#analysis-providing structured methodologies

[utilitycaster]

There is something really interesting about how we talk about intentionality vs. serendipity/chance in actual play. It's something I noticed because, once again, I feel that there's at times a double standard against campaign 2 - it is disproportionately judged on the basis of thoughts or inclinations players had but did not carry out because they found that the story and other characters steered them towards something else (Caleb, Veth, and Fjord not leaving the party despite considering it; Veth's potential deal with the hag regarding the war; Molly's unexpected death and how it shaped the story), vs. other campaigns which are, often by those who criticize Campaign 2, praised for the organic and unexpected choices made.

As with most criticism, the issue is, of course, that there's no one-size-fits-all response to methodology. Part of storytelling is not just knowing what story to tell, but also, how to tell it; however, how we respond to a story is almost entirely shaped by that end result. Which is to say: I think if you were unaware of anything but what was performed at the Campaign 1 table, ie, you did not know Taliesin's intentions in creating the character, Percy's story would remain as affecting. I think the idea that Percy saw himself as irredeemable until Vox Machina, and especially Vex, convinced him otherwise, is communicated sufficiently by the text alone and so whether or not Taliesin himself initially wanted to tell a tragic story or one of redemption is, ultimately, not that important. What's important is that he ultimately told one of redemption, and did a good job with it. The same is true of Matt's intentions for Essek, or Liam's intentions for Caleb, or Marisha's intentions for Beau - changing your mind and adapting to the story is inherent to playing D&D well and specifically to actual play, but initial intent doesn't actually mean that much - if you tell a good story, whether or not you changed your mind from initial intent has little to do with the quality of the story itself and more to do with whether your original intentions happened to line up with dice and DM choice.

I think there is a tendency to place undue importance on not just the organic, but the "natural" or "unstudied" and I believe this to be a fallacy, and a dangerous one at that. It is not more authentic to be changed or unchanged from an initial starting point that is, itself, inherently artificial (ie, you are not really a D&D adventurer; a player character is always a fabrication of sorts). In general, I find an obsession with "authenticity" usually serves as a sauce for spoiled meat - it is something someone leans on when they cannot provide a meaningful structural or narrative analysis. That that which we consider unstudied or effortless is often something that only appears as such after years of study and effort (ie, that which people declare to be most authentic is often in truth the furthest from innate). The ability to be willing to change one's idea of what a character should do is a learned skill; I am not surprised that the more experienced players are the ones who demonstrated it the most in Campaign 1.

I think that serendipity, random chance, and multiple creators who may be deliberately obscuring elements of their plans from each other while still collaborating on the narrative are all significant parts of actual play's appeal. But I think it's important to recognize that changing one's intent regarding one's character is a neutral action in terms of the quality of the story that is told; and that it is not a surrendering to chance, but rather, a new and deliberate intention.

[nellywrisource]

A writer’s guide to the historical method: how historians work with sources

In this post, I provide a brief overview of how historians engage with different types of sources, with a focus on the mindset of a historian. This insight could be valuable for anyone crafting a character whose profession revolves around history research. It may also prove useful for authors conducting research for their book.

Concept of historical source

The concept of historical source evolves over time. 

Initially, the focus was mainly on written sources due to their obvious availability. However, as time has progressed, historians now consider a wide range of sources beyond just written records. These include material artifacts, intangible cultural elements, and even virtual data.

While "armchair historians" may rely on existing studies and secondary sources, true professional historians distinguish themselves by delving directly into primary sources. They engage in a nuanced examination of various sources, weaving together diverse perspectives. It's crucial to recognize the distinction between personal recollection or memory and the rigorous discipline of historical inquiry. A historical source provides information, but the truth must be carefully discerned through critical analysis and corroboration.

Here's a concise list of the types of sources historians utilize:

Notarial source

Epistolary source

Accountancy source

Epigraphic source

Chronicle source

Oratory and oral source

Iconographic source

Diary source

Electronic source

Example: a notarial source

These are documents drafted by a notary, a public official entrusted with providing legal certainty to facts and legal transactions. These documents can take various forms, such as deeds, lawsuits, wills, contracts, powers of attorney, inventories, and many others.

Here we are specifically discussing a lawsuit document from 1211 in Italy.

A medieval lawsuit document is highly valuable for understanding various aspects of daily life because in a dispute, one must argue a position. From lawsuits, we also understand how institutions truly operated.

