#nonlinear structural analysis

[michaeldemanega]

Nonlinear Engineering

Lineare Systeme sind Systeme, bei denen ein lineares, also proportionelles Verhältnis zwischen Eingangsreiz und Ausgangsreiz besteht. Die Nichtlinearität ist im Bereich des Bauwesens eine geometrische Nichtlinearität, welche von der Unverformbarkeit abrückt, sowie eine physikalische Nichtlinearität, die die Stoff- und Materialgesetze betrifft. Oftmals kommen beide Nichtlinearitäten zugleich zur…

[therentyoupay]

TRYP thoughts on characterization

jack frost // elsa | characterization considerations

PART 4 | THEIR ICE POWERS (OR AU-EQUIVALENT) & SHARED CONNECTIONS:

ground rules + intro

overview: the tl;dr of my personality + dialogue choices

deep dive: characterization, personality, + identity

shared ice powers (or AU-equivalent) + shared connections

questions/points to consider as you write

PART 4 | THEIR ICE POWERS (OR AU-EQUIVALENT) & SHARED CONNECTIONS:

(STAY TUNED FOR AN IN-DEPTH ANALYSIS OF ❄️❄️❄️SNOWFLAKES, FROST, ICE, AND STRUCTURES❄️❄️❄️ [and more???] BY CALLI, @callimara, FORTHCOMING, but also initially explored in mtyk.)

you didn’t think i was going to not mention it, right?? but super honestly i almost forgot to write this section. 😂 i’ve written so many AUs, this point is not even always at the front of my mind anymore, but let’s dig in. ❄️❄️❄️

❄️❄️❄️❄️ this is usually how it goes, i feel, more or less:

a double-edged sword: 

elsa’s ice powers are an integral part of her identity, initially a source of both immense strength and bone-deep, soul-wrenching fear. throughout much of her life, she has been taught to suppress her abilities (her emotions), leading her to associate her powers with danger and potential harm, especially to those she cares about. this repression has caused her to view her powers as something to be controlled, rather than fully embraced, leaving her constantly on guard, always fearing that she might lose control and hurt someone.

jack’s unique perspective and transformative influence:

however, as elsa’s relationship with jack develops (in whatever universe), she begins to see her powers in a new light. jack, who also wields ice magic—and is OFTEN the FIRST and ONLY other ice-wielder that she knows of—understands the intricacies and challenges that come with such abilities. unlike others who have only seen elsa’s powers as something to fear or control, jack sees them as a natural extension of who she is—a gift, rather than a curse. 

this perspective is transformative for elsa. it allows her to slowly let go (👀) of the fear that has always accompanied her powers and begin to explore their full potential without the constant need to hold back.

liberation, trust, journey toward embracing her powers:

with jack by her side, elsa experiences—depending on the story—a newfound sense of security in using her powers. she no longer feels the need to restrain herself out of fear of causing harm (unless…….. it is angst. 👀). jack’s presence and his own mastery of ice magic provide her with a sense of understanding and acceptance that she has rarely experienced. 

unique bond and understanding:

this shared connection through their powers creates a bond between them that goes beyond the physical—it’s in some ways a sort of spiritual trust that they are safe with each other (unless it’s angst, at the center readers, you remember the scene 👀), that they can fully be themselves without judgment or fear. (unless, of course: ANGST.)

eventually, gradually: empowerment and healing:

if/when this freedom does occur, for elsa, this new sense of safety and security in using her powers is a liberating experience. she begins to experiment with her powers in ways she never dared before, discovering new facets of her abilities and pushing the boundaries of what she thought was possible. the trust she feels in jack’s presence allows her to let go of her inhibitions and truly explore the extent of her magic. 

it’s a process of self-discovery that is both empowering and healing, as she starts to view her powers not as something to be feared, but as an essential part of who she is.

cycle of growth and healing, strengthening their connection:

this newfound freedom also deepens her connection with jack, thereby pushing them into a cycle of growing and healing (unless: angst 😂, in which case, this cycle may still happen but SLOWLY and PAINFULLY, in stops and starts, in nonlinear patterns).

mutual understanding, support, and solidarity:

as they face challenges together, elsa finds that she can rely on jack not just as a partner in battle (or daily life, depending on the story, which could also be a “battle” 😂), but as someone who understands the unique burden of their shared powers (and, in the cases where they don’t share powers… their shared sense of Sacrifice 🥹 and their love for their younger sisters). this mutual understanding creates a sense of solidarity and, occasionally, camaraderie between them, making both of them feel less alone in their struggles.

growing confidence, deeper connection, shared purposes:

ultimately, elsa’s ice powers, once a source of fear and repression, become a symbol of her growing confidence and self-acceptance. through her relationship with jack, she learns to embrace her abilities without fear, finding strength in the trust and security they share. this transformation allows elsa to not only harness her powers more fully but also to connect with jack on a deeper level, creating a bond that is rooted in mutual understanding, trust, and a shared sense of purpose.

but what about the specific differences in features of their ice/snow/frost/powers?

for specific ice MAGIC features, patterns, habits, and geometrical dendrites (🤣 thanks calli), please note that @callimara will be dropping a DETAILED comparison analysis one day, so stay tuned!

in the meantime, you can see my quick takes in the woman in white, or in ch. 4 of more than you know. 

[dipperdesperado]

notes towards changing the world

i feel like the main way to change the world is to not think on a global scale. one of the coolest things about complex systems is how agents in the system, focusing on the local level and following simple rules while cooperating with other agents can create global behaviors. a vision of solarpunk could be the attempt at imagining and prefiguring a world that embraces complexity for the betterment of all of the agents in the system.

so, to change things, thankfully we don't have to figure out how to bring about worldwide upheaval. if we can change the local milieu, that can reverberate outward. this is not to say that it's easy, but it's possible. this is also not to say to never think globally. it's a dialogue between spheres of distance. you have to have a revolution internally to have a revolution interpersonally to have a revolution locally to have a revolution regionally and so on and so on.

now, we can finally talk about how to change things. the general (relatively nonlinear) phases of this process are analyzing and trying to understand the world, creating visions, goals, and values from that analysis, creating strategies to reach those goals, and enacting tactics birthed out of those strategies.

