#Agricultural data analysis

[jcmarchi]

Harvesting Intelligence: How Generative AI is Transforming Agriculture

New Post has been published on https://thedigitalinsider.com/harvesting-intelligence-how-generative-ai-is-transforming-agriculture/

Harvesting Intelligence: How Generative AI is Transforming Agriculture

In the age of digital transformation, agriculture is no longer just about soil, water, and sunlight. With the advent of generative AI, agriculture is becoming smarter, more efficient, and increasingly data driven. From predicting crop yields with unprecedented accuracy to developing disease-resistant plant varieties, generative AI enables farmers to make precise decisions that optimize yields and resource use. This article examines how generative AI is changing agriculture, looking at its impact on traditional farming practices and its potential for the future.

Understanding Generative AI

Generative AI is a type of artificial intelligence designed to produce new content—whether it’s text, images, or predictive models—based on patterns and examples it has learned from existing data. Unlike traditional AI, which focuses on recognizing patterns or making predictions, generative AI creates original outputs that closely mimic the data it was trained on. This makes it a powerful tool for enhancing decision-making and driving innovation. A key feature of generative AI is to facilitate building AI applications without much labelled training data. This feature is particularly beneficial in fields like agriculture, where acquiring labeled training data can be challenging and costly.

The development of generative AI models involves two main steps: pre-training and fine-tuning. In the pre-training phase, the model is trained on extensive amounts of data to learn general patterns. This process establishes a “foundation” model with broad and versatile knowledge. In the second phase, the pre-trained model is fine-tuned for specific tasks by training it on a smaller, more focused dataset relevant to the intended application, such as detecting crop diseases. These targeted uses of generative AI are referred to as downstream applications. This approach allows the model to perform specialized tasks effectively while leveraging the broad understanding gained during pre-training.

How Generative AI is Transforming Agriculture

In this section, we explore various downstream applications of generative AI in agriculture.

Generative AI as Agronomist Assistant: One of the ongoing issues in agriculture is the lack of qualified agronomists who can offer expert advice on crop production and protection. Addressing this challenge, generative AI can serve as an agronomist assistant by offering farmers immediate expert advice through chatbots. In this context, a recent Microsoft study evaluated how generative AI models, like GPT-4, performed on agriculture-related questions from certification exams in Brazil, India, and the USA. The results were encouraging, showing GPT-4’s ability to handle domain-specific knowledge effectively. However, adapting these models to local, specialized data remains a challenge. Microsoft Research tested two approaches—fine-tuning, which trains models on specific data, and Retrieval-Augmented Generation (RAG), which enhances responses by retrieving relevant documents, reporting these relative advantages.

Generative AI for Addressing Data Scarcity in Agriculture: Another key challenge in applying AI to agriculture is the shortage of labeled training data, which is crucial for building effective models. In agriculture, where labeling data can be labor-intensive and costly, generative AI offers a promising way forward. Generative AI stands out for its ability to work with large amounts of unlabeled historical data, learning general patterns that allow it to make accurate predictions with only a small number of labeled examples. Additionally, it can create synthetic training data, helping to fill gaps where data is scarce. By addressing these data challenges, generative AI improves the performance of AI in agriculture.

Precision Farming: Generative AI is changing precision farming by analyzing data from sources such as satellite imagery, soil sensors, and weather forecasts. It helps with predicting crop yields, automating fruit harvesting, managing livestock, and optimizing irrigation. These insights enable farmers to make better decisions, improving crop health and yields while using resources more efficiently. This approach not only increases productivity but also supports sustainable farming by reducing waste and environmental impact.

Generative AI for Disease Detection: Timely detection of pests, diseases, and nutrient deficiencies is crucial for protecting crops and reducing losses. Generative AI uses advanced image recognition and pattern analysis to identify early signs of these issues. By detecting problems early, farmers can take targeted actions, reduce the need for broad-spectrum pesticides, and minimize environmental impact. This integration of AI in agriculture enhances both sustainability and productivity.

How to Maximize the Impact of Generative AI in Agriculture

While current applications show that generative AI has potential in agriculture, getting the most out of this technology requires developing specialized generative AI models for the field. These models can better understand the nuances of farming, leading to more accurate and useful results compared to general-purpose models. They also adapt more effectively to different farming practices and conditions. The creation of these models, however, involves gathering large amounts of diverse agricultural data—such as crop and pest images, weather data, and insect sounds—and experimenting with different pretraining methods. Although progress is being made, there’s still a lot of work needed to build effective generative AI models for agriculture. Some of the potential use cases of generative AI for agriculture are mentioned below.