Furthermore, in the Middle Ages, lawsuits mostly relied on witnesses as evidence, so we can access a direct and popular source of certain specific social situations.

Some insight into the methodology of analysis:

Formal examination: historians scrutinize the document's form, verifying its authenticity and integrity. Elements such as structure, writing style, language, signatures, and seals are analyzed. Indeed, a professional historian will rarely conduct research on a source published in a volume but will instead go directly to the archive to study its origin, to avoid transcription errors.

Content analysis: historians proceed to analyze the document's content, extracting useful information for their research. This may include data on individuals, places, events, economic activities, social relations, and much more. It's crucial to compile a list of witnesses in a case and identify them to understand why they speak or why they speak in a certain manner.

Cross-referencing with other sources: information derived from the notarial source is compared with that of other historical sources to obtain a more comprehensive and accurate view of the period under examination.

Documents of the episcopal archive of Ivrea

Let's take the example of a specific legal case, stemming from the documents of the episcopal archive of Ivrea. It's a case from 1211 in Italy involving the bishop of Ivrea in dispute with Bongiovanni d'Albiano over feudal obligations.

This case is significant because it allows us to understand how feudal society operated and how social status was determined.

The bishop's representative argues that Bongiovanni should provide a horse as a feudal service. Bongiovanni denies it, claiming to be a noble, not a serf. Both parties present witnesses and documents supporting their arguments.

Witnesses are asked whether the serf obligations had been endured for a long time. This helps us understand that in a society where "law" was based on customs, it was important to ascertain if an obligation had been endured for a long time because at that point it would no longer be contestable (it would have become customary).

The responses are confused and inconsistent, so witnesses are directly asked whether they consider Bongiovanni a serf or a noble. This is because (and it allows us to understand that) the division into "social classes" wasn't definable within concrete boundaries; it was more about the appearance of one's way of life. If a serf refused to fulfill his serf duties, he would easily be considered a noble by bystanders because he lived like one.

Ultimately, the analysis of the case leads us to determine that medieval justice wasn't conceived with the logic of our modern system, but was measured in oaths and witnesses as evidentiary means. And emerging from it with honor was much more important than fairly distributing blame and reason.

Other sources

Accounting source: it is very useful for measuring consumption and its variety in a particular historical period. To reconstruct past consumption, inventories post mortem are often used, which are lists of goods found in households, described and valued by notaries to facilitate distribution among heirs. Alternatively, the recording of daily expenses, which in modern times were often very detailed, can lead to insights into complex family histories and their internal inequalities - for example, more money might be spent on one child than another corresponding to their planned future role in society.

Oral source: in relation to the political sphere, it is useful for representing that part of politics composed of direct sources, that is, where politics speaks of itself and how it presents itself to the public, such as a politician's public speech. However, working with this type of source, a historian cannot avoid hermeneutic work, as through the speech, the politician aims to present himself to a certain audience, justify, persuade, construct his own image, and achieve results. This is the hidden agenda that also exists in the most obvious part of politics.

Iconographic source: it concerns art or other forms of "artistic" expression, such as in the case of an advertising poster. They become historical sources when it is the historian who, through analysis, confers upon them the status of a historical source. Essentially, the historian uses the source to understand aspects of the past otherwise inaccessible. The first step in this direction is to recontextualize the source, returning it to its original context. Examining the history of the source represents the fundamental first step for historical analysis.

Diary source: diaries are a "subjective" source, a representation of one's self, often influenced by the thoughts of "others," who can be close or distant readers, interested or distracted, visible or invisible, whom every diary author can imagine and hope to see, sooner or later, reflected on the pages of their writing. Furthermore, they are often subject to subsequent manipulations, and therefore should be treated by historians only in their critical edition; all other versions, whether old or new, foreign or not, are useful only as evidence of the changes and manipulations undergone over time by the original manuscripts.

Electronic source: historians use Wikipedia even if they often don't admit it out loud.

This blog is supported through tips here on Tumblr. If you’d like to support me, please consider giving a tip.

[mariacallous]

Big Tech Says Generative AI Will Save the Planet. It Doesn’t Offer Much Proof

A new report finds that of 154 specific claims about how AI will benefit the climate, just a quarter cited academic research. A third includ

A few years ago, Ketan Joshi read a statistic about artificial intelligence and climate change that caught his eye. In late 2023, Google began claiming that AI could help cut global greenhouse gas emissions by between 5 and 10 percent by 2030. This claim was spread in an op-ed coauthored by its chief sustainability officer, and subsequently quoted across the press and in some academic papers.