Analysis

Identify the social issue: figure out the bad thing. try to understand the bad thing using complexity theory/systems theory. in other words, understand what the system is, what are the agents/elements are within that system (people, institutions, environments, etc), where the resources flow, what feedback loops exist, and what the vulnerabilities are.

Set your goals: The goal is to promote systemic change that addresses the root causes of the social issue, rather than simply addressing individual symptoms or surface-level manifestations. The guiding principles may include holistic thinking and interdisciplinary collaboration.

Visions, Goals, and Values

Visions: think about what you want the world to look like. put on your utopian lenses. what are the dreams that you have for the community (and maybe even the world at large)? Try to stretch your imagination as far as you can.

Goals: based on your visions, what are some more concrete targets to aim for? for ex., if one of the aspects of your visions is a city that is in harmony with nature, what are the material changes that need to happen?

Values: what are the guiding lights that will inform how you reach your goals? This can be thought of as the ethical/philosophical foundation of your praxis. these would also be points of unity for other folks. some good ones to include in this would be solidarity, free association, and a militant anti-oppressive orientation.

Strategy

Universal goals: make some specific goals for your specific context. ex. “everyone can access healthy, culturally relevant, and dietarily conscious food.”

Understand where the community is: how is the community, on average, doing at reaching this goal?

Understand the population: figure out the population segments in the given community, and discover their “distance” from the goal. take not of those differences.

explore the system: look at and make visible the structures that support or impede each group or community from achieving the universal goal.

create bespoke responses: develop and implement targeted strategies for each group to reach the universal goal. using our food example, rich folks in a community might not need any help reaching this goal, while unhoused neighbors might be furthest from this goal. this allows us to create responses that help each group in the ways that benefit them the most.

dismantle the system: while providing an equitable response to the issue through your bespoke responses, work towards removing the root causes of the issue. equity is good, but we want to move toward the abolition of oppressive systems and enter a space of liberation.

Tactics

tactics are the actions that we take, informed by our strategy, which stems from our goals, visions, and values. to organize specific tactics, a useful mental model is to think of “encircling” your goal. essentially, we want to try to reach our goal using multiple tactics in parallel (or relative parallel), attacking issues from multiple angles. I think that it’s ideal to have as many tactics deployed as is reasonable, but I like the idea of 2-4 campaigns of tactics going at the same time. I also like these tactics to be across the spectrum of commitment, from very simple, low-lift, and “reformist”, to more high-lift and “radical” or “revolutionary”. So, while our goal is to destroy the rule of authority (and all of its associated ills), actions that directly do that are the most extreme, and most folks don't start off ready for that level of commitment. To build that capacity in practice, this strategy could look like:

One campaign starts by making appeals to authority and getting more radical from there (foot in the door). Some examples are basically all of the things that liberals say to make changes: calling representatives, signing petitions, writing letters, op-eds, case studies, and things like that. Realistically, if the goal is widespread change, these things won’t really be able to cut it. You can’t vote a revolution in. So, we can use the more liberal actions as a gateway into more revolutionary actions. We also HAVE TO make sure we’re transparent about this so that people retain their ability to freely choose how they engage. In this campaign, we’re marching towards more and more radical action.

One or Two campaigns that start with civil disobedience (foot in the face). Some tactics here could be boycotts, strikes, sit-ins, and the like. Similar to the above action, we march towards more radical stuff but start from a more antagonistic position.

One campaign that starts with more directly confronting authority (door in the face). This campaign would start at as far of a place that the people would want to go. This would escalate into more radical action from a very radical place.

The purpose of formatting our tactics in this way is to be fighting on multiple fronts in ways that makes the movement harder to clamp down on, while having folks move in a more radical direction.

Note that all of these tactics are informed by what comes before them. Like everything else, it is useful to think of this as a dialogue. We don’t want tactics with no conception of a strategy and such, and by doing our tactics, we should and can reframe our strategy.

[genericruleroftheflies]

Ditty Diego War Chant | Head (1968)

Analysis… kinda

I actually want to just praise it.

One think I really want to highlight is how well in my opinion the war chant early on frames such a nonlinear film. It’s let’s you know what you’re in for and CLEARLY states that this “nonsense” is on purpose. And purposeful.

And as someone who has studied film, I do think that giving the audience a little heads up on a nonlinear film is smart. And I think the chant does it effectively. I’m going to quote some lines I love. But sometimes the hardest thing about watching nonlinear film is that as the audience you are confused obviously but also unsure if the filmmakers meant to do it this way. War Chant clears that right up.

“We hope you like our story, although there isn’t one” Boom done. There. Mike just let you know enjoy this story that doesn’t exist. Early on in Head you are told flat out this isn’t a traditional story. In fact there’s no “story” at all.

“…and give it 1, 2, 3! / but it may come 3, 2, 1, 2 or jump from 9 to 5” here the chant tells you that the film or “story” is not going to be linear or told in order. It might jump around. So be ready.

“And when you see the end in sight the beginning may arrive!” I love this part it’s just so fun that the film tells you early on that it is going to be a circular “story”. The ending of the film is going to be the beginning of the film. He just told you and I’m pretty sure it’s shown as well on the little screen.

“For those who look for meanings in form as they do fact—” Mike calling out me here… but also I would maybe suggest that this line is saying looking at the form/structure of the film… but now I feel a little embarrassed lol. “—we might tell you one thing, but we’d only take it back” see this like I’m not totally sure how to interpret but it’s so fun I love it! I could guess that maybe it’s just saying the film might suggest one thing and then contradict itself. But also (and I’m being conspiratorial here) it could be about how anyone involved with the film might say it means one thing and then they might take it back later… which I think people have done.

