Review on Hair Problem and its Solution

Hair is simple in structure.  Hair is formed of an extreme protein called Keratin. Cleanser may be a hair care item, ordinarily as a gooey fluid, that's utilized for cleaning hair. the problems related with it incorporates male pattern baldness, raucous hair, absence of hair volume, molding, youthful turning gray, dandruff, diminishing of hair, bluntness then on. Male pattern baldness are often caused due to various reasons, for instance , hereditary propensities, ecological triggers and presentation to synthetic compounds, medications, healthful inadequacy, outrageous pressure or long ailment then on. Gentle dandruff can for the foremost part be settled by washing the hair a day with a mellow cleanser hair. Sedated hostile to dandruff cleanser clean both the hair and scalp and leave the hair reasonable, not bother sebaceous organs. It contains an enemy of microbial to forestall development of expanded occurrence of microorganisms. Dynamic material ought not sharpen the scalp and diminish the extent of tingling and scaling. the most objective of article give idea about hairs problem, the way to solve these problems with cost effectiveness and also help to pick the which sort of treatment with selective dosage form preparation as per hairs problem by researcher for society. Keywords Antidandruff, Surfactants, Shampoo, Scalp

OVERVIEW OF MUCOADHESIVE BIOPOLYMERS FOR BUCCAL DRUG DELIVERY SYSTEMS

Mucoadhesive dosage forms may be intended for facilitation of prolonged retention time at the application site hence providing drug release in a controlled rate for enhanced improvement of therapeutic activity and its outcome. The buccal mucosa has been investigated for systemic drug delivery and local drug treatment or therapy that is subjected to first pass metabolism. The applicability of bio-adhesion approach in buccal drug delivery proved great therapeutic potential to overcome the limitation of conventional buccal drug delivery. The delivery via buccal route using mucoadhesive biopolymers such as various natural gums e.g. carrageenans, gum karaya, gum arabic, locust bean gum, khaya gum, gum ghatti, albizia gum, guar gum, starch, cellulose, larch gum and pectin etc. and various thiolated and carboxymethylated polymers has been the subject of interest since the early 20th century. The present article is focused mainly on the oral mucosa, mechanism of drug permeation, and characteristics of the desired polymers, the manuscript then proceeds to cover the theories behind the adhesion of bioadhesive polymers to the mucosal epithelium followed by the factors affecting mucoadhesion. Further the author has also discussed on the new generation of mucoadhesive polymers and their properties, recent mucoadhesive formulations for enhanced buccal drug delivery, various marketed products and patent literature. Various online search engines and scientific journals were employed for the collection of literature and scientific data and information related to the topic using keywords like mucoadhesive polymers, buccal drug delivery, buccal patches, tablets, films, gels, powder from the year 2002 and above

Comparison of Machining Performance under MQL and Ultra-High Voltage EMQL Conditions Based on Tribological Properties

This novel work presents the comparison of a newly developed ultra-high voltage electrostatic minimum quantity lubrication (EMQL) using a customized nozzle with the MQL technique as an alternative cooling/lubricating method in turning processes of 15-5 PHSS. The optimum voltage for EMQL within the range of 0-25 kV has been identified based on tribological performance. Besides, surface roughness has been measured to identify the impact of electrostatically charged mist for turning 15-5 PHSS. Finally, tool wear tests are performed for MQL and EMQL at optimized voltage. The EMQL at optimized electrostatic voltage resulted in 38% decreased tool wear as compared to conventional MQL for 2400 mm cutting length

Preparation and characterization of biocomposite films of carrageenan/locust bean gum/montmorrillonite for transdermal delivery of curcumin

