Search results for: Promise Chidi Kenkwo

Authors: Ahmedou Moulaye Idriss, Tfeil Yahya, Tamas Ungi, Gabor Fichtinger

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Image-guided therapy (IGT) plays a crucial role in minimally invasive procedures for liver interventions. Accurate segmentation of the liver and precise needle placement is essential for successful interventions such as needle aspiration. In this study, we propose a deep learning-based liver 3D slicer designed to enhance segmentation accuracy and facilitate needle aspiration procedures. The developed 3D slicer leverages state-of-the-art convolutional neural networks (CNNs) for automatic liver segmentation in medical images. The CNN model is trained on a diverse dataset of liver images obtained from various imaging modalities, including computed tomography (CT) and magnetic resonance imaging (MRI). The trained model demonstrates robust performance in accurately delineating liver boundaries, even in cases with anatomical variations and pathological conditions. Furthermore, the 3D slicer integrates advanced image registration techniques to ensure accurate alignment of preoperative images with real-time interventional imaging. This alignment enhances the precision of needle placement during aspiration procedures, minimizing the risk of complications and improving overall intervention outcomes. To validate the efficacy of the proposed deep learning-based 3D slicer, a comprehensive evaluation is conducted using a dataset of clinical cases. Quantitative metrics, including the Dice similarity coefficient and Hausdorff distance, are employed to assess the accuracy of liver segmentation. Additionally, the performance of the 3D slicer in guiding needle aspiration procedures is evaluated through simulated and clinical interventions. Preliminary results demonstrate the effectiveness of the developed 3D slicer in achieving accurate liver segmentation and guiding needle aspiration procedures with high precision. The integration of deep learning techniques into the IGT workflow shows great promise for enhancing the efficiency and safety of liver interventions, ultimately contributing to improved patient outcomes.

Keywords: deep learning, liver segmentation, 3D slicer, image guided therapy, needle aspiration

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Authors: Janani Selvaraj, M. Prashanthi Devi, P. B. Harathi

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Epidemiologic studies conducted over several decades have provided evidence to suggest that long-term exposure to elevated ambient levels of particulate air pollution is associated with increased mortality. Air quality risk management is significant in developing countries and it highlights the need to understand the role of ecologic covariates in the association between air pollution and mortality. Several new methods show promise in exploring the geographical distribution of disease and the identification of high risk areas using epidemiological maps. However, the addition of the temporal attribute would further give us an in depth idea of the disease burden with respect to forecasting measures. In recent years, new methods developed in the reanalysis were useful for exploring the spatial structure of the data and the impact of spatial autocorrelation on estimates of risk associated with exposure to air pollution. Based on this, our present study aims to explore the spatial and temporal distribution of the lung cancer cases in the Coimbatore district of Tamil Nadu in relation to air pollution risk areas. A spatio temporal moving average method was computed using the CrimeStat software and visualized in ArcGIS 10.1 to document the spatio temporal movement of the disease in the study region. The random walk analysis performed showed the progress of the peak cancer incidences in the intersection regions of the Coimbatore North and South taluks that include major commercial and residential regions like Gandhipuram, Peelamedu, Ganapathy, etc. Our study shows evidence that daily exposure to high air pollutant concentration zones may lead to the risk of lung cancer. The observations from the present study will be useful in delineating high risk zones of environmental exposure that contribute to the increase of cancer among daily commuters. Through our study we suggest that spatially resolved exposure models in relevant time frames will produce higher risks zones rather than solely on statistical theory about the impact of measurement error and the empirical findings.

Keywords: air pollution, cancer, spatio-temporal analysis, India

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Authors: Pavel Damborsky, Martina Zamorova, Jaroslav Katrlik

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Prostate cancer is annually the most common newly diagnosed cancer among men. An extensive number of evidence suggests that traditional serum Prostate-specific antigen (PSA) assay still suffers from a lack of sufficient specificity and sensitivity resulting in vast over-diagnosis and overtreatment. Thus, the early-stage detection of prostate cancer (PCa) plays undisputedly a critical role for successful treatment and improved quality of life. Over the last decade, particular altered glycans have been described that are associated with a range of chronic diseases, including cancer and inflammation. These glycans differences enable a distinction to be made between physiological and pathological state and suggest a valuable biosensing tool for diagnosis and follow-up purposes. Aberrant glycosylation is one of the major characteristics of disease progression. Consequently, the aim of this study was to develop a more reliable tool for early-stage PCa diagnosis employing lectins as glyco-recognition elements. Biosensor and biochip technology putting to use lectin-based glyco-profiling is one of the most promising strategies aimed at providing fast and efficient analysis of glycoproteins. The proof-of-concept experiments based on sandwich assay employing anti-PSA antibody and an aptamer as a capture molecules followed by lectin glycoprofiling were performed. We present a lectin-based biosensing assay for glycoprofiling of serum biomarker PSA using different biosensor and biochip platforms such as label-free surface plasmon resonance (SPR) and microarray with fluorescent label. The results suggest significant differences in interaction of particular lectins with PSA. The antibody-based assay is frequently associated with the sensitivity, reproducibility, and cross-reactivity issues. Aptamers provide remarkable advantages over antibodies due to the nucleic acid origin, stability and no glycosylation. All these data are further step for construction of highly selective, sensitive and reliable sensors for early-stage diagnosis. The experimental set-up also holds promise for the development of comparable assays with other glycosylated disease biomarkers.

Keywords: biomarker, glycosylation, lectin, prostate cancer

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Authors: Fama Jallow, Melody Neaves, Professor Mcgregor

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The quest for sustainable energy sources has driven significant interest in hydrogen production as a clean and efficient fuel. Alkaline water electrolysis (AWE) has emerged as a prominent method for generating hydrogen, necessitating the development of advanced electrode designs with improved performance characteristics. Additive manufacturing (AM) by laser powder bed fusion (LPBF) method presents an opportunity to tailor electrode microstructures and properties, enhancing their performance. This research proposes investigating the AM of electrodes with different lattice structures to optimize hydrogen production. The primary objective is to employ advanced modeling techniques to identify and select two optimal lattice structures for electrode fabrication. LPBF will be used to fabricate electrodes with precise control over lattice geometry, pore size, and distribution. The performance evaluation will encompass energy consumption and porosity analysis. AWE will assess energy efficiency, aiming to identify lattice structures with enhanced hydrogen production rates and reduced power requirements. Computed tomography (CT) scanning will analyze porosity to determine material integrity and mass transport characteristics. The research aims to bridge the gap between AM and hydrogen production by investigating lattice structures potential in electrode design. By systematically exploring lattice structures and their impact on performance, this study aims to provide valuable insights into the design and fabrication of highly efficient and cost-effective electrodes for AWE. The outcomes hold promise for advancing hydrogen production through AM. The research will have a significant impact on the development of sustainable energy sources. The findings from this study will help to improve the efficiency of AWE, making it a more viable option for hydrogen production. This could lead to a reduction in our reliance on fossil fuels, which would have a positive impact on the environment. The research is also likely to have a commercial impact. The findings could be used to develop new electrode designs that are more efficient and cost-effective. This could lead to the development of new hydrogen production technologies, which could have a significant impact on the energy market.

