Extension Technique Research Articles

Leveraging Open Big Data from R&D Projects with Large Language Models

Recent studies have highlighted the potential of Large Language Models (LLMs) to become experts in specific areas of knowledge through the utilization of techniques that enhance their context. Nevertheless, an interesting and underexplored application in the literature is the creation of an LLM that specializes in research projects, as it could streamline the process of project ideation and accelerate the advancement of research initiatives. In this regard, the aim of this work is to develop a tool based on LLM technology capable of assisting the employees of technology centers in answering their queries related to research projects funded under the Horizon 2020 program. By facilitating the identification of suitable funding calls and the formation of consortia with partners meeting specific requirements, tasks that are traditionally time-intensive, the proposed tool has the potential to improve operational efficiency and enable technology centers to allocate their resources more effectively. To improve the model’s baseline performance, context extension techniques such as Retrieved Augmented Generation (RAG) and prompt engineering were explored. Specifically, different RAG approaches and configurations, along with a specialized prompt, were tested on the LLaMA 3 70B model, and their results were compared to those obtained without context extension. The proposed evaluation metrics, which aligned with human judgment while maintaining objectivity, revealed that RAG systems outperformed the standalone LLaMA 3 70B, achieving a rate of optimal responses of up to 46% compared to 0% for the baseline model. These findings emphasize that integrating RAG and prompt engineering pipelines into LLMs can address key limitations, such as generating accurate and informative answers. Moreover, this study demonstrates the practical feasibility of leveraging advanced LLM configurations to support research-driven organizations, highlighting a pathway for the further development of intelligent tools that enhance productivity and foster innovation in the research domain.

5β-hydroxycostic acid from Laggera alata ameliorates sepsis-associated acute kidney injury through its anti-inflammatory and anti-ferroptosis effects via NF-κB and MAPK pathways.

The whole plant of Laggera alata is frequently utilize to remedy inflammatory diseases including nephritis as a traditional Chinese medicine. However, its active ingredients and mechanism of action against sepsis-associated acute kidney injury (SA-AKI) are unknown. This study aimed to identify active compounds from L. alata that inhibit renal inflammation and ameliorate SA-AKI, and to elucidate their mechanisms of action. The chemical constituents were separated from the ethyl acetate layer of L. alata methanol extract by column chromatography over silica gel, medium-pressure liquid chromatography and semipreparative high-performance liquid chromatography. Extensive spectroscopic techniques were applied to determine the chemical structures. The anti-inflammatory efficiency was measured by analyzing the NO production in RAW 264.7 cells. The levels of IL-6, IL-1β, CCL-2 and CCL-5 mRNA were determined by qRT-PCR. Cecal ligation and puncture (CLP) surgery is a frequently applied method to establish the mouse sepsis model. Sepsis was thus induced in mice via CLP. The effect in the treatment of SA-AKI was evaluated by H&E staining and ELISA detection. Western blotting was used to evaluate the protein levels involved in ferroptosis, NF-κB and MAPK signaling pathways. Twelve compounds were obtained from L.alata including four unreported sesquiterpenoids (1-4). Compound 5 exhibited the most significant inhibitory effect on NO production with the IC50 value of 6.034 μM and could restrain the mRNA expression of inflammatory factors IL-6, IL-1β, CCL-2 and CCL-5. The in vivo results demonstrated that compound 5 alleviated the renal injury by decreasing the serum IL-6, IL-1β, Cr, and BUN levels, reducing the kidney contents of Cys-C and KIM-1, and regulating the kidney levels of MDA, GSH, ferrous iron, GPX4, FTH1, and SLC7A11. Furthermore, Compound 5 also repressed the NF-κB and MAPK pathways in vitro and in vivo. This study revealed that compound 5 could ameliorate SA-AKI through exerting its anti-inflammatory and anti-ferroptosis effects via NF-κB and MAPK pathways. The current research supported the traditional use of L.alata in the treatment of renal diseases.

Quantitative Analysis of Nonhistone Lysine Methylation Sites and Lysine Demethylases in Breast Cancer Cell Lines.

