#Predictive Modeling

[lumenore-datalytics]

How AI-Powered Analytics Is Transforming Healthcare in 2025

In healthcare, seconds save lives. Imagine AI predicting a heart attack hours before symptoms strike or detecting cancer from a routine scan. This isn’t science fiction—AI-powered analytics in healthcare is making this a reality, turning data into life-saving insights. 

By analyzing vast amounts of data, AI healthcare analytics help decode hidden patterns, improving diagnoses and personalizing treatments, which were unimaginable until a few years ago. The global healthcare analytics market is projected to hit $167 billion by 2030, growing at a 21.1% CAGR, thereby proving that data is becoming the foundation of modern medicine. 

From real-time analytics in healthcare to AI-driven insights, the industry is witnessing a revolution—one that enhances patient care, optimizes hospital operations, and accelerates drug discovery. The future of healthcare is smarter, faster, and data-driven. 

What Is AI-Powered Analytics in Healthcare?

AI-powered analytics uses artificial intelligence and machine learning to analyze patient data, detect patterns, and predict health risks. This empowers healthcare providers to make smarter, faster, and more personalized decisions. Here’s how this data revolution is reshaping healthcare:

1. Early Diagnosis and Predictive Analytics 

AI-powered analytics can analyze massive datasets to identify patterns beyond human capability. Traditional diagnostic methods often rely on visible symptoms, but AI can detect subtle warning signs long before they manifest. 

For example, real-time analytics in healthcare is proving life-saving in sepsis detection. Hospitals that employ AI-driven early warning systems have reported a 20% drop in sepsis mortality rates as these systems detect irregularities in vitals and trigger timely interventions. 

2. Personalized Treatment Plans 

AI-powered analytics can customize plans for individual patients based on genetic data, medical history, and lifestyle. This shift towards precision medicine eliminates the conventional one-size-fits-all approach. 

AI also enables real-time patient monitoring and adjusting treatments based on continuous data collection from wearable devices and electronic health records (EHRs). This level of personalization is paving the way for safer, more effective treatments. 

3. Smarter Hospital Operations 

Hospitals generate 2,314 exabytes of data annually, yet much of it remains underutilized. AI-powered analytics is changing that by optimizing hospital operations to reduce inefficiencies and improve patient flow management. 

For instance, Mount Sinai Hospital in New York uses AI-powered analytics for patient care by predicting life-threatening complications before they escalate. A clinical deterioration algorithm analyzes patient data daily, identifying 15 high-risk patients for immediate intervention by an intensive care rapid response team. Beyond emergency care, AI also prevents falls, detects delirium, and identifies malnutrition risks, ensuring proactive treatment. 

4. Drug Discovery and Development 

Developing a new drug is expensive and time-consuming, often taking 10-15 years and costing over $2.6 billion. However, AI-powered analytics is significantly reducing both time and costs by analyzing millions of chemical compounds, predicting potential drug candidates, and streamlining clinical trials faster than traditional methods. 

During the COVID-19 pandemic, AI played a crucial role in identifying potential antiviral treatments by rapidly analyzing millions of drug interactions – a process that would have taken human researchers years. Additionally, AI is now being used to repurpose existing drugs, optimize trial designs, and predict patient responses, making pharmaceutical development faster, more efficient, and data-driven. 

5. 24/7 Patient Support with AI Chatbots and Virtual Assistants 

A survey by Accenture estimates that AI applications, including chatbots, could save the U.S. healthcare system around $150 billion annually by 2026. These savings stem from improved patient access and engagement, as well as a reduction in costs linked to in-person medical visits. AI-driven healthcare analytics is making healthcare more efficient, patient-centric, and responsive to individual needs. 

Challenges in AI-Driven Healthcare

Despite its potential to revolutionize healthcare, AI-powered healthcare data & analytics come with challenges that must be addressed for widespread adoption. Some of the challenges are: 

Data Privacy and Security: Healthcare systems handle sensitive patient data, making them prime targets for cyberattacks. Ensuring robust encryption, strict access controls, and compliance with HIPAA and GDPR is critical to maintaining patient trust and regulatory adherence. 