Keywords: hydrogen production, electrode, lattice structure, Africa

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Authors: S. Padma, D. H. Tejavathi

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Somatic embryogenesis is a remarkable illustration of the dictum of plant totipotency where developmental reconstruction of somatic cells takes place towards the embryogenic pathway. It recapitulates the morphological and developmental process that occurs in zygotic embryogenesis. S. androgynous commonly called as multivitamin plant. The leaves are consumed as green leafy vegetable by the Southeast Asian communities due to their rich nutritional profile. Despite being a good nutritional vegetable with proteins, vitamins, minerals, amino acids, it is warned for excessive intake due to the presence of alkoloid called papaverine. Papaverine at higher concentrations is toxic and leads to a syndrome called Bronchiolitis Obliterans. In the present study, morphogenetic potential of shoot tip, leaf and nodal explants of Sauropus androgynous was investigated to develop and enhance the reliable plant regeneration protocol via somatic embryogenesis. Somatic embryos were derived directly from the embryogenic callus derived from shoot tip, node and leaf cultures on Phillips and Collins (L2) medium supplemented with NAA at various concentrations ranging from 5.3 µM/l to 26.85 µM/l within two months of inoculation. Thus obtained embryos were sub cultured to modified L2 media supplemented with increased vitamin level for the further growth. Somatic embryos with well-developed cotyledons were transferred to normal and modified L2 basal medium for conversion. The plantlets thus obtained were subjected to brief acclimatization before transferring them to land. About 95% of survival rate was recorded. The augmentation process of culturing various explants through somatic embryogenesis using synthetic medium with various plant growth regulators under controlled conditions have aggrandized the commercial production of Sauropus making it easily available over the conventional propagation methods. In addition, regeneration process through somatic embryogenesis has ameliorated the development of desired character in Sauropus with low papaverine content thereby providing a valuable resource to the food and pharmaceutical industry. Based on this research, plant tissue culture techniques have shown promise for economical and convenient application in Sauropus androgynous breeding.

Keywords: L2 medium, multivitamin plant, NAA, papaverine

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Authors: Shashi Sharma, V. K. Katiyar, Uaday Singh

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The ability to manipulate magnetic particles in fluid flows by means of inhomogeneous magnetic fields is used in a wide range of biomedical applications including magnetic drug targeting (MDT). In MDT, magnetic carrier particles bounded with drug molecules are injected into the vascular system up-stream from the malignant tissue and attracted or retained at the specific region in the body with the help of an external magnetic field. Although the concept of MDT has been around for many years, however, wide spread acceptance of the technique is still looming despite the fact that it has shown some promise in both in vivo and clinical studies. This is because traditional MDT has some inherent limitations. Typically, the magnetic force is not very strong and it is also very short ranged. Since the magnetic force must overcome rather large hydrodynamic forces in the body, MDT applications have been limited to sites located close to the surface of the skin. Even in this most favorable situation, studies have shown that it is difficult to collect appreciable amounts of the MDCPs at the target site. To overcome these limitations of the traditional MDT approach, Ritter and co-workers reported the implant assisted magnetic drug targeting (IA-MDT). In IA-MDT, the magnetic implants are placed strategically at the target site to greatly and locally increase the magnetic force on MDCPs and help to attract and retain the MDCPs at the targeted region. In the present work, we develop a mathematical model to study the capturing of magnetic nanoparticles flowing in a fluid in an implant assisted cylindrical channel under the magnetic field. A coil of ferromagnetic SS 430 has been implanted inside the cylindrical channel to enhance the capturing of magnetic nanoparticles under the magnetic field. The dominant magnetic and drag forces, which significantly affect the capturing of nanoparticles, are incorporated in the model. It is observed through model results that capture efficiency increases from 23 to 51 % as we increase the magnetic field from 0.1 to 0.5 T, respectively. The increase in capture efficiency by increase in magnetic field is because as the magnetic field increases, the magnetization force, which is attractive in nature and responsible to attract or capture the magnetic particles, increases and results the capturing of large number of magnetic particles due to high strength of attractive magnetic force.

Keywords: capture efficiency, implant assisted-magnetic drug targeting (IA-MDT), magnetic nanoparticles, modelling

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Authors: Tomokazu Nagata, Jawid Ahmad Baktash

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The goal of this paper is to explore the use of formal methods for Electronic Identity Card System. Nowadays, one of the core research directions in a constantly growing distributed environment is the improvement of the communication process. The responsibility for proper verification becomes crucial. Formal methods can play an essential role in the development and testing of systems. The thesis presents two different methodologies for assessing correctness. Our first approach employs abstract interpretation techniques for creating a trace based model for Electronic Identity Card System. The model was used for building a semi decidable procedure for verifying the system model. We also developed the code for the eID System and can cover three parts login to system sending of Acknowledgment from user side, receiving of all information from server side and log out from system. The new concepts of impasse and spawned sessions that we introduced led our research to original statements about the intruder’s knowledge and eID system coding with respect to secrecy. Furthermore, we demonstrated that there is a bound on the number of sessions needed for the analysis of System.Electronic identity (eID) cards promise to supply a universal, nation-wide mechanism for user authentication. Most European countries have started to deploy eID for government and private sector applications. Are government-issued electronic ID cards the proper way to authenticate users of online services? We use the eID project as a showcase to discuss eID from an application perspective. The new eID card has interesting design features, it is contact-less, it aims to protect people’s privacy to the extent possible, and it supports cryptographically strong mutual authentication between users and services. Privacy features include support for pseudonymous authentication and per service controlled access to individual data items. The article discusses key concepts, the eID infrastructure, observed and expected problems, and open questions. The core technology seems ready for prime time and government projects deploy it to the masses. But application issues may hamper eID adoption for online applications.

Keywords: eID, event-B, Pro-B, formal method, message passing

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Authors: Yik Sin Chan, Nam Weng Sit, Fook Yee Chye, van Ofwegen Leen, de Voogd Nicole, Kong Soo Khoo

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Chikungunya fever is an arboviral disease transmitted by the Aedes mosquitoes. It has resulted in epidemics of the disease in tropical countries in the Indian Ocean and South East Asian regions. The recent spread of this disease to the temperate countries such as France and Italy, coupled with the absence of vaccines and effective antiviral drugs make chikungunya fever a worldwide health threat. This study aims to investigate the anti-chikungunya virus activity of selected marine organism samples collected from Malaysian coastal areas, including seaweeds (Caulerpa racemosa, Caulerpa sertularioides and Kappaphycus alvarezii), a soft coral (Lobophytum microlobulatum) and a sponge (Spheciospongia vagabunda). Following lyophilization (oven drying at 40C for K. alvarezii) and grinding to powder form, each sample was subjected to sequential solvent extraction using hexane, chloroform, ethyl acetate, ethanol, methanol and distilled water in order to extract bioactive compounds. The antiviral activity was evaluated using monkey kidney epithelial (Vero) cells infected with the virus (multiplicity of infection=1). The cell viability was determined by Neutral Red uptake assay. 70% of the 30 extracts showed weak inhibitory activity with cell viability ≤30%. Seven of the extracts exhibited moderate inhibitory activity (cell viability: 31%-69%). These were the chloroform, ethyl acetate, ethanol and methanol extracts of C. racemosa; chloroform and ethyl acetate extracts of L. microlobulatum; and the chloroform extract of C. sertularioides. Only the hexane and ethanol extracts of L. microlobulatum showed strong inhibitory activity against the virus, resulting in cell viabilities (mean±SD; n=3) of 73.3±2.6% and 79.2±0.9%, respectively. The corresponding mean 50% effective concentrations (EC50) for the extracts were 14.2±0.2 and 115.3±1.2 µg/mL, respectively. The ethanol extract of the soft coral L. microlobulatum appears to hold the most promise for further characterization of active principles as it possessed greater selectivity index (SI>5.6) compared to the hexane extract (SI=2.1).

