Miracle-dz/UniFAR-FTUs huggingface dataset:
Mimicking Molecular Pathways in the Design of Smart Hydrogels for the Design of Vascularized Engineered Tissues

Biomaterials are pivotal in supporting and guiding vascularization for therapeutic applications. To design effective, bioactive biomaterials, understanding the cellular and molecular processes involved in angiogenesis and vasculogenesis is crucial. Biomaterial platforms can replicate the interactions between cells, the ECM, and the signaling molecules that trigger blood vessel formation. Hydrogels, with their soft and hydrated properties resembling natural tissues, are widely utilized; particularly synthetic hydrogels, known for their bio-inertness and precise control over cell–material interactions, are utilized. Naturally derived and synthetic hydrogel bases are tailored with specific mechanical properties, controlled for biodegradation, and enhanced for cell adhesion, appropriate biochemical signaling, and architectural features that facilitate the assembly and tubulogenesis of vascular cells. This comprehensive review showcases the latest advancements in hydrogel materials and innovative design modifications aimed at effectively guiding and supporting vascularization processes. Furthermore, by leveraging this knowledge, researchers can advance biomaterial design, which will enable precise support and guidance of vascularization processes and ultimately enhance tissue functionality and therapeutic outcomes.
On the computation of plane-wave impressed fields and far-zone fields of arbitrary current sources in planar multilayer dielectric and plasmonic media

Abstract A unified and succinct transmission line formulation is presented for the computation of the plane-wave impressed fields and far-zone radiation fields due to arbitrary electric and magnetic current source distributions embedded in planar layered media. The equivalence principle and the spectral-domain transmission-line network analogue of the multilayer medium are used to express the fields in terms of the voltage and current transmission-line Green functions. The new formulation is applied to sample problems involving both dielectric and plasmonic multilayer media.
Semantic segmentation of point cloud based on graph neural network

As a key technology in the field of computer vision, point cloud semantic segmentation has been widely used in intelligent life and has become a hot spot and a difficult area of research in 3D vision. Current research on the semantic segmentation of 3D point clouds is mainly focused on the field of deep learning, however, there are two problems with such models: insufficient local feature representation and long model training period. To this end, we propose a point cloud semantic segmentation model based on geometric segmentation and graph neural network to improve the performance of semantic segmentation and model training efficiency. In our method, Firstly geometric segmentation is used for sparse and feature representation of the original point cloud. A directed graph is then constructed to partition the point cloud into "superpoints" and "superedges" with specific feature descriptors. followed by a spatial transformer network (STN) as the backbone of embedded network to encode local features from these superpoints. Finally, the graph neural network is employed for the classification of architectural components such as walls, ceilings, floors, and beams. Experiments were conducted on the Stanford University Large Scale 3D Indoor Spatial Dataset (S3DIS) dataset. Compared with the SPG network that also takes the entire large scene as input, the proposed algorithm improves the overall accuracy (OA), mean accuracy (mAcc), and per-class intersection over union (mIoU) by 1.0%, 2.9%, and 3. 0%.
Automatic crack detection and calculation based on deep learning and digital image processing

Structural health monitoring is an important way to ensure the safe and reliable work of the structures during the service period. Surface crack is one of the important indicators for monitoring and assessing the damage of structures. However, crack detection still mainly relies on human visual observation and recording. Besides, people could only record the crack width of a few points instead of all points. To solve this problem, this paper aims to propose a method which can automatically detect crack and calculate the width of every point on the crack but not several points. Thus, this paper built a large database containing more than 100,000 pictures which were taken in different scenarios, and used this database and self-designed deep convolutional neural network to detect cracks. And the prediction accuracy of CNN reached 95%. In addition, combined with image processing technology, this paper further carried out crack extraction and width calculation. Thus, the whole process of crack detection, extraction and width calculation is fully automated, and the calculation results were proved to be close to the actual results, which meets the engineering requirements and perform better than traditional OTSU algorithm.
Synthesis of vancomycin functionalized fluorescent gold nanoparticles and selective sensing of mercury (II)