Bias in AI Models: If AI systems are trained on biased datasets, they can perpetuate healthcare disparities, thereby leading to misdiagnoses and unequal treatment recommendations. Developing diverse, high-quality datasets and regularly auditing AI models can help mitigate bias. 

Regulatory Compliance: AI-driven healthcare solutions must align with strict regulations to ensure ethical use. Organizations must work closely with regulatory bodies to maintain transparency and uphold ethical AI practices. 

What’s Next in Smart Healthcare?

AI-Powered Surgeries: Robotic assistance enhances precision and reduces risks.

Smart Wearables: Track vital signs in real-time and alert patients to anomalies.

Mental Health Tech: Predictive tools offer proactive support and personalized therapy.

Why It Matters

AI isn’t replacing doctors—it’s augmenting their decision-making with data-driven insights. Healthcare systems that adopt analytics will see:

Improved patient outcomes

Reduced costs

Streamlined operations

[codeflixglobal]

The Process of Predictive Modeling!

Transform raw data into future insights! Explore the process of Predictive Modeling in our latest infographic.

[elainepaco]

Data-Driven Decisions: How Sabermetrics and AI are Shaping Baseball Strategy

Baseball, a sport steeped in tradition, is increasingly embracing the power of data analysis to gain a competitive edge. Sabermetrics, the empirical analysis of baseball statistics, has transformed the way teams evaluate players, make strategic decisions, and build winning rosters. The emergence of artificial intelligence (AI) is further enhancing these capabilities, providing even more sophisticated tools for analyzing data and predicting outcomes.

Optimizing In-Game Decisions

Sabermetrics provides valuable insights into various aspects of game strategy. For instance, research has identified the most important factors influencing stolen base success, including the number of pitches thrown during the at-bat, the speed of the pitch, the baserunner's sprint speed, and the game situation. This information can help coaches make more informed decisions about when to attempt a stolen base.

Building a Winning Team

Data analysis also sheds light on team performance and roster construction. Studies have shown that the depth of a team's relief pitching, rather than the performance of a single star reliever, is a crucial factor influencing a team's winning percentage. This suggests that general managers should prioritize building a strong bullpen with quality depth over acquiring a single high-performing relief pitcher.

Predicting Player Performance

Sabermetrics and AI are also valuable tools for evaluating and predicting player performance. Traditional statistics, such as batting average and ERA, are being supplemented with advanced metrics like exit velocity, launch angle, and spin rate. AI models can analyze these data points to predict player development, identify potential draft picks, and even forecast contract renewals.

Advanced Modeling Techniques

Sophisticated models are being developed to analyze and predict various aspects of the game. The zero-inflated bivariate binomial (ZIBVB) model effectively captures zero-inflation patterns in baseball data, allowing for simultaneous inference on two success probabilities and the zero-inflation parameter. This can help make more informed decisions about player selection and game strategy.

Challenging Traditional Beliefs

Data analysis is also challenging some traditional beliefs in baseball. For example, research has challenged the Time Through the Order Penalty (TTOP), the belief that batters perform significantly better against pitchers the more times they face them in a game. This suggests that managers should consider a pitcher's overall quality, rather than relying solely on TTOP when making decisions about pulling a pitcher.

AI and the Future of Baseball Analytics

AI is poised to play an even greater role in baseball analytics in the future. Machine learning models, such as Long Short-Term Memory (LSTM) neural networks, offer a promising approach for predicting home run performance. AI can also be used to develop more sophisticated scouting reports, optimize defensive positioning, and even predict the outcome of games.

Sabermetrics and AI are transforming the way baseball is played and analyzed. By harnessing the power of data, teams can make more informed decisions, optimize performance, and gain a competitive edge. As these technologies continue to evolve, we can expect even more innovative applications in the future, further revolutionizing the game of baseball.