Keywords: antiviral, seaweed, sponge, soft coral, vero cell

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Authors: Dhruv Saini, Megha Sharma, Sharad Gupta

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This research is showing the use of celebrity endorsement and social media influencers and how they help in enhancing the brand loyalty of the consumers. The study aims at keeping brand image of the brand as the link between the two. However, choosing the right celebrity or social media influencer is not an easy task and it is very essential for a brand to select the right ambassador for advertising their products and for selling the product to the ultimate consumer. The purpose of the study is to create a relationship of Celebrity endorsement with brand image and with brand loyalty and creating a relationship of Social media influencers with brand image and with brand loyalty and then making a comparison between the two by measuring the effects of both simultaneously. And then by analyzing which among the two has a greater impact on brand loyalty of the consumers. The study mainly focuses on four major variables namely Celebrity endorsement, Social media influencers, Brand image and Brand loyalty. The study also focuses on interdependence and relationships that these variables have with each other and how they are linked with each other. The study also aims at looking which among Celebrity endorsement and Social media influencer has a greater impact on increasing or enhancing the loyalty for a brand. Earlier celebrity endorsers had a major impact on brand loyalty of the consumers but with time social media influencers are also playing a very vital role in impacting the brand loyalty of the consumers and are giving a fight to the celebrity endorsers as well. Also, Brand image also has a very vital role to play in enhancing the brand loyalty of a brand in the minds of the consumers as a well-known and a better perception of a brand leads to retention of more and more consumers. Also, both Celebrity endorsement and Social media influencers are two-way swords as both have a number of positives and a number of negatives as well, so these are to be compared keeping in mind their adverse effects. Examination of the current market situation has shown that the recommendations of celebrities when properly integrated by comparing product strengths. Advertisers agree that celebrity authorization does not guarantee sales but it can create buzz and make the consumer feel better by-product, which is also what customers should expect as a real star by delivering the promise. On the other hand, depending on the results of the studies, there should be a variety of conclusions planned. Some of the influential people on social media had a positive impact on the product portrait. One of the conclusions is that the product image had a positive impact on consumers. Moreover, the results of the following study states that the most influential influencers consumers in their intended purpose of the purchase, but instead produced a positive result indirectly with Brand image which would further lead to brand loyalty .

Keywords: brand image, brand loyalty, celebrity endorsement, social media influencer

Procedia PDF Downloads 162

Authors: Chiqian Zhang, Kyle D. McIntosh, Nathan Sienkiewicz, Ian Struewing, Erin A. Stelzer, Jennifer L. Graham, Jingrang Lu

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Phytoplankton plays an essential role in freshwater aquatic ecosystems and is the primary group synthesizing organic carbon and providing food sources or energy to ecosystems. Therefore, the identification and quantification of phytoplankton are important for estimating and assessing ecosystem productivity (carbon fixation), water quality, and eutrophication. Microscopy is the current gold standard for identifying and quantifying phytoplankton composition and abundance. However, microscopic analysis of phytoplankton is time-consuming, has a low sample throughput, and requires deep knowledge and rich experience in microbial morphology to implement. To improve this situation, quantitative polymerase chain reaction (qPCR) was considered for phytoplankton identification and quantification. Using qPCR to assess phytoplankton composition and abundance, however, has not been comprehensively evaluated. This study focused on: 1) conducting a comprehensive performance comparison of qPCR and microscopy techniques in identifying and quantifying phytoplankton and 2) examining the use of qPCR as a tool for assessing eutrophication. Twelve large rivers located throughout the United States were evaluated using data collected from 2017 to 2019 to understand the relation between qPCR-based phytoplankton abundance and eutrophication. This study revealed that temporal variation of phytoplankton abundance in the twelve rivers was limited within years (from late spring to late fall) and among different years (2017, 2018, and 2019). Midcontinent rivers had moderately greater phytoplankton abundance than eastern and western rivers, presumably because midcontinent rivers were more eutrophic. The study also showed that qPCR- and microscope-determined phytoplankton abundance had a significant positive linear correlation (adjusted R² 0.772, p-value < 0.001). In addition, phytoplankton abundance assessed via qPCR showed promise as an indicator of the eutrophication status of those rivers, with oligotrophic rivers having low phytoplankton abundance and eutrophic rivers having (relatively) high phytoplankton abundance. This study demonstrated that qPCR could serve as an alternative tool to traditional microscopy for phytoplankton quantification and eutrophication assessment in freshwater rivers.

Keywords: phytoplankton, eutrophication, river, qPCR, microscopy, spatiotemporal variation

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Authors: Matthew Baucum

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With the increasing popularity of fMRI as an experimental method, psychology and neuroscience can greatly benefit from advanced techniques for summarizing and synthesizing large amounts of data from brain imaging studies. One promising avenue is automated meta-analyses, in which natural language processing methods are used to identify the brain regions consistently associated with certain semantic concepts (e.g. “social”, “reward’) across large corpora of studies. This study builds on this approach by demonstrating how, in fMRI meta-analyses, individual voxels can be treated as vectors in a semantic space and evaluated for their “proximity” to terms of interest. In this technique, a low-dimensional semantic space is built from brain imaging study texts, allowing words in each text to be represented as vectors (where words that frequently appear together are near each other in the semantic space). Consequently, each voxel in a brain mask can be represented as a normalized vector sum of all of the words in the studies that showed activation in that voxel. The entire brain mask can then be visualized in terms of each voxel’s proximity to a given term of interest (e.g., “vision”, “decision making”) or collection of terms (e.g., “theory of mind”, “social”, “agent”), as measured by the cosine similarity between the voxel’s vector and the term vector (or the average of multiple term vectors). Analysis can also proceed in the opposite direction, allowing word cloud visualizations of the nearest semantic neighbors for a given brain region. This approach allows for continuous, fine-grained metrics of voxel-term associations, and relies on state-of-the-art “open vocabulary” methods that go beyond mere word-counts. An analysis of over 11,000 neuroimaging studies from an existing meta-analytic fMRI database demonstrates that this technique can be used to recover known neural bases for multiple psychological functions, suggesting this method’s utility for efficient, high-level meta-analyses of localized brain function. While automated text analytic methods are no replacement for deliberate, manual meta-analyses, they seem to show promise for the efficient aggregation of large bodies of scientific knowledge, at least on a relatively general level.

Keywords: FMRI, machine learning, meta-analysis, text analysis

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Authors: M. Sakthivel, A. Devaki, D. Balasubramanyam, P. Kumarasamy, A. Raja, R. Anilkumar, H. Gopi