Potential Use Cases

A specialized generative AI model for agriculture could open several new opportunities in the field. Some key use cases include:

Smart Crop Management: In agriculture, smart crop management is a growing field that integrates AI, IoT, and big data to enhance tasks like plant growth monitoring, disease detection, yield monitoring, and harvesting. Developing precision crop management algorithms is challenging due to diverse crop types, environmental variables, and limited datasets, often requiring integration of varied data sources such as satellite imagery, soil sensors, and market trends. Generative AI models trained on extensive, multi-domain datasets offer a promising solution, as they can be fine-tuned with minimal examples for various applications. Additionally, multimodal generative AI integrates visual, textual, and sometimes auditory data, providing a comprehensive analytical approach that is invaluable for understanding complex agricultural situations, especially in precision crop management.

Automated Creation of Crop Varieties: Specialized generative AI can transform crop breeding by creating new plant varieties through exploring genetic combinations. By analyzing data on traits like drought resistance and growth rates, the AI generates innovative genetic blueprints and predicts their performance in different environments. This helps identify promising genetic combinations quickly, guiding breeding programs and accelerating the development of optimized crops. This approach aids farmers in adapting to changing conditions and market demands more effectively.

Smart Livestock Farming: Smart livestock farming leverages IoT, AI, and advanced control technologies to automate essential tasks like food and water supply, egg collection, activity monitoring, and environmental management. This approach aims to boost efficiency and cut costs in labor, maintenance, and materials. The field faces challenges due to the need for expertise across multiple fields and labor-intensive job. Generative AI could address these challenges by integrating extensive multimodal data and cross-domain knowledge, helping to streamline decision-making and automate livestock management.

Agricultural robots: Agricultural robots are transforming modern farming by automating tasks such as planting, weeding, harvesting, and monitoring crop health. AI-guided robots can precisely remove weeds and drones with advanced sensors can detect diseases and pests early, reducing yield losses. Developing these robots requires expertise in robotics, AI, plant science, environmental science, and data analytics, handling complex data from various sources. Generative AI offers a promising solution for automating various tasks of agricultural robots by providing advanced vision, predictive, and control capabilities.

 The Bottom Line

Generative AI is reshaping agriculture with smarter, data-driven solutions that improve efficiency and sustainability. By enhancing crop yield predictions, disease detection, and crop breeding, this technology is transforming traditional farming practices. While current applications are promising, the real potential lies in developing specialized AI models tailored to the unique needs of agriculture. As we refine these models and integrate diverse data, we can unlock new opportunities to help farmers optimize their practices and better navigate the challenges of modern farming.

[projectchampionz]

Explore These Exciting DSU Micro Project Ideas

Explore These Exciting DSU Micro Project Ideas Are you a student looking for an interesting micro project to work on? Developing small, self-contained projects is a great way to build your skills and showcase your abilities. At the Distributed Systems University (DSU), we offer a wide range of micro project topics that cover a variety of domains. In this blog post, we’ll explore some exciting DSU…

[airises]

"Revolutionizing Biotech: How AI is Transforming the Industry"

The biotech industry is on the cusp of a revolution, and Artificial Intelligence (AI) is leading the charge. AI is transforming the way biotech researchers and developers work, enabling them to make groundbreaking discoveries and develop innovative solutions at an unprecedented pace. “Accelerating Scientific Discovery with AI” AI is augmenting human capabilities in biotech research, enabling…

[ceresana]

Tear-resistant and flexible: Ceresana examines the world market for plastic films Stretching towards success: Plastic films get stronger and more transparent if they are stretched longitudinally and crosswise during production. The "biaxially oriented" plastics polypropylene (BOPP), polyethylene (BOPE) and polyester (BOPET) are processed into packaging, bags and sacks, shrink and stretch films, but also, for example, agricultural films, insulation material and industrial films. According to the latest Ceresana study on the global market for flexible plastic films, which is already the third edition, the revenues generated with these films will increase to around USD 339 billion by 2032. Further information about the new market study “Plastic Films – World (3rd edition)”: https://ceresana.com/en/produkt/plastic-films-market-report-world

[ai-azura]

Using Artificial Intelligence and Computer Vision to Count Livestock with High Accuracy

Using Artificial Intelligence and Computer Vision to Count Livestock with High Accuracy

Technology can be used to improve various industries, including livestock farming. An artificial intelligence platform allows farmers and producers to count livestock with 99.7% accuracy. This is achieved through the use of computer vision, which allows the system to “see” and interpret data in near-real time. The data is gathered at the edge and analyzed at the right speed and velocity to…

[probablyasocialecologist]

RPTU University of Kaiserslautern-Landau has shown for the first time, in a joint study with BOKU University, that permaculture brings about a significant improvement in biodiversity, soil quality and carbon storage. In view of the challenges of climate change and species extinction, this type of agriculture proved to be a real alternative to conventional cultivation—and reconcile environmental protection and high yields. Permaculture uses natural cycles and ecosystems as blueprint. Food is produced in an agricultural ecosystem that is as self-regulating, natural and diverse as possible. For example, livestock farming is integrated into the cultivation of crops or the diversity of beneficial organisms is promoted in order to avoid the use of mineral fertilizers or pesticides. In a study, published in the journal Communications Earth & Environment, researchers from RPTU and BOKU have now, for the first time, comprehensively investigated the effects of this planning and management concept on the environment.