Growing evidence shows that lysine methylation is a widespread protein post-translational modification (PTM) that regulates protein function on histone and nonhistone proteins. Numerous studies have demonstrated that the dysregulation of lysine methylation mediators contributes to cancer growth and chemotherapeutic resistance. While changes in histone methylation are well-documented with extensive analytical techniques available, there is a lack of high-throughput methods to reproducibly quantify changes in the abundances of the mediators of lysine methylation and nonhistone lysine methylation (Kme) simultaneously across multiple samples. Recent studies by our group and others have demonstrated that antibody enrichment is not required to detect lysine methylation, prompting us to investigate the use of tandem mass tag (TMT) labeling for global Kme quantification without antibody enrichment in four different breast cancer cell lines (MCF-7, MDA-MB-231, HCC1806, and MCF10A). To improve the quantification of KDMs, we incorporated a lysine demethylase (KDM) isobaric trigger channel, which enabled 96% of all KDMs to be quantified while simultaneously quantifying 326 Kme sites. Overall, 142 differentially abundant Kme sites and eight differentially abundant KDMs were identified among the four cell lines, revealing cell line-specific patterning.

Sesquiterpene Monomers and Dimers From the Flower of Inula japonica Thunb. With Selective Antiproliferation Against Breast Cancer Cells.

A total of 34 sesquiterpene derivates were obtained from the flower of Inula japonica Thunb. Compounds 2, 14-34 were identified as sesquiterpene monomers, whereas the other 12 isolates (1, 3-13) were characterized as sesquiterpene dimers. Among them, japonicone Z (1), a present undescribed sesquiterpene dimer, and another undescribed monomer, japonicol A (2), were discovered. Their structures were determined through extensive spectroscopic techniques. Structurally, these dimers shared a common guaiane moiety that fused with either 1,10-seco-eudesmanes or germacrenes by a [4+2] biogenetic pathway. The antiproliferative activity of these isolates was assayed on triple negative breast cancer cell line (MDA-MB-231), breast cancer cell line (MCF-7), and normal human mammary epithelial cells (MCF-10A). As a result, most of these compounds exhibited significant effects toward cancer cell lines. More importantly, they possessed promising selectivity toward TNBC cell line with a dramatically increased selectivity index, as they showed similar or superior IC50 values compared to paclitaxel and exhibited minimal or weak inhibitory effects on normal cells. These findings provided valuable insight for natural anti-TNBC sources.

Two New α-Glucosidase Inhibitors from Haplophyllum tuberculatum: Inhibition Kinetics and Mechanistic Insights Through in Vitro and in Silico Approaches.

Diabetes is a multifactorial global health disorder marked by unusually high plasma glucose levels, which can lead to serious consequences including diabetic neuropathy, kidney damage, retinopathy, and cardiovascular disease. One effective therapy approach for reducing hyperglycemia associated with type 2 diabetes is to target α-glucosidase, enzymes that catalyze starch breakdown in the intestine. In the current study, two new (1, 2) and nine known (3-11) compounds were isolated from the rutaceous plant Haplophyllum tuberculatum and characterized by extensive nuclear magnetic resonance spectroscopic techniques and high-resolution electrospray ionization mass spectrometry. After structural elucidation, nine compounds were evaluated for their ability to inhibit α-glucosidase, a target for the treatment of type-2 diabetes. Among them, three compounds (7, 5, and 2) exhibited notable inhibition with half-maximal inhibitory concentration (IC50) values of 3.42 ± 0.12, 5.79 ± 0.28, and 6.75 ± 1.18µM, respectively, while the remaining six compounds (1, 3, 4, 6, 8, and 9) had a moderate activity with IC50 values ranging from 12.14 ± 0.35 to 24.60 ± 0.57µM, compared to the standard drug acarbose (IC50 = 875.75 ± 1.24µM). A kinetic study of compounds 5 and 7 exhibited the competitive type of inhibition with Ki values of 4.82 ± 0.0036 and 3.92 ± 0.0062µM, respectively. Furthermore, a structure-based prediction of the compounds' binding mode suggested that these inhibitors fitted exceptionally well within the active site of the target enzyme, α-glucosidase, forming multiple hydrogen and hydrophobic interactions with its active site residues. In conclusion, compounds with potent α-glucosidase inhibitory activity are abundant in nature and can be explored and further developed for treating diabetes mellitus.

Ultra-sensitive fiber optic LSPR biosensor with enhanced enzyme functionalization for real-time putrescine detection.

