Diverse Parameters Research Articles

Thompson and Troian slip effects on ternary hybrid nanofluid flow over a permeable plate with chemical reaction

This study seeks to elucidate the electromagnetic hydro-thermal aspects of ternary hybrid nanofluid (THNF) (MoS2-SiO2-GO/water) flow over a permeable plate in the presence of Thomson and Troian slip, suction/blowing, and chemical reaction. Through the utilization of similarity transformations, the nonlinear partial differential equations (PDEs) representing the conservation of mass, momentum, energy, and concentration, are converted into ordinary differential equations (ODEs). The subsequent step involves solving these ODEs utilizing the bvp4c technique in MATLAB. The detailed graphical representations precisely elucidate the action of diverse parameters, such as Hartmann number, electric parameter, velocity slip parameter, Lewis number, critical shear rate, suction/blowing, thermophoresis, and Brownian motion parameter on the velocity, thermal, and concentration profiles. The momentum and temperature distributions respond oppositely to the parameters Hartmann number, electric parameter, and suction/blowing. Also, the absolute values of Nusselt and Sherwood numbers increase with elevated critical shear rate, velocity slip, and suction/blowing parameter but decrease with higher Lewis number and chemical reaction parameter. Moreover, adjusting the velocity slip parameter from 0.2 to 0.6 results in a 24.14% decrease in the skin friction coefficient and a 16.79% increase in the local Nusselt number.

Numerical simulation of shelter effect assessment for single-row windbreaks on the periphery of oasis farmland

Dust storms, resulting from aeolian erosion, pose significant environmental hazards, while farmland is one of the main sources of dust storm release. An effective strategy to mitigate surface wind speed and curb dust emissions involves the establishment of windbreaks on the periphery of oasis farmland. This study conducts a series of numerical simulations using computational fluid dynamics (CFD) to explore the airflow fields around windbreaks with diverse systematic structural parameters, encompassing porosity, planting spacing, and fence effects. The main findings are as follows: (1) When the vegetation porosity is consistent (e.g., porosity α = 0.7, 0.8, and 0.9), exact geometry results can effectively reflect the distribution of wall shear stress, while the porous medium model overlooks these details. (2) The “Venturi effect” contributes to the acceleration of surface erosion and improper planting spacing results in an elevation of near-surface velocity. Planting spacing of 0.5 m demonstrates superior wind speed reduction performance, mitigating aeolian erosion and accumulation. (3) When the fence is positioned at l = 5h (h represents the height of the windbreaks), the flow field around the windbreaks is minimally influenced. The optimal placement distance for fences should be close to the windbreaks, featuring minimal porosity (l = 0 h, α = 0.1), extending the shelter distance from 3 to 4 h to 5–6 h. The research results can provide a theoretical basis for optimizing the plant configuration of biological desertification control and soil erosion control measures.

Multiple Disease Prediction Using Machine Learning

In various regions worldwide, the incidence of life-threatening diseases such as brain tumors, cataracts, pneumonia, and malaria remain alarmingly high, posing significant challenges to public health systems. Multiple factors contribute to the onset of these conditions, including genetic predispositions, environmental exposures, and lifestyle choices. This research endeavors to develop and evaluate a data-driven predictive model for early detection of brain tumors, cataracts, pneumonia, and malaria utilizing convolutional neural network (CNN) algorithms trained on medical imaging data. The proposed method integrates diverse patient parameters, including demographic information, medical history, and imaging features, to forecast the risk of developing these diseases. CNN architecture is chosen as the preferred model due to its ability to effectively analyze complex image data. Ethical considerations and privacy concerns regarding the handling of sensitive medical information are thoroughly examined, emphasizing the importance of responsible model development. Furthermore, the interpretability of CNN models is addressed to facilitate understanding among healthcare professionals and patients. The developed predictive system demonstrates promising accuracy and reliability, with CNN achieving notable performance metrics across all disease categories. A web-based platform is implemented to facilitate easy input and disease prediction based on medical images. The dataset utilized in this study is sourced from reputable medical institutions and research organizations, ensuring data quality and integrity. The findings of this research contribute valuable insights into the application of CNN-based predictive models in healthcare, offering a pathway for integrating such systems into clinical practice for early disease diagnosis and intervention

Nephroprotective Effect of Hibiscus Sabdariffa Leaf Flavonoid Extracts via KIM-1 and TGF-1β Signaling Pathways in Streptozotocin-Induced Rats.

