Processing Methodology Research Articles

Photooxidation Cross-Linked, Glutaraldehyde Cross-Linked, or Enzyme and Hydrostatic Pressure Processed Decellularized Biomaterials for Cardiovascular Repair Do Not Affect Host Response in a Rat Right Ventricular Outflow Flow Tract Reconstruction (RVOT) Model.

Cardiovascular diseases (CVDs) were responsible for approximately 19 million deaths in 2020, marking an increase of 18.7% since 2010. Biological decellularized patches are common therapeutic solutions for CVD such as cardiac and valve defects. The preparation of biomaterials for cardiac patches involves two main processing methods: glutaraldehyde or photooxidation cross-linking (fixation) and noncross-linked (nonfixation) processing. Despite the variety of products available in the market, cardiac patches still suffer from significant limitations, failing to adequately mimic the properties of biological tissue and restore its function. This study assesses the impact of different processing methodologies on the biological and biomechanical outcomes of three commercially available cardiac patches (CorPatch, CardioCel, PhotoFix) and one newly developed decellularized cardiac patch (Adeka) when implanted as right ventricular outflow tract (RVOT) repair material on a rat model. Four different patches for cardiovascular repair were selected based on their processing approaches and included: photooxidation crosslinked (PhotoFix), glutaraldehyde crosslinked (CardioCel), noncross-linked small intestine submucosa (CorPatch) or enzyme, and hydrostatic pressure (Adeka) processed decellularized biomaterials. Structure and function were characterized prior to implantation via thickness mapping, cross-section morphology, 2D surface topography, 3D volume microstructure, biaxial testing, uniaxial tensile testing, ball burst, and suture retention. Their host-biomaterials response was assessed invivo using a relevant model for cardiovascular repair: a rat (RVOT) reconstruction with 8 and 16-week timepoints. Topological analysis showed that the crosslinked cardiac patches had a more homogeneous thickness distribution when compared to the noncrosslinked patches. This agreed with histological evaluation, where cross-linking processed materials better preserved collagen content than noncrosslinked patches who were also more delaminated. Biaxial data demonstrated that all patches, except CorPatch, recapitulated the anisotropic behavior of healthy left ventricle tissue. The Adeka patch in-plane mechanics at 16 weeks was the one who better resembled the mechanics of healthy cardiac tissue. All patches showed appropriate biocompatibility and function at 8- and 16-week timepoints for RVOT patching. This included echocardiographic assessment, biomechanics, macrophage infiltration and polarization, and angiogenesis. Consistently with a more porous laminae structure, explants histology showed higher cell infiltration in non-crosslinked Adeka when compared to the crosslinked PhotoFix. Overall, both invitro and invivo tests indicate that the material processing does not impact the function, biomechanical performance, and the host response of the patches that can be considered as equally effective as materials based cardiac repair solutions.

Value of multi-parameter 123I-MIBG scintigraphy in the differential diagnosis of Parkinson’s disease

Background123I-MIBG scintigraphy plays a significant role in diagnosing Parkinson's disease (PD), with most studies primarily targeting cardiac uptake and relying on traditional ratio-based parameters for assessment. However, due to variations in scanning conditions and image processing methodologies, the clinical utility of different parameters remains a subject of debate. This study aims to evaluate the diagnostic accuracy of multi-parameter 123I-3-Iodobenzylguanidine (MIBG) scintigraphy and to identify the most reliable metrics for distinguishing PD from Parkinson-plus syndromes.ResultWe recruited 56 PD patients and 44 non-PD controls, who underwent 123I-MIBG scintigraphy and clinical evaluation. MIBG uptake and washout rates (WR) for the heart, salivary glands, and thyroid were assessed, with semi-quantitative methods used to calculate heart-to-mediastinum (H/M) and gland-to-background (P/B, S/B, T/B) ratios. Diagnostic efficacy was evaluated using receiver operating characteristic (ROC) curves. The H/M ratio and cardiac WR parameters scintigraphy exhibited excellent diagnostic efficacy for PD and PPS. The 4 h H/M ratio showed superior diagnostic performance with an area under the ROC curve (AUC) of 0.980. Background and time decay correction improved cardiac WR diagnostic performance (AUC = 0.963). Although P/B, S/B, and T/B ratios showed no significant differences for differential diagnosis, the WR values of the parotid and thyroid glands exhibited moderate diagnostic efficacy with AUCs of 0.648 and 0.638, respectively.ConclusionThe heart H/M ratio and cardiac WR in 123I-MIBG scintigraphy show high diagnostic efficacy for both diagnosing and differentiating PD. Including cardiac WR in routine assessments is recommended. The diagnostic potential of WR in the parotid and thyroid glands also holds promise for PD diagnosis. Incorporating these parameters could enhance the accuracy of PD diagnosis, particularly in clinical scenarios where concurrent cardiac disease may confound the diagnosis.