PASS's Services:

Annual Guidebooks: PASS publishes annual guidebooks for various sports, summarizing the latest research and providing practical recommendations for training and performance optimization.

Custom Reports: PASS offers custom reports that delve deep into specific performance challenges, providing tailored solutions based on the latest scientific evidence.

Researcher Exchange: PASS facilitates workshops and Q&A sessions with leading sports scientists, allowing coaches and athletes to gain valuable insights and connect with experts in the field.

The potential of sports science to revolutionize training and performance is vast, but it remains largely untapped in many areas of athletics. By embracing research-backed principles and utilizing the services of organizations like PASS, coaches and athletes can unlock new levels of performance, reduce the risk of injuries, and achieve their full potential.

About PASS | Practical Application of Sport Science:

PASS helps top sports teams make better decisions using science. The teams ask questions like: “how to manage workload; how to improve decision-making; what is an optimal periodization program”. PASS takes a deep dive into all relevant research articles, figures out what's useful, and gives the teams specific advice they can immediately implement – only things that have been scientifically proven.

Explore the resources available at PASS (https://sportscience.pro/) and discover how sports science can transform your approach to training and performance.

[mrmhilo]

Predictive Modeling: Elevating Maritime Risk Management with Advanced Statistical Analysis

Insurance companies and maritime risk management (MRM) firms have traditionally relied on predictive modeling to identify and mitigate risks. However, the digital transformation of the maritime industry is reshaping how risk management is conducted by enabling more accurate, detailed, and thorough statistical analysis.

Maritime digital solutions can assess the likelihood of various events and estimate the potential damage they might cause, similar to how life insurance companies predict the probability of longevity or how racetrack odds are calculated.

Full statistical analysis models go beyond basic predictive modeling, which only identifies direct causal relationships between events. At HiLo, our advanced fleet risk management statistical analysis not only traces causal links after events occur to understand their origins but also uses this information to identify trends and predict unforeseen future risks.

How Maritime Statistical Analysis Works

At HiLo, we gather data from both public and private sources, including over 4,000 clients. This data helps us model potential incidents, their probabilities, and recommend preventive measures for maritime companies.

The ultimate result is a model of potential risks that allows leaders in maritime shipping companies to mitigate and prevent damage or injury. This maritime digital transformation creates a risk decision tool for the maritime industry that outperforms anything that’s ever existed before.

The Benefits of Our Maritime Statistical Analysis

Traditional predictive models can be unreliable and even harmful to companies. For instance, when predictive modeling is used to rate bonds, the model might suggest an entity is likely to default, despite insufficient evidence. This can result in negative ratings for companies that are beyond their control.

Our fleet risk management statistical analysis, however, focuses on delivering predictions that save lives, cargo, ships, and money. By processing massive amounts of data, we provide highly accurate risk assessments.

Confidence

With the vast datasets available through maritime digitalization, predictions are now more precise. For example, Big Data—like that used by tech giants such as Google and Facebook—offers the ability to forecast consumer behavior with unprecedented accuracy. Similarly, HiLo’s fleet risk management model leverages large datasets to improve both predictive accuracy and risk mitigation strategies.

Information

Thanks to the immense volume of data drawn from numerous maritime digital solutions, our system keeps maritime company leadership well-informed of emerging threats. If an unexpected danger arises, management is alerted as soon as information is available, ensuring proactive asset protection and preventing unpleasant surprises.

Proactivity

Armed with the right fleet risk management analysis, maritime leaders can take proactive measures to avoid incidents before they occur. For example, if icebergs are detected moving further south into North Atlantic shipping lanes, ships can be rerouted to avoid collision.

Being proactive rather than reactive is crucial to preventing losses. While learning from past incidents is important, preventing them in the first place is the most effective strategy.