“Not back like in a box back” is such fun set up/foreshadowing to how the monkees are going to be put back in the box a couple times throughout this film.

And then of course the reoccurring theme of the war chant is about how the Monkees are a manufactured band, they are denying that. This “band” was created to make money, so let’s have fun with our nonsense! Yay— *real life Vietnam gun shot footage*

I don’t feels this way, I actually feel the opposite, but I could see some film people saying it spoon feeds the film’s themes, messages, etc. Personally I think because it’s such a confusing film for some people letting them know the situation is nice. It works well imo.

[world-cinema-research]

The Green Knight (2021)

by Jacob Christopher

The movie I chose to rewatch for week one of class is The Green Knight (2021) directed by David Lowrey, a cinematic readaptation of the 14th century poem of the Arthurian tale Sir Gawain and the Green Knight. The story follows that of the nephew of King Arthur, Sir Gawain played by Dev Patel, and his quest for honor in order to please his lineage. Accepting the challenge of the mysterious Green Knight on Christmas day to strike him with a blow, on the condition that Gawain must seek him out a year later to receive an equal blow in return. The film explores themes of honor, destiny, and mortality as Gawain embarks on a perilous journey filled with encounters that test his character and courage. It's praised for its stunning visuals, atmospheric storytelling, and thought-provoking exploration of the human condition. The impact left on me was more so the curiosity left from the end of the film, left wondering what it was that captivated my attention. 

The indie film is a financial success considering the budget of 15 million compared to the grossed 20 million worldwide and 3 million in the home market. Arguments can be made looking at the box office opening night of just under 7 million isn't considered an impressive metric. However looking at the historical context, for an indie movie to be released during the covid-19 pandemic many restrictions for public safety were still implemented. To add inclination for people to view the film the production studio known as A24 acts as a seal of theatrical approval for audiences. Despite being based on a pre-established text, the obscurity of it’s age doesn’t exactly tempt the audience with it’s popularity. What differentiates The Green Knight from prior medieval films is the usage of it’s psychological horror and abstract cinematography to add a sense of surrealism to it’s story. A24 before releasing the film released a video on YouTube titled as An Oral History of the Green Knight to engage audiences as well as act as a guide for background and historical context for the film. The critical response for the film has remained the same with the film’s popularity peaking during it’s theater release.

Only adding to it’s strength, the unconventionality of the film drew in many to witness and marvel at the almost confusing film.Rather than focusing solely on chivalry and heroism, the film delves into themes of mortality, honor, and the nature of destiny, offering a more introspective take on the Arthurian legend. Combined with director David Lowery's visual storytelling, blending elements of fantasy and realism, creating a dreamlike atmosphere that sets it apart from typical historical dramas. Taking a non-linear approach to storytelling, often meandering through dream sequences and nonlinear timelines for a muddied lens of fantasy and realism it challenges that of conventional narrative structures. Instead embracing ambiguity and symbolism, leaving much more to be open to interpretation. The meaning of certain events and characters is left intentionally vague to invitie viewers to engage in deeper analysis. This movie has it all, etching itself into the A24 vault as arguably one of their most fascinating films. 

My first viewing of this film I was puzzled and amused by the creativity and storytelling capabilities expressed. I watched half of it, rewinded it, went to bed and watched the rest in the morning with my roommate with bowls of cereal in hand. By the ending scene I was left in awe of how well written it was, and when the credits had started rolling the two of us sat in silence for what felt like minutes. Before my rewatching of the film once more I had done more research on the intricaciesof the film and the Arthurian poem of Sir Gawian and the Green Knight. With one of the bigger questions of the purpose and symbolism of the Green Knight himself being answered by one of the commonly agreed on interpretaions. Being the Green Knight is an embodiment of Nature and the cycle of life itself, and the actions Gawian takes againt the Green Knight exposes his human nature and all of it’s blemishes. The poem is a much more cheery tale of bravery and the heroics of what is expected of a knight.

The quote I chose to embody the film is by Essel, a woman from the lower class of society as well as the lover of Gawain, asking Gawain before his venture to find the Green Knight and seek his fate, “Why Greatness? Why is goodness not enough?” It is within this dialogue we find the message of the film. What is honor if not an excuse to run from your own humanity?

[banananutmilk]

The Green Knight Film Essay(2021)

by Jacob Christopher

The movie I chose to rewatch for week one of class is The Green Knight (2021) directed by David Lowrey, a cinematic readaptation of the 14th century poem of the Arthurian tale Sir Gawain and the Green Knight. The story follows that of the nephew of King Arthur, Sir Gawain played by Dev Patel, and his quest for honor in order to please his lineage. Accepting the challenge of the mysterious Green Knight on Christmas day to strike him with a blow, on the condition that Gawain must seek him out a year later to receive an equal blow in return. The film explores themes of honor, destiny, and mortality as Gawain embarks on a perilous journey filled with encounters that test his character and courage. It's praised for its stunning visuals, atmospheric storytelling, and thought-provoking exploration of the human condition. The impact left on me was more so the curiosity left from the end of the film, left wondering what it was that captivated my attention. 

The indie film is a financial success considering the budget of 15 million compared to the grossed 20 million worldwide and 3 million in the home market. Arguments can be made looking at the box office opening night of just under 7 million isn't considered an impressive metric. However looking at the historical context, for an indie movie to be released during the covid-19 pandemic many restrictions for public safety were still implemented. To add inclination for people to view the film the production studio known as A24 acts as a seal of theatrical approval for audiences. Despite being based on a pre-established text, the obscurity of it’s age doesn’t exactly tempt the audience with it’s popularity. What differentiates The Green Knight from prior medieval films is the usage of it’s psychological horror and abstract cinematography to add a sense of surrealism to it’s story. A24 before releasing the film released a video on YouTube titled as An Oral History of the Green Knight to engage audiences as well as act as a guide for background and historical context for the film. The critical response for the film has remained the same with the film’s popularity peaking during it’s theater release.