Diabetes-induced kidney damage represents a substantial health hazard, emphasizing the imperative to explore potential therapeutic interventions. This study investigates the nephroprotective activity of flavonoid-rich extracts from Hibiscus sabdariffa leaves in streptozotocin-induced diabetic rats. The flavonoid-rich extracts of H. sabdariffa leaves was obtained using a standard procedure. The animals were induced with streptozotocin and thereafter treated with both low (LDHSFL) and high doses (HDHSFL) of flavonoid-rich extracts from H. sabdariffa leaves and metformin (MET), and other groups are diabetic control (DC) and normal control (NC). The study assesses diverse renal parameters, encompassing kidney redox stress biomarkers, serum electrolyte levels, kidney inflammatory biomarkers, serum concentrations of creatinine, urea, and uric acid, kidney phosphatase activities, renal histopathology, and relative gene expressions of kidney injury molecule-1 (KIM-1) and transforming growth factor beta-1 (TGF-1β), comparing these measurements with normal and diabetic control groups (NC and DC). The findings indicate that the use of extracts from H. sabdariffa leaves markedly (p < 0.05) enhanced renal well-being by mitigating nephropathy, as demonstrated through the adjustment of various biochemical and gene expression biomarkers, indicating a pronounced antioxidative and anti-inflammatory effect, improved kidney morphology, and mitigation of renal dysfunction. These findings suggest that H. sabdariffa leaf flavonoid extracts exhibit nephroprotective properties, presenting a potential natural therapeutic approach for the treatment of diabetic nephropathy.

Modeling of the shape of Taylor cone in EHD printing based on backpropagation neural network and genetic algorithm

Electrohydrodynamic (EHD) printing has been recognized as a promising additive manufacturing technology with superior pattern resolution and economic viability. The shape of Taylor cone is deemed a pivotal element for the amplification of deposition efficiency, and the maintenance of consistent operational steadiness in EHD printing. The correlations between diverse operating parameters and the shape of Taylor cone are presently not well investigated. In this paper, modeling of relationships between operating parameters and the shape of Taylor cone was conducted with a backpropagation neural network (BPNN) and a genetic algorithm optimized backpropagation (GA-BP) neural network. Taylor cone semi-vertical angle and the meniscus height were employed as two indexes to characterize the shape of Taylor cone. The prediction accuracies of BPNN model and GA-BP model were 92.79 % and 95.46 %, respectively. The GA-BP model demonstrated higher precision in forecasting the shape of Taylor cone. A predictive framework for the shape of Taylor cone was proposed, which provided a practical tool for process optimization in EHD printing.

Analysis of Elimination Effects of Inbreeding on Genotype Frequency in Larval Stages of Chinese Shrimp.

Marine animals possess genomes of considerable complexity and heterozygosity. Their unique reproductive system, characterized by high fecundity and substantial early mortality rates, increases the risk of inbreeding, potentially leading to severe inbreeding depression during various larval developmental stages. In this study, we established a set of inbred families of Fenneropenaeus chinensis, with an inbreeding coefficient of 0.25, and investigated elimination patterns and the manifestations of inbreeding depression during major larval developmental stages. Reduced-representation genome sequencing was utilized to explore the genotype frequency characteristics across two typical elimination stages. The results revealed notable mortality in hatching and metamorphosis into mysis and post-larvae stages. Inbreeding depression was also evident during these developmental stages, with depression rates of 24.36%, 29.23%, and 45.28%. Segregation analysis of SNPs indicated an important role of gametic selection before hatching, accounting for 45.95% of deviation in the zoea stage. During the zygotic selection phase of larval development, homozygote deficiency and heterozygote excess were the main selection types. Summation of the two types explained 82.31% and 89.91% of zygotic selection in the mysis and post-larvae stage, respectively. The overall distortion ratio decreased from 22.37% to 12.86% in the late developmental stage. A total of 783 loci were identified through selective sweep analysis. We also found the types of distortion at the same locus could change after the post-larvae stage. The predominant shifts included a transition of gametic selection toward normal segregation and other forms of distortion to heterozygous excess. This may be attributed to high-intensity selection on deleterious alleles and genetic hitchhiking effects. Following larval elimination, a greater proportion of heterozygous individuals were preserved. We detected an increase in genetic diversity parameters such as expected heterozygosity, observed heterozygosity, and polymorphic information content in the post-larvae stage. These findings suggest the presence of numerous recessive deleterious alleles and their linkage and suggest a major role of the partial dominance hypothesis. The results provide valuable insights into the mechanisms of inbreeding depression in marine animals and offer guidance for formulating breeding strategies in shrimp populations.