Dynamic Contrast-enhanced MRI Processing Comparison for Distinguishing True Progression From Pseudoprogression in High-grade Glioma.

Treatment-related changes may occur due to radiation and temozolomide in glioblastoma and can mimic tumor progression on conventional MRI. DCE-MRI enables quantification of the extent of blood-brain barrier (BBB) disruption, providing information about areas of suspicious postcontrast T1 enhancement. We compared DCE-MRI processing methods for distinguishing true disease progression from pseudoprogression in high-grade gliomas (HGGs). We identified 110 patients with HGG treated with surgery and chemoradiation who underwent DCE-MRI to further interrogate areas of new/increasing enhancement. All patients had confirmatory surgery/biopsy with pathology-confirmed progression or pseudoprogression. Scans were performed at 3T and analyzed using nordicICE. The MCA, SSS, and Parker models are three standardized processing methodologies used to create ktrans maps, a parameter that quantifies BBB permeability. Three equal regions of interest were placed at sites of peak contrast enhancement within each lesion. Data from each method was processed for mean and maximum ktrans. We conducted several rounds of analysis and finalized a strategy on penalized support vector machines based on engineered features with bootstrap sampling. The Parker method was significant for ktrans maximum in the combined pathology and clinical as well as the pathology-only data sets. MCA and SSS did not perform well under the SVM classifier for pathology only. For clinical follow-up subjects, the Parker method yielded statistically significant results for maximum and mean ktrans. The Parker method was effective in distinguishing PD and PsP for pathology and clinical data sets. MCA and SSS techniques were effective for the clinical data set.

Multi-Modal Twitter Data Analysis for Identifying Offensive Posts Using a Deep Cross Attention based Transformer Framework

In today’s society dissemination of information among the individuals occur very rapidly due to the widespread usage of social media platforms like Twitter (now-a-days acclaimed as X). However, information may pose challenges to maintaining a healthy online environment because often it contains harmful content. This paper presents a novel approach to identify different categories of offensive posts such as hate speech, profanity, targeted insult, and derogatory commentary by analyzing multi-modal image and text data, collected from Twitter. We propose a comprehensive deep learning framework, “Value Mixed Cross Attention Transformer” (VMCA-Trans) that leverage a combination of computer vision and natural language processing methodologies to effectively classify the posts into four classes with binary labels. We have created an in-house dataset (OffenTweet) comprising of Twitter posts having textual content, accompanying with images to build the proposed model. The dataset is carefully annotated by several experts with offensive labels such as hate speech, profanity, targeted insult, and derogatory commentary. VMCA-Trans utilizes fine-tuned state-of-the-art transformer based backbones such as ViT, BERT, RoBERTA etc. The combined representation of image and text embeddings obtained by these fine-tuned transformer encoders, is fed into a classifier to categorize the posts into offensive and non-offensive classes. To assess its effectiveness, we extensively evaluate the VMCA-Trans model using various performance metrics. The results indicate that the proposed multi-modal approach, achieves superior performance compared to traditional unimodal methods.