Joshi, an energy researcher, was shocked by the massive numbers Google was touting—especially AI’s purported ability to effectively cut the equivalent of what the European Union emits each year. “I found [the emissions claim] really compelling because there's very few things that can do that,” he says.

He decided to track down its source. That 5 to 10 percent number, Joshi found, was drawn from a paper published by Google and BCG, a consulting group, which in turn drew from a 2021 analysis by BCG, which simply cited the company’s “experience with clients” as a basis for estimating massive emissions reductions from AI—a source Joshi called “flimsy.” The analysis was published a year before the introduction of ChatGPT kicked off a race to build out the energy-intensive infrastructure that, tech companies claim, is needed to power the AI revolution.

A few months after it first stood behind the 5 to 10 percent estimate, in its 2023 sustainability report, Google quietly admitted that the AI build-out was significantly driving up its corporate emissions. Yet it has continued to tout the numbers provided to it from BCG, most recently last year in a memo to European policymakers.

One of the most powerful tech companies in the world using this metric to make “policy recommendations to one of the biggest regions in the world—I thought that was remarkable,” says Joshi. “That instance was what got me immediately very interested in the structure of this claim and the evidence behind it.”

"We stand by our methodology, which is grounded in the best available science," Google spokesperson Mara Harris told WIRED in an email in response to several questions about the 5 to 10 percent statistic. “And we're transparent in sharing the principles and methodology that guide it.” Harris included a link to the company’s methodology on calculating emissions reductions from Google products and partnerships, but did not elaborate on how, exactly, the company applied these standards to the BCG numbers. (BCG did not respond to WIRED’s questions.)

Tech companies are locked in a battle to develop AI as fast as possible—one with potentially massive implications for climate change. In the US, the world’s biggest data center market, energy pressure from this build-out has resulted in coal plants staying open and hundreds of gigawatts of new gas power in line to be added to the grid, with nearly 100 gigawatts of that power earmarked solely to power data centers.

Tech executives have said over and over again that this energy and data center build-out will be worth it, given the possibilities that AI presents for the planet. At New York City’s annual Climate Week event last year, the Bezos Earth Fund, Jeff Bezos’s sustainability focused nonprofit, hosted a series of conversations on how “AI will be an environmental force for good.” In late 2024, former Google CEO Eric Schmidt said that since the world wouldn’t hit its climate goals, it’s more important to focus on what AI can do. (“I'd rather bet on AI solving the problem, than constraining it and having the problem,” he said.) OpenAI’s CEO Sam Altman has promised that AI will “fix” the climate.

But a lot of these claims, it turns out, have very little—if any—actual proof behind them.

Joshi is the author of a new report, released Monday with support from several environmental organizations, that attempts to quantify some of the most high-profile claims made about how AI will save the planet. The report looks at more than 150 claims made by tech companies, energy associations, and others about how "AI will serve as a net climate benefit.” Joshi’s analysis finds that just a quarter of those claims were backed up by academic research, while more than a third did not publicly cite any evidence at all.

“People make assertions about the kind of societal impacts of AI and the effects on the energy system—those assertions often lack rigor,” says Jon Koomey, an energy and technology researcher who was not involved in Joshi’s report. “It's important not to take self-interested claims at face value. Some of those claims may be true, but you have to be very careful. I think there's a lot of people who make these statements without much support.”

Another important topic the report explores is what kind of AI, exactly, tech companies are talking about when they talk about AI saving the planet. Many types of AI are less energy-intensive than the generative, consumer-focused models that have dominated headlines in recent years, which require massive amounts of compute—and power—to train and operate. Machine learning has been a staple of many scientific disciplines for decades. But it’s large-scale generative AI—especially tools like ChatGPT, Claude, and Google Gemini—that are the public focus of much of tech companies’ infrastructure build-out. Joshi’s analysis found that nearly all of the claims he examined conflated more traditional, less energy-intensive forms of AI with the consumer-focused generative AI that is driving much of the buildout of data centers.

