Synthesis and Characterization of α-Al2O3 by Sol-Gel Process and Development of Zn-Al2O3 Composites by Powder Metallurgy Route

In the present study α-Al2O3 powders of nanometric size were successfully synthesized by sol-gel method using two different precursors, one using aluminium chloride(AlCl3),ethanol(C2H5OH) and ammonia(NH3) followed by calcination at 1200oC for 2 h in a furnace, and the second process using aluminium nitrate (Al(NO3)3), malic acid (C4H6O5) and polyvinylpyrrolidone, PVP ((C6H9NO)n) followed by heat treating at 1250°C for 2 h. The alumina formed were analysed by X-ray diffraction (XRD) and particle size analysis to characterize the powders in terms of their crystallinity and their crystallite size. Further characterizations of particles were carried out using scanning electron microscopy (SEM), high resolution transmission electron microscope (HRTEM), differential scanning calorimetry (DSC) and Fourier transform infrared spectroscopy (FTIR) techniques. It was found that much finer particle size of α-Al2O3 could be achieved when Al(NO3)3 was used as a precursor. The decrease in size accompanied by an increase in aspect ratio makes it an ideal reinforcing agent for high strength composites and results in its highly superior properties. The present study also involved the development of Zn-Al2O3 composites by reinforcing the Zn-matrix with α-Al2O3 as reinforcement. α-Al2O3 is very attractive due to their unique combination of excellent mechanical properties and high thermal stability which makes them an ideal reinforcing agent for high strength composites. Zn-Al2O3 composites were developed containing 50 and 60 vol.% α-Al2O3 by an ex-situ process that includes blending commercially available Zn and α-Al2O3 powders together in required compositions followed by compaction at 300 MPa and heat treatment at 500oC for 2 h. The Zn-Al2O3 composites fabricated were analysed by using optical microscopy, SEM and EDX. Mechanical properties like wear resistance and hardness of the composites were analysed to find out the effect of the addition of α-Al2O3 in the Zn-matrix. It was found that addition Al2O3 to Zn matrix improved the hardness as well as the wear resistance of Z

Factify 2: A Multimodal Fake News and Satire News Dataset

The internet gives the world an open platform to express their views and share their stories. While this is very valuable, it makes fake news one of our society's most pressing problems. Manual fact checking process is time consuming, which makes it challenging to disprove misleading assertions before they cause significant harm. This is he driving interest in automatic fact or claim verification. Some of the existing datasets aim to support development of automating fact-checking techniques, however, most of them are text based. Multi-modal fact verification has received relatively scant attention. In this paper, we provide a multi-modal fact-checking dataset called FACTIFY 2, improving Factify 1 by using new data sources and adding satire articles. Factify 2 has 50,000 new data instances. Similar to FACTIFY 1.0, we have three broad categories - support, no-evidence, and refute, with sub-categories based on the entailment of visual and textual data. We also provide a BERT and Vison Transformer based baseline, which acheives 65% F1 score in the test set. The baseline codes and the dataset will be made available at https://github.com/surya1701/Factify-2.0.Comment: Defactify@AAAI202

Findings of Factify 2: Multimodal Fake News Detection

With social media usage growing exponentially in the past few years, fake news has also become extremely prevalent. The detrimental impact of fake news emphasizes the need for research focused on automating the detection of false information and verifying its accuracy. In this work, we present the outcome of the Factify 2 shared task, which provides a multi-modal fact verification and satire news dataset, as part of the DeFactify 2 workshop at AAAI'23. The data calls for a comparison based approach to the task by pairing social media claims with supporting documents, with both text and image, divided into 5 classes based on multi-modal relations. In the second iteration of this task we had over 60 participants and 9 final test-set submissions. The best performances came from the use of DeBERTa for text and Swinv2 and CLIP for image. The highest F1 score averaged for all five classes was 81.82%.Comment: Defactify2 @AAAI 202

Overview of Memotion 3: Sentiment and Emotion Analysis of Codemixed Hinglish Memes