Adapting Deep-Learning in Early Yam Disease Detection Using Lenet-5 (Adam Optimizer) Convolutional Neural Network Architecture to Improve Productivity and Enhance Farmers Social Habits in the Digital Age

The agriculture sector faces significant challenges due to diseases affecting crop yields, particularly in yam cultivation. This study explores the adaptation of deep learning techniques for early detection of yam diseases using a LeNet-5 Convolutional Neural Network (CNN) architecture optimized with the Adam optimizer. The fam sides considered are; Ardokola, Zing and Mutum Biu in Taraba State, Nigeria. By leveraging advanced image processing and machine learning methodologies, we aim to develop an effective diagnostic tool that empowers farmers to identify and manage diseases promptly, ultimately improving productivity. This research not only enhances the technological capabilities of farmers in the digital age but also promotes better agricultural practices, fostering social habits that encourage knowledge sharing and community engagement. The proposed system is tested on a comprehensive dataset of yam leaf images, demonstrating its ability to accurately detect various disease conditions at 17.84%. Results indicate a significant improvement in recognition accuracy, suggesting that the integration of AI-driven solutions can transform disease management approaches in yam farming, contributing to sustainable agricultural practices and improved livelihoods for farmers.

Physical modeling of motion and evaporation of polydisperse water droplets in a high-enthalpy air flow

This paper presents the results of a study aimed at obtaining new experimental data on the regularities of evaporation of droplets of polyfractional composition in a high-enthalpy gas flow. This study considers the features of heating and subsequent vaporization of water droplets with diameters of 5…100 μm in an air flow with initial temperature up to ∼600 K and velocity up to ∼100 m/s at subcritical Weber numbers. The description of the laboratory setup equipped with laser-shadow means of diagnostics of two-phase flows and the proposed methodology of processing of the registered parameters are presented. On the basis of the conducted experiments, quantitative indicators of the intensity of the process of evaporation of polydisperse water droplets in the air flow are determined. The database of experimental data on the motion and evaporation of water droplets in the air flow, which has been developed according to the available literature sources, has been supplemented with new results, which makes it possible to clarify the corresponding physical and mathematical models. The results obtained can be used in planning and conducting subsequent studies.

Fractal-domain Transformer Based on Learnable Multifractal Spectrum for Chaotic Systems Classification

Conventional deep learning in the spatiotemporal-frequency domain frequently encounter challenges in terms of slow convergence rates and limited generalization, particularly for classification of chaotic systems. To address these limitations, this paper introduces a novel fractal-inspired deep network model, specifically, the Multifractal Spectrum Transformer (MFS-Transformer), grounded in learnable multifractal analysis. Initially, we put forward the conceptual framework of fractal learning, compared with traditional fractal signal processing methodologies and spatial-temporal domain learning paradigms. Subsequently, a Learnable Multifractal Spectrum (LMFS) derived from 3D spatial gridding, coupled with fractal-domain filtering, is proposed to construct the iterative learning process within the fractal domain. Further, we formulate the MFS-Transformer, an innovative architecture that integrates multi-channel embedding, LMFS, fractal-domain filtering, residual fusion mechanisms, a mixer module, and a classifier, tailored for chaotic system classification. Ultimately, we evaluate the efficacy of our model in classifying 3D chaotic systems under stringent conditions of short-term sequences and low Signal-to-Noise Ratio (SNR). Experimental outcomes underscore the substantial performance gains achieved by the MFS-Transformer, with classification accuracy enhancements of 13.34% and 5.00% over existing Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), respectively, under SNR=0dB and 32-sample sequences. These findings validate the superiority of the MFS-Transformer in addressing the complexities of chaotic system classification under complex scenarios. This research not only advances the frontier of fractal deep learning but also presents a novel perspective and methodology for tackling intricate spatiotemporal classification problems.

Nanomembrane Etching and Release Process for the Realization of Hybrid InP/Si Photonic‐Crystal Surface‐Emitting Lasers Using Micro‐Transfer Printing

Herein, the fabrication of hybrid InP/Si 1.55 μm range photonic‐crystal surface‐emitting lasers (PCSELs) using micro‐transfer printing is reported on. This fabrication technology is expected to have important advantages in manufacturing, performance, and silicon integration, but it relies on a fine‐tuned process for selective etching, release, and subsequent printing of active InP membranes on top of a Si photonic‐crystal layer. Herein, the appropriate wet etching chemistry and processing methodology for preserved layer and interface integrity are discussed and the technology by optically pumped PCSELs is demonstrated, showing continuous‐wave operation at room temperature.