Mercury ions (Hg2+) are widely found in the environment; it is considered a major pollutant. Therefore, the rapid and reliable detection of Hg2+ is of great technical interest. In this study, a highly fluorescent, sensitive, and selective fluorometric assay for detecting Hg2+ ions was developed using vancomycin functionalized and polyethyleneimine stabilized gold nanoparticles (PEI-f-AuNPs@Van). The as-made gold nanoparticles were highly fluorescent, with excitation and emission maxima occurring at 320 and 418 nm, respectively. The size of nanoparticles was ~7 nm; a zeta potential of ~38.8 mV was determined. The XRD analysis confirmed that the nanoparticles possessed crystalline structure with face centerd cubic symmetry. Using the PEI-f-AuNP@Van probe, the detection limit of Hg2+ ion was achieved up to 0.988 nM (within a linear range) by calculating the KSV. However, the detection limit in a natural environmental sample was shown to be 12.5 nM. Furthermore, the selectivity tests confirmed that the designed probe was highly selective to mercury (II) cations among tested other divalent cations. Owing to its sensitivity and selectivity, this approach for Hg2+ ions detection can be utilized for the analysis of real water samples.
Class Identification Efficacy in Piecewise GMM with Unknown Turning Points

ABSTRACT Piecewise GMM with unknown turning points is a new procedure to investigate heterogeneous subpopulations' growth trajectories consisting of distinct developmental phases. Unlike the conventional PGMM, which relies on theory or experiment design to specify turning points a priori, the new procedure allows for an optimal location of turning points based on data. The advantage of the procedure has gained increasing attention in educational and behavioral research, but a major challenging issue, class enumeration performance of the model, has not yet been investigated. The current simulation study compared the performance of PGMMs with unknown turning points in identifying the correct number of latent classes under both Bayesian and ML/EM estimation methods.
A comparative convergence and accuracy study of composite guided-ultrasonic wave solution methods: Comparing the unified analytic method, SAFE method and DISPERSE

Multiple approaches and programs are available to the public to predict ultrasonic guided-wave propagation dispersion curves in a material. Each approach and program will have its own advantage and disadvantage making it suitable for a specific end use and less suitable for others. This manuscript aims to compare three different guided-ultrasonic wave dispersion curves retrieval methods for multiple cases. First a single layer of isotropic, unidirectional, orthotropic and monoclinic material is examined, followed by a multi-layer case (consisting of 10 layers of the aforementioned materials) and finally four different laminates and one sandwich laminate are evaluated. The goal of this manuscript is to give a concise overview of the advantages and limitations of each approach to assist the end user in choosing the right program to use.
Review of Text Mining Techniques

Data mining is a process of discovering potential and practical, previously unknown patterns from large pre existing databases. Text mining is a realm of data mining in which large amount of structured and unstructured text data is analyzed to produce information of high commercial value. Analyzing textual data requires context analysis. This paper represents the current research status of text mining. Association rules, a novel technique in text mining is gaining increasing currency among research scholars is discussed. Based on studied attempts, the potential future research activities have been proposed.
Low flow sensitivity to water withdrawals in Central and Southwestern Europe under 2 K global warming

A sufficient freshwater supply is vital for humans, ecosystems, and economies, but anticipated climate and socio-economic change are expected to substantially alter water availability. Across Europe, about two-third of the abstracted freshwater comes from rivers and streams. Various hydrological studies address the resulting need for projections on changes in river discharge. However, those assessments rarely specifically account for the impact of various water withdrawal scenarios during low flow periods. We present here a novel, high-resolution hydrological modeling experiment using pseudo-global warming climate data to investigate the effects of changing water withdrawals under 2 K global warming. Especially in Western and Central Europe the projected impacts on low flows highly depend on the chosen water withdrawal assumption and can severely decrease under the worst case assumptions. Our results highlight the importance of accounting for future water withdrawals in low flow projections, showing that climate-focused impact assessments in near-natural catchments provide only one piece of the anticipated response and do not necessarily reflect changes in heavily managed river basins.
An overview of Chinese fine-grained sentiment analysis methods based on multi-kernel plant intelligence model