Making Predictive Modeling the Standard

Risk assessment in fleet management has traditionally been carried out by experienced captains and industry veterans. However, digitalization now allows for the creation of a more sophisticated risk decision tool that provides greater insight and accuracy than ever before.

Historically, data sharing in the maritime industry would only occur after a major incident, and typically only the most catastrophic events would be made public. Now, with data from thousands of companies being filtered into predictive models, we can anticipate risks and offer solutions without waiting for major disasters. Small incidents that foreshadow larger problems can now be identified in advance.

Today, more and more maritime companies are adopting the power of HiLo’s statistical analysis to enhance safety and minimize risk.

The Human Element

Despite the impressive capabilities of maritime digital solutions, algorithms, and vast datasets, human expertise remains essential. That’s why the best fleet risk management analysis companies still employ former captains and seasoned professionals. These experts interpret predictions and translate them into actionable steps to prevent incidents on the ground.

The Key Factor

“Unsuccessful predictive modeling can always be traced back to one factor—insufficient data. Predictive modeling is only as reliable as the data it is built on. Companies using only basic publicly available data will miss valuable insights and fail to deliver effective reports.” (Source: Maritime Executive)

The more data, the better the predictions. This is why tech giants like Google and Facebook continuously gather vast amounts of data—they understand that more information leads to more accurate forecasts and improved decision-making.

The most crucial element of any fleet risk management analysis is the size of the dataset. A small dataset means missing out on critical signs of potential larger problems.

Enhancing Fleet Safety

At HiLo, our mission is to make maritime operations safer by leveraging the data we collect, our analytical capabilities, and the recommendations we provide. The more maritime companies that share anonymized data, the more effectively we can reduce risks in the industry.

Join us today and help make the seas safer for everyone.

[einnosyssecsgem]

Revolutionizing Industrial Efficiency: AI/ML-Based Pump & Motor Health Monitoring and Predictive Maintenance

In today’s fast-paced industrial landscape, minimizing downtime and optimizing operational efficiency are crucial for maintaining a competitive edge. Pumps and motors are essential components in numerous industries, and their failure can lead to significant operational disruptions and financial losses. Traditional maintenance approaches, often based on reactive or scheduled maintenance, are no longer sufficient. The solution lies in leveraging advanced technologies: AI and Machine Learning (ML) for predictive maintenance. The Power of Predictive Maintenance

Predictive maintenance uses AI and ML algorithms to analyze data from pumps and motors, predicting potential failures weeks in advance. This proactive approach allows maintenance teams to address issues before they escalate into costly downtime or catastrophic failures.

Key Benefits of AI/ML-Based Health Monitoring

AI/ML algorithms can detect anomalies in pump and motor performance far earlier than human operators or traditional monitoring systems. By identifying subtle changes in vibration, temperature, or sound patterns, these systems can predict failures weeks before they occur, providing ample time for corrective action.

Reduced Downtime and Maintenance Costs

By predicting and preventing failures, companies can significantly reduce unplanned downtime and the associated costs. Maintenance can be scheduled at optimal times, avoiding the need for emergency repairs and minimizing production disruptions.

Extended Equipment Lifespan

Regular, condition-based maintenance helps keep pumps and motors running at peak efficiency, extending their operational lifespan. This reduces the frequency of equipment replacements and lowers capital expenditure.

Improved Safety and Reliability

Predictive maintenance ensures that equipment is always in good working condition, enhancing the overall safety of operations. Reliable equipment also means fewer interruptions and more consistent production output.

How AI/ML-Based Systems Work

AI/ML-based health monitoring systems use a combination of sensors, data analytics, and machine learning models to continuously monitor the condition of pumps and motors.

Data Collection

Sensors attached to pumps and motors collect real-time data on various parameters, including vibration, temperature, pressure, and electrical currents.

Data Processing and Analysis

The collected data is processed and analyzed using advanced ML algorithms. These algorithms learn the normal operating conditions and identify patterns that indicate potential issues.