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The genetic parameters of growth traits in the New Zealand White rabbits maintained at Sheep Breeding and Research Station, Sandynallah, The Nilgiris, India were estimated by partitioning the variance and covariance components. The (co)variance components of body weights at weaning (W42), post-weaning (W70) and marketing (W135) age and growth efficiency traits viz., average daily gain (ADG), relative growth rate (RGR) and Kleiber ratio (KR) estimated on a daily basis at different age intervals (1=42 to 70 days; 2=70 to 135 days and 3=42 to 135 days) from weaning to marketing were estimated by restricted maximum likelihood, fitting six animal models with various combinations of direct and maternal effects. Data were collected over a period of 15 years (1998 to 2012). A log-likelihood ratio test was used to select the most appropriate univariate model for each trait, which was subsequently used in bivariate analysis. Heritability estimates for W42, W70 and W135 were 0.42 ± 0.07, 0.40 ± 0.08 and 0.27 ± 0.07, respectively. Heritability estimates of growth efficiency traits were moderate to high (0.18 to 0.42). Of the total phenotypic variation, maternal genetic effect contributed 14 to 32% for early body weight traits (W42 and W70) and ADG1. The contribution of maternal permanent environmental effect varied from 6 to 18% for W42 and for all the growth efficiency traits except for KR2. Maternal permanent environmental effect on most of the growth efficiency traits was a carryover effect of maternal care during weaning. Direct maternal genetic correlations, for the traits in which maternal genetic effect was significant, were moderate to high in magnitude and negative in direction. Maternal effect declined as the age of the animal increased. The estimates of total heritability and maternal across year repeatability for growth traits were moderate and an optimum rate of genetic progress seems possible in the herd by mass selection. The estimates of genetic and phenotypic correlations among body weight traits were moderate to high and positive; among growth efficiency traits were low to high with varying directions; between body weights and growth efficiency traits were very low to high in magnitude and mostly negative in direction. Moderate to high heritability and higher genetic correlation in body weight traits promise good scope for genetic improvement provided measures are taken to keep the inbreeding at the lowest level.

Keywords: genetic parameters, growth traits, maternal effects, rabbit genetics

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Authors: Zakaria Kharbouch, Mustapha Bouchaara, F. Elkouihen, A. Habbal, A. Ratnani, A. Faik

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Solid-state lithium-ion batteries have garnered increasing interest in modern energy research due to their potential for safer, more efficient, and sustainable energy storage systems. Among the critical components of these batteries, the electrolyte plays a pivotal role, with LLZO garnet-based electrolytes showing significant promise. Garnet materials offer intrinsic advantages such as high Li-ion conductivity, wide electrochemical stability, and excellent compatibility with lithium metal anodes. However, optimizing ionic conductivity in garnet structures poses a complex challenge, primarily due to the multitude of potential dopants that can be incorporated into the LLZO crystal lattice. The complexity of material design, influenced by numerous dopant options, requires a systematic method to find the most effective combinations. This study highlights the utility of machine learning (ML) techniques in the materials discovery process to navigate the complex range of factors in garnet-based electrolytes. Collaborators from the materials science and ML fields worked with a comprehensive dataset previously employed in a similar study and collected from various literature sources. This dataset served as the foundation for an extensive data refinement phase, where meticulous error identification, correction, outlier removal, and garnet-specific feature engineering were conducted. This rigorous process substantially improved the dataset's quality, ensuring it accurately captured the underlying physical and chemical principles governing garnet ionic conductivity. The data refinement effort resulted in a significant improvement in the predictive performance of the machine learning model. Originally starting at an accuracy of 0.32, the model underwent substantial refinement, ultimately achieving an accuracy of 0.88. This enhancement highlights the effectiveness of the interdisciplinary approach and underscores the substantial potential of machine learning techniques in materials science research.

Keywords: lithium batteries, all-solid-state batteries, machine learning, solid state electrolytes

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Authors: Masome Moeni, Roya Abedizadeh, Elham Aram, Hamid Sadeghi-Abandansari, Davood Sabour, Robert Menzel, Ali Hassanpour

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Significant number of studies and preclinical research in formulation of cancer nano-pharmaceutics have led to an improvement in cancer care. Nonetheless, the antineoplastic agents have ‘failed to live up to its promise’ since their clinical performance is moderately low. For almost ninety years, iron oxide nanoparticles (IONPS) have managed to keep its reputation in clinical application due to their low toxicity, versatility and multi-modal capabilities. Drug Administration approved utilization of IONPs for diagnosis of cancer as contrast media in magnetic resonance imaging, as heat mediator in magnetic hyperthermia and for the treatment of iron deficiency. Furthermore, IONPs have high drug-loading capacity, which makes them good candidates as therapeutic agent transporters. There are yet challenges to overcome for successful clinical application of IONPs, including stability of drug and poor delivery, which might lead to (i) drug resistance, (ii) shorter blood circulation time, and (iii) rapid elimination and adverse side effects from the system. In this study, highly stable and super paramagnetic IONPs were prepared for efficient and targeted drug delivery in cancer treatment. The synthesis procedure was briefly involved the production of IONPs via co-precipitation followed by coating with tetraethyl orthosilicate and 3-aminopropylethoxysilane and grafting with folic acid for stability targeted purposes and controlled drug release. Physiochemical and morphological properties of modified IONPs were characterised using different analytical techniques. The resultant IONPs exhibited clusters of 10 nm spherical shape crystals with less than 100 nm size suitable for drug delivery. The functionalized IONP showed mesoporous features, high stability, dispersibility and crystallinity. Subsequently, the functionalized IONPs were successfully loaded with oxaliplatin, a chemotherapeutic agent, for a controlled drug release in an actively targeting cancer cells. FT-IR observations confirmed presence of oxaliplatin functional groups, while ICP-MS results verified the drug loading was ~ 1.3%.

Keywords: cancer treatment, chemotherapeutic agent, drug delivery, iron oxide, multi-functional nanoparticle

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Authors: Richard Roberts, Anna Kravtcova

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Introduction: The realisation that personality can change over the course of a lifetime has led to a new companion model to the Big Five, the behavioural, emotional, and social skills approach (BESSA). BESSA hypothesizes that this set of skills represents how the individual is thinking, feeling, and behaving when the situation calls for it, as opposed to traits, which represent how someone tends to think, feel, and behave averaged across situations. The five major skill domains share parallels with the Big Five Factor (BFF) model creativity and innovation (openness), self-management (conscientiousness), social engagement (extraversion), cooperation (agreeableness), and emotional resilience (emotional stability) skills. We point to noteworthy limitations in the current operationalisation of BESSA skills (i.e., via Likert-type items) and offer up a different measurement approach: forced choice. Method: In this forced-choice paradigm, individuals were given three skill items (e.g., managing my time) and asked to select one response they believed they were “worst at” and “best at”. The Thurstonian IRT models allow these to be placed on a normative scale. Two multivariate studies (N = 1178) were conducted with a 22-item forced-choice version of the BESSA, a published measure of the BFF, and various criteria. Findings: Confirmatory factor analysis of the forced-choice assessment showed acceptable model fit (RMSEA<0.06), while reliability estimates were reasonable (around 0.70 for each construct). Convergent validity evidence was as predicted (correlations between 0.40 and 0.60 for corresponding BFF and BESSA constructs). Notable was the extent the forced-choice BESSA assessment improved upon test-criterion relationships over and above the BFF. For example, typical regression models find BFF personality accounting for 25% of the variance in life satisfaction scores; both studies showed incremental gains over the BFF exceeding 6% (i.e., BFF and BESSA together accounted for over 31% of the variance in both studies). Discussion: Forced-choice measurement models offer up the promise of creating equated test forms that may unequivocally measure skill gains and are less prone to fakability and reference bias effects. Implications for practitioners are discussed, especially those interested in selection, succession planning, and training and development. We also discuss how the forced choice method can be applied to other constructs like emotional immunity, cross-cultural competence, and self-estimates of cognitive ability.