[...]

"Permaculture appears to be a much more ecologically sustainable alternative to industrial agriculture," said Julius Reiff . At the same time, the yields from permaculture are comparable to those of industrial agriculture, as the researchers' not yet published data shows. "In view of the challenges of climate change and biodiversity loss, the observed improvements would represent a real turnaround when applied to larger areas," says ecosystem analysis expert Martin Entling from RPTU.

4 July 2024

[reasonsforhope]

Determined to use her skills to fight inequality, South African computer scientist Raesetje Sefala set to work to build algorithms flagging poverty hotspots - developing datasets she hopes will help target aid, new housing, or clinics.

From crop analysis to medical diagnostics, artificial intelligence (AI) is already used in essential tasks worldwide, but Sefala and a growing number of fellow African developers are pioneering it to tackle their continent's particular challenges.

Local knowledge is vital for designing AI-driven solutions that work, Sefala said.

"If you don't have people with diverse experiences doing the research, it's easy to interpret the data in ways that will marginalise others," the 26-year old said from her home in Johannesburg.

Africa is the world's youngest and fastest-growing continent, and tech experts say young, home-grown AI developers have a vital role to play in designing applications to address local problems.

"For Africa to get out of poverty, it will take innovation and this can be revolutionary, because it's Africans doing things for Africa on their own," said Cina Lawson, Togo's minister of digital economy and transformation.

"We need to use cutting-edge solutions to our problems, because you don't solve problems in 2022 using methods of 20 years ago," Lawson told the Thomson Reuters Foundation in a video interview from the West African country.

Digital rights groups warn about AI's use in surveillance and the risk of discrimination, but Sefala said it can also be used to "serve the people behind the data points". ...

'Delivering Health'

As COVID-19 spread around the world in early 2020, government officials in Togo realized urgent action was needed to support informal workers who account for about 80% of the country's workforce, Lawson said.

"If you decide that everybody stays home, it means that this particular person isn't going to eat that day, it's as simple as that," she said.

In 10 days, the government built a mobile payment platform - called Novissi - to distribute cash to the vulnerable.

The government paired up with Innovations for Poverty Action (IPA) think tank and the University of California, Berkeley, to build a poverty map of Togo using satellite imagery.

Using algorithms with the support of GiveDirectly, a nonprofit that uses AI to distribute cash transfers, the recipients earning less than $1.25 per day and living in the poorest districts were identified for a direct cash transfer.

"We texted them saying if you need financial help, please register," Lawson said, adding that beneficiaries' consent and data privacy had been prioritized.

The entire program reached 920,000 beneficiaries in need.

"Machine learning has the advantage of reaching so many people in a very short time and delivering help when people need it most," said Caroline Teti, a Kenya-based GiveDirectly director.

'Zero Representation'

Aiming to boost discussion about AI in Africa, computer scientists Benjamin Rosman and Ulrich Paquet co-founded the Deep Learning Indaba - a week-long gathering that started in South Africa - together with other colleagues in 2017.

"You used to get to the top AI conferences and there was zero representation from Africa, both in terms of papers and people, so we're all about finding cost effective ways to build a community," Paquet said in a video call.

In 2019, 27 smaller Indabas - called IndabaX - were rolled out across the continent, with some events hosting as many as 300 participants.

One of these offshoots was IndabaX Uganda, where founder Bruno Ssekiwere said participants shared information on using AI for social issues such as improving agriculture and treating malaria.

Another outcome from the South African Indaba was Masakhane - an organization that uses open-source, machine learning to translate African languages not typically found in online programs such as Google Translate.

On their site, the founders speak about the South African philosophy of "Ubuntu" - a term generally meaning "humanity" - as part of their organization's values.

"This philosophy calls for collaboration and participation and community," reads their site, a philosophy that Ssekiwere, Paquet, and Rosman said has now become the driving value for AI research in Africa.

Inclusion

Now that Sefala has built a dataset of South Africa's suburbs and townships, she plans to collaborate with domain experts and communities to refine it, deepen inequality research and improve the algorithms.

"Making datasets easily available opens the door for new mechanisms and techniques for policy-making around desegregation, housing, and access to economic opportunity," she said.

African AI leaders say building more complete datasets will also help tackle biases baked into algorithms.

"Imagine rolling out Novissi in Benin, Burkina Faso, Ghana, Ivory Coast ... then the algorithm will be trained with understanding poverty in West Africa," Lawson said.

"If there are ever ways to fight bias in tech, it's by increasing diverse datasets ... we need to contribute more," she said.