This paper proposes a new model, PGB_GPT, for Chinese fine-grained sentiment analysis, which combines Bidirectional Long Short-Term Memory (BiLSTM), Graph Convolutional Neural Network (GCN), and Generative Pre-Training Model (GPT). Additionally, a multi-core plant intelligent model is introduced to extract comprehensive symbolic meaning and improve the precision and accuracy of sentiment analysis. PGB_GPT outperforms other combination models and the possibility of merging a multi-core plant intelligence model with BiLSTM, GCN, and GPT for more extensive and accurate emotion analysis is highlighted. For Chinese fine-grained sentiment analysis, the PGB_GPT model combines BiLSTM, GCN, and GPT, with "P" representing "Plant Intelligence," "G" representing "Graph Convolutional Neural Network," "B" representing "Bidirectional Long Short-Term Memory," and "GPT" representing "Generative Pre-Training Model. " As evidenced by the sentiment analysis dataset evaluation, each component greatly contributes to the model's enhanced performance.
Tailoring Ceria-Based Nanocatalysts for Enhanced Performance in Steam Reforming Processes: Exploring Fundamentals and Morphological Modulations

Ceria-based nanostructures, employed as catalytic supports for noble and non-noble metals, are well-known for their remarkable activity in steam-reforming reactions, exceptional resistance to degradation, and thermal stability. However, the catalytic activity and selectivity of such systems are strongly dependent on the size and shape of ceria, making it possible to tune the oxide properties, affecting catalyst design and performance. The rational manipulation of ceria nanostructures offers various features that directly impact steam-reforming transformations, including the possibility of tuning oxygen vacancies, redox properties, and oxygen storage capacity. Thus, the importance of shape control in ceria nanomaterials is highlighted herein, emphasizing how the surface atomic configurations (exposure of different facets) significantly impact their efficiency. Although the main focus of this review is to discuss how the catalyst design may affect the performance of hydrogen production, some other elemental studies are shown, when necessary, to exemplify the level of deepness (or not) that literature has reached. Thus, an overview of ceria properties and how the physicochemical control of nanostructures contributes to their tuning will be presented, as well as a discussion regarding elemental materials design and the most prominent synthetic procedures; then, we select some metals (Ni, Co, and Pt) to discuss the understanding of such aspects for the field. Finally, challenges and perspectives for nanoengineering catalysts based on shape-controlled ceria nanostructures will be described to possibly improve the performance of designed catalysts for steam-reforming reactions. Although there are other literature reviews on ceria-based catalysts for these reactions, they do not specifically focus on the influence of the size and shape of the oxide.
Analysis of Hyperspectral Data to Develop an Approach for Document Images

Hyperspectral data analysis is being utilized as an effective and compelling tool for image processing, providing unprecedented levels of information and insights for various applications. In this manuscript, we have compiled and presented a comprehensive overview of recent advances in hyperspectral data analysis that can provide assistance for the development of customized techniques for hyperspectral document images. We review the fundamental concepts of hyperspectral imaging, discuss various techniques for data acquisition, and examine state-of-the-art approaches to the preprocessing, feature extraction, and classification of hyperspectral data by taking into consideration the complexities of document images. We also explore the possibility of utilizing hyperspectral imaging for addressing critical challenges in document analysis, including document forgery, ink age estimation, and text extraction from degraded or damaged documents. Finally, we discuss the current limitations of hyperspectral imaging and identify future research directions in this rapidly evolving field. Our review provides a valuable resource for researchers and practitioners working on document image processing and highlights the potential of hyperspectral imaging for addressing complex challenges in this domain.
EXISTENCE OF GEODESICS FOR THE GRADIENT METRIC AND THE EBIN METRIC ON THE SPACE OF SASAKIAN METRICS