Putrescine is a kind of physical diamine that is closely related to food deterioration and food quality safety. This study employs a novel fiber optic biosensor based on S-tapered and waist extension techniques, as well as localized surface plasmon resonance (LSPR), to detect putrescine accurately. The gold nanoparticles (AuNPs) are fixed on the fiber to excite LSPR. Furthermore, the multi-walled carbon nanotubes (MWCNTs) and niobium carbide (Nb2CTx) are used to increase the binding sites of enzymes to improve the sensing performance of probes. In this work, the fiber probe surface is functionalized by diamine oxidase (DAO) to improve selectivity. The sensitivity and limit of detection of the fiber optic biosensor are 2.04 nm/lg(µM) and 0.267 µM over a linear range of 0-100 µM, respectively. In addition, the tests of repeatability, reusability, selectivity, pH value, and real samples of the sensor probe are assessed to demonstrate the potential in measuring putrescine.

SegcsvdWMH: A Convolutional Neural Network-Based Tool for Quantifying White Matter Hyperintensities in Heterogeneous Patient Cohorts.

White matter hyperintensities (WMH) of presumed vascular origin are a magnetic resonance imaging (MRI)-based biomarker of cerebral small vessel disease (CSVD). WMH are associated with cognitive decline and increased risk of stroke and dementia, and are commonly observed in aging, vascular cognitive impairment, and neurodegenerative diseases. The reliable and rapid measurement of WMH in large-scale multisite clinical studies with heterogeneous patient populations remains challenging, where the diversity of imaging characteristics across studies adds additional complexity to this task. We present segcsvdWMH, a convolutional neural network-based tool developed to provide reliable and accurate WMH quantification across diverse clinical datasets. segcsvdWMH was developed using a large dataset consisting of over 700 fluid-attenuated inversion recovery MRI scans from seven multisite studies, spanning a wide range of clinical populations, WMH burdens, and imaging protocols. Model training incorporated anatomical information through a novel hierarchical segmentation approach, together with extensive data augmentation techniques to improve performance across varied imaging conditions. Benchmarked against three widely available segmentation tools, segcsvdWMH demonstrated superior accuracy, achieving mean Dice score improvements of 7.8% ± 9.7% over HyperMapp3r, 21.8% ± 8.6% over SAMSEG, and 43.5% ± 7.1% over WMH-SynthSeg across four diverse test datasets. segcsvdWMH also maintained consistently high Dice scores across these test datasets (mean DSC = 0.86 ± 0.08), and exhibited strong, stable correlations with periventricular, deep, and total WMH ground truth volumes (mean r = 0.99 ± 0.01). Additionally, segcsvdWMH was robust to low and moderate levels of simulated MRI spike noise artifacts and maintained strong performance across a range of binary segmentation thresholds and WMH burden levels. These findings suggest that segcsvdWMH may provide more accurate and robust WMH segmentation performance for heterogeneous clinical datasets characterized by varying degrees of CSVD severity.

Identification and evaluation of human errors in the care of pregnant mothers using the extensive CREAM technique

Background Care of pregnant mothers is one of the most important services provided in the healthcare system. Objective This study aims to identify and evaluate human errors in the care process of pregnant mothers using the extensive Cognitive Reliability and Error Analysis Method (CREAM). Methods In this study, tasks and activities related to the care of pregnant mothers in the health center and hospital were first analyzed using the HTA (hierarchical task analysis). Then, using extensive CREAM, human errors in tasks related to the care of pregnant mothers in the health center and hospital were identified and evaluated. Results In general, 34 main tasks and 106 subtasks were identified in the process of caring for pregnant Mothers. Thirty seven activities are related to midwifery associates or experts, and 19 activities are related to gynecologists. The highest Cognitive Failure Probability total (CFPt) was related to the duties of measures related to the infant, preparing the pregnant mother for transfer to the operating room, routine obstetric measures, taking delivery, measures related to resuscitation, and measures related to the control of the condition after delivery. Conclusions The process of caring for pregnant mothers is very vital and prone to errors due to different levels of service. The presence of experienced and capable personnel can largely prevent the occurrence of human errors. Increasing the number of personnel can be effective in reducing workload and preventing errors.

Attention and dilated convolutions inclusive deep-CNN with multiplexed texture features to diagnose Pathological and High Myopia.