Development of three biofuel CRMs for the quality parameters in biodiesel and wood pellet via a joint research project.

Biomass is a key element in biofuels which can be defined as a fuel produced through contemporary biological processes, and its increased use can support the EU's aims of reducing greenhouse gas emissions. Information on the nature and the quality of the biomass or biofuel is important in order to support the optimization of their combustion with respect to realizing higher efficiencies and lower emissions during energy production. Three reference materials were produced by a collaborative approach among national metrology institutes and designated institutes within the scope of the EMPIR project: BIOFMET.The project was aimed to establish advanced traceable measurement standards for the determination of the calorific value, impurities, and other parameters such as density, kinematic viscosity, moisture, and ash. This paper presents the sampling and processing methodology, homogeneity, stability, characterization campaign, the assignment of property values, and their associated uncertainties in compliance with ISO 17034 for biofuel reference materials: biodiesel, wood pellet powder, and wood pellet. Parameters of interest in biodiesel reference material-UME BIOFMET CRM 01 are gross calorific value (GCV), density, viscosity, and mass fractions of Ca, K, Mg, Na, P, and S elements. Parameters to be certified in wood pellet powder reference material-UME BIOFMET CRM 02 are GCV, moisture, ash, and mass fractions of Al, Cr, K, Mg, Mn, Ni, S, and Zn elements. Parameters to be certified in the wood pellet reference material-UME BIOFMET CRM 03 are GCV and moisture. The homogeneity and stability of the materials were assessed in accordance with ISO 33405. The materials were characterized by interlaboratory comparison studies among competent metrology institute and designated institute laboratories. Assigned values and uncertainties of the certified values were calculated in accordance with ISO 33405, and uncertainties include characterization, homogeneity, and stability components. The developed CRMs are intended to be used for the development and validation of measurement procedures for the determination and quality control/assurance purposes of the quality parameters for biofuels. It should be emphasized that the UME BIOFMET CRM 01-Biodiesel CRM is the first biodiesel reference material certified for calorific value. Among the developed wood CRMs, thepellet form, UME BIOFMET CRM 03, was found to be more stable than the powder one, UME BIOFMET CRM 02, for the moisture parameter. Sixfold lower relative uncertainty value for short-term stability at 45°C and twofold lower relative uncertainty value for long-term stability at 22°C were obtained for the moisture parameter of the CRM in pellet form compared to the CRM in powder form.

Ultrasound-Assisted Extraction of Betalains from the Amaranthus Using Natural Deep Eutectic Solvents.

Due to increasing consumer awareness, there is a growing demand for healthy foods by replacing synthetic colorants with natural alternatives. Amaranthus is a sustainable rich source of one such natural colorant, namely, betalains. Conventional extraction methods have various challenges, such as high solvent requirements and low extraction yield. To overcome these problems, the present study aims at developing a green extraction method for betalains from Amaranth by ultrasound-assisted extraction (UAE) employing natural deep eutectic solvents (NADES). Of the three Amaranth species screened, the leafy Amaranth is found to have the highest betalains content (24.79 ± 2.88mg/100g FW). Among the five NADES screened, lactic acid: fructose was identified as the most efficient solvent with a betalains yield of 79.68 ± 4.81mg/100g FW. The Box-Behnken Design (BBD) was employed to identify the best combinations of extraction process parameters. This approach enabled optimization through a reduced number of experiments. Subsequently, a machine learning model was employed to predict yield for all possible process parameter combinations. On identifying the most effective combination process parameters that resulted in the highest possible yield (306.72mg/100g FW), the same is experimentally validated (297.28 ± 10.76mg/100g FW). UAE employing NADES has resulted in a 3.7-fold increase in the yield of betalains over extraction with NADES alone and a 12-fold increase over conventional extraction. HPLC analysis confirmed that the structure remained intact even after processing. Based on the results, UAE employing NADES was demonstrated to be an attractive extraction methodology for downstream processing of betalains in particular and biomolecules, in general.