Keywords: Big Five, forced-choice method, BFF, methods of measurements

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Authors: Ali Hamza

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Accurate segmentation of breast ultrasound images is of paramount importance in enhancing the diagnostic capabilities of breast cancer detection. This study presents an approach utilizing the U-Net architecture for segmenting breast ultrasound images aimed at improving the accuracy and reliability of mass identification within the breast tissue. The proposed method encompasses a multi-stage process. Initially, preprocessing techniques are employed to refine image quality and diminish noise interference. Subsequently, the U-Net architecture, a deep learning convolutional neural network (CNN), is employed for pixel-wise segmentation of regions of interest corresponding to potential breast masses. The U-Net's distinctive architecture, characterized by a contracting and expansive pathway, enables accurate boundary delineation and detailed feature extraction. To evaluate the effectiveness of the proposed approach, an extensive dataset of breast ultrasound images is employed, encompassing diverse cases. Quantitative performance metrics such as the Dice coefficient, Jaccard index, sensitivity, specificity, and Hausdorff distance are employed to comprehensively assess the segmentation accuracy. Comparative analyses against traditional segmentation methods showcase the superiority of the U-Net architecture in capturing intricate details and accurately segmenting breast masses. The outcomes of this study emphasize the potential of the U-Net-based segmentation approach in bolstering breast ultrasound image analysis. The method's ability to reliably pinpoint mass boundaries holds promise for aiding radiologists in precise diagnosis and treatment planning. However, further validation and integration within clinical workflows are necessary to ascertain their practical clinical utility and facilitate seamless adoption by healthcare professionals. In conclusion, leveraging the U-Net architecture for breast ultrasound image segmentation showcases a robust framework that can significantly enhance diagnostic accuracy and advance the field of breast cancer detection. This approach represents a pivotal step towards empowering medical professionals with a more potent tool for early and accurate breast cancer diagnosis.

Keywords: mage segmentation, U-Net, deep learning, breast cancer detection, diagnostic accuracy, mass identification, convolutional neural network

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Authors: Nikolai N. Bogolubov, Andrey V. Soldatov

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Terahertz radiation occupies a range of frequencies somewhere from 100 GHz to approximately 10 THz, just between microwaves and infrared waves. This range of frequencies holds promise for many useful applications in experimental applied physics and technology. At the same time, reliable, simple techniques for generation, amplification, and modulation of electromagnetic radiation in this range are far from been developed enough to meet the requirements of its practical usage, especially in comparison to the level of technological abilities already achieved for other domains of the electromagnetic spectrum. This situation of relative underdevelopment of this potentially very important range of electromagnetic spectrum is known under the name of the 'terahertz gap.' Among other things, technological progress in the terahertz area has been impeded by the lack of compact, low energy consumption, easily controlled and continuously radiating terahertz radiation sources. Therefore, development of new techniques serving this purpose as well as various devices based on them is of obvious necessity. No doubt, it would be highly advantageous to employ the simplest of suitable physical systems as major critical components in these techniques and devices. The purpose of the present research was to show by means of conventional methods of non-equilibrium statistical mechanics and the theory of open quantum systems, that a thoroughly studied two-level quantum system, also known as an one-electron two-level 'atom', being driven by external classical monochromatic high-frequency (e.g. laser) field, can radiate continuously at much lower (e.g. terahertz) frequency in the fluorescent regime if the transition dipole moment operator of this 'atom' possesses permanent non-equal diagonal matrix elements. This assumption contradicts conventional assumption routinely made in quantum optics that only the non-diagonal matrix elements persist. The conventional assumption is pertinent to natural atoms and molecules and stems from the property of spatial inversion symmetry of their eigenstates. At the same time, such an assumption is justified no more in regard to artificially manufactured quantum systems of reduced dimensionality, such as, for example, quantum dots, which are often nicknamed 'artificial atoms' due to striking similarity of their optical properties to those ones of the real atoms. Possible ways to experimental observation and practical implementation of the predicted effect are discussed too.

Keywords: terahertz gap, two-level atom, resonant fluorescence, quantum dot, resonant fluorescence, two-level atom

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Authors: Amrit Vasdev, Edwin Rho, Gurinder Vasdev

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Robotic surgery has enabled a new spectrum of minimally invasive breast reconstruction by improving visualization, surgeon posturing, and improved patient outcomes.1 The DaVinci robot system can be utilized in nipple sparing mastectomies and reconstructions. The process involves the insufflation of the subglandular space and a dissection of the mammary gland with a combination of cautery and blunt dissection. This case outlines a 35-year-old woman who has a long-standing family history of breast cancer and a diagnosis of a deleterious BRCA2 genetic mutation. She has decided to proceed with bilateral nipple sparing mastectomies with implants. Her perioperative mammogram and MRI were negative for masses, however, her left internal mammary lymph node was enlarged. She has taken oral contraceptive pills for 3-5 years and denies DES exposure, radiation therapy, human replacement therapy, or prior breast surgery. She does not smoke and rarely consumes alcohol. During the procedure, the patient received a standardized anesthetic for out-patient surgery of propofol infusion, succinylcholine, sevoflurane, and fentanyl. Aprepitant was given as an antiemetic and preoperative Tylenol and gabapentin for pain management. Concerns for the patient during the procedure included CO2 insufflation into the subcutaneous space. With CO2 insufflation, there is a potential for rapid uptake leading to severe acidosis, embolism, and subcutaneous emphysema.2To mitigate this, it is important to hyperventilate the patient and reduce both the insufflation pressure and the CO2 flow rate to the minimal acceptable by the surgeon. For intraoperative monitoring during this 6-9 hour long procedure, it has been suggested to utilize an Arterial-Line for end-tidal CO2 monitoring. However, in this case, it was not necessary as the patient had excellent cardiovascular reserve, and end-tidal CO2 was within normal limits for the duration of the procedure. A BIS monitor was also utilized to reduce anesthesia burden and to facilitate a prompt discharge from the PACU. Minimal Invasive Robotic Surgery will continue to evolve, and anesthesiologists need to be prepared for the new challenges ahead. Based on our limit number of patients, robotic mastectomy appears to be a safe alternative to open surgery with the promise of clearer tissue demarcation and better cosmetic results.

Keywords: anesthesia, mastectomies, robotic, hypercarbia

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Authors: Krupali Mehta, Sandip Bhatt, Umesh Trivedi, Bhavesh Bharatiya, Mukesh Ranjan, Atindra D. Shukla

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Owing to the dominating surface forces and large-scale surface interactions, the nano-scale particles face difficulties in getting suspended in various media. Magnetic nanoparticles of iron oxide offer a great deal of promise due to their ease of preparation, reasonable magnetic properties, low cost and environmental compatibility. We intend to modify the surface of magnetic Fe₂O₃ nanoparticles with selected surface modifying agents using simple and effective single-step chemical reactions in order to enhance dispersibility of magnetic nanoparticles in non-polar media. Magnetic particles were prepared by hydrolysis of Fe²⁺/Fe³⁺ chlorides and their subsequent oxidation in aqueous medium. The dried particles were then treated with Octadecyl quaternary ammonium silane (Terrasil™), stearic acid and gallic acid ester of stearyl alcohol in ethanol separately to yield S-2 to S-4 respectively. The untreated Fe₂O₃ was designated as S-1. The surface modified nanoparticles were then analysed with Dynamic Light Scattering (DLS), Fourier Transform Infrared spectroscopy (FTIR), X-Ray Diffraction (XRD), Thermogravimetric Gravimetric Analysis (TGA) and Scanning Electron Microscopy and Energy dispersive X-Ray analysis (SEM-EDAX). Characterization reveals the particle size averaging 20-50 nm with and without modification. However, the crystallite size in all cases remained ~7.0 nm with the diffractogram matching to Fe₂O₃ crystal structure. FT-IR suggested the presence of surfactants on nanoparticles’ surface, also confirmed by SEM-EDAX where mapping of elements proved their presence. TGA indicated the weight losses in S-2 to S-4 at 300°C onwards suggesting the presence of organic moiety. Hydrophobic character of modified surfaces was confirmed with contact angle analysis, all modified nanoparticles showed super hydrophobic behaviour with average contact angles ~129° for S-2, ~139.5° for S-3 and ~151° for S-4. This indicated that surface modified particles are super hydrophobic and they are easily dispersible in non-polar media. These modified particles could be ideal candidates to be suspended in oil-based fluids, polymer matrices, etc. We are pursuing elaborate suspension/sedimentation studies of these particles in various oils to establish this conjecture.