In today's era, precise and timely diagnosis of ocular diseases are crucial as these disorders jeopardize millions of visions. Early detection and proactive management can minimize vision threatening complications from these disorders. High Myopia(HM) and Pathological Myopia(PM), are the globally prevalent ocular diseases, which can impair vision acuity and productivity across all age groups. Routine screening via computer aided diagnosis(CAD) is required to detect HM and PM early and halt their progression. Previous studies majorly relied only on deep convolutional neural network(deep-CNN) features and independent analysis of PM and HM. This work seeks to provide a holistic analysis of PM and HM pathology by creating a novel ternary classifier on colour fundus images to detect normal vision, PM and HM respectively. We built the classifier by integrating texture features generated using gray level co-occurrence matrix(GLCM) within the deep-CNN model. Deep-CNN model comprises of spatial attention(SA) to locate lesion, squeeze-excitation(SE) to model interdependent channel attention & atrous or dilated convolutions to capture salient multiscale features of the related disease. Investigations employing extensive ablation techniques have elucidated the significance of optic disc(OD) and retinal vessel(RV) in the fundus images and their respective alterations in the HM or PM fundus. On the diverse dataset of 3212 colour fundus images, our ternary classifier has achieved 5-fold cross validation mean accuracy of 0.9754(+/-0.014), test accuracy of 0.9767 on 645 test fundus images and the kappa score of 0.9622 indicating the clinical viability of our classifier. The performance of our classifier excelled that of other pertinent studies for both PM and HM colour fundus images. Our classifier's test findings, comprehensive aetiology analysis and class activation maps are validated by the expert ophthalmologists making it reliable to serve as a virtual doctor alleviating the concerns with the lack of skilled ophthalmologists and expensive optometry tools by facilitating remote diagnostics and telemedicine applications.

Electrode Development for Electro‐Organic Kolbe–Schmitt C–H Carboxylation of Benzene Using Cu Nanoparticles on Graphene Oxide Modified With MIL‐101 MOF for CO2 Capture in a DES Electrolyte System

ABSTRACTThis study focuses on developing an advanced electrode for the electro‐organic Kolbe–Schmitt C–H carboxylation of benzene. The electrode uses copper (Cu) nanoparticles supported on graphene oxide modified with MIL‐101 metal–organic framework (MOF). The MIL‐101 MOF enhances CO2 capture, improving the electrode's efficiency in a deep eutectic solvent (DES) system. The study reports high yields (89%–97%) of synthesized benzoic acids 4(a–l), facilitated by urea/ChCl as a cost‐effective electrolyte, by applying an electric current of 20 mA, in 2 h and at ambient temperature. The innovative electrode design facilitates efficient benzoic acid synthesis, while the use of urea/ChCl as an accessible and cost‐effective electrolyte and solvent enhances the electro‐organic reaction rate. The synthesized benzoic acids were characterized through melting point analysis, 1HNMR spectroscopy, and CHN elemental composition. To assess the electrode's properties, extensive characterization techniques such as XPS, TGA, SEM, EDS, FT‐IR, BET surface area analysis, CV, and FT‐IR were used.

Impact of Red Wine Vinegar-Based Solution on Bacterial Communities of Squid and Shrimp Skewers: A Classic and Genomic Approach

AbstractSeafood is an essential component of a balanced and healthy diet, which increases its demand. However, its biological composition and high moisture content make these products extremely perishable. To prevent spoilage, and the consequent food waste and financial expenses throughout the seafood supply chain, new technologies have been successfully developed to inhibit bacterial growth, the main cause of seafood spoilage. This work aimed to test a shelf life extension technique for seafood skewers whilst maintaining an all-natural label using a financially feasible red wine vinegar treatment applied by immersion or pulverisation. Bacterial growth was monitored by classical methods and by 16S rRNA gene amplicon sequencing during the 5 days of storage. Immersion of samples in a vinegar-based solution effectively reduced Pseudomonas and Enterobacterales counts (by 2 log cfu/g), immediately after application and throughout storage. The overall structure and diversity of the bacterial community were analysed, and a strong reduction in bacterial diversity and impact on bacterial composition was observed immediately after immersion in the red wine vinegar solution. In untreated samples, Pseudomonadota (especially the Gammaproteobacteria class) was the principal phylum, whereas the microbiota of the treated samples was dominated by Bacillota (mainly the Bacilli class). Sensory analysis revealed a mild vinegar or vinaigrette flavour in treated samples; however, these characteristics were not unpleasant. Although applying a vinegar-based solution by immersion promoted a significant reduction in the growth of spoilage bacteria during the first days of storage, further tests are required to confirm the shelf life extension.