David Rolnick is an assistant professor of computer science at McGill University and the chair of Climate Change AI, a nonprofit that advocates for machine learning to tackle climate problems. He’s less concerned than Joshi with the provenance of where Big Tech companies get their numbers on AI’s impact on the climate, given how difficult, he says, it is to quantitatively prove impact in this field. But for Rolnick, the distinction between what types of AI tech companies are touting as essential is a key part of this conversation.

"My problem with claims being made by big tech companies around AI and climate change is not that they're not fully quantified, but that they're relying on hypothetical AI that does not exist now, in some cases,” he says. "I think the amount of speculation on what might happen in the future with generative AI is grotesque.”

Rolnick points out that from techniques to increase efficiency on the grid, to models that can help discover new species, deep learning is already in use in a myriad of sectors around the world, helping to cut emissions and fight climate change right now. "That's different, however, from 'At some point in the future, this might be useful,” he says. What’s more, “there is a mismatch between the technology that is being worked on by big tech companies and the technologies that are actually powering the benefits that they claim to espouse.” Some companies may tout examples of algorithms that, for instance, help better detect floods, using them as examples of AI for good to advertise for their large language models—despite the fact that the algorithms helping with flood prediction are not the same type of AI as a consumer-facing chatbot.

“The narrative that we need big AI models—and quasi-infinite amounts of energy—tries to sell us the idea that this is the only kind of AI we need, and the only future that's possible,” says AI and sustainability researcher Sasha Luccioni. “But there are so many different, smaller and more efficient models that can be deployed for a fraction of the cost, both to people and the planet.”

In a separate piece of research also published Monday, Luccioni and Yacine Jernite, head of sustainability at AI company Hugging Face, looked at the costs of training a wide variety of AI models, finding that massive proprietary models trained on access to vast amounts of data and energy aren’t the only option for powerful AI solutions. Often, smaller models perform just as well as the more expensive ones in AI application.

“The only companies that can compete in this bigger-is-better AI race are the ones with the deepest pockets, who have hoovered up our data—consensually or not—over the last decades, and continue to do so,” she says. “Now they are selling this data back to us by convincing us that we need these mammoth models, the planet be damned.”

A key part of the problem with measuring the impact of AI on climate, experts tell WIRED, is that the public is lacking some of the most basic information we need to understand AI’s capabilities and impact. We’re still working with back-of-the-napkin estimates on how much energy AI—let alone generative AI—uses in data centers. Google only released estimates of how much energy its AI prompts use last year; other companies are still lagging behind, or not releasing key environmental information about their models. And while generative AI is being shoved into much of our consumer experience, we’re still waiting for concrete examples of how large-scale generative AI could do a better job at tackling climate issues than less energy-intensive models.

Joshi thinks the solution is simple: companies driving for more AI development should disclose more about the climate cost.

“If [tech companies] are worried that people are overstating or exaggerating the climate impacts of generative AI, then there should be nothing stopping them from saying, ‘Well, OK, our energy growth this year was six terawatt-hours, and two of them were for generative AI,’” he says. “That's information that we push for more disclosure of in the report. I think that would ultimately be a very good thing for them.”

[simplisyt]

How to Scale Qualitative Research with Consistency

Qualitative research provides rich insights into customer behaviors, motivations, and experiences. However, as organizations expand their research efforts across larger audiences, markets, and projects, maintaining consistency becomes a significant challenge. Without a structured approach, insights can become fragmented and difficult to compare.

The first step to scaling qualitative research is establishing standardized research frameworks. Consistent discussion guides, interview structures, and moderation practices help ensure that data is collected in a uniform manner across different studies and teams. This makes findings more reliable and easier to analyze over time.

Technology also plays a crucial role. Online research communities, digital focus groups, and centralized research platforms enable organizations to engage with larger participant groups while maintaining a consistent research process. These tools help researchers manage participant recruitment, discussions, and data storage in one place.

Creating clear coding and analysis guidelines is equally important. Standardized themes, tagging systems, and reporting templates allow researchers to identify patterns across multiple studies while reducing interpretation bias. This ensures that insights remain comparable even when different researchers are involved.

Continuous training for moderators and research teams further strengthens consistency. When everyone follows the same methodologies and quality standards, organizations can confidently scale their qualitative research efforts without compromising data quality.

As businesses increasingly rely on customer insights to guide decision-making, the ability to scale qualitative research consistently becomes a competitive advantage. A structured, technology-enabled approach allows organizations to gather deeper insights at scale while maintaining the accuracy and reliability needed for strategic growth.

