Power transformers are among the most costly assets in power networks, of which differential protection is one of their main protections. A major challenge in differential protection is discriminating internal and winding faults from inrush current (IC), sympathetic inrush current (SIC), overexcitation (OE), and external faults, with and without IC, SIC, and OE. Various methods have been proposed for this purpose; however, they are not necessarily simple to implement and often do not cover all disturbances. In this paper, a disturbance detection approach based on the fast discrete orthonormal Stockwell transform (FDOST) of the differential current is proposed. The average, kurtosis, and relative maximum of the absolute value of the FDOST coefficients are considered as discriminative features. Advantages of the approach include simple implementation and a low computational burden. The results demonstrate the satisfactory performance of the approach in achieving an accuracy of over 99% with an average processing time of 0.2 ms and a detection time of approximately 13 ms. Furthermore, the results are compared with those of the well-known second harmonic restraint method, verifying that the proposed method achieves higher accuracy with a shorter detection time. Moreover, the approach maintains robust performance under noisy conditions, current transformer saturation, and variations in sampling frequency. Additionally, experimental results obtained from a laboratory transformer confirm the high accuracy of the approach in discriminating internal faults from ICs.
The growing mismatch between energy generation and consumption highlights the need for efficient energy storage solutions. Lithium-ion batteries dominate current applications due to their high energy density and long cycle life, yet their safe and optimal operation relies heavily on accurately monitoring two internal states: the state of charge (SOC) and the core temperature. Real-time estimation of these states remains difficult because the battery’s electrical and thermal behaviors are strongly coupled and vary with operating conditions. This paper presents an enhanced electro-thermal modeling and estimation framework that explicitly accounts for the mutual dependence between SOC and core temperature. Unlike conventional fixed-parameter approaches, the model parameters are defined as nonlinear functions of SOC and core temperature, capturing their inherent interdependence. These relationships are identified experimentally through controlled charge–discharge tests at 5°C, 25°C, and 45°C. A physics-based thermal model is used offline as a virtual core temperature sensor to generate reference temperature trajectories for parameter identification, since only surface and ambient temperatures are measured. The ARMAX technique is applied to estimate the electrical and thermal sub-model parameters from experimental data, and an Extended Kalman Filter is subsequently implemented on the coupled nonlinear model to jointly estimate SOC and core temperature under varying load and ambient conditions. Simulation results demonstrate that the proposed method offers higher accuracy and robustness than traditional fixed-parameter models, reducing mean estimation errors by more than 40
The ongoing opioid crisis has highlighted the urgent need for novel analgesics with low addiction potential that provide effective pain relief. In this study, we present an integrated computational framework that combines quantum chemical descriptors with classical molecular descriptors to predict the bioactivity of synthetic opioid analogs across multiple opioid receptor subtypes. Using a curated dataset of over 7000 compounds with experimentally measured activities against the delta opioid receptor (DOR), kappa opioid receptor (KOR), and mu opioid receptor (MOR), we generated several classical and quantum chemical descriptors. These high-dimensional descriptors were used to train and validate several supervised models, including random forest (RF), gradient boosting (GB) with XGBoost, support vector regression (SVR), and linear regression. The RF achieved the highest predictive performance, outperforming GB, SVR, and linear regression across all metrics. Among the descriptors, dipole moment and TPSA were the most effective, significantly enhancing model interpretability and mechanistic insight into ligand–receptor interactions. Clustering and chemical space visualization were used to identify ligand subpopulations with receptor subtype preferences, guiding rational drug design. The integration of quantum-informed descriptors, classical physicochemical features, structural clustering, and interpretable machine learning—particularly RF represents a novel contribution to computational opioid ligand design and advances the field by enabling large-scale, mechanistic analysis of opioid bioactivity. Our approach uniquely combines quantum-informed GNN descriptors with interpretable ML and classical molecular descriptors, enabling mechanistic insights beyond classical QSAR. Unlike prior works relying on empirical fingerprints or small-scale docking, this quantum–ML fusion reveals mechanistic insights into stereo electronic features driving biased agonism, offering a scalable platform for GPCR drug design amid the opioid crisis.
This paper investigates the nonlinear post-buckling behavior of functionally graded porous double-curved nanoshells (FGP-DC-NS), which rest on elastic foundations and are subjected to uniform lateral pressure in a thermal environment. The temperature-dependent properties of the nanoshell, are graded across its thickness by employing a modified rule of mixture and power-law function. The formulation of the double-curved nanoshell employs the nonlocal elasticity theory and classical shell theory (CST), incorporating von Kármán type kinematic nonlinearity. The nonlinear system of equilibrium equations for the double-curved nanoshell is derived using the principle of minimum total potential energy. The governing equations are reformulated in their non-dimensional form to address the case of functionally graded porous double-curved nanoshells with immovable edges. Closed-form solutions are obtained in this study by adopting the two-step perturbation technique. The numerical outcomes are centered on Si3N4/SUS304 FGP double-curved nanoshell. Numerical parametric analysis and three types of porosity distribution are carried out to examine the effects of the small-scale parameter, geometric parameters, material properties, and temperature rise, on the post-buckling behavior of the FGP-DC-NS. These results indicate that the post-buckling behavior of the FGP-DC-NS remains stable in the face of uniform lateral pressure and thermal environment.
The generated heat from photovoltaic (PV) cells, electronic device, transformer, internal combustion (IC) engine, and so forth can be mentioned as the most critical issues confronting modern industries. Leading to high temperature causes a problem in devices and sometimes cause damage. A mini-heat sink is one of the common ways to cool and remove created heat from these engineering devices. This research used compact heat sinks, combined pins, and plate fins, with using SiO2-water nanofluids for enhancing the hydro-thermal performance and entropy generation of heat sinks. The computational investigation and simulation of compact heat sinks are performed by using ANSYS-FLUENT 14.5. The compact heat sinks include three different pins cross-section: circular (PCP), square (PSP), and elliptic (PEP) as well as flat finned heat sinks as a standard case. SiO2-water nanofluids with the various nanoparticles volume fraction of 0% to 5% have been examined for Reynolds number range between 100 and 1,000. The main data display that the supreme Nusselt number is for PCP around 93% and 100% for 0% and 5% SiO2-water, respectively compared with plate fins heat sink. The PCP and PSP have the lowest base temperature, around 25% for 0% and 5% nanofluids. Furthermore, at Re = 1,000, the highest hydro-thermal performance is for PEP at 1.44 and 1.52 for pure water and SiO2-water, respectively. While, the most magnificent hydro-thermal performance is for PCP at 1.44 and 1.50 for pure water and SiO2-water, respectively, as Re = 800. Moreover, the PCP and PSP have the smallest total entropy generation, approximately 42% for pure water and 5% SiO2-water among other heat sinks. Thus, it is recommended to use this kind of heat sinks with nanofluids instead of traditional coolant of PV cells.