Manifold Component Analysis: a novel technique to spatially profile tau tangles across Alzheimer’s disease stages

AbstractBackgroundIntracellular accumulation of tau tangles in the brain is one of the most prominent manifestations of Alzheimer’s disease (AD). Progression thereof across the AD stages has specific temporal and spatial patterns, wherein time is informative of space and vice versa. Here we introduce a novel method, Manifold Component Analysis (MCA), to represent tangle accumulation in 2D, reflecting the spatial aspect of tau propagation stages to further relate it to the temporal aspect thereof.MethodMCA represents a neuroinformatics technique serving to smooth out transitions between neighbouring Braak stage regions (Fig. 1A) and thus creating “sub‐stages” (Fig. 1B) which are subsequently used to sample neocortical (tau tangle) data and represent them in a continuous 2D graph with the spatially earliest sub‐stage appearing in the leftmost, and the latest spatial sub‐stage appearing on the right (Fig. 1C). This method was applied to 18F‐MK‐6240 tau PET tracer data from the TRIAD cohort (https://triad.tnl‐mcgill.com/; N = 753; 123 AD, 170 MCI, 416 cognitively unimpaired).ResultWe obtained MCA profiles of tau‐PET for each subject in the cohort, and evaluated them across multiple visits (Fig. 1D; mean inter‐visit interval 1.9 years), showing that the largest change (about 25%) occurs at stages 4 and 5. Partial correlations between sub‐stages within the MCA profiles for each subject, and their corresponding neuropsychological assessment measurements showed unique spatial signatures for each specific cognitive test, generally favouring earlier stage correlations for memory, and later stage correlations for higher cognition (Fig. 1E); the covariates included age, sex, APOEe4 status and years of education.ConclusionThe MCA method proves useful as an intuitive lookup tool for tau tangle accumulation across the brain in AD. The obtained profiles help resolve correspondence between “space” and “time” contexts of the pathology spread, as well as the spatial aspect of the timeline of cognitive decline. As MCA is a subject‐specific, easily applicable, fast and extensible technique, numerous new applications and extensions will follow, providing a necessary aid in staging, forecasting, and potentially treating AD.

Secured Anatomy-Driven Flap Extension (SAFE) for Guided Bone Regeneration: A Modified Flap Release Technique Description and Retrospective Study.

Current flap-releasing designs for guided bone regeneration (GBR) emphasize preserving subperiosteal microvasculature by adapting a deep-slit approach, separating the periosteum from the flap. Although biologically sound, a biomechanical disadvantage may be encountered. This study aimed to describe a modified design, the Secured Anatomy-driven Flap Extension (SAFE) technique, for effective facial flap release and to preliminarily evaluate the clinical outcomes of this technique retrospectively. Chart reviews were conducted to identify patients treated by facial flap release in staged GBR procedures between May 2020 and March 2022. The anatomical, biological, and biomechanical rationale of this technique were described. The following clinical data were collected: intraoperative and postoperative complications, initial and final horizontal ridge width before and 5-6 months after the GBR, and implant performance. A total of 10 patients were identified. At baseline, these patients presented with a mean ridge width of 2.05 ± 0.52 mm. No intraoperative and postoperative complications were observed in these patients (bleeding, wound opening, neurosensory disturbance, etc.) at the 2-3-week follow-up visit. At the re-entry, a mean ridge width of 6.50 ± 0.55 mm was measured (P < .01), resulting in a mean of 4.45 ± 0.65 mm ridge width gain. Twenty-one implants were successfully placed, integrated, and in function without signs/symptoms of peri-implantitis after a mean 21.5 ± 9.2 months follow-up period. Preliminary results suggest that the SAFE technique is a safe and predictable approach for releasing facial flaps during GBR procedures.

Innominate Artery Translocation with Hemiarch Replacement Strategy for Acute Type A Aortic Dissection: a Single-Center Study.

