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This research explores the application of an artificial intelligence (AI)-assisted approach to enhance the selectivity of microwave sensors used for liquid mixture sensing. We utilized a planar microwave sensor comprising two coupled rectangular complementary split-ring resonators operating at 2.45 GHz to establish a highly sensitive capacitive region. The sensor’s quality factor was markedly improved from 70 to approximately 2700 through the incorporation of a regenerative amplifier to compensate for losses. A deep neural network (DNN) technique is employed to characterize mixtures of methanol, ethanol, and water, using the frequency, amplitude, and quality factor as inputs. However, the DNN approach is found to be effective solely for binary mixtures, with a maximum concentration error of 4.3%. To improve selectivity for ternary mixtures, we employed a more sophisticated machine learning algorithm, the convolutional neural network (CNN), using the entire transmission response as the 1-D input. This resulted in a significant improvement in selectivity, limiting the maximum percentage error to just 0.7% (≈6-fold accuracy enhancement).

Microwave planar sensors employ conventional passive complementary split ring resonators (CSRR) as their sensitive region. In this work, a novel planar reflective sensor is introduced that deploys CSRRs as the front-end sensing element at fres=6 GHz with an extra loss-compensating negative resistance that restores the dissipated power in the sensor that is used in dielectric material characterization. It is shown that the S11 notch of −15 dB can be improved down to −40 dB without loss of sensitivity. An application of this design is shown in discriminating different states of vanadium redox solutions with highly lossy conditions of fully charged V5+ and fully discharged V4+ electrolytes.

Microwave sensors are principally sensitive to effective permittivity, and hence not selective to a specific material under test (MUT). In this work, a highly compact microwave planar sensor based on zeroth-order resonance is designed to operate at three distant frequencies of 3.5, 4.3, and 5 GHz, with the size of only λg−min/8 per resonator. This resonator is deployed to characterize liquid mixtures with one desired MUT (here water) combined with an interfering material (e.g., methanol, ethanol, or acetone) with various concentrations (0%:10%:100%). To achieve a sensor with selectivity to water, a convolutional neural network (CNN) is used to recognize different concentrations of water regardless of the host medium. To obtain a high accuracy of this classification, Style-GAN is utilized to generate a reliable sensor response for concentrations between water and the host medium (methanol, ethanol, and acetone). A high accuracy of 90.7% is achieved using CNN for selectively discriminating water concentrations.

This manuscript addresses the critical challenge of fault classification and localization within smart distribution networks, exacerbated by the complex integration of distributed energy resources and the dynamic nature of modern power systems. Traditional methods fall short in accurately and efficiently managing these tasks due to their reliance on linear models and manual inspection, which cannot cope with the data-rich and variable environment of current distribution networks. We introduce a novel data-driven framework utilizing convolutional neural networks to enhance fault classification accuracy and localization precision. Our framework uniquely incorporates an online continual learning algorithm, adapting to system changes and evolving fault patterns without requiring retraining. The method demonstrated significant improvements in fault classification and localization performance. Quantitatively, our CNN-based approach achieved a fault classification accuracy of 98.5% and a fault localization accuracy of 97.9%, outperforming traditional AI models in simulated environments. These results underline the potential of our framework to significantly contribute to the reliability and efficiency of fault management in smart distribution systems, offering a robust solution to the challenges posed by the integration of and the variability of load conditions.

Application of signaling molecules has gained immense importance in improving the phytoremediative capacity of plants. This study investigated the possible role of melatonin (MEL) as a signaling molecule in ameliorating lead (Pb)-induced oxidative injury in safflower seedlings. Pot grown 10-day-old safflower seedlings were exposed to 50 μM Pb (NO3)2 alone and in combination with different MEL concentrations (0–300 μM). Exposure to Pb, resulted in a severe oxidative stress, which was indicated by reducing biomass production and enhancing the level of oxidative stress markers (e.g. MDA and H2O2). Addition of exogenous MEL considerably decreased Pb uptake and its root-to-shoot translocation while, biomass production of roots, stems and leaves increased significantly. With MEL application a marked increase in reduced glutathione (GSH) content in leaves and roots was noted as compared with Pb treatment alone. In leaves the activity of enzymes involved in glyoxalase system increased markedly by adding MEL to Pb-sressed plants. In response to increasing MEL treatments, the phytochelatin content of leaves increased substantially in comparison with Pb treatment alone. These findings confirmed that MEL can alleviate Pb toxicity by reducing Pb uptake and its root-to-shoot translocation along with modulating different antioxidant systems. The results also showed that despite the insignificant effect of melatonin on the improvement of Pb phytoremediation potential, the application of this signaling molecule can improve the survival of safflower in Pb-contaminated soils by stimulating antioxidant defense mechanisms.

Cytotoxic T-lymphocyte-associated protein-4 (CTLA-4) is the most important human immune checkpoint that modulates T cells activity and brings about immune-homeostasis. Accordingly, checkpoint inhibitor cancer therapy has been approved as a growing method to block over-expressed immune checkpoints, such as CTLA-4 receptors. Considering the competitive characteristics of single-domain antibodies with monoclonal antibodies, we tried to develop a camelid Nanobody against human CTLA-4. We have constructed the VHH gene library by using immunized-camel peripheral blood mononuclear cells and carrying out the Nested-PCR technique. VHH-library was screened by phage display technique and specific nanobodies against CTLA-4 protein were selected and amplified with bio-panning steps. Stronger binders were screened by Periplasmic Extract-ELISA, followed by estimating the complexity of the library. Specific anti-CTLA-4 Nanobody and 3hCTL55, with longer CDR3 and a higher binding rate, were selected for more assays. Results revealed the existence of two different clones in the library with 10 binders. In comparison with seven different antigens, using the ELISA technique confirmed the specificity of Nanobody 3hCTL55 against human CTLA-4 antigen. We calculated Nanobody 3hCTL55 affinity for human CTLA-4 antigen at 50×10 M, approximately. Performing western blot and Flow-cytometry techniques showed that Nanobody 3hCTL55 was able to specifically detect and attach both commercial human CTLA-4 protein and human CTLA-4 antigen on the cell surface and in the cell lysate. Taken together, this developed camelid-specific anti-CTLA-4 Nanobody 3hCTL55, selected from a high-quality immune library by phage display technique, may be effective for further study about cancer diagnosis and cancer-therapy purposes.

A variety of signaling molecules have been identified that play a role in angiogenesis, of prime importance, vascular endothelial growth factor (VEGF) and its resceptor (VEGFR), which is highly expressed in most human solid tumors. Targeting VEGF or/and VEGFR with immunotoxin may be a promising approach to directly affect cancer cells. Immunotoxins are for targeted treatment comprising two functional moieties, an antibody that binds to target cells along with toxin that kills molecules. In this study, an immunotoxin comprising domain of diphtheria toxin subunit A (DT386) genetically fused to mouse VEGF (mVEGF-DT) was developed. The second construct, which contains the DT386 domain, was made to investigate the action of the DT386 domain on tumor cells. Both gene constructs were cloned, expressed, and were further purified. The biological activity of mVEGF-DT and DT386 proteins was assessed on the TC1 cell line bearing mouse model. Proteins were injected intra-tumoral in mice, in separate groups. Tumors in the mVEGF-DT group started to dwindle after six injections, but tumor size in both control groups (DT386 and PBS), continued to grow. Successful targeting of solid tumor cells by mVEGF-DT immunotoxin demonstrates the therapeutic potential utility of these conjugates for tumor targeting.