Keywords: iron nanoparticles, modification, hydrophobic, dispersion

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Authors: Claes Gunnarsson, Karin Fröding, Nina Hasche

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Innovation is a necessity for the evolution of societies and it is also a driving force in human life that leverages value creation among cross-sector participants in various network arrangements. Social innovations can be characterized as the creation and implementation of a new solution to a social problem, which is more effective and sustainable than existing solutions in terms of improvement of society’s conditions and in particular social inclusion processes. However, barriers exist which may restrict the potential of social innovations to live up to its promise as a societal welfare promoting driving force. The literature points at difficulties in tackling social problems primarily related to problem complexity, access to networks, and lack of financial muscles. Further research is warranted at detailed at detail clarification of these barriers, also connected to recognition of the interplay between institutional logics on the development of cross-sector collaborations in networks and the organizing processes to achieve innovation barrier break-through. There is also a need to further elaborate how obstacles that spur a difference between the actual and desired state of innovative value creating service systems can be overcome. The purpose of this paper is to illustrate barriers to social innovations, based on qualitative content analysis of 36 dialogue-based seminars (i.e. research circles) with nine Swedish focus groups including more than 90 individuals representing civil society organizations, private business, municipal offices, and politicians; and analyze patterns that reveal constituents of barriers to social innovations. The paper draws on central aspects of innovation barriers as discussed in the literature and analyze barriers basically related to internal/external and tangible/intangible characteristics. The findings of this study are that existing institutional structures highly influence the transformative potential of social innovations, as well as networking conditions in terms of building a competence-propelled strategy, which serves as an offspring for overcoming barriers of competence extension. Both theoretical and practical knowledge will contribute to how policy-makers and SI-practitioners can facilitate and support social innovation processes to be contextually adapted and implemented across areas and sectors.

Keywords: barriers, research circles, social innovation, service systems

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Authors: Sangwoo Han, Saeed Khaleghi Rahimian, Ying Liu

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Vehicle electrification is gaining momentum, and many car manufacturers promise to deliver more electric vehicle (EV) models to consumers in the coming years. In controlling the battery pack, the battery management system (BMS) must maintain optimal battery performance while ensuring the safety of a battery pack. Tasks related to battery performance include determining state-of-charge (SOC), state-of-power (SOP), state-of-health (SOH), cell balancing, and battery charging. Safety related functions include making sure cells operate within specified, static and dynamic voltage window and temperature range, derating power, detecting faulty cells, and warning the user if necessary. The BMS often utilizes an RC circuit model to model a Li-ion cell because of its robustness and low computation cost among other benefits. Because an equivalent circuit model such as the RC model is not a physics-based model, it can never be a prognostic model to predict battery state-of-health and avoid any safety risk even before it occurs. A physics-based Li-ion cell model, on the other hand, is more capable at the expense of computation cost. To avoid the high computation cost associated with a full-order model, many researchers have demonstrated the use of a single particle model (SPM) for BMS applications. One drawback associated with the single particle modeling approach is that it forces to use the average current density in the calculation. The SPM would be appropriate for simulating drive cycles where there is insufficient time to develop a significant current distribution within an electrode. However, under a continuous or high-pulse electrical load, the model may fail to predict cell voltage or Li⁺ plating potential. To overcome this issue, a multi-particle reduced-order model is proposed here. The use of multiple particles combined with either linear or nonlinear charge-transfer reaction kinetics enables to capture current density distribution within an electrode under any type of electrical load. To maintain computational complexity like that of an SPM, governing equations are solved sequentially to minimize iterative solving processes. Furthermore, the model is validated against a full-order model implemented in COMSOL Multiphysics.

Keywords: battery management system, physics-based li-ion cell model, reduced-order model, single-particle and multi-particle model

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Authors: Edvard Kokanyan, Narine Babajanyan, Ninel Kokanyan, Marco Bazzan

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Lithium niobate (LN) crystals, renowned for their exceptional nonlinear optical, electro-optical, piezoelectric, and photorefractive properties, stand as foundational materials in diverse fields of study and application. While they have long been utilized in frequency converters of laser radiation, electro-optical modulators, and holographic information recording media, LN crystals doped with rare earth ions represent a compelling frontier for modern compact devices. These materials exhibit immense potential as key components in infrared lasers, optical sensors, self-cooling systems, and radiation-balanced laser setups. In this study, we present the successful synthesis of Ho-doped lithium niobate (LN:Ho) thin films on sapphire substrates employing the Sol-Gel technique. The films exhibit a strong crystallographic orientation along the perpendicular direction to the substrate surface, with X-ray diffraction analysis confirming the predominant alignment of the film's "c" axis, notably evidenced by the intense (006) reflection peak. Further characterization through Raman spectroscopy, employing a confocal Raman microscope (LabRAM HR Evolution) with exciting wavelengths of 532 nm and 785 nm, unraveled intriguing insights. Under excitation with a 785 nm laser, Raman scattering obeyed selection rules, while employing a 532 nm laser unveiled additional forbidden lines, reminiscent of behaviors observed in bulk LN:Ho crystals. These supplementary lines were attributed to luminescence induced by excitation at 532 nm. Leveraging data from anti-Stokes Raman lines facilitated the disentanglement of luminescence spectra from the investigated samples. Surface scanning affirmed the uniformity of both structure and luminescence across the thin films. Notably, despite the robust orientation of the "c" axis perpendicular to the substrate surface, Raman signals indicated a stochastic distribution of "a" and "b" axes, validating the mosaic structure of the films along the mentioned axis. This study offers valuable insights into the structural properties of Ho-doped lithium niobate thin films, with the observed luminescence behavior holding significant promise for potential applications in optoelectronic devices.

Keywords: lithium niobate, Sol-Gel, luminescence, Raman spectroscopy.

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Authors: Francis C. Emejeamara, Alison S. Tomlin, James Gooding

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Due to the highly turbulent nature of urban air flows, and by virtue of the fact that turbines are likely to be located within the roughness sublayer of the urban boundary layer, proposed urban wind installations are faced with major challenges compared to rural installations. The challenge of operating within turbulent winds can however, be counteracted by the development of suitable gust tracking solutions. In order to assess the cost effectiveness of such controls, a detailed understanding of the urban wind resource, including its turbulent characteristics, is required. Estimating the ambient turbulence and total kinetic energy available at different control response times is essential in evaluating the potential performance of wind systems within the urban environment should effective control solutions be employed. However, high resolution wind measurements within the urban roughness sub-layer are uncommon, and detailed CFD modelling approaches are too computationally expensive to apply routinely on a city wide scale. This paper therefore presents an alternative semi-empirical methodology for estimating the excess energy content (EEC) present in the complex and gusty urban wind. An analytical methodology for predicting the total wind energy available at a potential turbine site is proposed by assessing the relationship between turbulence intensities and EEC, for different control response times. The semi-empirical model is then incorporated with an analytical methodology that was initially developed to predict mean wind speeds at various heights within the built environment based on detailed mapping of its aerodynamic characteristics. Based on the current methodology, additional estimates of turbulence intensities and EEC allow a more complete assessment of the available wind resource. The methodology is applied to 4 UK cities with results showing the potential of mapping turbulence intensities and the total wind energy available at different heights within each city. Considering the effect of ambient turbulence and choice of wind system, the wind resource over neighbourhood regions (of 250 m uniform resolution) and building rooftops within the 4 cities were assessed with results highlighting the promise of mapping potential turbine sites within each city.