Emerging Photoreforming Process to Hydrogen Production: A Future Energy

AbstractIn the quest of renewable energy technologies, solar photoreforming emerges as one of the affordable yet challenging process for converting biomass into hydrogen, hydrocarbon fuels, and chemicals. This review highlights the state‐of‐the‐art photoreforming, elucidating its underlying mechanisms for the conversion of dissipated polymers into H2 and valuable chemicals. Biomass feedstocks such as carbohydrates, agricultural residues, glycopolymers, food wastes, and waste plastics are evaluated based on their chemical composition, energy content, and sustainability aspects, exploring the selection of appropriate bio‐renewable resources, considering their abundance, availability, and potential for hydrogen production. The impact of diverse process parameters on photoreforming efficiency is explored, encompassing factors like reaction temperature, pH, catalyst loading, reactor design, solvent effect, and light intensity across various sacrificial substrates. The discussion also considers their correlation with hydrogen production rate, selectivity, and energy efficiency. This review buckles on the design and synthesis of functional photocatalysts for biomass‐derived feedstock, highlighting their photocatalytic (PC) properties in biomass reforming processes and related feedstock into valuable chemicals and biofuel. The review also delves into potential pathways for future advancements including artificial intelligence (AI) and machine learning (ML), alongside addressing the challenges and insightful perspectives within this evolving field of future green energy.

Exploring the interplay between the core microbiota, physicochemical factors, agrobiochemical cycles in the soil of the historic tokaj mád wine region.

A Hungarian survey of Tokaj-Mád vineyards was conducted. Shotgun metabarcoding was applied to decipher the microbial-terroir. The results of 60 soil samples showed that there were three dominant fungal phyla, Ascomycota 66.36% ± 15.26%, Basidiomycota 18.78% ± 14.90%, Mucoromycota 11.89% ± 8.99%, representing 97% of operational taxonomic units (OTUs). Mutual interactions between microbiota diversity and soil physicochemical parameters were revealed. Principal component analysis showed descriptive clustering patterns of microbial taxonomy and resistance gene profiles in the case of the four historic vineyards (Szent Tamás, Király, Betsek, Nyúlászó). Linear discriminant analysis effect size was performed, revealing pronounced shifts in community taxonomy based on soil physicochemical properties. Twelve clades exhibited the most significant shifts (LDA > 4.0), including the phyla Verrucomicrobia, Bacteroidetes, Chloroflexi, and Rokubacteria, the classes Acidobacteria, Deltaproteobacteria, Gemmatimonadetes, and Betaproteobacteria, the order Sphingomonadales, Hypomicrobiales, as well as the family Sphingomonadaceae and the genus Sphingomonas. Three out of the four historic vineyards exhibited the highest occurrences of the bacterial genus Bradyrhizobium, known for its positive influence on plant development and physiology through the secretion of steroid phytohormones. During ripening, the taxonomical composition of the soil fungal microbiota clustered into distinct groups depending on altitude, differences that were not reflected in bacteriomes. Network analyses were performed to unravel changes in fungal interactiomes when comparing postveraison and preharvest samples. In addition to the arbuscular mycorrhiza Glomeraceae, the families Mycosphaerellacae and Rhyzopodaceae and the class Agaricomycetes were found to have important roles in maintaining soil microbial community resilience. Functional metagenomics showed that the soil Na content stimulated several of the microbiota-related agrobiogeochemical cycles, such as nitrogen and sulphur metabolism; steroid, bisphenol, toluene, dioxin and atrazine degradation and the synthesis of folate.

