Browse Articles

Optimizing wind-PV-battery microgrids for sustainable and resilient residential communities

Abstract Integrating solar and wind energy with battery storage systems into microgrids is gaining prominence in both remote areas and high-rise urban buildings. Optimally designing all distributed energy resources (DERs) within a microgrid enhances self-sufficiency, reliability, and economic feasibility. However, due to the inherent unpredictability of DERs, a robust stochastic-based optimization approach is crucial. This article proposes a Grey Wolf-based multi-objective optimization technique for wind-solar-battery-assisted residential microgrids. The method aims to minimize renewable energy costs by determining the optimal sizing of components based on a given microgrid load profile. To address the global energy trilemma, the microgrid is modeled with economic, reliability, and energy indices, ensuring a balanced three-dimensional objective. The proposed algorithm is evaluated across three different configurations, with a numerical analysis of the capacity degradation factor to assess battery lifetime.

Batch gradient based smoothing L2/3 regularization for training pi-sigma higher-order networks

Association between uric acid to high-density lipoprotein cholesterol ratio and diabetic kidney disease in US adults

Residual capsule network with threshold convolution and attention mechanism for forest fire detection using UAV imagery

Evaluating Mandarin tone pronunciation accuracy for second language learners using a ResNet-based Siamese network

Identification of low-molecular compounds that inhibit envelope formation of hepatitis B virus

Assessing the limits of delay of gratification in bumble bees

Gut microbiota composition is related to anxiety and aggression scores in companion dogs

SOD2 genetics regulating mitochondrial management of oxidative stress is tied to chemical sensitivity in Gulf war veterans

Abstract Multiple chemical sensitivity (MCS), though viewed by many as psychogenic, was presumptively tied to genetic variation in superoxide dismutase 2 (SOD2), involved in conversion of mitochondrial superoxide to hydrogen peroxide, in Japanese paper pulp workers. Gulf War veterans (GWV) have increased MCS compared to nondeployed persons. In data from GWV and nonveterans, we assessed whether altered oxidative stress management via SOD2 polymorphism was tied to self-rated chemical sensitivity. Sixty white predominantly male GWV and age-similar nonveterans completed self-ratings of chemical sensitivity, and underwent both nuclear DNA analysis for SOD2 variants and mitochondrial haplogroup assessment. SOD2 Ala16 (vs. Val16) significantly predicted self-rated chemical sensitivity in GWV and in the total sample (ordinal logistic regression with robust SEs): OR(SE)[95% CI] = 3.56(1.83)[1.30, 9.74], p = 0.013 (total sample); OR[95% CI](SE) = 6.36(4.85)[1.42, 28.4], p = 0.015 (Gulf-deployed). Significance was sustained with adjustment for mitochondrial haplogroup U (not itself significant) and when reappraised with nonparametric trend tests (Kendall’s Tau, Spearman Ranked Correlation Coefficient). The findings extend evidence of SOD2 polymorphism ramifications for chemical sensitivity to a new chemically exposed sample, implicating mitochondrial oxidative stress management as a (though not necessarily the exclusive) key factor in chemical sensitivity. Findings comport with burgeoning evidence inculpating mitochondrial impairment and oxidative stress in many drug/chemical/environmental factors’ toxicity irrespective of the agent’s nominal mechanism of action. This supports chemical sensitivity as a physiological, not a psychogenic condition.

Predicting CO2 adsorption in KOH-activated biochar using advanced machine learning techniques

Machine learning models using non-invasive tests & B-mode ultrasound to predict liver-related outcomes in metabolic dysfunction-associated steatotic liver disease

In silico investigation of ketamine and methylphenidate drug-drug conjugate for MDD and ADHD treatment using MD simulations and MMGBSA

Research on water quality prediction of Jiangshan Port based on SCV-CBA model

ResNet-based image processing approach for precise detection of cracks in photovoltaic panels