Introduction: Skin can be used as a site for local and systemic drug administration. Diffusion of drugs through the skin has led to the development of different transdermal drug delivery systems. Curcumin is a wound healing and anti-inflammatory agent. Curcumin was incorporated into biocomposite films of carrageenan (κC)/locust bean gum (LBG)/ montmorillonite (MMT) prepared by a solvent casting method. Methods: Film-forming solutions were prepared by adding and 2.5% v/v of propylene glycol and MMT (30% w/w). The curcumin loaded polymer composite transdermal films were characterized by scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR) spectroscopy and X-ray diffraction (XRD) analysis. Mechanical properties in terms of tensile strength and extensibility were studied. Films were also evaluated for moisture content, moisture uptake, thickness, folding endurance, swelling ratio and water vapor transmission rate (WVTR). Results: κC and κC/L40 showed the highest percent cumulative release of 80.42±1.61% and 69.38±1.26% among all of the polymer composite transdermal films in 8 hours and 24 hours respectively. Conclusion: In vitro release profiles showed that increasing concentration of LBG and MMT sustained the release of the drug from the polymer composite transdermal films. Decreased percent cumulative release as the concentration of LBG and MMT increases in polymer composite transdermal film

No "zero-shot" without exponential data: pretraining concept frequency determines multimodal model performance

Web-crawled pretraining datasets underlie the impressive “zero-shot” evaluation performance of multimodal models, such as CLIP for classification/retrieval and Stable-Diffusion for image generation. However, it is unclear how meaningful the notion of “zero-shot” generalization is for such multimodal models, as it is not known to what extent their pretraining datasets encompass the downstream concepts targeted for during “zero-shot” evaluation. In this work, we ask: How is the performance of multimodal models on downstream concepts influenced by the frequency of these concepts in their pretraining datasets? We comprehensively investigate this question across 34 models and five standard pretraining datasets (CC-3M, CC-12M, YFCC-15M, LAION-400M, LAION-Aesthetics), generating over 300GB of data artifacts. We consistently find that, far from exhibiting “zero-shot” generalization, multimodal models require exponentially more data to achieve linear improvements in downstream “zero-shot” performance, following a sample inefficient log-linear scaling trend. This trend persists even when controlling for sample-level similarity between pretraining and downstream datasets [79], and testing on purely synthetic data distributions [51]. Furthermore, upon benchmarking models on long-tailed data sampled based on our analysis, we demonstrate that multimodal models across the board perform poorly. We contribute this long-tail test set as the Let it Wag! benchmark to further research in this direction. Taken together, our study reveals an exponential need for training data which implies that the key to “zero-shot” generalization capabilities under large-scale training paradigms remains to be found

Vision-Language Models can Identify Distracted Driver Behavior from Naturalistic Videos

Recognizing the activities, causing distraction, in real-world driving scenarios is critical for ensuring the safety and reliability of both drivers and pedestrians on the roadways. Conventional computer vision techniques are typically data-intensive and require a large volume of annotated training data to detect and classify various distracted driving behaviors, thereby limiting their efficiency and scalability. We aim to develop a generalized framework that showcases robust performance with access to limited or no annotated training data. Recently, vision-language models have offered large-scale visual-textual pretraining that can be adapted to task-specific learning like distracted driving activity recognition. Vision-language pretraining models, such as CLIP, have shown significant promise in learning natural language-guided visual representations. This paper proposes a CLIP-based driver activity recognition approach that identifies driver distraction from naturalistic driving images and videos. CLIP's vision embedding offers zero-shot transfer and task-based finetuning, which can classify distracted activities from driving video data. Our results show that this framework offers state-of-the-art performance on zero-shot transfer and video-based CLIP for predicting the driver's state on two public datasets. We propose both frame-based and video-based frameworks developed on top of the CLIP's visual representation for distracted driving detection and classification task and report the results.Comment: 15 pages, 10 figure

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Comparison of Machining Performance under MQL and Ultra-High Voltage EMQL Conditions Based on Tribological Properties

This novel work presents the comparison of a newly developed ultra-high voltage electrostatic minimum quantity lubrication (EMQL) using a customized nozzle with the MQL technique as an alternative cooling/lubricating method in turning processes of 15-5 PHSS. The optimum voltage for EMQL within the range of 0-25 kV has been identified based on tribological performance. Besides, surface roughness has been measured to identify the impact of electrostatically charged mist for turning 15-5 PHSS. Finally, tool wear tests are performed for MQL and EMQL at optimized voltage. The EMQL at optimized electrostatic voltage resulted in 38% decreased tool wear as compared to conventional MQL for 2400 mm cutting length