Automated analysis of pectoralis major thickness in pec-fly exercises: evolving from manual measurement to deep learning techniques

This study addresses a limitation of prior research on pectoralis major (PMaj) thickness changes during the pectoralis fly exercise using a wearable ultrasound imaging setup. Although previous studies used manual measurement and subjective evaluation, it is important to acknowledge the subsequent limitations of automating widespread applications. We then employed a deep learning model for image segmentation and automated measurement to solve the problem and study the additional quantitative supplementary information that could be provided. Our results revealed increased PMaj thickness changes in the coronal plane within the probe detection region when real-time ultrasound imaging (RUSI) visual biofeedback was incorporated, regardless of load intensity (50% or 80% of one-repetition maximum). Additionally, participants showed uniform thickness changes in the PMaj in response to enhanced RUSI biofeedback. Notably, the differences in PMaj thickness changes between load intensities were reduced by RUSI biofeedback, suggesting altered muscle activation strategies. We identified the optimal measurement location for the maximal PMaj thickness close to the rib end and emphasized the lightweight applicability of our model for fitness training and muscle assessment. Further studies can refine load intensities, investigate diverse parameters, and employ different network models to enhance accuracy. This study contributes to our understanding of the effects of muscle physiology and exercise training.

Increasing the complexity of lipoprotein characterization for cardiovascular risk in type 2 diabetes.

The burden of cardiovascular disease is particularly high among individuals with diabetes, even when LDL cholesterol is normal or within the therapeutic target. Despite this, cholesterol accumulates in their arteries, in part, due to persistent atherogenic dyslipidaemia characterized by elevated triglycerides, remnant cholesterol, smaller LDL particles and reduced HDL cholesterol. The causal link between dyslipidaemia and atherosclerosis in T2DM is complex, and our contention is that a deeper understanding of lipoprotein composition and functionality, the vehicle that delivers cholesterol to the artery, will provide insight for improving our understanding of the hidden cardiovascular risk of diabetes. This narrative review covers three levels of complexity in lipoprotein characterization: 1-the information provided by routine clinical biochemistry, 2-advanced nuclear magnetic resonance (NMR)-based lipoprotein profiling and 3-the identification of minor components or physical properties of lipoproteins that can help explain arterial accumulation in individuals with normal LDLc levels, which is typically the case in individuals with T2DM. This document highlights the importance of incorporating these three layers of lipoprotein-related information into population-based studies on ASCVD in T2DM. Such an attempt should inevitably run in parallel with biotechnological solutions that allow large-scale determination of these sets of methodologically diverse parameters.

Compact quad element dual band high gain MIMO rectangular dielectric resonators antenna for 5G millimeter wave application

In this article, a compact dual-band quad-port Multiple Input Multiple Output (MIMO) Rectangular Dielectric Resonator Antenna (RDRA) with a defected connected ground Geometry (DCGG) for 5G millimeter-wave (mm-wave) application is presented. The antenna is operating at 24 GHz to 32 GHz frequency range. The antenna configuration comprises four identical rectangular microstrip radiators mounted on an RT Duroid 5880 substrate (20 × 20 × 0.254) mm3. To enhance bandwidth, each radiator is equipped with both rectangular and circular-shaped ring slots. To improve the performance of the 4 port MIMO antenna, RDRAs are placed over each radiator and optimized. The MIMO RDRs exhibit dual-band with 4.24 GHz and 0.46 GHz bandwidths. The MIMO RDRA achieves exceptional isolation (>27 dB), high gain (8.24 dB), and more than 94 % radiation efficiency. The diversity parameters exhibit a low Envelope Correlation Coefficient (ECC) of 0.0006, and a high Diversity Gain (DG) of 9.97 dB is achieved, highlighting the effectiveness of the antenna system in providing diversity reception and improving communication reliability in challenging environments. Hence, the proposed MIMO antenna is compact, making it suitable for integration into small 5G devices or systems where space is limited, such as IoT devices, mobile phones, or small base stations at mm-wave frequency bands.

The Combined Effects of Azoxystrobin and the Biosurfactant-Producing Bacillus sp. Kol B3 against the Phytopathogenic Fungus Fusarium sambucinum IM 6525.