Link to Trends

Only adding to it’s strength, the unconventionality of the film drew in many to witness and marvel at the almost confusing film. Rather than focusing solely on chivalry and heroism, the film delves into themes of mortality, honor, and the nature of destiny, offering a more introspective take on the Arthurian legend. Combined with director David Lowery's visual storytelling, blending elements of fantasy and realism, creating a dreamlike atmosphere that sets it apart from typical historical dramas. Taking a non-linear approach to storytelling, often meandering through dream sequences and nonlinear timelines for a muddied lens of fantasy and realism it challenges that of conventional narrative structures. Instead embracing ambiguity and symbolism, leaving much more to be open to interpretation. The meaning of certain events and characters is left intentionally vague to invitie viewers to engage in deeper analysis. This movie has it all, etching itself into the A24 vault as arguably one of their most fascinating films. 

My first viewing of this film I was puzzled and amused by the creativity and storytelling capabilities expressed. I watched half of it, rewinded it, went to bed and watched the rest in the morning with my roommate with bowls of cereal in hand. By the ending scene I was left in awe of how well written it was, and when the credits had started rolling the two of us sat in silence for what felt like minutes. Before my rewatching of the film once more I had done more research on the intricaciesof the film and the Arthurian poem of Sir Gawian and the Green Knight. With one of the bigger questions of the purpose and symbolism of the Green Knight himself being answered by one of the commonly agreed on interpretaions. Being the Green Knight is an embodiment of Nature and the cycle of life itself, and the actions Gawian takes againt the Green Knight exposes his human nature and all of it’s blemishes. The poem is a much more cheery tale of bravery and the heroics of what is expected of a knight.

The quote I chose to embody the film is by Essel, a woman from the lower class of society as well as the lover of Gawain, asking Gawain before his venture to find the Green Knight and seek his fate, “Why Greatness? Why is goodness not enough?” It is within this dialogue we find the message of the film. What is honor if not an excuse to run from your own humanity?

[and-then-there-were-n0ne]

Scholars have long known that youthful marriage is a strong predictor of divorce. For instance, someone who marries at 25 is over 50 percent less likely to get divorced than is someone who weds at age 20. Most youthful couples simply do not have the maturity, coping skills, and social support it takes to make marriage work. In the face of routine marital problems, teens and young twenty-somethings lack the wherewithal necessary for happy resolutions.

What about age at marriage past the twenties? Delaying marriage from the teens until the early twenties produces the largest declines in divorce risk, for totally understandable reasons: we’re all changing a lot more from year to year as teenagers than when we’re in our twenties or thirties. [...] Still, earlier scholarship found that the risk of divorce continued to decline past that point, albeit at a milder rate (as the figure using 1995 data shows below). And why wouldn’t it? Couples in their thirties are more mature and usually have a sounder economic foundation. Conversely, youthful marriage is correlated with lower educational attainment, which compounds divorce risk no matter how old you are.

But what was true for decades no longer seems to be the case. I analyzed data collected between 2006 and 2010 from the National Survey of Family Growth (NSFG). The trick is to use statistical methods that permit nonlinear relationships to emerge (click here for more information on these methods). My data analysis shows that prior to age 32 or so, each additional year of age at marriage reduces the odds of divorce by 11 percent. However, after that the odds of divorce increase by 5 percent per year. The change in slopes is statistically significant. The graph below shows what the relationship between age at marriage and divorce looks like now.

This is a big change. To the best of my knowledge, it’s only recently that thirty-something marriage started to incur a higher divorce risk. It appears to be a trend that’s gradually developed over the past twenty years: a study based on 2002 data observed that the divorce risk for people who married in their thirties was flattening out, rather than continuing to decline through that decade of life as it previously had.

How can this change be explained? The first step to answering this question is to see if the pattern persists after adjusting for a variety of social and demographic differences between survey respondents. Indeed it does: thirty-something marriage continues to yield a higher divorce rate even after controlling for respondents’ sex, race, family structure of origin, age at the time of the survey, education, religious tradition, religious attendance, and sexual history, as well as the size of the metropolitan area that they live in. Note that the NSFG is a cross-sectional survey, which means that all respondent information was collected at a single point in time. Consequently the effects of education, religious attendance, and other impermanent demographic attributes on marital stability may not be causal. Results involving these variables should therefore be treated as provisional.

None of these variables seem to have much of an effect on the relationship between age at marriage and divorce risk. Additional tests revealed that the relation seems to function more or less the same for everyone: male or female, less or more educated, religious or irreligious, intact or nonintact family of origin, and limited versus extensive sexual history prior to marriage. For almost everyone, the late twenties seems to be the best time to tie the knot. [...]

My money is on a selection effect: the kinds of people who wait till their thirties to get married may be the kinds of people who aren’t predisposed toward doing well in their marriages. [...] More generally, perhaps people who marry later face a pool of potential spouses that has been winnowed down to exclude the individuals most predisposed to succeed at matrimony.

There are obvious strengths and weaknesses to this sort of explanation. [...] But we do know beyond a shadow of a doubt that people who marry in their thirties are now at greater risk of divorce than are people who wed in their late twenties. This is a new development. This finding changes the demographic landscape of divorce, and lends credence to scholars and pundits making the case for earlier marriage.

Note: Unadjusted estimates of divorce in NSFG in 1995 and 2006-2010.