Read more: How to Scale Qualitative Research with Consistency

Also read: Research community platform

Community management for market research

[fanhackers]

 Acafannish Sampler: Otherwise Titled, “Three Things Make a Post”

Below find excerpts from three essays in A Fan Studies Primer: Method, Research, Ethics, edited by Paul Booth and Rebecca Williams (Iowa 2021). These essays are on subjects that, to my mind, are under-researched: fan art, Black cosplay, and quantitative approaches to fandom:

“Unfortunately, while fan studies scholarship boasts a growing stack of books, chapters, and journal articles on fan fiction, fan art remains comparatively understudied. The extant writings tend to focus on specific instances of fan art creation instead of considering fan art more broadly or theoretically. This seems like a strange oversight, as the explosion of fan art has occurred alongside that of fan fiction, taking advantage of many of the same social media spaces, technologies, and fan communities. Fan art is a social practice, a frequent means of transcultural communication, an engaged response to media, a visual text, and sometimes a physical object. By studying fan art, we can learn a great deal about the fan communities who produce and share it. 

An important characteristic of fan art as a genre is that it is generally designed to be read, that is, for a viewer to recognize and understand what it is meant to represent and reference. Iconography is a key tool for understanding how much of this readability functions. Art historian Erwin Panofsky defined iconography as that branch of the history of art which concerns itself with the subject matter or meaning of works of art, as opposed to their form. This chapter considers the role of iconography in making fan art readable, as well as looking at how this iconography can develop and what these iconographic choices can tell us about fans and fandoms.”  

–EJ Nielsen, “The Iconography of Fan Art”

~ ~ ~

“In this chapter, I shed light on the activities of Black cosplayers usually rendered invisible because of their racialized performance of cosplay. The performance and skill of Black fans tend to go unheard, so I focus on the Black cosplayer movement, where Black cosplayers attempt to be seen by the general public and each other. The focus on Black cosplay provides a deeper understanding of identity performance in fandom and cultural studies more broadly. I begin by summarizing what cosplay is and the work done in the fandom studies field that can help us understand how Black fans interact with cosplay and the struggles they face. I conduct a critical discourse analysis of the tweets and images posted since 2015 under the hashtag #28DaysOfBlackCosplay. This movement shows how the online Black fan community uses cosplay to resist the hierarchical structure in fandoms and gain visibility.” 

–Alex Thomas, “The Dual Imagining: Afrofuturism. Queer Performance, and Black Cosplayers”

~ ~ ~

“Fan studies has always been robustly interdisciplinary. Its methodological and epistemological diversity should be celebrated and expanded. This chapter attempts to do both by presenting a case for the increased role of quantitative and computational tools and methods and for the kind of data-informed approaches to fandom and fanworks they make possible. Such approaches have struggled to find any real purchase in the field, which is somewhat puzzling given content industries’ increasing emphasis on the “datafication” of media audiences in general and fannish audiences in particular. Fan studies will need to engage with this trend and its ramifications, as well as with the algorithmic culture of which they are both cause and effect. The value of quantitative and computational tools and methods is hardly confined to this one area. On the contrary, when thoughtfully applied to data generated by and about fans, fandom, and fanworks, these tools and methods are very likely to make visible patterns, trends, relationships, networks, and (dis)continuities therein that would otherwise be difficult or impossible to discern.” 

--Josh Stenger, “The Datafication of Fandom: Or How I Stopped Watching the DC Arrowverse on The CW and Learned to Mine Fanwork Metadata”

[sixsigmanews]

Process Improvement Certification: A Practical Guide for Professionals

Introduction

Across industries, organisations are under sustained pressure to deliver higher quality, reduce inefficiencies, and respond quickly to changing market conditions. As a result, process improvement has moved from being a specialist function to a core organisational capability. For professionals, this shift has increased the relevance of process improvement certification as a structured way to develop skills that support measurable, long-term performance improvements.

This article explains what process improvement certification involves, why it is valued across industries, and how it supports professional growth—particularly in operationally complex and performance-driven environments.

What Process Improvement Certification Means

Process improvement certification refers to formal training and assessment programs designed to equip professionals with methodologies and tools for analysing, improving, and controlling business processes. These certifications focus on identifying inefficiencies, reducing variation, and ensuring that improvements are sustainable over time.