Abstract Advancing renewable energy solutions requires efficient and durable solar Photovoltaic (PV) modules. A novel mechanism based on Deep Learning (DL) and Residual Network (ResNet) for accurate cracking detection using Electroluminescence (EL) images of PV panels is proposed in this paper. Different kinds of ResNet architectures, where ResNet34, ResNet50, and ResNet152 were tested, came out with an F1-Score of 86.63%, 87.37%, and 88.89%, respectively. Although the accuracy for ResNet152 is slightly higher, ResNet34 was chosen as the best model since it gives us a trade-off between detection performance and computational performance. The main contribution in this research is the design of an efficient crack detection system trained on a large PV power dataset composed of 2000 EL images collected from different polycrystalline and monocrystalline cells. Although the dataset has some imperfections, to guarantee the presence of many cell states in each subset, it was split into training (70%), validating (20%), and testing (10%). This research demonstrates the application of advanced DL frameworks for early defect diagnosis from raw data to enhance PV panel maintenance, thereby bolstering the sustainability of solar systems. This research also has a significant impact on the academic industry, offering practical solutions for the renewable energy sector during periods of sustainable energy instability, particularly when new materials supplement PV panel usage. The technology preserves the efficiency of solar modules and encourages clean energy solutions by accurately identifying PV panel faults. The study lays a foundation for the further development of image-based defect detection methods in PV systems.

CAV-1 regulates osteocyte communication with osteoclast and osteoblast precursors

Inhibitory mechanisms of amentoflavone on amyloid-β peptide aggregation revealed by replica exchange molecular dynamics

Motor imagery EEG signal classification using novel deep learning algorithm

Solitary phytoplankton cells sink in the mesopelagic ocean

Phytoplankton, and their carbon, are typically exported from the surface ocean when packaged inside larger, sinking detrital particles. This process draws carbon out of the atmosphere, where it can be sequestered for long time periods in the deep ocean. Phytoplankton can also sink as solitary cells, but direct observations are scarce and the ecological significance is unknown. We collected unprecedented observations of solitary sinking cells during month-long observations in the upper 500 m at two contrasting ocean locations. While these cells account for only a small fraction of the total particulate organic carbon flux (<5%), they provide essential nutrients and a persistent source of food to deep sea ecosystems while preserving a seed bank for future phytoplankton blooms. In one case, observed depth changes over time allowed calculation of a sinking speed of 6 m d -1 . The disaggregation of detrital aggregates collected at the same time could not account for the magnitude or composition of individually sinking cells in our samples, although in some instances their fluxes were correlated. Instead, the data imply that these solitary cells were transferred through the upper mesopelagic as individually sinking particles and play key roles in ocean ecology and biogeochemistry.

A copper-doped ZIF-8: a high-performance catalyst for eco-friendly Ullmann and Biginelli reactions

Influence of bodyweight on prednisolone pharmacokinetics in dogs

Background Larger dogs may be at greater risk of prednisolone side effects, yet there is limited research about how bodyweight affects prednisolone pharmacokinetics in dogs. Hypothesis/objectives To describe the relationship between prednisolone dose, bodyweight, body surface area (BSA) and prednisolone area under the curve (AUC) in dogs receiving prednisolone for medical reasons. Animals 25 client owned dogs receiving prednisolone for medical reasons. Methods Observational population pharmacokinetic study. Liquid chromatography tandem mass spectrometry was used for plasma prednisolone quantification. Data analysis was conducted in a two-stage approach using non-compartmental modelling. A Bayesian non-linear regression model described the relationship between AUC over 8 hours ( A U C 8 h r ,   ng·min/mL), bodyweight and prednisolone dose. Results From the allometric scaling model of the form AUC 8h = A · BW B , the scaling exponent B was.83 (90% credible interval (CrI):.60–1.06) and the coefficient A was 22.8 (90% CrI: 11.8–43.4). This model suggests that equivalent exposure would be obtained using an intermediate strategy between BSA and bodyweight dosing, but the total evidence provided was relatively weak. Conclusions and clinical importance Evidence was obtained regarding the nonlinear relationship between prednisolone pharmacokinetics and bodyweight in dogs; however, this model is currently too imprecise for clinical dose determination.