Sustainable Enhancement of Mechanical and Wear Properties in Natural Fiber Composites: Integration of Bio-Waste Fillers and Taguchi Optimization for Bio Medical Applications

<p style="line-height:200%;text-align:justify;"><span style="font-family:"Times New Roman",serif;font-size:12.0pt;">This study investigates the effect of teak wood dust (TWD) particles on the mechanical properties and erosion wear characteristics of Luffa acutangula fiber (LAF) reinforced phenol formaldehyde (PF) composites. Test samples were prepared with 20% LAF and varying TWD content (5%, 15%, and 25%). The addition of 15% TWD showed a significant improvement in mechanical performance, with tensile strength increasing by 17.56%, flexural strength by 48.78%, and impact strength by 54.64%. The optimum parameters for minimizing erosion wear were determined using Taguchi analysis, achieving a minimum erosion rate of 199.82 mg/Kg. This combination included 15% TWD, 40 m/s impact velocity, 60° impingement angle, 450 µm erodent size, and 60 mm standoff distance. Inspired by signal processing and classification methodologies, this study employs a structured approach to enhance mechanical and erosive wear properties, akin to wavelet-based data optimization techniques. The results demonstrate that TWD particles significantly improve the properties of natural fiber-reinforced composites, presenting a novel, sustainable material solution for industrial applications. This study provides a novel approach to enhancing composite materials by integrating TWD into LAF/PF composites, offering sustainable solutions for lightweight and durable industrial applications. These composites hold potential for applications beyond traditional industries, extending to biomechanical systems and prosthetic design, where lightweight and durability are critical, much like EMG-based gesture recognition systems in human-machine interaction.</span></p>

Performance of Individual Tree Segmentation Algorithms in Forest Ecosystems Using UAV LiDAR Data

Forests are crucial for biodiversity, climate regulation, and hydrological cycles, requiring sustainable management due to threats like deforestation and climate change. Traditional forest monitoring methods are labor-intensive and limited, whereas UAV LiDAR offers detailed three-dimensional data on forest structure and extensive coverage. This study primarily assesses individual tree segmentation algorithms in two forest ecosystems with different levels of complexity using high-density LiDAR data captured by the Zenmuse L1 sensor on a DJI Matrice 300RTK platform. The processing methodology for LiDAR data includes preliminary preprocessing steps to create Digital Elevation Models, Digital Surface Models, and Canopy Height Models. A comprehensive evaluation of the most effective techniques for classifying ground points in the LiDAR point cloud and deriving accurate models was performed, concluding that the Triangular Irregular Network method is a suitable choice. Subsequently, the segmentation step is applied to enable the analysis of forests at the individual tree level. Segmentation is crucial for monitoring forest health, estimating biomass, and understanding species composition and diversity. However, the selection of the most appropriate segmentation technique remains a hot research topic with a lack of consensus on the optimal approach and metrics to be employed. Therefore, after the review of the state of the art, a comparative assessment of four common segmentation algorithms (Dalponte2016, Silva2016, Watershed, and Li2012) was conducted. Results demonstrated that the Li2012 algorithm, applied to the normalized 3D point cloud, achieved the best performance with an F1-score of 91% and an IoU of 83%.

Implementation of a Big Data Ecosystem for Interdisciplinary Analysis in Academic Projects: Collaboration between Engineering and Communication in the Study of the 2024 Mexican Elections

This article presents the development and application of a Big Data ecosystem at FES Aragón, UNAM, framed within a PAPIIT Project. It details the collaboration with the Electoral Observatory of the Communication program at the same Faculty for analyzing political communication on social networks during the 2024 election campaigns in Mexico. Through massive data processing methodologies, which included comparisons between conventional tools like Python and distributed platforms on a Hadoop cluster and the use of Spark, large volumes of data were structured and cleaned. This methodology allowed for processing information for subsequent comprehensive qualitative and quantitative analysis from the perspectives of ethos, pathos, and logos in the discourse of the candidates participating in the electoral process. The results revealed that, while Python is useful for small datasets, Hadoop and Spark offer superior efficiency in large-scale data processing, highlighting the importance of choosing the appropriate infrastructure for each task. The findings demonstrate the value of the intersection between computer engineering and communication in social and political research, setting a precedent for future interdisciplinary collaborations.

