The value of R′ & C′ and identify cause and effect group.

Personalized Nutrition Robot Platform Market Outlook

According to our latest research, the global personalized nutrition robot platform market size reached USD 1.74 billion in 2024, driven by rising consumer demand for tailored health and dietary solutions powered by advanced robotics and artificial intelligence. The market is projected to grow at a robust CAGR of 17.6% during the forecast period, reaching an estimated USD 8.12 billion by 2033. This impressive growth is primarily fueled by the convergence of health awareness, technological advancements, and the increasing prevalence of chronic diseases, which is prompting both individuals and healthcare providers to seek more precise and automated nutrition management tools.

The primary growth factor for the personalized nutrition robot platform market is the surging adoption of AI-driven dietary assessment and meal planning solutions. With the global population becoming more health-conscious and the incidence of lifestyle-related disorders such as obesity, diabetes, and cardiovascular diseases on the rise, there is an acute need for individualized nutrition recommendations. Personalized nutrition robot platforms leverage big data analytics, machine learning, and real-time biomarker monitoring to deliver highly customized meal and supplement plans. These platforms not only enhance user engagement but also improve outcomes by continuously adapting to individual metabolic profiles and lifestyle changes, thus offering a significant advantage over traditional one-size-fits-all nutrition approaches.

Another significant driver is the integration of personalized nutrition robot platforms into various healthcare and wellness environments, including hospitals, clinics, fitness centers, and even homecare settings. The ability of these platforms to automate diet tracking, provide instant feedback, and deliver professional-grade nutritional counseling at scale is revolutionizing how nutrition is managed across the continuum of care. Furthermore, the proliferation of wearable health devices and the growing ecosystem of connected health technologies are enabling seamless data collection and real-time personalization, thereby amplifying the value proposition of nutrition robot platforms for both end-users and healthcare professionals.

The market is also benefiting from favorable regulatory frameworks and increasing investments from both public and private sectors. Governments and health organizations worldwide are recognizing the potential of personalized nutrition in preventive healthcare, leading to supportive policies and funding initiatives. Additionally, the entry of major technology and healthcare companies into the personalized nutrition robot platform market is accelerating innovation and driving down costs, making these solutions more accessible to a broader audience. The emergence of cloud-based deployment models and subscription-based service offerings is further lowering barriers to adoption, especially among small and medium-sized enterprises and individual consumers.

From a regional perspective, North America currently dominates the personalized nutrition robot platform market, accounting for the largest revenue share in 2024, owing to its advanced healthcare infrastructure, high digital literacy, and early adoption of health technologies. However, the Asia Pacific region is expected to exhibit the fastest growth over the forecast period, driven by rising disposable incomes, increasing health awareness, and rapid urbanization. Europe also remains a significant market, characterized by strong regulatory support for digital health and a growing focus on preventive healthcare. Meanwhile, Latin America and the Middle East & Africa are gradually emerging as promising markets, supported by expanding healthcare investments and the rising prevalence of chronic diseases.

Component Analysis

The component segment of the personalized nutrition robot platform market is broadly categorized into hardware, software, and services. Hardware components encompass robotic arms, sensors, wearable devices, and other physical interfaces th

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Seneca Foods (SENEA) Stock: Is the Future Bright for This Food Giant?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

The total-relation matrix.

The Food Security Simulator is an innovative and easy-to-use, MS-Excel-based tool for assessing the potential short-term impacts of food price or household income shocks on food security and people’s diets. The Simulator is an ideal tool for first-cut forward-looking evaluations of direct, household-level outcomes of economic crises and policy responses in a timely manner. The tool allows users to enter positive and negative price or income changes in percentage terms and provides simulated changes for a diverse set of food-consumption- and diet-quality-related indicators. In addition to detailed tabular presentations of all simulation results by household income quintile and residential area, key indicator results are summarized in concise overview tables and visualized in graphs for easy export and use in reports. The underlying data include estimates from representative household survey data and rigorous, sophisticated food demand models to capture consumer behavior.

Modify total-relation matrix.

Despite recent improvements, low height-for-age, a key indicator of inadequate child nutrition, is an ongoing public health issue in low-income and middle-income countries. Paid maternity leave has the potential to improve child nutrition, but few studies have estimated its impact.

BackgroundTraditional methods of dietary assessment have their limitations and commercial sources of food sales and purchase data are increasingly suggested as an additional source to measuring diet at the population level. However, the potential uses of food sales data are less well understood. The aim of this review is to establish how sales data on food and soft drink products from third-party companies have been used in public health nutrition research.MethodsA search of five electronic databases was conducted in February-March 2018 for studies published in peer-reviewed journals that had used food sales or purchase data from a commercial company to analyse trends and patterns in food purchases or in the nutritional composition of foods. Study quality was evaluated using the National Institutes of Health (NIH) Quality Assessment Tool for Cohort and Cross-Sectional Studies.ResultsOf 2919 papers identified in the search, 68 were included. The selected studies used sales or purchase data from four companies: Euromonitor, GfK, Kantar and Nielsen. Sales and purchase data have been used to evaluate interventions, including the impact of the saturated fat tax in Denmark, the soft drink and junk food taxes in Mexico and supplemental nutrition programmes in the USA. They have also been used to identify trends in the nutrient composition of foods over time and patterns in food purchasing, including socio-demographic variations in purchasing.ConclusionFood sales and purchase data are a valuable tool for public health nutrition researchers and their use has increased markedly in the last four years, despite the cost of access, the lack of transparency on data-collection methods and restrictions on publication. The availability of product and brand-level sales data means they are particularly useful for assessing how changes by individual food companies can impact on diet and public health.