But contributing more will require increased funding for African projects and wider access to computer science education and technology in general, Sefala said.

Despite such obstacles, Lawson said "technology will be Africa's savior".

"Let's use what is cutting edge and apply it straight away or as a continent we will never get out of poverty," she said. "It's really as simple as that."

-via Good Good Good, February 16, 2022

[mindblowingscience]

Tropical forests are essential to sustain high biodiversity and mitigate climate change. They suffer from deforestation, the cutting and converting of forests for agriculture, mining, or infrastructure purposes. However, significant human impacts on the remaining forests that lead to their degradation are often overlooked. By using multiple remote sensing data streams and cutting-edge data analysis, researchers have acquired an unprecedented view of the extent and long-lasting effects of such degradation in tropical moist forests. Their study, published in Nature, reveals that the effects of human-driven degradation and fragmentation are greater than previously estimated.

Continue Reading.

[rjzimmerman]

Here's the link to the report from the Union of Concerned Scientists described in this story from EcoWatch:

A new report from the Union of Concerned Scientists (UCS) has found that Tyson Foods dumped hundreds of millions of pounds of pollutants into U.S. waterways from 2018 to 2022. The pollutants came from company facilities including slaughterhouses and processing plants.

UCS analyzed publicly available data from the U.S. Environmental Protection Agency (EPA) and found that Tyson Foods processing plants released 371.72 million pounds of pollutants into waterways from 2018 to 2022. Half of the pollutants were dumped in waterways of Nebraska, Illinois and Missouri. The group published the findings in a report titled Waste Deep: How Tyson Foods Pollutes US Waterways and Which States Bear the Brunt.

“As the nation’s largest meat and poultry producer, Tyson Foods plays a huge role in our food and agriculture system and has for decades exploited policies that allow big agribusiness corporations to pollute with impunity,” Omanjana Goswami, co-author of the report and an interdisciplinary scientist with the Food and Environment Program at UCS, said in a press release. “In 2022, the latest year for which we have data, Tyson plants processed millions of cattle and pigs and billions of chickens, and discharged over 18.5 billion gallons of wastewater, enough to fill more than 37,000 Olympic swimming pools.”

Waterways in Nebraska had the most wastewater pollutants dumped by Tyson Foods plants, about 30% of the total or 111 million pounds, UCS reported. The pollutants dumped in Nebraska included 4.06 million pounds of nitrate, which a 2021 study linked to increased risks of central nervous system cancers in children.

According to the National Provisioner, Tyson Foods is one of the top meat and poultry processing companies in the U.S. From 2018 to 2022, it generated 87 billion gallons of wastewater, based on EPA data. This wastewater can include pathogens and microorganisms (such as E. coli) and slaughterhouse byproducts, such as body parts of animals, feces and blood.

As noted in the report, the dumped pollutants contained high amounts of nitrogen (34.25 million pounds) and phosphorus (5.06 million pounds), which can contribute to algal blooms in waterways. As UCS pointed out in its analysis, many Tyson Foods facilities are located near waterways that are home to threatened and endangered species. 

The facilities are also positioned near historically underserved communities, leading to additional pollution near and burden on vulnerable populations.

“Pollution from these plants also raises environmental justice concerns,” Stacy Woods, co-author of the report and research director for the Food and Environment Program at UCS, said in a press release. “We know from previous research that almost 75% of water-polluting meat and poultry processing facilities are located within one mile of communities that already shoulder heavy economic, health or environmental burdens. In mapping these plants, we found Tyson largely fit that pattern, with many plants located near communities where people live with more pollution, less socioeconomic and political power, and worse health compared to other areas of the United States.”

The report provides insight into a larger problem. As The Guardian reported, meat processing pollution in the U.S. is much higher and goes beyond Tyson Foods.

“There are over 5,000 meat and poultry processing plants in the United States, but only a fraction are required to report pollution and abide by limits,” Goswami told The Guardian. “As one of the largest processors in the game, with a near-monopoly in some states, Tyson is in a unique position to treat even hefty fines and penalties for polluting as simply the cost of doing business. This has to change.”

[jcmarchi]

Oleksandr (Sasha) Strozhemin, Co-Founder & CEO of Trinetix – Interview Series

New Post has been published on https://thedigitalinsider.com/oleksandr-sasha-strozhemin-co-founder-ceo-of-trinetix-interview-series/

Oleksandr (Sasha) Strozhemin, Co-Founder & CEO of Trinetix – Interview Series

Oleksandr (Sasha) Strozhemin is a сo-founder and CEO of Trinetix – a global technology company that provides strategy, design, and innovation services to Fortune 500 and fast-growing businesses operating in diverse areas, from finance and professional services to logistics, healthcare, and agriculture.