A Low Temperature Calciothermic Reduction Approach for the Synthesis of LaB6 Powders

In this work, we report the synthesis of a polycrystalline LaB6 powders with a uniform morphology by a low-temperature metallothermic reduction route using calcium as a reductant. The processing methodology and heat treatment parameters are optimized based on thermodynamic estimates in order to favour the reaction kinetics and for complete formation of LaB6 powders with highest purity. The synthesised powder was subjected to comprehensive characterization studies. X-Ray Diffraction (XRD) revealed the complete formation of LaB6 compound without any traces of reactants. Scanning Electron Microscopy (FE-SEM) along with chemical mapping shows the morphology and elemental distribution of LaB6 powder. X-Ray Photoelectron Spectroscopy (XPS) results show the futuristic peaks with photon energies that are assigned to the core levels of the La and B without significant impurities whereas the Fourier transform infrared (FTIR) spectrum indicates an apparent presence of La-B bonds at 1045.4 cm−1, 585.5 cm−1, and at 463.1 cm−1.

Automatic Detection of Seafloor Bedforms for 3D Bathymetric Data

Seafloor bedforms are sedimentary structures that can reveal the local hydrodynamics conditions as they are the result of the bottom sediments' response to the dominant flow. The studying of these dynamic bottom shapes is important given that they can provide auxiliary information for the mapping of benthic habitats and can present a risk to navigation and marine structures. Mapping of the sea bottom is often done with dense and spatially extensive 3D bathymetry data (point clouds) resulting in a more precise representation of the targeted area. However, the most common praxis in this field is to rasterize the original data for the easiness in computation and lack of yet well-established methodology for 3D bathymetric data processing. As a consequence, the rasterization process causes information loss and increase of data processing time. The advantage of points clouds is that they comprise a larger volume of information in the same file, e.g. depth, intensity, RGB, and point classes, enabling a closer representation of reality and simultaneous generation of multiple products as potential habitat maps, Landscape Information Model (LIM), and denser Digital Bathymetry Model (DBM). Therefore, the purpose of this work is to apply a modified U-Net convolutional neural network for detecting and classifying bedform types in the bathymetry point cloud. The methodology will be applied to two datasets collected on the Espirito Santo Continental Shelf: Recifes Esquecidos (RE) and Doce River (DR).The following methodological steps will be carried out: (1) data collection, (2) generation of bathymetry derivatives as slope, curvature, geomorphons, aspect, and data tiling up for data augmentation, (3) image labeling, (4) model implementation including the training, validation, and testing steps, (5) calculation of model’s performance metrics: Intersection over Union (IoU), mean Average Precision (mAP), recall, and precision. This study will also present a performance comparison between the modified U-Net and a Random Forest (RF) forest. The selected areas are very rich in sedimentary features, so it is expected as the final product a classified points cloud in transversal, parallel, transitional, and artifacts (errors related to the surveying method) classes. It is also expected to have better performance from the non-convolutional model, RF.

Vehicle trajectory dataset from drone videos including off-ramp and congested traffic – Analysis of data quality, traffic flow, and accident risk

Vehicle trajectory data have become essential for many research fields, such as traffic flow, traffic safety, and automated driving. To make trajectory data useable for researchers, an overview of the included road section and traffic situation as well as a description of the data processing methodology is necessary. In this paper, we present a trajectory dataset from a German highway with two lanes per direction, an off-ramp and congested traffic in one direction, and an on-ramp in the other direction. The dataset contains 8,648 trajectories and covers 87 ​min and an ∼1,200 ​m long section of the road. The trajectories were extracted from drone videos using a posttrained YOLOv5 object detection model and projected onto the road surface via three-dimensional (3D) camera calibration. The postprocessing methodology can compensate for most false detections and yield accurate speeds and accelerations. The trajectory data are also compared with induction loop data and vehicle-based smartphone sensor data to evaluate the plausibility and quality of the trajectory data. The deviations of the speeds and accelerations are estimated at 0.45 ​m/s and 0.3 ​m/s2, respectively. We also present some applications of the data, including traffic flow analysis and accident risk analysis.