We answer a question by Calabi about the existence of geodesics of the gradient metric for the space of Kahler potentials for any initial position and velocity. Moreover we find that the Ebin metric restricted to the space of type II Sasaki deformations is the sum of the Calabi metric and the gradient metric. We study its geometric properties and we prove the existence of short time geodesics as well.
Effect of humic acid and probiotics on immunity of broiler chickens

Background: The immune system in chickens has a fundamental role in controlling many diseases based on vaccination, thus enhancement of the immune system response is a priority. Aim: The aim of this experiment was to study the effect of probiotics and humic acid on immunity of broiler chickens. Methods: Day-old 300 Ross broiler chicks were segregated into 5 groups of 60 chicks per group. Group C was considered as a control. Groups T1, T2, T3, and T4 were given probiotics, antibiotics, humic acid for the first 7 days and humic acid for 42 days, respectively. Samples were collected on days 27 and 42 to assess the humoral immunity, cellular immunity, lymphoid organs weight, and differential leucocyte count (DLC). Results: The results showed a significant increase (p < 0.05) in antibodies titer against Newcastle disease virus in chickens given humic acid (T4) daily for 42 days as compared to the control. There was also a significant increase in antibodies titer in the T1 group given probiotic for the first week lasting up to day 27 as compared to the control. The skin thickness of T4 group showed a significant increase as compared to T1 and T2 groups after 24 hours of DNCB challenge. After 48 hours, the thickness was still significantly higher in the T4 group as compared to other groups except for the control. There were no significant differences in Bursa of Fabricius/Body weight (%) between the groups. Spleen/Body weight (%) was significantly higher in the control group and T1 than the other groups on day 42. The DLC remains normal in all groups. Conclusion: It is concluded that the humic acid has a stimulant and strengthening effect on the humoral and cellular immune system when given daily to broiler chickens. Moreover, the use of humic acid and probiotics with good hygiene in the first week of age may alternate the use of antibiotics which could be toxic and raise bacterial resistance.
Electrical dynamic modulation of THz radiation based on superconducting metamaterials

We demonstrate an electrically tunable superconducting metamaterial capable of modulating terahertz waves dynamically. The device is based on electromagnetically induced transparency-like metamaterials, and the maximum modulation depth reaches 79.8% in the transmission window. Controlled by an electrical sinusoidal signal, such a device could achieve a modulation speed of approximately 1 MHz. The superior property and simplicity of design make this device promising for the development of high performance THz systems.
Aerobic Exercise Training Improves Calcium Handling and Cardiac Function in Rats with Heart Failure Resulting from Aortic Stenosis

Aerobic exercise training (AET) has been used to manage heart disease. AET may totally or partially restore the activity and/or expression of proteins that regulate calcium (Ca2+) handling, optimize intracellular Ca2+ flow, and attenuate cardiac functional impairment in failing hearts. However, the literature presents conflicting data regarding the effects of AET on Ca2+ transit and cardiac function in rats with heart failure resulting from aortic stenosis (AoS). This study aimed to evaluate the impact of AET on Ca2+ handling and cardiac function in rats with heart failure due to AoS. Wistar rats were distributed into two groups: control (Sham; n = 61) and aortic stenosis (AoS; n = 44). After 18 weeks, the groups were redistributed into: non-exposed to exercise training (Sham, n = 28 and AoS, n = 22) and trained (Sham-ET, n = 33 and AoS-ET, n = 22) for 10 weeks. Treadmill exercise training was performed with a velocity equivalent to the lactate threshold. The cardiac function was analyzed by echocardiogram, isolated papillary muscles, and isolated cardiomyocytes. During assays of isolated papillary muscles and isolated cardiomyocytes, the Ca2+ concentrations were evaluated. The expression of regulatory proteins for diastolic Ca2+ was assessed via Western Blot. AET attenuated the diastolic dysfunction and improved the systolic function. AoS-ET animals presented an enhanced response to post-rest contraction and SERCA2a and L-type Ca2+ channel blockage compared to the AoS. Furthermore, AET was able to improve aspects of the mechanical function and the responsiveness of the myofilaments to the Ca2+ of the AoS-ET animals. AoS animals presented an alteration in the protein expression of SERCA2a and NCX, and AET restored SERCA2a and NCX levels near normal values. Therefore, AET increased SERCA2a activity and myofilament responsiveness to Ca2+ and improved the cellular Ca2+ influx mechanism, attenuating cardiac dysfunction at cellular, tissue, and chamber levels in animals with AoS and heart failure.
Impact of Integrated Reporting on the Value Relevance of Corporate Disclosures