[kreuzaderny]

Experimental Evidence of Quantum Radiation Reaction in Aligned Crystals

Radiation reaction is the influence of the electromagnetic field emitted by a charged particle on the dynamics of the particle itself. Here we report experimental radiation emission spectra from ultrarelativistic positrons in silicon in a regime where both quantum and radiation-reaction effects dominate the dynamics of the positrons. We found that each positron emits multiple photons with energy comparable to its own energy, revealing the importance of quantum photon recoil. Moreover, the shape of the emission spectra indicates that photon emissions occur in a nonlinear regime where positrons absorb several quanta from the crystal field. Our theoretical analysis shows that only a full quantum theory of radiation reaction is capable of explaining the experimental results, with radiation-reaction effects arising from the recoils undergone by the positrons during multiple photon emissions. This experiment is the first fundamental test of quantum electrodynamics in a new regime where the dynamics of charged particles is determined not only by the external electromagnetic fields but also by the radiation-field generated by the charges themselves. Future experiments carried out in the same line will be able to, in principle, also shed light on the fundamental question about the structure of the electromagnetic field close to elementary charges.    

[lairn]

Book 9/24: Pale Fire by Vladimir Nabokov Rating: 4/5

Pale Fire is framed as a poem written by the character, John Shade, and introduced and analyzed by the character, Dr. Kinbote. The poem, “Pale Fire,” is quite good and Kinbote is a self-obsessed and inadequate analyst more interested in telling his own story than examining the text. Reading this novel was a complicated experience. It took me over a month to finish, fighting through jet lag and final exams, but mostly fighting through the book itself.

From the first pages I knew I was going to struggle because the structure is a paradox of linear and wildly nonlinear. In conventional order, there is a forward to a poem, the poem, commentary, and an index. On the other hand, the author of the forward, Kinbote, recommends the reader skip the poem and go straight to the commentary, which itself is constantly imploring the reader to refer to other sections of the commentary, parts of the poem, and even the forward. The index is its own separate little maze pointing the reader from one entry to another.

I chose to read the book front-to-back, but still found myself jumping backwards to reread the poem’s cantos 3 or 4 times, and yes, even the forward at Kinbote’s pleading suggestion. By the time I finished I had a much clearer idea of the relationship Kinbote has to art, academia, and John Shade, but I still felt muddled and overwhelmed. There is a lot of vocabulary I was unfamiliar with and even more references to other works I had never heard of in both the poem and the analysis, but Kinbote does not properly research the poem, so I had to constantly second guess his statements and do my own research. I could never be sure if what Kinbote says is accurate, a lie, or an ignorant misstatement. I cannot even be sure the poem is presented as written by John Shade. On one level, the novel is about deception and misrepresentation, and I felt the wool pulled firmly over my eyes.

Basically, the book is exhausting but interesting. I was so relieved to be finished with it, but also immediately wished to buy a personal copy I could mark up with my own red-string-conspiracy-board-style notes.

[minadevivarma]

CSIR NET Mathematical Science Syllabus: A Detailed Overview 

The CSIR NET Mathematical Science syllabus is designed to test the mathematical knowledge and problem-solving skills of candidates aspiring for Junior Research Fellowship (JRF) and Lectureship in mathematical sciences. This syllabus can be divided into three main segments: Analysis, Algebra, and Linear Algebra. In addition, there are extra topics such as Ordinary and Partial Differential Equations, Numerical Analysis, and Topology. All these topics can be further explained in detail so that candidates can get a proper schedule for preparation: 

1. Analysis 

The real and complex analysis are included in this part. Sequences and series, convergence, continuity, differentiability, and even more Rieman integration, functions of several variables, and complex functions are covered in this part. Candidates should know contour integration, Cauchy's theorem, and residue calculus apart from the many solved advanced mathematical problems. 

2. Algebra 

Algebra covers the subjects of linear and abstract algebra. Group theory, ring theory, field theory, vector spaces, and modules are some of the key topics. Eigenvalues and eigenvectors, Cayley-Hamilton theorem, and canonical forms all fall under the scope. Being competent in algebra is crucial as it serves as the basis for several more advanced areas of study. 

3. Linear Algebra 

This area is critical in linear algebra and includes matrices, determinants, systems of linear equations, and diagonalization. Candidates should know the spectral theorem, inner product spaces, and bilinear and quadratic forms. In this section, both theoretical knowledge and practical problem-solving abilities are tested. 

4. Ordinary and Partial Differential Equations (ODEs and PDEs) 

This section focuses on the formation and solutions of ODEs and PDEs, including first-order equations, higher-order linear differential equations, and boundary value problems. Methods like separation of variables, Fourier and Laplace transforms, and characteristics for solving PDEs are important. 

5. Numerical Analysis 

Numerical Analysis involves topics such as numerical solutions of equations, interpolation, numerical integration, and finite differences. Candidates should be familiar with numerical methods for solving linear and nonlinear equations, as well as error analysis. 

6. Topology 

Topology tests the understanding of basic concepts like open and closed sets, compactness, connectedness, and metric spaces. The section also includes continuous functions and homeomorphisms. Topology is essential for understanding advanced mathematical structures. 

Exam Pattern 

The CSIR NET Mathematical Science paper is divided into three parts: 

Part A: General Aptitude questions. 

Part B: Subject-specific questions of a basic level. 

Part C: Advanced-level subject questions requiring detailed reasoning and problem-solving. 

Preparation Tips 

Focus on understanding fundamental concepts before moving to advanced topics. 

Solve previous years' question papers to identify important topics and patterns. 

Use standard reference books like Rudin’s Principles of Mathematical Analysis and Herstein’s Topics in Algebra. 

Regularly practice numerical problems and theoretical proofs. 

The CSIR NET Mathematical Science syllabus is vast but manageable with a well-planned strategy. Understanding the syllabus and focusing on high-weightage topics can significantly boost a candidate’s chances of success. 