Leveraging its diverse arsenal of AI and GenAI, intelligent digital assistants, data and analytics, digital workplaces, experience design, and cloud enablement, Trinetix is committed to enabling business leaders to turn their product ideas into competitive, one-of-a-kind market offerings.

To further enable industry-redefining, Trinetix acts as a dedicated technology partner, delivering future-proof strategies and tech talents necessary for scoring transformation goals and driving long-term business outcomes.

Can you share the story behind the founding of Trinetix and your journey to becoming a strategic technology partner for Fortune 100 companies?

I’ve always been fascinated by the pace of innovation, the way new things change our lives with a click. But I’ve also noticed how fast this evolution has led to an overheated market. In 2011, the competition for being the next revolutionary provider and game-changer was extremely intense.

To me, it was a time of opportunity for creating something unique and outstanding. So, I took action.

We started our work in the US in 2011, focusing on AR, experience design, and mobile app development. Of course, AR was a central part of our service offerings because it was a budding trend back then, and it held a promise of creating the exact one-of-a-kind experiences I wanted to deliver.

To find new clients, I took an unorthodox approach. While we targeted the US market, we were particularly interested in companies with a global presence. So, to engage them, we opened a delivery office in Eastern Europe (Kyiv, Ukraine), where we delivered around 20 AR projects for P&G, Nivea, ExxonMobile, and Coca-Cola. It was a great call: in addition to assisting our clients with establishing their presence in the local market, we worked in a region highly responsive to innovation and rich with tech talent.

A year and a half of proactive work passes—and we get an invitation to participate in a tender for experience design for a Fortune 100 enterprise. It turns out they have been monitoring our work closely and added us to the candidates list.

We accept — and out of all the candidates, we are the only ones to give a ready-to-show offer with all the requirements dissected, UI prototypes prepared, and interaction logic animated. The company rep takes our submission to the decision-making group — and we’re in!

In the next two years of developing CX design practice, we smoothly transition to project engineering, taking projects put on deep hiatus and turning them into complete value-driving products.

Naturally, when you work with an industry leader, you find yourself on the threshold of disruptions, navigating and orchestrating transformation. So, our story of embedding AI, intelligent automation, and data analytics into enterprise processes starts at that point.

This experience left me with a deeper understanding of our mission — to guide businesses as they manage digital change and adopt it with maximum impact. This is our commitment.

Trinetix has developed AI chatbots, digital assistants, and AI-powered data intelligence solutions. Can you elaborate on how these technologies are transforming operations for your clients?

In short, I’d say that leveraging AI enables clients to accomplish more in less time—and by more, I mean much, much more.

Today, businesses have a wealth of valuable insights at their fingertips, but finding them requires organizing, categorizing, and validating data. If done by hand, the entire process can take months. Sometimes, there is too much data, and even 10 experts working 24/7 for months won’t suffice.

This is where AI comes into play — and I believe this is its strongest and most game-changing aspect. Replacing months of manual research with instant delivery of the right information for the right objective is simply revolutionary.

How do you ensure that your AI solutions are tailored to meet the specific needs of each client, particularly in diverse industries like logistics and healthcare?

Research comes first. Always.

Any AI model is as strong as the data used for training it and the knowledge of the niche it’s built for.  So, we always start our work with a discovery session. It helps us explore how an enterprise operates, identify its strong points, and study its key competitors.

Our top priority is to put our clients’ vision and needs into features of the future solution—so we also build from their experience, research key enterprise processes, and discuss ways of addressing constraints.

We also establish the general digital dexterity levels across the enterprise and the needs of departments using the technology. That includes helping clients to onboard their teams and providing detailed yet straightforward instructions on operating with the solution.

Can you discuss a recent project where Trinetix integrated generative AI to solve a critical business challenge for a client? What were the key outcomes?

There has been such a case. A Fortune 500 client operating in freight management came to us with a request to transform their request-for-proposal (RPF) management processes.

Since they were handling their RPF tasks manually, they were dealing with slow response times and calculations while accumulating heaps of unstructured data (images, screenshots, emails) — which were never converted into value. Accordingly, great opportunities were either lost among the data or in delayed tasks.

A digital upgrade of key operations was in order.

We developed a multimodal solution that was powered by generative AI, transforming all the unstructured data into a comprehensible source of rich and robust insights. This solution enabled the client to generate quotes straight from the company’s mailboxes and provided end-to-end automation for faster task completion. As a result, the client accelerated their operations, increased their win rates, and optimized quote management, ultimately growing their revenue.

What are the main challenges you face when implementing generative AI solutions, and how do you overcome them?

The way I see it, many challenges stem from the human factor.  For instance, when an enterprise adopts GenAI, employees worry that they are being replaced. As a result, AI meets organizational resistance, which defeats the entire point of technology adoption.