Anomaly Detection

When the system detects anomalies that deviate from the learned normal patterns, it flags them for further analysis. These anomalies can indicate early signs of wear and tear, misalignment, or other potential failures.

Predictive Modeling

Based on historical data and identified anomalies, predictive models forecast the remaining useful life of the equipment and predict the likelihood of future failures. This allows maintenance teams to prioritize and schedule interventions proactively.

Actionable Insights

The system provides actionable insights and recommendations to maintenance teams, enabling them to address issues before they lead to failure. This could include instructions for specific repairs, adjustments, or replacements.

Applications Across Industries

AI/ML-based health monitoring and predictive maintenance systems are versatile and can be applied across various industries, including:

Manufacturing

Ensuring continuous operation of critical machinery, reducing production downtime, and optimizing maintenance schedules.

Oil and Gas

Monitoring pumps and motors in harsh environments, predicting failures, and preventing costly shutdowns.

Water and Wastewater Management

Ensuring the reliability of pumps and motors in treatment plants, preventing service interruptions, and reducing maintenance costs.

HVAC Systems

Monitoring the health of motors and pumps in heating, ventilation, and air conditioning systems, improving efficiency and reducing energy consumption.

The Future of Industrial Maintenance

As AI and ML technologies continue to evolve, the capabilities of predictive maintenance systems will only improve. Future advancements may include more sophisticated anomaly detection algorithms, better integration with other industrial systems, and enhanced user interfaces that provide more intuitive insights and recommendations.

Call to Action

Implementing an AI/ML-based health monitoring and predictive maintenance system is not just a technological upgrade; it's a strategic investment in your business's future. By adopting these advanced solutions, you can ensure the longevity and reliability of your pumps and motors, reduce maintenance costs, and maintain continuous, efficient operations.

Don't wait for unexpected failures to disrupt your operations. Embrace the future of maintenance today and see the difference predictive maintenance can make for your business. Contact us to learn more about how our AI/ML-based pump and motor health monitoring systems can transform your maintenance strategy and drive your business towards greater efficiency and reliability.

[archijain931]

Data Science in Finance: Predictive Modeling and Risk Management

Data Science in Finance: Predictive Modeling and Risk Management involves using statistical techniques and machine learning to predict financial trends and assess risk. By analyzing large datasets, financial institutions can forecast market movements, identify potential risks, and make data-driven decisions to optimize portfolios, enhance investment strategies, and ensure regulatory compliance.

Read More: https://blognow.co.in/data-science-in-finance-predictive-modeling-and-risk-management

[davidjones2]

Mastering Statistics Homework with XLMINER: Your Ultimate Guide

Discover how XLMINER revolutionizes statistics homework help, offering advanced tools and techniques to conquer challenges with ease.

In our quest to demystify statistics and empower learners worldwide, we're thrilled to introduce a game-changing ally in your academic and professional endeavors: XLMINER. This powerful tool harnesses the capabilities of data mining and predictive analytics to unravel intricate statistical patterns and insights, making it an invaluable asset for anyone seeking help with statistics homework using XLMINER.

Understanding the Essence of XLMINER

Before delving into the practical applications of XLMINER, let's grasp the essence of this cutting-edge software. XLMINER stands at the intersection of statistics, machine learning, and data visualization, offering a comprehensive suite of tools to analyze, interpret, and visualize complex data sets. From descriptive statistics to advanced predictive modeling, XLMINER empowers users to extract actionable insights with unparalleled efficiency and accuracy.

Navigating the Landscape of Statistics Homework

Statistics homework assignments often pose formidable challenges, requiring a blend of theoretical knowledge and practical application. Whether you're grappling with hypothesis testing, ANOVA, or time series analysis, the journey towards mastery can be fraught with obstacles. However, with the right tools and guidance, conquering these challenges becomes a feasible endeavor.

