacturing/assembly processes, and at the needed spatial resolution accuracy. We present here in this paper a novel method of rapid stress-induced warpage/distortion metrology for silicon semiconductor wafers/dies at high resolution (target: 1 micron). It is non-X-ray Diffraction, but high resolution based on thin plate geometry and local surface curvature model according to Kirchhoff theory, combined with recent advances in laser interferometry instrumentation. In this work, we demonstrate the first experimental findings on the technical feasibility of the novel technique on silicon thin plate samples and discuss paths for industrial implementation of the technique for use in semiconductor high volume manufacturing (HVM) as a metrology tool to enhance die placement accuracy and thus Cu-Cu bonding quality in the Die-to-Wafer Hybrid Bonding Integration (D2W HBI) Scheme crucial in realizing ultra-high density semiconductor packages and next generation assembly and systems packaging for AI and HPC chips. This is ongoing work between OIT, ASU and NAU with Intel Advanced Packaging Technology & Manufacturing (APTM) in Arizona.

Early detection of Alzheimer’s disease (AD) requires reliable modelling of both functional and structural brain alterations. This paper presents a multimodal EEG–MRI framework for (AD) detection and binary classification based on feature-level fusion of functional and structural biomarkers. EEG signals are processed using window-based embeddings extracted from multi-channel recordings, while MRI features are obtained from a convolutional neural network trained on structural brain slices from the OASIS dataset. The extracted EEG and MRI embeddings are concatenated and classified using a fully connected neural network. The suggested multimodal model achieves an accuracy of 94.87% with a macro F1-score of 0.95 and an AUC of 0.991, according to experimental results. Although the EEG-only model attains higher accuracy, the multimodal approach provides competitive performance while incorporating complementary structural information, improving robustness and interpretability for (AD) detection.

Environmental health management and public perception rely on this: standard fixed stations, which have limited range due to fixed positions, which bias localized information. This study introduces a new method of AQI monitoring by combining mobile and Internet of Things-based technology with geospatial analytics and artificial intelligence. With the help of drones, mobile sensors, and predictive modeling, the new strategy provides dynamic, real-time, and precise air quality information from multiple locations. This allows stakeholders to proactively address environmental threats and take data-driven actions, freeing the system from reliance on fixed locations. Future pollution trends, real-time geographic visualization, geofenced alerts, and integration of public data streams are just a few of the solutions. Scalable and cost-effective, it can be utilized for city-wide and area deployments, and it is a leap forward in sustainable monitoring and environmental management.

Background: Prevention of postoperative recurrence after endoscopic sinus surgery (ESS) relies on targeting specific pathological mechanisms according to individuals’ immunological profiles. However, essential biomarkers and biological characteristics of difficult-to-treat chronic rhinosinusitis (CRS) patients are not well clarified. Objective: To explore the immunologic profiles of subgroups of CRS patients and determine specific cytokines responsible for recalcitrant or recurrent CRS with nasal polyposis (rCRSwNP). Methods: We used 30-cytokine antibody arrays to determine key cytokines related to recurrent polypogenesis. Enzyme-linked immunosorbent assay (ELISA) experiments were conducted to assess levels of these key cytokines in 78 patients. Polymorphonuclear leukocytes (PMNs) isolated from nasal polyps were challenged with specific cytokines to examine levels of enhanced interleukin (IL)-8 production. Finally, we used immunohistochemistry (IHC) staining to check for the presence and distribution of the biomarkers within nasal polyps. Results: A cytokine antibody array revealed that IL-8, IL-13, IL-15, and IL-20 were significantly higher in the recalcitrant CRSwNP group. Subsequent ELISA screening showed a stepwise increase in tissue IL-8 levels in the CHR, CRSsNP and CRSwNP groups. PMNs isolated from 9 CRSwNP cases all demonstrated enhanced IL-8 production after IL-15 treatment. IHC staining labeled concurrent IL-8 and IL-15 expression in areas of prominent neutrophil infiltration. Conclusion: Our results suggest that IL-15 within the sinonasal mucosa plays a crucial role in promoting IL-8 secretion by infiltrating PMNs in recalcitrant nasal polyps. In addition, we propose a novel therapeutic strategy targeting the anti-IL-15/IL-8 axis in treating CRS with nasal polyposis.