This is where we work together with enterprise leaders, helping them promote the change across their company. I think it’s crucial to address fears from a fact-based angle, providing a realistic perspective on the strengths and weaknesses of the technology. We also include employees in the development process, establishing feedback loops and showing them how the technology works and why it benefits them.

There is also a fear of uncertainty.  Sometimes executives hesitate to proceed because they want to be confident that the ROI will be worth the overhaul. I think communication is once again the key. For instance, our teams always have a designated relationship manager who explains the change to stakeholders, updating them on our progress and the results they should expect.

Your approach emphasizes 360° value and innovation excellence. How do you ensure that your solutions continually provide value and stay ahead of market trends?

We base our work on three principles.

First,  it’s not about the trend but what the client needs. Sometimes, the client comes to us with a presumed solution, but then a discovery session reveals alternatives that better fit their goals. And when we have a fit, we know we have a foundation that will work for the client for years to come.

Second, we base our solution planning on enterprises’ growth, scale, and evolution, so the end product must synergize with these processes. In addition to discussing the client’s potential roadmap and growth trajectory, we also study compatibility, ensuring that the client can integrate new platforms and systems into their framework.

Third, it’s autonomy. While we share responsibility and provide support and success monitoring, we see that clients’ teams can use the product and onboard new users easily without requiring our intervention. So, we approach our solutions from the user POV, anticipating potential issues and introducing measures to prevent them.

What role does intelligent automation play in driving business transformation and innovation for your clients?

Today, we’re observing the arrival of new, flexible business models that embrace change as the only constant. That means rigid models that burden human resources with numerous repetitive routines are becoming a thing of the past.

Intelligent automation enables organizations to make the transition by restructuring their operations and liberating their talents for more complex and impactful tasks. With intelligent automation handling all the processes that can be replicated, a company becomes more agile, gaining a stronger competitive standing and greater confidence in its next steps.

Data underpins many of your services. How does Trinetix help organizations maximize the strategic potential of their data?

Our goal is to connect decision-makers with value-rich insights, enabling them to access necessary data right when needed. That includes removing blindspots, data silos, and bottlenecks by equipping enterprises with every tool they need to dissect information flowing their way.

Instant report generation that converts volumes of complex information into comprehensive storytelling, intuitive dashboards, and fast and responsive AI-powered data analysis tools—we provide all the building blocks for data-fueled strategies.

Can you discuss a success story where your data and analytics services significantly impacted a client’s business operations?

One of our most prominent cases is the business entity research transformation we did for our Fortune 500 client.

Previously, such research was executed by hand—meaning that managers had to send queries across several departments and wait for their response. Then, they had to manually aggregate information collected from over 10 enterprise data systems into a report.

Accordingly, making just one report took months and the risk of human error was high, which led to an inaccurate picture of legal and compliance risks.

We developed a data management system that enabled a single view across all 10+ enterprise sources, providing full visibility of business entity connections and relationships. It allowed managers to generate relevant and accurate reports within days, increasing productivity while reducing the probability of data discrepancies.

What are some emerging trends in AI and digital solutions that you believe will shape the future of enterprise technology?

I’m particularly passionate about advancing and evolving AI assistants into intelligent workplace partners that gather information, facilitate cross-department communication, and enable service personalization by combining machine capacity with human agility.

From my perspective, the integration of AI assistants is going to significantly improve the quality of life for not just customers but also enterprise employees. This will create more dynamic business environments and foster a culture of proactive problem-solving.

Thank you for the great interview, readers who wish to learn more should visit Trinetix.

[she-is-ovarit]

Women and girls are oppressed on the axis of sex:

Economic

 Around 2.4 billion women of working age are not afforded equal economic opportunity and 178 countries maintain legal barriers that prevent their full economic participation, according to the World Bank’s Women, Business and the Law 2022 report. In 86 countries, women face some form of job restriction and 95 countries do not guarantee equal pay for equal work.

Globally, women still have only three quarters of the legal rights afforded to men -- an aggregate score of 76.5 out of a possible 100, which denotes complete legal parity.

Gender inequality is a major cause and effect of hunger and poverty: it is estimated that 60 percent of chronically hungry people are women and girls. (Source: WFP Gender Policy and Strategy.)

Less than 20 percent of the world's landholders are women. Women represent fewer than 5 percent of all agricultural landholders in North Africa and West Asia, while in sub-Saharan Africa they make up an average of 15 percent.

In the United States, the labor force participation rate among females is 56.5% and among males is 67.5% for 2022

Vulnerable employment among women [in the US] has remained nearly the same since 1991. Workers in vulnerable employment are the least likely to have formal work arrangements, social protection, and safety nets to guard against economic shocks; thus they are more likely to fall into poverty. Vulnerable employment among women is 3.9% and among men is 4.6% in the United States for 2021.

In the United States, women spend 1.6 times as much time on unpaid domestic and care work than men. In 2019, women in the United States spent 15.3% of their day and men spent 9.7% of their day on unpaid work. 