[extremeloading11]

Enhancing Precision with Nonlinear Structural Analysis Tools

Engineers and architects seeking to elevate their structural designs can greatly benefit from nonlinear structural analysis tools. These advanced software solutions, such as those offered by Extreme Loading, allow professionals to simulate complex behaviors of materials under various load conditions, including dynamic and seismic events. By accurately predicting potential structural failures or deformations, engineers can optimize their designs for safety and efficiency. The Extreme Loading Structural Analysis Software stands out by providing intuitive interfaces and detailed visualizations, ensuring that even the most challenging projects are approached with confidence. Visit their website to learn more.

[s3dadesignfirm]

A Comprehensive Overview on Advanced Structural Analysis and Design

Structural analysis and design play a pivotal role in engineering, ensuring that buildings, bridges, and other structures are safe, functional, and efficient. Advanced structural analysis and design involve sophisticated techniques and tools to evaluate and enhance the performance of complex structures, meeting modern engineering challenges.

What is Advanced Structural Analysis?

Advanced structural analysis refers to the process of evaluating a structure's ability to withstand various forces and conditions using detailed mathematical models, simulations, and computational methods. It goes beyond traditional approaches by incorporating advanced materials, dynamic loading conditions, and innovative technologies.

Key components include:

Finite Element Analysis (FEA): Breaking down a structure into smaller elements to simulate and analyze its behavior under specific loads.

Dynamic Analysis: Assessing how structures respond to time-varying forces like earthquakes or wind.

Nonlinear Analysis: Evaluating structural performance when material properties or geometries change significantly under load.

Principles of Advanced Structural Design

Structural design involves creating safe and efficient structures that meet specified criteria and aesthetic requirements. Advanced structural design integrates state-of-the-art techniques to optimize performance and resource use.

Key principles include:

Material Efficiency: Utilizing advanced materials like high-strength concrete, composites, and alloys.

Sustainability: Incorporating eco-friendly design practices to reduce environmental impact.

Resilience: Designing structures to withstand natural disasters, heavy usage, and other unforeseen challenges.

Cost-Effectiveness: Balancing innovative solutions with budget constraints.

Tools and Software for Advanced Structural Analysis and Design

The rise of technology has revolutionized structural engineering. Here are some key tools used in advanced analysis and design:

ANSYS and SAP2000: Powerful tools for structural analysis and simulation.

ETABS: Ideal for designing multi-story buildings.

Revit Structure: Combines structural design with BIM (Building Information Modeling).

STAAD.Pro: Widely used for structural analysis across different types of projects.

Applications of Advanced Structural Analysis and Design

The versatility of advanced structural techniques makes them indispensable in various domains, including:

Skyscrapers and High-Rises: Designing buildings that can endure wind loads and seismic activity.

Bridges: Analyzing load distribution and optimizing materials for durability.

Industrial Structures: Developing robust frameworks for factories, warehouses, and plants.

Seismic Retrofitting: Reinforcing older buildings to meet current seismic standards.

Challenges in Advanced Structural Analysis and Design

Despite technological advancements, structural engineers face several challenges:

Complex Modeling: Representing intricate designs accurately.

Dynamic Behavior Prediction: Anticipating the structure's real-world response to various forces.

Interdisciplinary Collaboration: Coordinating with architects, mechanical engineers, and other stakeholders.

Regulatory Compliance: Adhering to evolving building codes and standards.

Future Trends in Structural Engineering

The field of structural analysis and design continues to evolve, with trends like:

AI and Machine Learning: Enhancing predictive modeling and automating repetitive tasks.

Digital Twins: Creating real-time virtual models of structures for ongoing monitoring and optimization.

3D Printing: Enabling rapid prototyping and construction of complex designs.

Sustainability Focus: Prioritizing renewable materials and energy-efficient designs.

Conclusion

Advanced structural analysis and design is essential for modern engineering, ensuring structures are safe, resilient, and efficient. By leveraging cutting-edge tools, sustainable practices, and multidisciplinary collaboration, engineers create awe-inspiring solutions for today’s challenges. For comprehensive and innovative structural engineering services, S3DA Design Firm is your trusted partner. Their expertise in advanced structural analysis and design ensures projects meet the highest safety and efficiency standards.

[csiespl]

Bridge Structural Analysis Software | CSI Bridge Software

CSI Bridge Structural Analysis Software is used for various types of bridge analysis, including linear and nonlinear static and dynamic analyses. It user-friendly interface facilitates the creation of detailed models, while its robust capabilities support load rating evaluations and seismic analysis. Additionally, CSI Bridge software complies with international design codes, ensuring that engineers can meet regulatory requirements effectively.

[engineering-courses]

Welcome to Episode 2 of our "CAE Simulation using SolidWorks" series! 🎉 Today, we're diving into Static and Dynamic Analysis—exploring the differences, applications, and how to set up each in SolidWorks. Whether you're a beginner or looking to deepen your understanding of Finite Element Analysis (FEA), this tutorial will guide you through setting up simulations, understanding stress-strain relationships, and when to choose linear or nonlinear analysis. Let’s get hands-on with SolidWorks and learn how to capture realistic behavior in mechanical parts and structures! 🌟 📌Key Highlights: Basics of Static vs Dynamic Analysis ⚖️ Linear and Nonlinear Analysis explained 📈 Real-world example: Tension Test on a Plate with a Hole 🧩 Step-by-step setup for Static and Nonlinear Analysis 🔧 When to use Static vs Dynamic simulations 💡

[starseedfxofficial]