Aggressive surgical methods for acute type A aortic dissection (ATAD) can cause extended operating times and postoperative complications. less extensive techniques may increase the risk of needing further aortic reintervention. To prevent the need for extensive aortic arch surgery and subsequent re-sternotomy, hemiarch replacement (HAR) with innominate artery (a.) translocation is performed to create a suitable proximal landing zone for future endovascular repair. Retrospective study of 112 patients with ATAD who underwent aortic surgery from January 2009 to December 2020. Forty-one patients underwent HAR with innominate artery translocation, 16 underwent total arch replacement (TAR), and 55 underwent only HAR. Multivariable Cox regression and logistic regression analyses were used to study the outcomes and risk factors. The TAR group had a higher incidence of postoperative acute kidney injury. The overall mortality rate of the TAR group was 25%, compared with 20% in the HAR group and 14.6% in the translocation group. The 5-year overall survival rates for the groups were 81.9%, 75.0%, and 77.7%, respectively. False lumen thrombosis at the aortic arch and descending aorta level were factors associated with reduced mortality in both univariable and multivariable analyses. The translocation group had a significantly higher reintervention rate of 41.5% compared with the TAR and HAR groups, with rates of 31.3% and 16.4%, respectively. The median reintervention time for the translocation group was 4.72 years. Despite the innominate translocation technique having a higher reintervention rate, it had similar mortality outcomes to HAR and TAR. Thus, it could be a more convenient option for reintervention, including creating a proximal landing zone, which could benefit patients needing endovascular repair.

A consistent, volume preserving, and adaptive mesh refinement-based framework for modeling non-isothermal gas–liquid–solid flows with phase change

This work expands on our recently introduced low Mach enthalpy method (Thirumalaisamy and Bhalla 2023) for simulating the melting and solidification of a phase change material (PCM) alongside (or without) an ambient gas phase. The method captures PCM’s volume change (shrinkage or expansion) by accounting for density change-induced flows. We present several improvements to the original work. First, we introduce consistent time integration schemes for the mass, momentum, and enthalpy equations, which enhance the method stability. Demonstrating the effectiveness of this scheme, we show that a system free of external forces and heat sources can conserve its initial mass, momentum, enthalpy, and phase composition. This allows the system to transition from a non-isothermal, non-equilibrium, phase-changing state to an isothermal, equilibrium state without exhibiting unrealistic behavior. Furthermore, we show that the low Mach enthalpy method accurately simulates thermocapillary flows without introducing spurious phase changes. To reduce computational costs, we solve the governing equations on adaptively refined grids. We investigate two cell tagging/untagging criteria and find that a gradient-based approach is more effective. This approach ensures that the moving thin mushy region is always captured at fine grid levels, even when it temporarily falls within a subgrid level. We propose an analytical model to validate advanced computational fluid dynamics (CFD) codes used to simulate metal manufacturing processes (welding, 3D printing). These processes involve a heat source (like a laser) melting metal or its alloy in the presence of an ambient (inert) gas. Traditionally, studies relied on artificially manipulating material properties to match complex experiments for validation purposes. Leveraging the analytical solution to the Stefan problem with a density jump, this model offers a straightforward approach to validating multiphysics simulations involving heat sources and phase change phenomena in three-phase flows. Lastly, we demonstrate the practical utility of the method in modeling porosity defects (gas bubble trapping) during metal solidification. A field extension technique is used to accurately apply surface tension forces in a three-phase flow situation. This is where part of the bubble surface is trapped within the (moving) solidification front.

Robust Multi-Class Classification for Real-Time Agricultural Applications Using Efficient and Adaptive Deep Learning

Plant diseases severely affect agricultural productivity, necessitating accurate and rapid detection methods. This research presents a robust, multi-class plant disease classification framework using adaptive deep learning. We utilize pre-trained convolutional neural networks (CNNs), specifically Xception, InceptionResNetV2, InceptionV3, ResNet50, and the proposed EfficientNetB3-based Adaptive Augmented Deep Learning (EfficientNetB3-AADL) model. Our approach leverages transfer learning combined with extensive data augmentation and trimming techniques to enhance model performance and mitigate overfitting. The EfficientNetB3-AADL architecture incorporates convolutional and max pooling layers, regularization strategies, and a dense feature learning layer, optimized to classify 52 disease categories from a publicly available leaf image dataset. The model’s performance is extensively evaluated using metrics such as accuracy, precision, recall, and F1 score. Notably, EfficientNetB3-AADL achieves superior accuracy over 98%, outperforming other CNN models. The proposed methodology highlights the efficacy of compound scaling and adaptive data augmentation in ensuring robust and efficient disease classification, suitable for real-time agricultural applications. This advancement supports sustainable farming by offering a scalable, computationally efficient solution for early and accurate disease detection in diverse crop species.