This study aimed to evaluate how the combined presence of the synthetic fungicide azoxystrobin (AZ) and the biosurfactant-producing Bacillus sp. Kol B3 influences the growth of the phytopathogenic fungus Fusarium sambucinum IM 6525. The results showed a noticeable increase in antifungal effectiveness when biotic and abiotic agents were combined. This effect manifested across diverse parameters, including fungal growth inhibition, changes in hyphae morphology, fungal membrane permeability and levels of intracellular reactive oxygen species (ROS). In response to the presence of Fusarium and AZ in the culture, the bacteria changed the proportions of biosurfactants (surfactin and iturin) produced. The presence of both AZ and/or Fusarium resulted in an increase in iturin biosynthesis. Only in 72 h old bacterial-fungal co-culture a 20% removal of AZ was noted. In the fungal cultures (with and without the addition of the bacteria), the presence of an AZ metabolite named azoxystrobin free acid was detected in the 48th and 72nd hours of the process. The possible involvement of increased iturin and ROS content in antifungal activity of Bacillus sp. and AZ when used together are also discussed. Biosurfactants were analyzed by liquid chromatography with tandem mass spectrometry (LC-MS/MS). Microscopy techniques and biochemical assays were also used.

Preparation and Characterization of Bismaleimide-Resin-Based Composite Materials.

This study utilized bismaleimide (BMI) resin, reinforced with introduced ether bonds, as a binding matrix, in combination with silicon carbide (SiC), for the fabrication of composite materials. A thorough investigation was conducted to assess the influence of diverse processing parameters on the mechanical properties and high-temperature thermo-oxidative stability of these composites. Experimental results indicate a notable improvement in the mechanical properties of the composites upon the incorporation of ether bonds, in contrast to their unmodified counterparts. The variation in performance among composites with different ratios and molding densities is apparent. Within a certain range, an increase in resin content and molding density is correlated with improved bending strength in the composites. With a resin content of 27.5 vol% and a molding density of 2.31 g/cm3, the composite achieved a maximum flexural strength of 109.52 MPa, representing a 24% increase compared to its pre-modification state. Even after exposure to high-temperature heat treatment, the composites displayed commendable mechanical properties compared to their pre-ether bond modification counterparts, maintaining 74.5% of the strength of the untreated composites at 300 °C. The scanning electron microscopy (SEM) microstructures of composite materials correlate remarkably well with their mechanical properties.

FSM-BC-BSP: Frequent Subgraph Mining Algorithm Based on BC-BSP

As graph models become increasingly prevalent in the processing of scientific data, the exploration of effective methods for the mining of meaningful patterns from large-scale graphs has garnered significant research attention. This paper delves into the complexity of frequent subgraph mining and proposes a frequent subgraph mining (FSM) algorithm. This FSM algorithm is developed within a distributed graph iterative system, designed for the Big Cloud (BC) environment of the China Mobile Corp., and is based on the bulk synchronous parallel (BSP) model, named FSM-BC-BSP. Its aim is to address the challenge of mining frequent subgraphs within a single, large graph. This study advocates for the incorporation of a message sending and receiving mechanism to facilitate data sharing across various stages of the frequent subgraph mining algorithm. Additionally, it suggests employing a standard coded subgraph and sending it to the same node for global support calculation on the large graph. The adoption of the rightmost path expansion strategy in generating candidate subgraphs helps to mitigate the occurrence of redundant subgraphs. The use of standard coding ensures the unique identification of subgraphs, thus eliminating the need for isomorphism calculations. Support calculation is executed using the Minimum Image (MNI) measurement method, aligning with the downward closure attribute. The experimental results demonstrate the robust performance of the FSM-BC-BSP algorithm across diverse input datasets and parameter configurations. Notably, the algorithm exhibits exceptional efficacy, particularly in scenarios with low support requirements, showcasing its superior performance under such conditions.