We conduct a scoping study to establish determinant sociological, infrastructural, biological, and agronomic factors that currently affect vegetable production and offer scope for future vegetable production in the study area. About the project Project title: Research in Sustainable Intensification in the Sub-Humid Maize-Based Cropping Systems of Babati, Tanzania Project abstract Scoping study to assess current status and future potential, increase awareness and develop partnerships for action research, technology development, and capacity building. Project website: http://africa-rising.net Citation APA Harvard MLA Vancouver Chicago IEEE CSE AMA NLM Turabian

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Agronomics (ANIC): Are Plant-Based Foods the Answer to Food Security?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Childhood undernutrition yearly kills 3.1 million children worldwide. For those who survive early life undernutrition, it can cause motor and cognitive development problems that translate into poor educational performance and limited work productivity later in life. It has been suggested that nutrition-specific interventions (e.g., micronutrient supplementation) that directly address the immediate determinants of undernutrition (e.g., nutrient intake) need to be complemented by nutrition-sensitive interventions that more broadly address the underlying determinants of undernutrition (e.g., food insecurity). Here, we argue that forest conservation represents a potentially important but overlooked nutrition-sensitive intervention. Forests can address a number of underlying determinants of undernutrition, including the supply of forest food products, income, habitat for pollinators, women's time allocation, diarrheal disease, and dietary diversity. We examine the effects of forests on stunting—a debilitating outcome of undernutrition—using a database of household surveys and environmental variables across 25 low- and middle-income countries. Our result indicates that exposure to forest significantly reduces child stunting (at least 7.11% points average reduction). The average magnitude of the reduction is at least near the median of the impacts of other known nutrition interventions. Forest conservation interventions typically cover large areas and are often implemented where people are vulnerable, and thus could be used to reach a large number of the world's undernourished communities that may have difficult access to traditional nutrition programs. Forest conservation is therefore a potentially effective nutrition-sensitive intervention. Efforts are needed to integrate specific nutrition goals and actions into forest conservation interventions in order to unleash their potential to deliver nutritional benefits.

The global plant-based food market is expected to reach **** billion U.S. dollars in 2025. Further growth is expected. The forecast projects that by 2030 the market will have more than ******** For 2022, the model expects a market worth **** billion U.S. dollars.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

USFD US Foods Holding Corp. Common Stock

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

The DEMATEL initial direct-relation matrix.

The normalized direct-matrix X.

BackgroundIt has previously been hypothesized that lower socio-economic status can accelerate biological ageing, and predispose to early onset of disease. This study investigated the association of socio-economic and lifestyle factors, as well as traditional and novel risk factors, with biological-ageing, as measured by telomere length, in a Glasgow based cohort that included individuals with extreme socio-economic differences. MethodsA total of 382 blood samples from the pSoBid study were available for telomere analysis. For each participant, data was available for socio-economic status factors, biochemical parameters and dietary intake. Statistical analyses were undertaken to investigate the association between telomere lengths and these aforementioned parameters. ResultsThe rate of age-related telomere attrition was significantly associated with low relative income, housing tenure and poor diet. Notably, telomere length was positively associated with LDL and total cholesterol levels, but inversely correlated to circulating IL-6. ConclusionsThese data suggest lower socio-economic status and poor diet are relevant to accelerated biological ageing. They also suggest potential associations between elevated circulating IL-6, a measure known to predict cardiovascular disease and diabetes with biological ageing. These observations require further study to tease out potential mechanistic links.

IntroductionThe prevalence of childhood obesity remains high in the United States, particularly among children living in low-income households. Diet quality plays an important role in obesity prevention, particularly among mothers as they serve as role models. Those served by the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) receive nutrient-rich foods aimed at increasing diet quality, yet redemption is low. Digital interventions targeting WIC parents show potential for behavior change and could be used for childhood obesity prevention.MethodsThis study describes the formative research conducted to understand perspectives on healthy eating practices, acceptance of WIC-approved foods, and preferences for the use of digital tools to improve the purchasing and consumption of WIC-approved foods to improve diet quality. In-depth interviews were conducted with 13 WIC parents and caregivers.ResultsA variety of definitions for and misconceptions about healthy eating exist among WIC caregivers. Most purchased foods were fruits, vegetables, milk, cheese, and eggs and the least purchased foods were yogurt and peanut butter. The biggest facilitator for purchasing WIC-approved foods was the preference of children and caregivers, whereas the biggest barrier was children’s picky eating behaviors. Most caregivers reported using their phone to get nutrition information. Most caregivers reported their interest in receiving weekly text messages and indicated preferences about receiving recipes.ConclusionA text messaging program that includes sending weekly messages, recipes, and nutrition tips is hypothesized to improve diet quality and increase redemption of WIC-approved foods.

Baseline and Outcome measures, intermediary factors and time points.