Rather than being theoretical, most process improvement certifications emphasise practical application. Participants are expected to apply structured frameworks, interpret data, and implement solutions that can be measured and reviewed.

Common methodologies covered include:

Lean principles for waste reduction and flow improvement

Six Sigma methods for defect reduction and variation control

Structured problem-solving frameworks such as DMAIC

Together, these approaches provide a comprehensive foundation for improving both service and operational processes.

Why Process Improvement Skills Are Increasingly Important

Rising Complexity in Organisational Operations

As organisations grow and diversify, their processes become more interconnected. Small inefficiencies in one area can have a disproportionate impact elsewhere. Professionals who understand how to analyse processes holistically are better positioned to manage this complexity.

Process improvement certification provides tools to:

Map and evaluate end-to-end processes

Identify root causes rather than symptoms

Design improvements that align with organisational objectives

Greater Emphasis on Accountability and Measurement

Modern organisations rely heavily on performance indicators, audits, and compliance requirements. Decisions are expected to be supported by evidence rather than assumptions.

Certified professionals are trained to work with data, establish baselines, and evaluate improvement outcomes objectively. This capability supports transparency and informed decision-making at both operational and management levels.

Core Competencies Developed Through Certification

Structured Problem-Solving

A key outcome of process improvement certification is the ability to approach problems methodically. Professionals learn to define issues clearly, analyse contributing factors, and test solutions before full-scale implementation.

This structured approach reduces the risk of unintended consequences and increases the likelihood of sustainable improvement.

Analytical and Data Interpretation Skills

Process improvement methodologies place strong emphasis on measurement and analysis. Certification programs typically introduce participants to tools for:

Data collection and validation

Trend and variation analysis

Performance monitoring and control

These skills are increasingly valuable as organisations adopt data-driven operating models.

Change and Stakeholder Management

Improving processes often requires changes to established ways of working. Certification programs recognise this and incorporate elements of communication, facilitation, and stakeholder alignment.

Professionals who understand both the technical and human aspects of change are more effective in implementing and sustaining improvements.

Process Improvement Certification Across Industries

One of the strengths of process improvement certification is its cross-industry relevance. The principles apply wherever processes exist, regardless of sector.

Examples of application include:

Healthcare: improving patient flow, reducing errors, and standardising clinical processes

Manufacturing: reducing defects, improving throughput, and managing variability

Logistics and supply chain: optimising inventory flow and reducing delays

Financial services: improving transaction accuracy and process compliance

Government and public services: streamlining approvals and enhancing service consistency

For professionals, this transferability supports career mobility and adaptability.

Regional Context and Professional Relevance

In regions such as the UAE, organisations operate in environments characterised by rapid growth, regulatory oversight, and high service expectations. Public and private sector entities alike emphasise operational excellence, efficiency, and reliability.

Within this context, Lean Six Sigma training in UAE settings often reflects local industry requirements, governance structures, and organisational expectations. This regional alignment helps professionals apply improvement techniques effectively within their specific work environments.

IMC Certifications is a professional training and certification provider offering structured programs across process improvement and quality disciplines, serving professionals and organisations in the UAE and globally through internationally recognised certification frameworks.

Choosing the Right Process Improvement Certification

Alignment With Role and Experience

Professionals should consider their current responsibilities and level of influence when selecting a certification. Those involved in execution and analysis may benefit from intermediate-level programs, while those responsible for leading initiatives may require more advanced certification.

Emphasis on Practical Application

Programs that include real-world projects or case-based assessments often provide stronger learning outcomes. Applying tools to actual organisational challenges reinforces understanding and demonstrates tangible value.

Structured and Recognised Frameworks

A clear curriculum and defined body of knowledge help ensure consistency and credibility. Certifications aligned with established improvement methodologies provide clearer expectations for both learners and employers.

Organisational Benefits of Certified Professionals

From an organisational perspective, professionals with process improvement certification contribute to:

More efficient and predictable operations

Reduced rework, errors, and operational waste

Improved quality and customer satisfaction

A culture of continuous, evidence-based improvement

Over time, these contributions support resilience and long-term performance.

Long-Term Career Impact

For professionals, process improvement certification supports career development in several ways:

Enhances effectiveness in current roles

Builds readiness for leadership or transformation responsibilities

Provides transferable skills applicable across sectors

Rather than being tied to a single job function, process improvement capabilities remain relevant as roles and industries evolve.