Keywords: excess energy content, small-scale wind, turbulence intensity, urban wind energy, wind resource assessment

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Authors: Daniel Thompson, Muhammad Peerbux, Sophie Cerutti, Hansraj Riteesh Bookun

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Patients with carotid stenosis, which may be asymptomatic or symptomatic in the form of transient ischaemic attack (TIA), amaurosis fugax, or stroke, often require an endarterectomy to reduce stroke risk. Risks of this procedure include stroke, death, myocardial infarction, and cranial nerve damage. Carotid endarterectomy is most commonly performed under general anaesthetic, however, it can also be undertaken with a regional anaesthetic approach. Our tertiary centre generally performs carotid endarterectomy under regional anaesthetic. Our major tertiary hospital mostly utilises regional anaesthesia for carotid endarterectomy. We completed a cross-sectional analysis of all cases of carotid endarterectomy performed under regional anaesthesia across a 10-year period between January 2010 to March 2020 at our institution. 350 patients were included in this descriptive analysis, and demographic details for patients, indications for surgery, procedural details, length of surgery, and complications were collected. Data was cross tabulated and presented in frequency tables to describe these categorical variables. 263 of the 350 patients in the analysis were male, with a mean age of 71 ± 9. 172 patients had a history of ischaemic heart disease, 104 had diabetes mellitus, 318 had hypertension, and 17 patients had chronic kidney disease greater than Stage 3. 13.1% (46 patients) were current smokers, and the majority (63%) were ex-smokers. Most commonly, carotid endarterectomy was performed conventionally with patch arterioplasty 96% of the time (337 patients). The most common indication was TIA and stroke in 64% of patients, 18.9% were classified as asymptomatic, and 13.7% had amaurosis fugax. There were few general complications, with 9 wound complications/infections, 7 postoperative haematomas requiring return to theatre, 3 myocardial infarctions, 3 arrhythmias, 1 exacerbation of congestive heart failure, 1 chest infection, and 1 urinary tract infection. Specific complications to carotid endarterectomy included 3 strokes, 1 postoperative TIA, and 1 cerebral bleed. There were no deaths in our cohort. This analysis of a large cohort of patients from a major tertiary centre who underwent carotid endarterectomy under regional anaesthesia indicates the safety of such an approach for these patients. Regional anaesthesia holds the promise of less general respiratory and cardiac events compared to general anaesthesia, and in this vulnerable patient group, calls for comparative research between local and general anaesthesia in carotid surgery.

Keywords: anaesthesia, carotid endarterectomy, stroke, carotid stenosis

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Authors: James P. Takona

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In a world that is experiencing sharp digital transformations guided by digital technologies, the potential of technology to drive transformation and evolution in the higher is apparent. Higher education is facing a paradigm shift that exposes susceptibilities and threats to fully online programs in the face of post-Covid-19 trends of commodification. This historical moment is likely to be remembered as a critical turning point from analog to digital degree-focused learning modalities, where the default became the pivot point of competition between higher education institutions. Fall 2020 marks a significant inflection point in higher education as students, educators, and government leaders scrutinize higher education's price and value propositions through the new lens of traditional lecture halls versus multiple digitized delivery modes. Online education has since tiled the way for a pedagogical shift in how teachers teach and students learn. The incremental growth of online education in the west can now be attributed to the increasing patronage among students, faculty, and institution administrators. More often than not, college instructors assume paraclete roles in this learning mode, while students become active collaborators and no longer passive learners. This paper offers valuable discernments into the threats, challenges, and opportunities of a massive digital revolution in servicing degree programs. To view digital instruction and learning demands for instructional practices that revolve around collaborative work, engaging students in learning activities, and an engagement that promotes active efforts to solicit strong connections between course activities and expected learning pace for all students. Appropriate digital technologies demand instructors and students need prior solid skills. Need for the use of digital technology to support instruction and learning, intelligent tutoring offers great promise, and failures at implementing digital learning may not improve outcomes for specific student populations. Digital learning benefits students differently depending on their circumstances and background and those of the institution and/or program. Students have alternative options, access to the convenience of learning anytime and anywhere, and the possibility of acquiring and developing new skills leading to lifelong learning.

Keywords: digi̇tized learning, digital education, collaborative work, high education, online education, digitize delivery

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Authors: Mehrnaz Mostafavi

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Perovskite nanocrystals and quantum dots have emerged as leaders in the field of photovoltaic technologies, demonstrating exceptional light-harvesting abilities and stability. This study investigates the substantial progress and potential of these nano-sized materials in transforming solar energy conversion. The research delves into the foundational characteristics and production methods of perovskite nanocrystals and quantum dots, elucidating their distinct optical and electronic properties that render them well-suited for photovoltaic applications. Specifically, it examines their outstanding light absorption capabilities, enabling more effective utilization of a wider solar spectrum compared to traditional silicon-based solar cells. Furthermore, this paper explores the improved durability achieved in perovskite nanocrystals and quantum dots, overcoming previous challenges related to degradation and inconsistent performance. Recent advancements in material engineering and techniques for surface passivation have significantly contributed to enhancing the long-term stability of these nanomaterials, making them more commercially feasible for solar cell usage. The study also delves into the advancements in device designs that incorporate perovskite nanocrystals and quantum dots. Innovative strategies, such as tandem solar cells and hybrid structures integrating these nanomaterials with conventional photovoltaic technologies, are discussed. These approaches highlight synergistic effects that boost efficiency and performance. Additionally, this paper addresses ongoing challenges and research endeavors aimed at further improving the efficiency, stability, and scalability of perovskite nanocrystals and quantum dots in photovoltaics. Efforts to mitigate concerns related to material degradation, toxicity, and large-scale production are actively pursued, paving the way for broader commercial application. In conclusion, this paper emphasizes the significant role played by perovskite nanocrystals and quantum dots in advancing photovoltaic technologies. Their exceptional light-harvesting capabilities, combined with increased stability, promise a bright future for next-generation solar cells, ushering in an era of highly efficient and cost-effective solar energy conversion systems.

Keywords: perovskite nanocrystals, quantum dots, photovoltaic technologies, light-harvesting, solar energy conversion, stability, device designs

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Authors: Daniel Osorio, Andreas Klenk, Stefan Weihe, Andreas Kopp, Frank Rödiger

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Future power plants currently face high design requirements due to worsening climate change and environmental restrictions, which demand high operational flexibility, superior thermal performance, minimal emissions, and higher cyclic capability. The aim of the paper is, therefore, to investigate the creep and thermo-mechanical material behavior of improved materials experimentally and welded joints at component scale under near-to-service operating conditions, which are promising for application in highly efficient and flexible future power plants. These materials promise an increase in flexibility and a reduction in manufacturing costs by providing enhanced creep strength and, therefore, the possibility for wall thickness reduction. At the temperature range between 550°C and 625°C, the investigation focuses on the in-phase thermo-mechanical fatigue behavior of dissimilar welded joints of conventional materials (ferritic and martensitic material T24 and T92) to nickel-based alloys (A617B and HR6W) by means of membrane test panels. The temperature and external load are varied in phase during the test, while the internal pressure remains constant. At the temperature range between 650°C and 750°C, it focuses on the creep behavior under multiaxial stress loading of similar and dissimilar welded joints of high temperature resistant nickel-based alloys (A740H, A617B, and HR6W) by means of a thick-walled-component test. In this case, the temperature, the external axial load, and the internal pressure remain constant during testing. Numerical simulations are used for the estimation of the axial component load in order to induce a meaningful damage evolution without causing a total component failure. Metallographic investigations after testing will provide support for understanding the damage mechanism and the influence of the thermo-mechanical load and multiaxiality on the microstructure change and on the creep and TMF- strength.