Analyzing memes on the internet has emerged as a crucial endeavor due to the impact this multi-modal form of content wields in shaping online discourse. Memes have become a powerful tool for expressing emotions and sentiments, possibly even spreading hate and misinformation, through humor and sarcasm. In this paper, we present the overview of the Memotion 3 shared task, as part of the DeFactify 2 workshop at AAAI-23. The task released an annotated dataset of Hindi-English code-mixed memes based on their Sentiment (Task A), Emotion (Task B), and Emotion intensity (Task C). Each of these is defined as an individual task and the participants are ranked separately for each task. Over 50 teams registered for the shared task and 5 made final submissions to the test set of the Memotion 3 dataset. CLIP, BERT modifications, ViT etc. were the most popular models among the participants along with approaches such as Student-Teacher model, Fusion, and Ensembling. The best final F1 score for Task A is 34.41, Task B is 79.77 and Task C is 59.82.Comment: Defactify2 @AAAI 202

A REVIEW ON LACTOFERRIN PRINCIPLE CONSTITUENT OF BOVINE COLOSTRUM: IN COVID19

The Covid sickness (COVID19) pandemic is quickly expanding across the world. There is no legitimate therapy for this illness except for by supporting our resistance we can battle with this infection so to battle Coronavirus contamination the famous dietary enhancement bovine colostrums having bunches of utilization to battle against viral disease. It go about an immunomodulatory, anti-inflammatory, Antibacterial effect. The most significant part found in colostrum is lactoferrin which is a 80 KDA glycoprotein containing around 703 amino corrosive and having its capacity to tie with iron by restricting with iron, lactoferrin retain iron from the climate and forestalls viral heap of pathogens. The Covid didn't get authoritative therapy because of viral transformation; this review is accommodating for helping the invulnerable framework in coronavirus patient

Development of newborn screening connect (NBS connect): a self-reported patient registry and its role in improvement of care for patients with inherited metabolic disorders

Abstract Background Newborn Screening Connect (NBS Connect) is a web-based self-reported patient registry and resource for individuals and families affected by disorders included in the newborn screening panel. NBS Connect was launched in 2012 by Emory University after years of planning and grassroots work by professionals, consumers, and industry. Individuals with phenylketonuria (PKU), maple syrup urine disease (MSUD) or tyrosinemia (TYR) have been recruited through distribution of outreach materials, presentations at parent organization meetings and direct recruitment at clinic appointments. Participants complete online profiles generating data on diagnosis, treatment, symptoms, outcomes, barriers to care, and quality of life. Resources such as education materials, information on the latest research and clinical trials, recipes, interactive health tracking systems, and professional support tools are described. In addition, to examine the ability of NBS Connect to generate data that guides hypothesis-driven research, data pertaining to age at diagnosis, bone health, and skin conditions in individuals with PKU were assessed. The objective of this paper is to describe the development of NBS Connect and highlight its data, resources and research contributions. Results In September 2016, NBS Connect had 442 registered participants: 314 (71%) individuals with PKU, 68 (15%) with MSUD, 20 (5%) with TYR, and 40 (9%) with other disorders on the NBS panel. Age at diagnosis was less than 4 weeks in 285 (89%) of 319 respondents to this question and between 1 month and 14 years in 29 (9%) individuals. Of 216 respondents with PKU, 33 (15%) had a DXA scan in the past year. Of 217 respondents with PKU, 99 (46%) reported at least one skin condition. Conclusions NBS Connect was built and refined with feedback from all stakeholders, including individuals with inherited metabolic disorders. Based on patient-reported data, future studies can be initiated to test hypotheses such as the relationship between PKU and skin conditions. Patient registries like NBS Connect can inform hypothesis-driven research, contributing to knowledge generation and following the current trend in moving from traditional medicine towards evidence-based practice. NBS Connect will help clinicians understand long-term outcomes of rare disorders, contributing to better patient care and quality of life

Hierarchical Ternary Sulfides as Effective Photocatalyst for Hydrogen Generation Through Water Splitting: A Review on the Performance of ZnIn₂S₄