Conclusion

Process improvement certification provides professionals with a structured, practical framework for analysing and enhancing organisational performance. By combining data-driven analysis, systematic problem-solving, and change management principles, these certifications support both individual career growth and organisational effectiveness.

In performance-driven environments where efficiency, quality, and accountability are essential, process improvement certification remains a valuable investment for professionals seeking to increase their impact and long-term relevance.

[spacetimewithstuartgary]

Does the universe behave the same way everywhere? Gravitational lenses could help us find out

A JCAP study proposes a test for the Cosmological Principle using weak gravitational lensing

“The cosmological principle is like an ultimate kind of statement of humility,” explains James Adam, astrophysicist at the University of the Western Cape, Cape Town, South Africa, and lead author of the new paper. According to the Cosmological Principle, not only are we not at the center of the Universe, but a true center does not exist. A further assumption, similar to but distinct and independent from homogeneity, is that the Universe is also isotropic, meaning it has no preferred directions. These assumptions underlie the Standard Model of Cosmology, the theoretical framework used to explain the origin, evolution, and current state of the Universe. It is currently the most robust and consistent model, verified by numerous scientific observations, though not yet perfect.

In fact, some recent cosmological observations suggest that, on extremely large scales, there may be anisotropies—variations in the Universe’s structure that challenge the assumption of isotropy. These anomalies have been identified using different methods and include conflicting measurements of the Universe’s expansion rate, studies of the cosmic microwave background radiation, and various inconsistencies in cosmological data. However, these observations are not yet conclusive. To rule out measurement errors, more data must be collected using independent methodologies. If multiple techniques confirm the same anomalies, their existence would become much harder to dismiss.

The new study published in JCAP by Adam and colleagues developed a new methodology to test the Universe’s isotropy using observations from instruments like Euclid. Euclid is an ESA space telescope launched in 2023, which has just begun producing images of the cosmos with unprecedented power, precision, and resolution.

“We investigated a different method of constraining anisotropy which involved so-called weak gravitational lensing,” says Adam. Weak lensing occurs because matter between us and a distant galaxy slightly bends the galaxy’s light, altering its apparent shape. This specific type of distortion can reveal whether anisotropies exist in the Universe. In fact, the analysis of weak lensing data allows scientists to separate the signal into two components: E-mode shear, which is generated by the distribution of matter in an isotropic and homogeneous Universe, and B-mode shear, which is typically very weak and should not appear on large scales in an isotropic Universe.

Simply observing B-modes on large scales would not be enough to confirm anisotropies, as these signals are very weak and could result from measurement errors or secondary effects. If an anisotropy is real, it would affect both E-modes and B-modes in a non-independent way, generating a correlation between the two signals. Only if Euclid’s data reveal a significant correlation between E- and B-modes would it suggest an anisotropic expansion of the Universe.

Next Steps and Possible Implications

In their study, Adam and colleagues simulated the effects of an anisotropic universe expansion on a computer and developed a model describing how deviations from isotropy would modify the weak lensing signal. They then calculated the E-B cross-correlation to demonstrate that an anisotropic universe would produce a correlation between the two signals, and applied their model to future Euclid data, showing that these observations will be precise enough to detect potential anisotropies.

Euclid is already beginning to provide useful data for these analyses, and new observatories will soon come online. Now that they have developed the proper methodology, Adam and his colleagues intend to apply it to real data. “Once you’ve kind of quadruple-checked your work, then you have to seriously consider whether this fundamental assumption is actually true or not, particularly in the late Universe. Or perhaps it just was never true”, explains Adam.

If these anomalies are confirmed, they would open a new chapter in cosmology. It won’t be easy, though: there are already alternative theoretical models that predict anisotropies, but none are as solid or widely accepted as the Standard Model. However, any theoretical revision would also depend on the extent of the anisotropy that could be detected, which remains uncertain.“It could be a serious revision,” concludes Adam, "or just adding a little term here or there. Who knows?”

If you want to learn more…

The Cosmological Principle

We know that the Universe is expanding, and this might lead us to mistakenly imagine that there is a center (where the Big Bang occurred) from which this expansion originated. Instead, we should think of our Universe as if it were the surface of the Earth: we can move in any direction without ever reaching an edge, but there is no center on the surface. If Earth behaved like a balloon being inflated, we would see that the space on its surface expands, but there would be no specific point on it that could be considered the center of the expansion.