Empowering Your Learning Journey

At Statistics Homework Helper, our mission is to empower learners of all levels to excel in statistics. Leveraging the capabilities of XLMINER, we're committed to providing tailored solutions to your homework assignments, ensuring a seamless learning experience. Whether you're seeking assistance with data analysis, interpretation, or report generation, our team of experts is equipped with the skills and expertise to elevate your academic performance.

Practical Applications of XLMINER

Let's delve into the practical applications of XLMINER, illuminating how this versatile tool can revolutionize your approach to statistics homework.

Descriptive Statistics: XLMINER simplifies the process of summarizing and interpreting data, enabling you to generate descriptive statistics with ease. Whether you're calculating measures of central tendency, dispersion, or correlation, XLMINER streamlines the analysis process, allowing you to focus on deriving meaningful insights.

Predictive Modeling: Harnessing the power of machine learning algorithms, XLMINER facilitates predictive modeling across diverse domains. From linear regression to decision trees, you can leverage advanced techniques to forecast trends, identify patterns, and make informed decisions based on data-driven insights.

Data Visualization: Effective data visualization is paramount in conveying complex statistical concepts in a comprehensible manner. With XLMINER's robust visualization capabilities, you can create insightful charts, graphs, and dashboards to communicate your findings with clarity and precision.

Unlock Your Potential with Statistics Homework Helper

Embark on a transformative journey towards statistical proficiency with the assistance of Statistics Homework Helper. Our team of seasoned professionals is dedicated to providing personalized support tailored to your unique needs and requirements. Whether you're struggling with basic concepts or tackling advanced analytical challenges, we're here to guide you every step of the way.

Conclusion

In conclusion, the journey towards mastering statistics can be both challenging and rewarding. By harnessing the power of XLMINER and leveraging the expertise of Statistics Homework Helper, you can overcome obstacles with confidence and unlock your full potential in the realm of statistical analysis. Embrace the possibilities, embark on your learning journey today, and watch as the realm of statistics unfolds before your eyes.

[lumenore-datalytics]

How Healthcare Analytics and Visualization Are Transforming Patient Care

Healthcare teams are doing incredible work—but imagine how much more they could do with the right insights at their fingertips.

At Lumenore, we just published a new blog on how healthcare analytics and data visualization are changing the game—from reducing patient wait times to helping doctors catch issues before they become critical.

Here’s what’s inside:

✔️ Real-time dashboards for better decisions ✔️ Predictive alerts that help reduce readmissions ✔️ Easy-to-use tools that work for everyone—not just data teams ✔️ Stories from real health systems using Lumenore to make a difference

We’re proud to be building solutions that help care teams do what they do best—care.

📖 Give it a read:

How Healthcare Analytics and Visualization Are Transforming Patient Care

[lucubratemagazine]

When will artificial intelligence replace human interaction and empathy?

Remember that AI cannot replace human interaction and empathy...

Lucubrate Magazine, November 17th, 2023 Artificial intelligence (AI) is no longer just a future concept. It has become an integral part of our daily lives. AI is there, making our lives easier and more convenient from when we wake up to when we go to bed. In the same way, AI has become an integral part of daily life in the school. Conversely, we must remember that AI cannot replace human…

[healthcaretechnologynews]

Predictive Modelling in Pharma Sales: Charting Market Trends with Data Analytics

The pharmaceutical industry has undergone significant transformations in recent years, with advancements in data analytics playing a pivotal role in decision-making processes. Predictive modelling, a branch of data analytics, has emerged as a powerful tool for pharmaceutical sales teams to anticipate market trends and make informed decisions. In this blog, we will delve into the world of predictive modelling in pharmaceutical sales, exploring its applications, benefits, and how it can revolutionize the industry's approach to forecasting and marketing.

Read Full Blog Here: https://www.anervea.com/predictive-modeling-in-pharmaceutical-sales-anticipating-market-trends-with-data-analytics-2

Applications of Predictive Modelling in Pharmaceutical Sales

Demand Forecasting: Predictive models analyze past sales data, seasonality, market fluctuations, and other relevant factors to forecast the demand for pharmaceutical products.