One of the major aspects and advantages of solar energy conversion is the photocatalytic hydrogen generation using semiconductor materials for an eco-friendly technology. Designing a low-cost efficient material to overcome limited light absorption as well as rapid recombination of photogenerated charge carriers is essential to achieve considerable hydrogen generation. In recent years, sulfide based semiconductors have attracted scientific research interest due to their excellent solar response and narrow band gap. The present review focuses on the recent approaches in the development of hierarchical ternary sulfide based photocatalysts with a special focus on ZnIn2S4. We also observe how the electronic structure of ZnIn2S4 is beneficial for water splitting and the various strategies involved for improving the material efficiency for photocatalytic hydrogen generation. The review places emphasis on the latest advancement/new insights on ZnIn2S4 being used as an efficient material for hydrogen generation through photocatalytic water splitting. Recent progress on essential aspects which govern light absorption, charge separation and transport are also discussed in detail

Anthropometry of deep-set eyes with respect to difficulty in docking during femtosecond laser procedures

Purpose: To identify the facial anthropometric parameters that predict the difficulty during femtosecond (FS) laser. Methods: This was a single-center observational study was conducted on participants between the ages 18 and 30 years who were planned for FS-LASIK (femtosecond laser-assisted laser in situ keratomileusis) or SMILE (small incision lenticule extraction) at Dr. Rajendra Prasad Centre for Ophthalmic Sciences, AIIMS, New Delhi, India. The front and side-facing images of the participants were analyzed using Image J software to measure different anthropometric parameters. The nasal bridge index, facial convexity, and other parameters were measured. The difficulty faced by the surgeon during docking was recorded for each subject. The data were analyzed on Stata 14. Results: A total of 97 subjects were included. The mean age was 24 (±7) years. Twenty-three (23.71%) subjects were females while the rest were males. Difficulty in docking was seen in 1 (4.34%) female and 14 (19%) males. The mean nasal bridge index was 92.58 (±4.01) in subjects with deep-set eyes and 89.72 (±4.30) in normal subjects. The mean total facial convexity was 129.28 (±4.24) in deep-set eyes, and 140.23 (±4.74) in normal subjects. Conclusion: Total facial convexity appeared as the most important feature, with the value being less than 133° in most subjects with unfavorable facial anthropometry

Recommendations for initial diabetic retinopathy screening of diabetic patients using large language model-based artificial intelligence in real-life case scenarios

Abstract Purpose To study the role of artificial intelligence (AI) to identify key risk factors for diabetic retinopathy (DR) screening and develop recommendations based on clinician and large language model (LLM) based AI platform opinions for newly detected diabetes mellitus (DM) cases. Methods Five clinicians and three AI applications were given 20 AI-generated hypothetical case scenarios to assess DR screening timing. We calculated inter-rater agreements between clinicians, AI-platforms, and the “majority clinician response” (defined as the maximum number of identical responses provided by the clinicians) and “majority AI-platform” (defined as the maximum number of identical responses among the 3 distinct AI). Scoring was used to identify risk factors of different severity. Three, two, and one points were given to risk factors requiring screening immediately, within a year, and within five years, respectively. After calculating a cumulative screening score, categories were assigned. Results Clinicians, AI platforms, and the “majority clinician response” and “majority AI response” had fair inter-rater reliability (k value: 0.21–0.40). Uncontrolled DM and systemic co-morbidities required immediate screening, while family history of DM and a co-existing pregnancy required screening within a year. The absence of these risk factors required screening within 5 years of DM diagnosis. Screening scores in this study were between 0 and 10. Cases with screening scores of 0–2 needed screening within 5 years, 3–5 within 1 year, and 6–12 immediately. Conclusion Based on the findings of this study, AI could play a critical role in DR screening of newly diagnosed DM patients by developing a novel DR screening score. Future studies would be required to validate the DR screening score before it could be used as a reference in real-life clinical situations. Clinical trial registration Not applicable