Structural complexity and benthic metabolism: resolving the links between carbon cycling and biodiversity in restored seagrass meadows

Abstract. Due to large losses of seagrass meadows worldwide, restoration is proposed as a key strategy for increasing coastal resilience and recovery. The emergence of a seagrass meadow is expected to substantially amplify biodiversity and enhance benthic metabolism by increasing primary productivity and respiration. Nevertheless, open questions remain regarding the metabolic balance of aging seagrass meadows and the roles benthic communities within the seagrass ecosystem play in overall metabolism. To address these questions, we investigated a chronosequence of bare sediments and adjacent Zostera marina meadows of 3 and 7 years since restoration alongside a natural meadow located within a high-temperate marine embayment in Gåsö, Sweden. We combined continuous measurements of O2 fluxes using underwater eddy covariance with dissolved inorganic carbon (DIC) and O2 fluxes from benthic chambers during the productive season (July). Based on the ratio between O2 and DIC, we derived site-specific photosynthetic and respiratory quotients, enabling the conversion of eddy covariance fluxes to DIC. We assessed benthic diversity parameters as potential drivers of metabolic flux variability. We observed high rates of gross primary productivity (GPP) spanning −18 to −82 mmolDICm-2d-1, which increased progressively with meadow age. Community respiration (CR) mirrored the GPP trend, and all meadows were net heterotrophic (GPP &lt; CR), with net community productivity (NCP) ranging from 16 to 28 mmolDICm-2d-1. While autotrophic biomass did not increase with meadow age, macrophyte diversity did, elucidating potential effects of niche complementarity among macrophytes on community metabolism. These findings provide valuable insights into how community composition and meadow development relate to ecosystem functioning, highlighting potential tradeoffs between carbon uptake and biodiversity.

Comparative Evaluation of Solite RS3 and HyFlex Remover Retreatment Files in Conserving Remaining Dentin Thickness During Endodontic Retreatment Using Cone Beam Computed Tomography: An In Vitro Analysis.

Introduction Non-surgical retreatment is seen as a conservative choice for dealing with recurrent infections, instead of opting for periapical surgery. The retreatment processes should be promptly and efficiently carried out, utilizing a suitable armamentarium. The objective of this experiment is to evaluate the quantity of root dentin that remains following the removal of gutta-percha (GP) from the root canal employing two distinct retreatment files. Materials and methods Sixty single-rooted teeth were selected for the examination. The process of shaping and cleaning was performed using the step-back approach, with a master apical file size of 40. The smear layer was effectively eliminated by rinsing with a solution of 3% sodium hypochlorite and 17% ethylenediaminetetraacetic acid. Paper points were employed to desiccate the canals. The obturation process involved the utilization of the lateral compaction technique with the AH Plus sealer (Dentsply Sirona, NC, USA). The teeth were classified into two groups: Group I (n=30) underwent retreatment using HyFlex Remover (Coletene India, Pvt., Ltd.), whereas Group II (n=30) received therapy with Solite RS3 retreatment files (Solite Dental in Chennai, India). The remaining dentin thickness (RDT) was assessed by cone beam computed tomography at levels 3, 6, and 9 mm from the cemento enamel junction after the removal of GP. The acquired data underwent examination using an independent t-test to determine statistical significance. Results The findings demonstrate that the utilization of Solite RS3 files led to a higher level of dentin thickness remaining at 3 mm, 6 mm, and 9 mm on the mesial side in comparison to HyFlex Remover retreatment files. The observed difference was found to be statistically significant at a significance level of p<0.05 on the mesial side. Nevertheless, there was no notable disparity seen between the two file types at these three levels on the distal side (p>0.05). Conclusion Based on the obtained results of the study, it can be concluded that Solite RS3 files show promise in preserving the RDT. However, further studies encompassing diverse parameters are needed to establish a conclusive and definitive conclusion.

Comprehensive Assessment of Cardiac Function in Breast Cancer Patients: Integrating Multiparametric Speckle Tracking Imaging and Conventional Echocardiography Parameters.