A 2013 study revealed that 7.6% of lesbian couples in the United States live in poverty compared to 5.7% of married different-sex couples. Similarly, one-third of lesbian couples without a high school diploma were in poverty compared to 18.8% of different-sex couples.

Study: Stereotypes of middle-aged women as less ‘nice’ can hold them back at work.

Women hold 66% of all student loan debt. 41% of women undergraduates take out student loans, compared to 35% of male undergraduates. Women take an additional two years on average to pay off student loans.

Education

Women make up more than two-thirds of the world's 796 million illiterate people.

While progress has been made in reducing the gender gap in urban primary school enrollment, data from 42 countries shows that rural girls are twice as likely as urban girls to be out of school.

Male violence against women

In the United States, the share of women who have experienced intimate partner violence is nearly the same as the world average, 27%. Intimate partner violence is by far the most prevalent form of violence against women globally and is defined as the percentage of ever-married women (ages 15-49) who have ever experienced physical or sexual violence committed by their husband or partner.

35% of women worldwide have experienced either physical and/or sexual intimate partner violence or non-partner sexual violence.

1 in 3 women, around 736 million, are subjected to physical or sexual violence by an intimate partner or sexual violence from a non-partner – a number that has remained largely unchanged over the past decade.

Globally, 7% of women have been sexually assaulted by someone other than a partner.

Globally, as many as 38% of murders of women are committed by an intimate partner.

200 million women have experienced female genital mutilation/cutting.

Violence against women in Mexico rises to over 70%, study finds

7 in 10 human trafficking victims are women and girls.

Women and girls represent 65 per cent of all trafficking victims globally. More than 90 per cent of detected female victims are trafficked for the purpose of sexual exploitation.

Politics, power, and Influence

28.7% of seats in national parliament were held by women in 2022 in the United States

Metadata analysis shows biographies of women on Wikipedia are deleted and marked non-notable at a significantly higher rate than those of men.

Women continue to be underrepresented in the fields of science, technology, engineering and mathematics, representing only slightly more than 35% of the world’s STEM graduates. Women are also a minority in scientific research and development, making up less than a third of the world’s researchers.

Medical discrimination, medical violence, and female healthcare

 Women 32% more likely to die after operation by male surgeon

Women are over-medicated because drug dosage trials are done on men.

Women are sometimes forcibly sterilized, without consent, across the globe.

Mental Health

A 2016 study investigating physical and mental health, and experiences of violence among male and female trafficking survivors in England found 78% of women and 40% of men reported high levels of depression, anxiety, or PTSD symptoms.

Female people in the US attempt suicide more frequently than men.

Adult women have higher rates of mental illness than adult men

Discrimination, bias, and sex-based stereotypes

 UN report finds 90% of men and women hold some sort of bias against females.

Men were 93 percent more likely to have their loans discharged when disclosing a medical condition, as compared to women who disclosed medical conditions.

We historically are not included in research, and when we are, are grouped in with men which is unhelpful (bonus question: What does this mean, then, if male people who "identify-as-women" are grouped in with women and not considered a separate category?)

The Madonna-Whore Dichotomy (MWD) denotes polarized perceptions of women in general as either “good,” chaste, and pure Madonnas or as “bad,” promiscuous, and seductive whores. Men who reported higher endorsement of the Madonna-whore-dichotomy rated their partner as less entitled to sexual pleasure. Women who reported higher endorsement of the Madonna-whore dichotomy devalued their own pleasure by rating their partner as more entitled to sexual pleasure than themselves.

“Their Great Shame is Poverty”: Women Portrayed as Among the “Undeserving Poor” are Seen as Deserving Sexual Assault

The Impact of Media Use on Girls' Beliefs About Gender Roles, Their Bodies, and Sexual Relationships: A Research Synthesis.

Mothers in China for decades pressured their daughters to bind their feet - often destroying the function and formation of their feet - in order to please and service men.

My one disclaimer to this post is that there is a tremendous amount of information left out of this post. This is because it is impossible to capture the vast amount of research and details within studies illuminating the sex-based oppression of women and girls. I have not gone into depth on the impact of media on teenage girls' body image, the role of trauma in girls influencing them to hate their bodies, FGM, the Iranian protests, etc. I hope others reblog and add more information.