Customer Segmentation: Pharmaceutical companies can use predictive modelling to segment their customer base based on various criteria, such as demographics, purchasing behavior, and prescription patterns

Market Trend Analysis: By analyzing historical market data and external variables like healthcare policies, economic conditions, and public health trends, predictive models can help identify emerging market trends.

Competitor Analysis: By tracking competitors' product launches, pricing strategies, and market share fluctuations, pharmaceutical companies can gain a competitive edge and respond proactively.

Benefits of Predictive Modelling in Pharmaceutical Sales

Improved Sales and Revenue: Anticipating market trends and understanding customer behavior allows pharmaceutical companies to align their sales strategies with market demand, leading to increased sales and revenue.

Cost Reduction: Effective demand forecasting and inventory management can reduce wastage due to overstocking or shortages, leading to cost savings.

Enhanced Marketing ROI: Targeted marketing efforts, based on customer segmentation and trend analysis, result in a higher return on investment (ROI) for marketing campaigns.

Better Product Development: Predictive models can guide research and development efforts by identifying potential blockbuster drugs or areas of high demand.

Competitive Advantage: Pharmaceutical companies that leverage predictive modeling gain a competitive edge by being more agile and responsive to market changes.

Challenges and Considerations

While predictive modelling offers numerous benefits, it comes with its own set of challenges. Ensuring data accuracy, privacy compliance (such as GDPR and HIPAA), and model interpretability are crucial considerations. Additionally, ongoing model validation and refinement are necessary to maintain predictive accuracy as market conditions evolve.

Conclusion

Predictive modelling has revolutionized pharmaceutical sales by enabling companies to anticipate market trends, optimize sales strategies, and make data-driven decisions. In an industry driven by innovation and competition, harnessing the power of data analytics is essential for staying ahead. By embracing predictive modelling, pharmaceutical companies can enhance their sales and marketing efforts, reduce costs, and ultimately improve patient access to vital medications, thus advancing the industry as a whole. As technology continues to evolve, the role of predictive modelling in pharmaceutical sales will only become more critical in shaping the future of healthcare.

Visit our website now: https://www.anervea.com/

[simplyforensic]

Persistence of Touch DNA: Understanding the Stability and Implications

Touch DNA, the microscopic genetic material left behind by human touch on surfaces has emerged as a valuable source of evidence in forensic investigations. Recent advancements in DNA analysis techniques have enabled forensic scientists to extract and analyze DNA from touch DNA samples, opening up new possibilities for solving crimes. However, touch DNA’s stability and persistence have remained a…

[tvntheatre]

A general, Post-Pacifist overdesigned (by obligation) Axis drawing. I don't know what to do with this, I just heard Axis' "birthday" may or may not be on this month and thunk (thought) it neato. And I'm out of storage so apolocheese, can't crop.

rant below, tumblr please don't shit on my parade.

I have also been reading a LOT of Axis fanfictions this past several existences and the three major doodles that included winged Axis are all inspired off of moments in the fanfics in question. I'll bring it up in a sec, just let me look them up again

Of course, Falling Out of Trouble is why Axis was drawn with wings at the last second, evidenced by Bird Mechanic in the side drawing.

thanks to you, is another. The primary reason why this was made AND is the major draw of ideas.

And there's RESURFACED MEMORY, which is the minor reason why Axis' eye is cracked.

please tell me if i got the links wrong i suck at thi

Why is it cracked in this context? Good question. I had only made this version of Axis a few hours ago but I had many ideas during making him.

I'm definitely gonna neglect this version of Axis I made the fuck up<3

[colesabi]

RE6R prediction:

ok so hear me out… this-

plus this…

Capcom - make it happen. If you f this up, I’m rioting and burning it all down.

[minweber]

Magos Theodora Oikodomos, the Heiress of the Condemned Forge, Artisan Majoris and a Cawl Minor.