Keywords: creep, creep-fatigue, component behaviour, weld joints, high temperature material behaviour, nickel-alloys, high temperature resistant steels

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Authors: Felicia N. Obialo, Aliza Wingo, Thomas Wingo

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Psychosocial factors such as personality traits and social networks influence cognitive aging and dementia outcomes both positively and negatively. The inherent complexity of these factors makes defining the underlying mechanisms of their influence difficult; however, exploring their interactions affords promise in the field of cognitive aging. The objective of this study was to elucidate some of these interactions by determining the relationship between social network size and dementia outcomes and by determining whether personality traits mediate this relationship. The longitudinal Alzheimer’s Disease (AD) database provided by Rush University’s Religious Orders Study/Memory and Aging Project was utilized to perform retrospective regression and mediation analyses on 3,591 participants. Participants who were cognitively impaired at baseline were excluded, and analyses were adjusted for age, sex, common chronic diseases, and vascular risk factors. Dementia outcome measures included cognitive trajectory, clinical dementia diagnosis, and postmortem beta-amyloid plaque (AB), and neurofibrillary tangle (NT) accumulation. Personality traits included agreeableness (A), conscientiousness (C), extraversion (E), neuroticism (N), and openness (O). The results show a positive correlation between social network size and cognitive trajectory (p-value = 0.004) and a negative relationship between social network size and odds of dementia diagnosis (p = 0.024/ Odds Ratio (OR) = 0.974). Only neuroticism mediates the positive relationship between social network size and cognitive trajectory (p < 2e-16). Agreeableness, extraversion, and neuroticism all mediate the negative relationship between social network size and dementia diagnosis (p=0.098, p=0.054, and p < 2e-16, respectively). All personality traits are independently associated with dementia diagnosis (A: p = 0.016/ OR = 0.959; C: p = 0.000007/ OR = 0.945; E: p = 0.028/ OR = 0.961; N: p = 0.000019/ OR = 1.036; O: p = 0.027/ OR = 0.972). Only conscientiousness and neuroticism are associated with postmortem AD pathologies; specifically, conscientiousness is negatively associated (AB: p = 0.001, NT: p = 0.025) and neuroticism is positively associated with pathologies (AB: p = 0.002, NT: p = 0.002). These results support the study’s objectives, demonstrating that social network size and personality traits are strongly associated with dementia outcomes, particularly the odds of receiving a clinical diagnosis of dementia. Personality traits interact significantly and beneficially with social network size to influence the cognitive trajectory and future dementia diagnosis. These results reinforce previous literature linking social network size to dementia risk and provide novel insight into the differential roles of individual personality traits in cognitive protection.

Keywords: Alzheimer’s disease, cognitive trajectory, personality traits, social network size

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Authors: Mehrnaz Mostafavi

Abstract:

Nanoliposomes, minute lipid-based vesicles at the nano-scale, show promise in the realm of photothermal therapy (PTT). This study presents an extensive overview of nanoliposomes in PTT, exploring their distinct attributes and the significant progress in this therapeutic methodology. The research delves into the fundamental traits of nanoliposomes, emphasizing their adaptability, compatibility with biological systems, and their capacity to encapsulate diverse therapeutic substances. Specifically, it examines the integration of light-absorbing materials, like gold nanoparticles or organic dyes, into nanoliposomal formulations, enabling their efficacy as proficient agents for photothermal treatment Additionally, this paper elucidates the mechanisms involved in nanoliposome-mediated PTT, highlighting their capability to convert light energy into localized heat, facilitating the precise targeting of diseased cells or tissues. This precise regulation of light absorption and heat generation by nanoliposomes presents a non-invasive and precisely focused therapeutic approach, particularly in conditions like cancer. The study explores advancements in nanoliposomal formulations aimed at optimizing PTT outcomes. These advancements include strategies for improved stability, enhanced drug loading, and the targeted delivery of therapeutic agents to specific cells or tissues. Furthermore, the paper discusses multifunctional nanoliposomal systems, integrating imaging components or targeting elements for real-time monitoring and improved accuracy in PTT. Moreover, the review highlights recent preclinical and clinical trials showcasing the effectiveness and safety of nanoliposome-based PTT across various disease models. It also addresses challenges in clinical implementation, such as scalability, regulatory considerations, and long-term safety assessments. In conclusion, this paper underscores the substantial potential of nanoliposomes in advancing PTT as a promising therapeutic approach. Their distinctive characteristics, combined with their precise ability to convert light into heat, offer a tailored and efficient method for treating targeted diseases. The encouraging outcomes from preclinical studies pave the way for further exploration and potential clinical applications of nanoliposome-based PTT.

Keywords: nanoliposomes, photothermal therapy, light absorption, heat conversion, therapeutic agents, targeted delivery, cancer therapy

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Authors: Laura Rueda-Gensini, Jader Rodríguez, Juan C. Cruz, Carolina Munoz-Camargo

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There is an emerging interest in botanicals and plant extracts for medicinal practices due to their widely reported health benefits. A large variety of phytochemicals found in plants have been correlated with antioxidant, immunomodulatory, and analgesic properties, which makes plant-derived products promising candidates for modulating the progression and treatment of numerous diseases. Non-centrifugal cane sugar (NCS), in particular, has been known for its high antioxidant and nutritional value, but composition-wise variability due to changing environmental and processing conditions have considerably limited its use in the nutraceutical and biomedical fields. This work is therefore aimed at assessing the effect of thermal exposure during NCS production over its bioactive composition and, in turn, its therapeutic value. Accordingly, two modified dehydration methods are proposed that employ: (i) vacuum-aided evaporation, which reduces the necessary temperatures to dehydrate the sample, and (ii) window refractance evaporation, which reduces thermal exposure time. The biochemical composition of NCS produced under these two methods was compared to traditionally-produced NCS by estimating their total polyphenolic and protein content with Folin-Ciocalteu and Bradford assays, as well as identifying the major phenolic compounds in each sample via HPLC-coupled mass spectrometry. Their antioxidant activities were also compared as measured by their scavenging potential of ABTS and DPPH radicals. Results show that the two modified production methods enhance polyphenolic and protein yield in resulting NCS samples when compared to traditional production methods. In particular, reducing employed temperatures with vacuum-aided evaporation demonstrated to be superior at preserving polyphenolic compounds, as evidenced both in the total and individual polyphenol concentrations. However, antioxidant activities were not significantly different between these. Although additional studies should be performed to determine if the observed compositional differences affect other therapeutic activities (e.g., anti-inflammatory, analgesic, and immunoprotective), these results suggest that reducing thermal exposure holds great promise for the production of natural products with enhanced nutritional value.

Keywords: non-centrifugal cane sugar, polyphenolic compounds, thermal processing, antioxidant activity

Procedia PDF Downloads 65