Purpose The purpose of this study is to comprehensively evaluate the role of different echocardiography parameters in breast cancer patients undergoing chemotherapy. While echocardiography examination with calculation of ejection fraction (EF), is pivotal for patient monitoring, its operator dependence and insensitivity to subtle changes in left ventricular (LV) contractility present challenges. Global longitudinal strain (GLS), derived from speckle tracking, is more sensitive and stable than EF. Our research aimed to delineate supplementary echocardiography measurements beneficial for the cardiological monitoring of breast cancer patients. Methods Patients were followed up with echocardiography at baseline, during, and after the chemotherapy. Conventional echocardiography and multiple speckle tracking imaging parameters including myocardial work index, atrial strain, twist, and automatic EF were investigated. Results A total of 25 patients were recruited. A subset (15/25) exhibited pronounced GLS reduction, associated with decreased EF and altered cardiac mechanics. Patients with unchanged GLS were often hypertensive and on specific medications, in particular angiotensin-converting enzyme inhibitors (ACE inhibitors)/angiotensin II receptor blockers (ARBs), potentially indicating protective effects. Despite stability in other parameters, GLS and EF sensitivity highlight their importance. A strong correlation between manual and automated EF measurement methods was also observed. Conclusion Despite the small sample size, across diverse echocardiography parameters, GLS and EF are primarily affected by chemotherapy. Hypertensive individuals exhibited lower susceptibility to chemotherapy-induced damage, likely attributed to the cardioprotective properties of ACE inhibitors and angiotensin II receptor blockers. A strong correlation between automatic and Simpson-based EF was found.

Impedance matching design of capacitively coupled plasma with fluid and external circuit coupled model

AbstractThis paper establishes a fully self‐consistent coupled model of fluid and external circuits. The Kirchhoff equation, the charge conservation equation, and Poisson equation are coupled via boundary conditions and integrated into the fluid model for iterative parameter solution. On the basis of this model, we investigate the influence of impedance matching on single‐frequency capacitively coupled plasma characteristics under different parameters and topological structures. The findings suggest that after several iterations the matching parameters converge. Using different initial circuit parameters, the adjustable capacitance and inductance are eventually adjusted to approximately equal values, resulting in the same optimal matching state, whereas diverse discharge parameters led to different outcomes. Under fixed parameters for two topologies, the power absorption efficiency increases, and the reflection coefficient approaches zero, and the best matching is found. This model can be extended to different fluid programs to investigate the impact of complex external circuits with impedance matching network on plasma discharge while simultaneously seeking best impedance matching.

Abstract LB245: Single cell guided identification of logic-gated cell surface combinations for selective and safe CAR therapy design

Abstract The advancement of chimeric antigen receptor (CAR) T-cell therapy has been groundbreaking in the treatment of hematological malignancies. However, its application in solid tumors is constrained by antigen variability and unintended off-tumor, on-target toxicities. Addressing these issues, we introduce a genetic algorithm to analyze single-cell transcriptomics data from patient tumors. This method pinpoints sets of surface antigens exclusive to cancer cells, utilizing the logical operators "AND," "OR," and "NOT." Our technique scales to gene combinations of any size and exhibits robust performance across diverse parameter settings. When applied to five breast cancer datasets, this algorithm successfully uncovered triplet antigen combinations superior to current clinical single antigen targets in distinguishing between cancerous and healthy cells. This strategy offers a promising pathway toward the design of more selective and safe CAR therapies. Citation Format: Sanna Madan, Tiangen Chang, Binbin Wang, Saugato Rahman Dhruba, Alejandro A. Schäffer, Eytan Ruppin. Single cell guided identification of logic-gated cell surface combinations for selective and safe CAR therapy design [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB245.

Overview of Biomass Hydrothermal Techniques for Carbon Quantum Dots Synthesis: Mini Review

The aim of this study is to provide a comprehensive overview of various biomass hydrothermal techniques used in the synthesis of carbon quantum dots (CQDs), as well as to identify recent developments in this field. Careful literature search methods were employed to gather information related to biomass hydrothermal techniques and CQD synthesis, applying inclusion and exclusion criteria to select articles that align with the research focus. Data analysis approaches were used to organize and present relevant information. The main findings of this review encompass a range of hydrothermal techniques used in CQD synthesis, including diverse biomass feedstocks and process parameters influencing the final outcomes. Furthermore, the review also reveals recent advancements in CQD applications, such as in photocatalysis, biomedicine, and optoelectronics. The findings of this review have significant implications for further development in CQD synthesis using biomass hydrothermal techniques, with the potential application of this technology in various high-tech fields, including renewable energy, biomedicine, and sensing. Overall, this review provides an in-depth understanding of recent advancements in biomass hydrothermal techniques for CQD synthesis, highlighting the substantial potential of this research in supporting innovation across various application domains.