Reinforcement Learning Meets Forex: Decoding the AUD/CAD Dance Why AUD/CAD Deserves Your Attention When it comes to currency pairs, the Australian Dollar (AUD) and Canadian Dollar (CAD) don’t always get the limelight—but they should. Think of them as the “underdogs” of Forex, quietly holding their own with compelling opportunities for those in the know. Both currencies are commodity-driven, influenced by oil, metals, and economic policies from Ottawa to Canberra. With reinforcement learning (RL) models entering the trading scene, there’s never been a better time to uncover the hidden patterns driving AUD/CAD. But here’s the twist: the same models predicting traffic lights for autonomous cars are now optimizing Forex strategies. This crossover between technology and trading is where magic—and profits—happen. Reinforcement Learning in Forex: The AUD/CAD Edge Reinforcement learning—a subset of machine learning where algorithms learn through trial and error—is revolutionizing trading. Unlike traditional algorithms that rely on fixed rules, RL models adapt dynamically to new data, making them ideal for AUD/CAD, a pair sensitive to commodity fluctuations and geopolitical shifts. Why AUD/CAD and RL Models Make a Perfect Match: - Volatility with Structure: AUD/CAD’s moderate volatility provides an ideal testing ground for RL’s adaptive strategies. - Commodity Dependencies: RL models excel at deciphering nonlinear relationships, such as oil’s effect on CAD or iron ore’s influence on AUD. - Niche Expertise: Unlike heavily traded pairs like EUR/USD, AUD/CAD offers unique opportunities for traders equipped with cutting-edge tools. The Hidden Patterns That Drive AUD/CAD Trading AUD/CAD without advanced analytics is like navigating the Outback without a map—possible, but why risk it? Here’s where RL models shine: - Detecting Supply and Demand Zones Traditional indicators like moving averages often lag. RL models, however, can identify supply and demand zones in real-time, adapting to shifts in sentiment or unexpected news. Imagine catching a trend reversal right before it happens—yes, that’s possible. - Forecasting with Multi-Layered Inputs While human traders might monitor oil prices and central bank statements separately, RL models digest these inputs simultaneously, uncovering connections most traders overlook. - Adaptive Stop-Loss Strategies Forget static stop-loss orders. RL algorithms adjust them dynamically based on market conditions, ensuring you’re not stopped out prematurely during a temporary spike. How to Apply RL Models to AUD/CAD Trading Ready to embrace the future of trading? Here’s how to integrate RL models into your AUD/CAD strategies: - Choose the Right Framework Popular RL libraries like TensorFlow and PyTorch offer tools for custom models. Start with frameworks designed for time-series forecasting, such as Deep Q-Networks (DQN). - Feed the Data Beast RL models thrive on data. Include historical AUD/CAD prices, oil benchmarks, central bank meeting notes, and sentiment analysis. The more diverse the data, the better the model. - Train, Test, Tweak Training an RL model is like raising a puppy—it requires patience, consistency, and the occasional treat. Test your model in simulated environments before going live. Trust me, you’ll thank yourself later. - Use Automation Wisely Automation is a double-edged sword. While RL models can execute trades autonomously, always monitor performance and intervene when necessary. Case Study: How an RL Model Nailed an AUD/CAD Trade In late 2023, an RL-powered trading bot analyzed AUD/CAD during an oil price spike. The model recognized an overreaction in CAD strength and anticipated a correction. While most traders waited for confirmation, the bot entered early, capitalizing on a swift 50-pip gain. The secret? It factored in not just oil prices but also Australia’s unexpectedly robust labor market report. Common Myths About RL Models in Forex (And the Truth) Myth 1: “RL Models Are Too Complex for Individual Traders” Truth: While the math might seem daunting, pre-built tools and communities like StarseedFX make RL accessible to retail traders. Myth 2: “AI Trading Is a Quick Path to Wealth” Truth: RL models enhance decision-making but don’t replace sound risk management. Think of them as a co-pilot, not the captain. Myth 3: “RL Models Are Only for Major Pairs” Truth: The adaptability of RL shines in niche pairs like AUD/CAD, where unique patterns often go unnoticed by traditional methods. Your Next Steps to Master AUD/CAD with RL Now that you’re equipped with the basics, take your trading to the next level: - Stay Updated: Bookmark StarseedFX’s Forex News Today for real-time insights. - Deepen Your Knowledge: Enroll in a free Forex course to learn advanced methodologies. - Join a Community: Network with like-minded traders in the StarseedFX community. - Plan Smartly: Download a free trading plan tailored to your goals. - Track Progress: Use a trading journal to refine your strategies. The Future of Forex Trading Is Here Integrating reinforcement learning models into AUD/CAD trading isn’t just a trend—it’s the future. By leveraging these cutting-edge tools, you’re not only staying ahead of the competition but also tapping into opportunities most traders will never see. So, why trade in the dark when AI can light the way? Start small, experiment boldly, and watch your trading game transform. And remember: trading isn’t just about making money. It’s about mastering a craft. So, go ahead, explore RL’s potential, and let the AUD/CAD pair become your ultimate training ground. —————– Image Credits: Cover image at the top is AI-generated Read the full article

[abhinav3045]

Test a Basic Linear Reggression

Simple linear regression is used to estimate the relationship between two quantitative variables. You can use simple linear regression when you want to know:

How strong the relationship is between two variables (e.g., the relationship between rainfall and soil erosion).

The value of the dependent variable at a certain value of the independent variable (e.g., the amount of soil erosion at a certain level of rainfall).

Regression models describe the relationship between variables by fitting a line to the observed data. Linear regression models use a straight line, while logistic and nonlinear regression models use a curved line. Regression allows you to estimate how a dependent variable changes as the independent variable(s) change. Simple linear regression example .You are a social researcher interested in the relationship between income and happiness. You survey 500 people whose incomes range from 15k to 75k and ask them to rank their happiness on a scale from 1 to 10.

Your independent variable (income) and dependent variable (happiness) are both quantitative, so you can do a regression analysis to see if there is a linear relationship between them.

If you have more than one independent variable, use multiple linear regression instead.

Table of contents

Assumptions of simple linear regression

How to perform a simple linear regression

Interpreting the results

Presenting the results

Can you predict values outside the range of your data?

Other interesting articles

Frequently asked questions about simple linear regression

Assumptions of simple linear regression

Simple linear regression is a parametric test, meaning that it makes certain assumptions about the data. These assumptions are:

Homogeneity of variance (homoscedasticity): the size of the error in our prediction doesn’t change significantly across the values of the independent variable.