Aurel_AI: Automating an Institutional Help Desk Using an LLM Chatbot

The Aurel_AI research project was born from the need to implement a virtual help desk for a university, providing accurate organizational information to both internal and external clients. The information includes details about academic programs, regulations, processes, and personnel. Aurel_AI is part of a broader research program on the use of AI in academia. Traditional solutions for a help desk, such as telephone call centers, present quality and efficiency issues that are difficult to solve. Call center staff generally lack comprehensive knowledge about the institution, rely on specific information that is sometimes outdated, require additional systems for information retrieval, and experience high turnover rates. This leads to associated costs and issues related with outdated information, resulting in inaccurate responses and long waiting times. Generative artificial intelligence models, known as Large Language Models (LLMs), offer an interesting alternative for an automated virtual help desk. These models can understand even vague and poorly structured questions and generate reasonably appropriate answers. However, they are not without flaws, as they tend to present issues like "hallucinations" when the required information is not present in their training data. To minimize this problem, it is crucial to ensure that the model has precise and comprehensive information, which needs a specific methodology for information collection, validation, and updating. Base models require an adaptation process to be used for specific cases, for which techniques like Fine-Tuning and Retrieval Augmented Generation (RAG) exist. Fine-tuning retrains a model's weights with new specific information, while RAG uses both proprietary information—in this case, from the university—and publicly available internet data. Both techniques have pros and cons that need to be evaluated to select the most suitable option. They also demand appropriate and specialized infrastructure, which is often expensive. Thus, another challenge is to find a balance between suitable equipment and reasonable costs. The final system, from the user's perspective, must be accurate, flexible, and adaptable to deliver a satisfactory experience. As the results show, Aurel_AI represents an advance in the digitalization of educational services, standing out for its ability to generate accurate and personalized responses. However, its current limitations, such as handling concurrent queries and hallucinations, underscore the need for adjustments to both infrastructure and data processing methodology. With strategic improvements, the system has the potential to consolidate itself as a replicable model for multiple university digital services.

Elucidating the dynamics of Integrin αIIb[beta]3 from native platelet membranes by cryo-EM with build and retrieve method.

Platelets fulfill their essential physiological roles sensing the extracellular environment through their membrane proteins. The native membrane environment provides essential regulatory cues that impact the protein structure and mechanism of action. Single-particle cryogenic electron microscopy (cryo-EM) has transformed structural biology by allowing high-resolution structures of membrane proteins to be solved from homogeneous samples. Our recent breakthroughs in data processing now make it feasible to obtain atomic-level-resolution protein structures from crude preparations in their native environments by integrating cryo-EM with the "Build-and-Retrieve" (BaR) data processing methodology. We applied this iterative bottom-up methodology on resting human platelet membranes for an in-depth systems biology approach to uncover how lipids, metal binding, post-translational modifications, and co-factor associations in the native environment regulate platelet function at the molecular level. Here, we report using cryo-EM followed by the BaR method to solve the first unmodified integrin αIIbβ3 structure directly from resting human platelet membranes in its inactivated and intermediate states at 2.75Å and 2.67Å, respectively. Further, we also solved a novel dimer conformation of αIIbβ3 at 2.85Å formed by two intermediate-states of αIIbβ3. This may indicate a previously unknown self-regulatory mechanism of αIIbβ3 in its native environment. In conclusion, our data show the power of using cryo-EM with the BaR method to determine three distinct structures including a novel dimer directly from natural sources. This approach allows us to identify unrecognized regulation mechanisms for proteins without artifacts due to purification processes. These data have the potential to enrich our understanding of platelet signaling circuitry.