[asurprizinglysanefangirl]

Name meanings of Star Trek main characters

TOS –

James - Supplanter; Holder of The Heel

Spock – I don't know

Leonard - Lion Strength; Descendant; Lover

Nyota – Stars

(Bonus - Uhura – Freedom (crafted from the Swahili word uhuru for the show))

Mongomery - Mountain Belonging to The Ruler; Man Power

Hikaru – Radiance; Light

Pavel - Small; Humble, Modest

Christine - Follower of Christ

TNG –

Jean-Luc - God Is Gracious; Bringer of Light

William - Resolute Protector; Will, Desire; Helmet, Protection

Data - Facts and statistics collected together for reference or analysis

(Bonus - Soong – Grandson, Descendant (it's a Chinese surname))

Deanna – Valley; Devine; Goddess; Church Leader

Beverly - Beaver Stream, Meadow

Geordie - Land-Worker, Farmer

Worf – I couldn’t find anything for either his first or last name

Tasha – Christmas Day, Birthday (Of Christ)

Wesley - Western meadow

DS9 –

Benjamin - Son of The Right Hand, Son of The South, Son of my Days

Kira - Mistress, Lady; Beam of Light; Black; Glitter, Shining

(Bonus - Nerys – Noblewoman; Hero)

Jadzia - Princess

Julian - Youthful; Jove's Child; Downy Bearded

Odo – Nothing [Cardassian meaning] (The following is extracted from the ST Wiki -  "Odo's name stemmed from the Cardassian word for "nothing", Odo'ital, which was the loose translation of the "unknown sample" label in Bajoran on his laboratory flask. After he was discovered to be sentient, the scientists began jokingly referring to him as "Odo Ital", in a similar manner to a Bajoran name, which eventually got shorted into simply "Odo".)

Odo - Wealthy; Inheritance; Richness [Human meaning] (Because apparently it's also a human name!)

Quark - I couldn't find anything

Miles O’Brien - Soldier; Who Is Like God?; Merciful

Elim - Place of Strong Trees

(Bonus - Garak – To Sojourn, To Dwell. Check this article out for more information)

Jake - Supplanter; Of Jacob

Nog – Agriculture (it’s a Chinese surname)

Ezri – Helper, My Help

Voyager –

Kathryn - Pure

Chakotay – Man who walks the Earth but only sees the Sky

Tuvok – I don't know. (There probably is someone who knows the Vulcan meaning of Tuvok and Spock out there.)

B’Elanna – (Belanna is an actual name) A noble woman who is beautiful

(Another bonus - Torres - Towers)

Tom - Twin; Innocence

Harry – Home Ruler

The Doctor – The name Jason means Healer, so I sometimes think of that as his name

Neelix – It was created for the show

Kes – I couldn’t find anything

Annika (Seven of Nine) - Grace; Favor; Gracious; Elegant

[cookie-nom-nom]

“No details regarding the methods of their analysis are presented, and the data which they analyzed was irrelevant to the question of field progress, leaving their conclusions without merit.”

omg things heating up in the agriculture fandom. I love when the academics get vicious.

[girlactionfigure]

Rosalind Franklin, chemist and DNA crystallographer.

Rosalind Franklin (1920-1958) has been called “the unsung hero of the double helix” after she failed to win recognition for her work in discovering the double-helix DNA.

Rosalind was born in London to a wealthy Jewish family. She studied chemistry at Cambridge University. She then went on to study the structures of carbon in coal, and later, viruses in plants and animals. In the 1940s, she studied plant viruses that blighted important agricultural crops, including the potato, turnip, tomato and pea. In 1957, she conducted research into the virus that causes polio.

In 1953, she made the most important discovery in her career. Using x-ray equipment and a micro-camera, Franklin photographed and analyzed samples of DNA. In May 1952, Franklin and a graduate student took a ground-breaking photo, labelled #51, which unequivocally provided the first clear image of DNA and its helical pattern.

Franklin’s research results had been passed to fellow scientists James Crick and Francis Watson without her knowledge or permission. Her photo, and her precise analysis of the x-ray diffraction data inspired Crick and Watson to reject their initial idea of a three-helix molecule and make the necessary calculations to develop the double helix model of the DNA strand we now know. Without Franklin, there likely would have been no global recognition for Watson and Crick and no Nobel Prize.

Crick and Watson were awarded the Nobel Prize in 1962. Franklin had already died of ovarian cancer in 1957 at the tragically young age of 37. When Crick and Watson were given their prize they not only did not acknowledge Franklin’s contribution to their work, they never even mentioned her name in any of their publications.

Franklin may not have been recognized during her lifetime, but years later, Watson and Crick did eventually acknowledge her contributions. In a book published in 1968, “The Double Helix” Watson wrote, “The instant I saw the picture my mouth fell open and my pulse began to race. The black cross of reflections which dominated the picture could only arise from a helical structure.”

There is now a Rosalind Franklin University of Medicine and Science in Chicago, which was originally founded in 1912 as the Chicago Hospital-College of Medicine, and was renamed after Franklin in 2004. Franklin has also become greater known among the general public, and was placed fifth in the 2018 BBC History Magazine poll of the world’s most influential women.

Finally, years after her death, Rosalind Franklin, whose work was instrumental in unlocking the structure of DNA, is receiving the recognition she deserved for so long.

Historical Photos of Women's Stories