Independence of observations: the observations in the dataset were collected using statistically valid sampling methods, and there are no hidden relationships among observations.

Normality: The data follows a normal distribution.

Linear regression makes one additional assumption:

The relationship between the independent and dependent variable is linear: the line of best fit through the data points is a straight line (rather than a curve or some sort of grouping factor).

If your data do not meet the assumptions of homoscedasticity or normality, you may be able to use a nonparametric test instead, such as the Spearman rank test. Example: Data that doesn’t meet the assumptions.You think there is a linear relationship between cured meat consumption and the incidence of colorectal cancer in the U.S. However, you find that much more data has been collected at high rates of meat consumption than at low rates of meat consumption, with the result that there is much more variation in the estimate of cancer rates at the low range than at the high range. Because the data violate the assumption of homoscedasticity, it doesn’t work for regression, but you perform a Spearman rank test instead.

If your data violate the assumption of independence of observations (e.g., if observations are repeated over time), you may be able to perform a linear mixed-effects model that accounts for the additional structure in the data.

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How to perform a simple linear regression

Simple linear regression formula

The formula for a simple linear regression is:

y is the predicted value of the dependent variable (y) for any given value of the independent variable (x).

B0 is the intercept, the predicted value of y when the x is 0.

B1 is the regression coefficient – how much we expect y to change as x increases.

x is the independent variable ( the variable we expect is influencing y).

e is the error of the estimate, or how much variation there is in our estimate of the regression coefficient.

Linear regression finds the line of best fit line through your data by searching for the regression coefficient (B1) that minimizes the total error (e) of the model.

While you can perform a linear regression by hand, this is a tedious process, so most people use statistical programs to help them quickly analyze the data.

Simple linear regression in R

R is a free, powerful, and widely-used statistical program. Download the dataset to try it yourself using our income and happiness example.

Load the income.data dataset into your R environment, and then run the following command to generate a linear model describing the relationship between income and happiness: R code for simple linear regressionincome.happiness.lm <- lm(happiness ~ income, data = income.data)

This code takes the data you have collected data = income.data and calculates the effect that the independent variable income has on the dependent variable happiness using the equation for the linear model: lm().

To learn more, follow our full step-by-step guide to linear regression in R.

Interpreting the results

To view the results of the model, you can use the summary() function in R:summary(income.happiness.lm)

This function takes the most important parameters from the linear model and puts them into a table, which looks like this:

This output table first repeats the formula that was used to generate the results (‘Call’), then summarizes the model residuals (‘Residuals’), which give an idea of how well the model fits the real data.

Next is the ‘Coefficients’ table. The first row gives the estimates of the y-intercept, and the second row gives the regression coefficient of the model.

Row 1 of the table is labeled (Intercept). This is the y-intercept of the regression equation, with a value of 0.20. You can plug this into your regression equation if you want to predict happiness values across the range of income that you have observed:happiness = 0.20 + 0.71*income ± 0.018

The next row in the ‘Coefficients’ table is income. This is the row that describes the estimated effect of income on reported happiness:

The Estimate column is the estimated effect, also called the regression coefficient or r2 value. The number in the table (0.713) tells us that for every one unit increase in income (where one unit of income = 10,000) there is a corresponding 0.71-unit increase in reported happiness (where happiness is a scale of 1 to 10).

The Std. Error column displays the standard error of the estimate. This number shows how much variation there is in our estimate of the relationship between income and happiness.

The t value column displays the test statistic. Unless you specify otherwise, the test statistic used in linear regression is the t value from a two-sided t test. The larger the test statistic, the less likely it is that our results occurred by chance.

The Pr(>| t |) column shows the p value. This number tells us how likely we are to see the estimated effect of income on happiness if the null hypothesis of no effect were true.

Because the p value is so low (p < 0.001), we can reject the null hypothesis and conclude that income has a statistically significant effect on happiness.

The last three lines of the model summary are statistics about the model as a whole. The most important thing to notice here is the p value of the model. Here it is significant (p < 0.001), which means that this model is a good fit for the observed data.

Presenting the results

When reporting your results, include the estimated effect (i.e. the regression coefficient), standard error of the estimate, and the p value. You should also interpret your numbers to make it clear to your readers what your regression coefficient means: We found a significant relationship (p < 0.001) between income and happiness (R2 = 0.71 ± 0.018), with a 0.71-unit increase in reported happiness for every 10,000 increase in income.

It can also be helpful to include a graph with your results. For a simple linear regression, you can simply plot the observations on the x and y axis and then include the regression line and regression function:

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Can you predict values outside the range of your data?

No! We often say that regression models can be used to predict the value of the dependent variable at certain values of the independent variable. However, this is only true for the range of values where we have actually measured the response.

We can use our income and happiness regression analysis as an example. Between 15,000 and 75,000, we found an r2 of 0.73 ± 0.0193. But what if we did a second survey of people making between 75,000 and 150,000?

The r2 for the relationship between income and happiness is now 0.21, or a 0.21-unit increase in reported happiness for every 10,000 increase in income. While the relationship is still statistically significant (p<0.001), the slope is much smaller than before.

What if we hadn’t measured this group, and instead extrapolated the line from the 15–75k incomes to the 70–150k incomes?

You can see that if we simply extrapolated from the 15–75k income data, we would overestimate the happiness of people in the 75–150k income range.

If we instead fit a curve to the data, it seems to fit the actual pattern much better.

It looks as though happiness actually levels off at higher incomes, so we can’t use the same regression line we calculated from our lower-income data to predict happiness at higher levels of income.

Even when you see a strong pattern in your data, you can’t know for certain whether that pattern continues beyond the range of values you have actually measured. Therefore, it’s important to avoid extrapolating beyond what the data actually tell you.