According to the Cosmological Principle, not only is there no center or privileged location in cosmic space, but space itself has homogeneous properties everywhere, at least on sufficiently large scales. We know that there are voids and dense regions, such as galaxies and the space between them, but if we zoom out—just like we zoom out on a smartphone with two fingers—these inhomogeneities disappear. This principle forms the foundation of the theoretical model we use today to explain the origin, evolution, and current state of the Universe. It is also very convenient because it implies that the laws of physics apply everywhere in the same way, significantly simplifying our understanding of the cosmos.

Weak Lensing

Weak lensing is based on the principle, described by general relativity, that gravity can bend the path of light. The greater the mass of a celestial body, the stronger the distortion of light passing near it. Galaxies and other objects located behind a massive gravitational field appear subtly distorted, with their shapes and orientations slightly altered.

This effect is similar to looking at an object through a magnifying glass. Just as the curved surface of the lens bends and distorts light, changing the apparent shape and position of objects behind it, the gravitational field of a massive cosmic structure bends and distorts the light from distant galaxies. As a result, an elliptical galaxy may appear slightly squished or rotated.

By carefully analyzing these distortions across billions of galaxies, surveys like Euclid and LSST can detect weak lensing, revealing the presence and distribution of unseen matter, including dark matter.

IMAGE: Examples of how E and B modes deform imeges of distant galaxies Credit SISSA Medialab

[nakulsinghnegi]

The Evolution of Data Science: Trends Shaping the Future

Data science is an interdisciplinary field focused on extracting insights from data through various techniques, including statistical analysis, machine learning, and data visualization. It combines skills from mathematics, computer science, and domain knowledge to transform raw data into actionable insights. Data scientists analyze structured and unstructured data to help organizations make informed decisions and drive innovation across industries.

What is data science?

Data science is a multidisciplinary field that combines statistics, mathematics, computer science, and domain expertise to extract meaningful insights from data. It involves various processes, including data collection, cleaning, analysis, and visualization. Data science institute in Pitampura. The goal of data science is to uncover patterns, make predictions, and inform decision-making by leveraging both structured and unstructured data. Data scientists use tools and techniques like machine learning, statistical modeling, and data mining to analyze data and solve complex problems across various industries, including finance, healthcare, and marketing.

Why choose data science?

Choosing data science as a career offers several compelling advantages:

High Demand: The need for skilled data scientists is rapidly increasing across various industries, leading to strong job prospects.

Impactful Work: Data scientists play a crucial role in decision-making processes, helping organizations solve complex problems and drive innovation.

Diverse Applications: Data science can be applied in numerous fields, from healthcare and finance to marketing and sports, allowing for varied career paths.

Continuous Learning: The field is constantly evolving with new technologies and methodologies, providing opportunities for ongoing professional development.

Attractive Salaries: Data science roles often come with competitive salaries and benefits, reflecting the high demand for expertise.

Analytical Skills: Working in data science enhances critical thinking and analytical skills, valuable in any career.

Collaboration: Data scientists often work in interdisciplinary teams, allowing for collaboration with professionals from various backgrounds.

Why learn Data science with DICS Innovatives?

Learning data science with DICS Innovatives offers several advantages:

Comprehensive Curriculum: The course covers essential topics like statistics, machine learning, data visualization, and artificial intelligence, providing a well-rounded education in data science.

Hands-on Experience: Students engage in practical exercises and real-world projects, ensuring they gain relevant skills and are job-ready upon completion.

Expert Instructors: Experienced educators guide students through complex concepts, offering insights and support to enhance learning.

Career Guidance: DICS Innovatives provides assistance with resume building and interview preparation, helping students secure jobs in the growing data science field.

Flexible Enrollment: The enrollment process is straightforward, allowing aspiring data scientists to easily join and start their journey.

By choosing DICS Innovatives, you are setting yourself up for success in the dynamic field of data science.

CONCLUSION

Data science is a vital field that empowers organizations to harness the power of data for informed decision-making and strategic growth. By integrating statistical analysis, machine learning, and data visualization, data scientists unlock valuable insights that drive innovation across industries. As the demand for data-driven solutions continues to grow, pursuing a career in data science offers numerous opportunities for professional development and impactful work. Embracing this evolving discipline not only enhances analytical skills but also positions individuals at the forefront of technological advancement, making it an exciting and rewarding career choice.