Bifurcation analysis, phase portraits and optical soliton solutions of the perturbed temporal evolution equation in optical fibers

The perturbed nonlinear Schrödinger equation plays a crucial role in various scientific and technological fields. This equation, an extension of the classical nonlinear Schrödinger equation, incorporates perturbative effects that are essential for modeling real-world phenomena more accurately. In this paper, we investigate the traveling wave solutions of the perturbed nonlinear Schrödinger equation using the bifurcation theory of dynamical systems. Graphical presentations of the phase portrait are provided, revealing the traveling wave solutions under various conditions. By employing the auxiliary equation method, we derive a variety of solutions including periodic, dark, singular and bright optical solitons. To provide comprehensive and clearer depiction of the model’s behavior 2D, contour and 3D graphical representations are offered. We also highlight specific constraint conditions that ensure the presence of these obtained solutions. This study expands the scope of known exact solutions and their stability qualities which is offering an extensive analytical technique which enhances previous research. The novelty of our research lies in its examination of bifurcation analysis and the auxiliary equation method within the context of a perturbed nonlinear Schrödinger wave equation for the first time. By integrating these two perspectives, this paper contributes to establishing the complex dynamics and stability characteristics of soliton solutions under perturbations.

Entropically driven binding of Camptothecin in the minor groove of salmon testes DNA

The present study focuses on binding association of Camptothecin (CMT) towards natural deoxy-ribonucleic acid (salmon testes, ST) under physiological conditions of pH 7.4. Extensive spectroscopic and computational techniques have been employed to elucidate thermodynamics of the said interaction. UV and fluorescence analyses portrays significant intensity changes (hyper-chromic and hypsochromic) in the spectra, which confirms effective CMT binding to ST DNA. The McGhee-von Hipple method and Scatchard plot analyses estimated the binding affinities in 105 M−1 range. Associated thermodynamic data revealed spontaneous and exothermic nature of binding. Temperature-dependent fluorescence showed negative change in enthalpy and positive change in entropy, leading to the formation of a 1:1 adduct. Non-polyelectrolytic forces appeared to be the driving force of the ligand-DNA interaction, according to salt-dependent fluorescence. Dye displacement assay, viscosity study, DNA melting, iodide quenching, urea denaturation assay examined the minor groove nature of CMT. In silico docking study examined precise molecular representations of the minor groove binding mechanism that formed between the complex, and the study's findings were consistent with the experimental results. Simulation studies also validated the experimental analysis and docking data. These findings could expedite the process of creating new and improved CMT molecular derivatives and help in the creation of DNA-targeted medicines, which may be beneficial from a pharmaceutical point of view.

Vacuum Polarization in the Point Impurity Background

The vacuum polarization effect of a massive scalar field φ(x) near the point δ-like source is considered. The corresponding interaction is introduced within a technique of self-adjoint extension of the Laplace operator. This method has been widely discussed within the framework of quantum mechanics. We propose to use it to investigate vacuum field effects. This approach allows computing the renormalized Hadamard function and the renormalized vacuum energy density hT00(x)iren for massive real scalar field with minimal curvature coupling. The dependence of the vacuum polarization effect upon the fields’s mass is analyzed.

Investigation of surface structuring and oxidation performance of Inconel 718 superalloy by laser remelting with different patterns

The current paper deals with the effect of CO2 laser remelting on the surface structuring and oxidation performance of Inconel 718 superalloy, pointing out different laser-induced surface patterns that make a difference in their oxidation properties vital for high-temperature applications. Surface roughness, microstructure, and oxidation behavior characterization were performed using 3D optical profilometry, SEM, and XRD techniques. The oxidation tests conducted at 1000 °C for 24 h have revealed that remelted surfaces offered better oxidation resistance compared to untreated samples. Notably, the patterned sample showed the lowest weight gain under oxidation, and a parabolic regime in oxidation occurred after 100 min; while in an untreated reference sample, this appears only after 200 min. This improvement in performance results from the formation of a chromium-rich oxide layer on the melt pools, which acts as an effective barrier against further oxidation. The findings show that laser remelting, especially with grid-like surface patterns, results in an improvement in both durability and high-temperature performance of Inconel 718; therefore, it is apparently a very promising technique for lifespan extension of industrially relevant materials.