Purpose: The integrated reporting (IR) process allows companies to present their business in a clear and concise manner. It was intended to investigate whether corporate disclosures are more beneficial when integrated reporting is adopted. Theoretical framework: According to agency theory, market value of shares can be increased by providing investors with more concise, useful and relevant information. Integrated reporting which is the most recent advancement in the realm of corporate reporting facilitates to growing information needs of stakeholders. Thereby, this study examines whether integrated reporting enhance the value relevance of corporate disclosures which enhance the market value of firm. Design/Methodology/Approach: This study was conducted using data collected from Colombo Stock Exchange (CSE) listed companies. There were 121 companies included in the sample, representing each sector proportionately. The data was gathered from corporate annual reports from 2012 to 2019. In the analysis, the return model and price model were both used. Descriptive statistics, correlation and panel regression analysis were conducted for achieving the research objectives. Findings: The study discovered that Sri Lankan-listed companies had a low level of corporate disclosures, although it has improved significantly over time. Further, results of the study revealed that increasing the level of disclosures in annual reports had no significant impact on improving the firm value, indicating that there is no any value relevance of disclosures. Finally, it also concluded that the IR is not enhancing the value relevance of corporate disclosures. Research, practical and social implications: The study suggest that financial managers should encourage in communicating more timely and more relevant information in order to increase their market capitalization which represent the value relevance. Further financial reporting guidelines and practices need to be more regulated. At the same time this study highlights the importance of implementing innovative corporate reporting techniques to enhance investor confidence, thereby stimulating economic growth and benefiting a wider range of stakeholders. Originality/Value: Many researchers have investigated whether integrated reporting enhance the value relevance of accounting information. Still, existing research on the value relevance of corporate disclosures has not extensively explored the effects of adopting integrated reporting (IR) on this aspect.
Effect of dehydration methods on quality parameters of drumstick (Moringa oleifera Lam.) leaf powder

A study was undertaken to assess the suitable drying methods for retention of quality parameters of drumstick (Moringa oleifera Lam.) leaf powder. The experiment was laid out in Factorial CRD involving three methods of drying (T1: Sun drying, T2: Shade drying and T3: Cabinet tray dryer) with three pre-treatments (B1: Unblanched, B2: Blanched with plain water and B3: Blanched followed by KMS dip) replicated three times. All the pre-treatments had significant effect on biochemical characteristics of drumstick leaves. Among the pre-treatments, unblanched leaves (B1) retained higher nutrient contents compared to other pre-treatments. The results ofthe investigation revealed that among the three different drying methods, shade dried sample was found to retain better nutritional properties. Significantly maximum values for moisture (11.18 %), ascorbic acid (156.27 mg/100g), vitamin-A (22.71 mg/100g), iron (16.54 mg/100g), oxalate (378.66 mg/100g), antioxidant activity (77.11%) and phenol (140.04 mg/100g) were recorded in shade dried sample. The interaction effect between pre-treatment and drying methods showed variation in results. However, the treatment combination T1B1 (Unblanched sun dried) was found to retain high protein (26.43 g/100g), magnesium (318.70 mg/100g) and potassium (1378.79 mg/100g) whereas T2B1 (unblanched shade dried) showed higher ascorbic acid (179.47 mg/100g), saponin (3.66 %), oxalate (541.47 mg/100g) and antioxidant (80.33 %) than rest of the treatment combinations.