Description of a new species of sucking louse Hoplopleura kuhnludwigi (Phthiraptera: Anoplura: Hoplopleuridae) from the Cape York rat Rattus leucopus cooktownensis (Rodentia: Muridae) in Australia using conventional methodology and novel epiflourescence microscopy

A new species of sucking louse, Hoplopleura kuhnludwigi (Phthiraptera: Hoplopleuridae), from the Cape York rat Rattus leucopus cooktownensis Tate, 1951 (Rodentia: Muridae) is described and illustrated with photos and drawings. The host rat(s) were trapped in Northeast Queensland, Australia and identified using morphological characters and DNA sequencing. Hoplopleura kuhnludwigi is the first louse species to be described on R. leucopus. The morphology of the new louse species is contrasted with the morphology of Hoplopleura spp. previously described in Australian murids and shown to be different from other louse species that infest Australian native murids. Both conventional lice processing and staining methodology and a novel method, based on autofluorescence of chitin, have been used to describe and illustrate the diagnostic characters of H. kuhnludwigi. Processing of H. kuhnludwigi for epifluorescence microscopy (EM) is fast and easy, and the examination of the lice processed by this method can improve the accuracy of the description and identification of the lice species from genus Hoplopleura and potentially other louse species.

Development of Vertical Structured TFT Using Interfacial Oxidation

As advancements in display technology persist, ongoing research endeavors into Thin-Film Transistors (TFTs) to move forwards display evolution endure. Among the various semiconductors in TFT technologies, oxide TFTs have garnered considerable attention due to their manifold advantages, including heightened electron mobility in comparison to a-Si, superior transmittance, and suitability for medium to large-scale applications.Recent developments in display engineering suggest that Augmented Reality (AR), Virtual Reality (VR), and flexible displays are the vanguards of next-generation displays. These rapidly expanding applications desire several specifications for TFTs, such as minimalized occupied area, high resolution, low-power operation, etc. Given the challenges associated with fulfilling these mentioned requirements using conventional TFT technologies is difficult due to the facing physical limitations. Therefore, a novel enhancement methodology becomes necessary. In response to this necessity, there has been a rapidly growing interest in Vertical TFTs (VTFTs), which represent a structural refinement of next-generation TFTs in recent years.VTFT was propounded by Professor Y. Uchida of Japan in 1984 utilizing a-Si [1]. In the V-TFT structure, the source and drain electrodes are vertically separated by an insulator spacer. The annel lies between them, attached to the spacer's sidewall, directing carrier flow vertically. Therefore, VTFTs can easily achieve short channel lengths under 1µm, larger aspect ratios W/L, and higher packaging density, greatly reducing the occupied area over conventional TFTs. Nevertheless, the development of previously proposed VTFTs facing into unresolved challenges, including process complexity attributable to the requisite spacers and interlayer dielectrics (ILDs), as well as reduced yields due to the process complexity.M. Chandra Sekhar et al., have postulated a methodology [2] employing the oxide layer formed at the interface as a leading layer during the deposition of metal and oxide, subsequently evaluating its reliability. This oxide film is termed Interfacial Oxidation.As illustrated in Figure 1, our study introduces a novel VTFT processing methodology assured to improve process complexity and low yield rates, which represent inherent challenges in conventional VTFT fabrication.Tantalum (Ta), characterized by low Gibbs's free energy, is harnessed as the gate electrode, while Indium Tin Oxide (ITO) serves as the source and drain electrodes. Concurrently, an interfacial oxide film occurs through the heat treatment-induced interfacial oxidation reaction between ITO and Ta, serving as an insulating film. This innovative VTFT processing protocol offers the following advantages: 1) Substituting the gate electrode prevents the necessity for conventional spacers and prevents the deposition of an interlayer insulating film, thereby simplifying the fabrication process considerably. 2) The insulating layer conforms to the contour of the gate electrode, obviating the deposition process for the insulating layer. This prevents defects that may emerge during additional insulating layer processing, mitigating electrode damage and averting diminished yields. 3) As a result, VTFT configuration offers an exceedingly short channel in the nm range, corresponding to the gate thickness, thereby producing a low power consumption—an outstanding advantage for driving applications.Consequently, this research holds the potential to substantially contribute to the advancement of low-power electronic devices and the realization of next-generation display applications, encompassing form factor innovations, automotive integration, and AR/VR implementation, while simultaneously addressing the challenges posed by vertical TFT technology. Figure 1