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Retail India News: Lumaè Unveils a New Era in Skincare with High-Quality, Inclusive Solutions
The beauty and skincare industry is set for a transformative shift with the launch of Lumaè, a new brand focused on high-quality skincare solutions that prioritize skin health. Founded on principles of efficacy, sustainability, and inclusivity, Lumaè is redefining skincare through meticulously crafted formulations and a transparent approach to product development.
Lumaè is the brainchild of siblings Gunjan Sharma and Shan Sharma, who are united in their vision of making high-performance skincare accessible to everyone, irrespective of skin tone or gender. What began as a passion project in 2020 under the name Daisybloom, a homemade skincare company driven by a love for natural and effective solutions, has now evolved into Lumaè.
The brand’s debut collection introduces two standout products, each designed to tackle major skin concerns such as acne and aging. Lumaè unveils an anti-ageing night cream, infused with Astaxanthin, a powerful antioxidant 6000 times stronger than Vitamin C. This ingredient helps boost collagen production, reduce wrinkles, and enhance skin elasticity. This is a first in Indian skincare, as Astaxanthin was previously only available in supplements. In addition, Lumaè’s anti-acne serum works to combat bacteria, control oil, unclog pores, and fade dark spots, featuring key ingredients like Saffron, Ashwagandha, Salicylic Acid, and Glycolic Acid, all of which contribute to clearer, brighter skin. With this launch, Lumaè is expanding its range to provide more targeted skincare solutions like nourishing night creams and anti-acne treatments, ensuring comprehensive care for diverse skin needs.
Maintaining its focus on quality, Lumaè promises to offer premium, high-performance ingredients that promote true skin health. All of Lumaè's products are meticulously tested through independent lab analysis to ensure both efficacy and safety, ensurin Jianheng Tang, Qifan Zhang, Yuhan Li, Nuo Chen, Jia Li The “arms race” of Large Language Models (LLMs) demands new benchmarks to examine their progresses. In this paper, we introduce GraphArena, a benchmarking tool designed to evaluate LLMs on real-world graph computational problems. It offers a suite of four polynomial-time tasks (e.g., Shortest Distance) and six NP-complete challenges (e.g., Traveling Salesman Problem). GraphArena features a rigorous evaluation framework that classifies LLM outputs as correct, suboptimal (feasible but not optimal), hallucinatory (properly formatted but infeasible), or missing. Evaluation of over 10 LLMs reveals that even top-performing LLMs struggle with larger, more complex graph problems and exhibit hallucination issues. We further explore four potential solutions to address this issue and improve LLMs on graph computation, including chain-of-thought prompting, instruction tuning, code writing, and scaling test-time compute, each demonstrating unique strengths and limitations. GraphArena complements the existing LLM benchmarks and is open-sourced at https://github.com/squareRoot3/GraphArena. Evaluating LLMs, especially their advanced reasoning capabilities, remains a significant long-term challenge. While standardized tests in fields like mathematics Hendrycks et al. (2021); Cobbe et al. (2021), medicine Chen et al. (2023); Li et al. (2023a), and multi-task collections Hendrycks et al. (2020); Srivastava et al. (2022) have become popular LLM benchmarks, concerns about data leakage in pretraining corpora Aiyappa et al. (2023); Xu et al. (2024) raise questions about whether LLMs are genuinely reasoning or merely memorizing information. Crowd-sourced evaluations can address this issue but ar BMC Women's Healthvolume 25, Article number: 78 (2025) Cite this article Despite the well-documented benefits of full ANC- which includes at least four visits starting in the first trimester, two or more tetanus shots, and over 100 days of iron-folic acid (IFA) supplementation coverage remains alarmingly low, particularly among socioeconomically disadvantaged Scheduled Caste (SC) mothers. However, there is a dearth of research focusing specifically on this population. Therefore, this study aims to investigate the change in the coverage of full ANC and its determinants among SC mothers in India. Using data from the two latest rounds of the National Family Health Survey (2015-16 and 2019-21), a pooled sample of 51,705 SC mothers was analysed. Bivariate statistics was used to assess the significance of association between full ANC utilisation and the independent characteristics. Furthermore, to investigates the net effect of the predictor variables on the receipt of full ANC, multivariable binary logistic regression was applied. The coverage of full ANC in India increased from 20.7% in 2015-16 to 26.9% in 2019-21. While there were substantial gains in the coverage of ANC within the 1st trimester (68.4–74.8%) and IFA (39.1–48.6%), the coverage of two or more tetanus toxoid injections (87.1–85.2%) and 4 or more ANC visits (63.6–62.4%) registered a slight decline. Among the states, West Bengal (16.4% in NFHS-4 to 45.1% in NFHS-5) experienced the largest gain, while Gujarat had the lowest increase, 41.2% in NFHS-4 to 40.9% in NFHS-5. In South India, Kerala (59.9–75.4%) and Tamil Nadu (35.8–56.1%) saw substantial increases of 15–20 percentage points (PP), whereas Karnataka and Andhra Pradesh experienced marginal declines of 1–3PP. Rajasthan (10.0–21.0%), Madhya Pradesh (11.3–27.2%), Uttarakhand Food, feed, and fuel production are vital for human well-being. Yet current agricultural practices have resulted in extensive multi-media damages, primarily due to reactive nitrogen (Nr) emissions (NH3, N2O, NOx). Managing Nr sustainably to alleviate food and feed insecurity has been identified as a Grand Engineering Challenge. Systematically analysing source contributions, flows, and impacts of Nr is crucial for an agro-dominant country like India that faces the dual challenge of food and environmental security for 1.6 billion people by 2050. Here, we construct an Environmentally Extended Input-Output model for Nr in the Indian agriculture sector (cropland + livestock) for 2000–2020. Our findings indicate an increase in total N input to cropland from 23 Tg-N to 33 Tg-N (2000–2020), largely attributed to synthetic fertilizers (62%), biological N fixation (17%), atmospheric nitrogen deposition (11%), and livestock manure use (7%). Despite these increases, nitrogen use efficiency has only improved marginally (45% in 2000 to 57% in 2020). Nr losses to hyrosphere constitute 55%-60% of total N, with atmospheric emissions accounting for 40%-45% of total N. Key pollutants include nitrites/nitrates lost through runoff (40%), NH3 emissions (34%), and NO3 leakage to groundwater (20%). Noteworthy are NH3 emissions from fertilizer (55%) and manure (28%) application, and nitrogen deposition (12%). Flows from the cropland sector serves as an input to the livestock sector, e.g., the production of grain and straw as feed to turn plant protein into animal protein, with efficiency varying from 4% to 10%. The type of animal and manure management systems and practices influences the N flow outputs from the livestock sector. Nitrogen within the remaining fraction (90–96%) is found in urine and dung, leading to potential nitrogen losses, i.e., 13.7TgN in the year 2020 due to the volatilization, leaching, GraphArena: Evaluating and Exploring Large Language Models on Graph Computation
Hong Kong University of Science and Technology (Guangzhou)
jtangbf@connect.ust.hk, jialee@ust.hk
Corresponding author.Abstract
1 Introduction
Utilization of full antenatal care among Scheduled Caste mothers in India, 2015–2021
Abstract
Background
Methods
Results
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