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زیر نظر دکتر عهدیه آقامحمدی
ارتباط با ما 👇
@tephd4
@mpp_phd
تبلیغات نداریم.
کانال اطلاع رسانی کارگاههای آموزشی👇
@mppphd
Created
Between 1 January 2016 and 31 October 2016— estimated from Telegram’s id allocation, not measured. How this range is calculated.
Technology — a classification, not a measurement. An on-box language model (Qwen3.6-35B-A3B-UD-Q6_K_XL, prompt version 1) read this channel’s own recent posts on 20 August 2026 and assigned it the closest of 31 fixed categories, at 83% confidence. This is a model’s judgement about what the channel is likely to be about, not a fact this register measured the way a subscriber count or a view count is measured — it can be revised on a later pass, and it carries no weight anywhere else on this page. How this classification works, and why it has no browse page of its own yet.
Growth
16 measurements spanning 15 days, net -266. Dots are measurements; the straight line between them is drawn to join them, not to claim we know the path taken in between — snapshots are recorded only when a count changes, so gaps mean “no change observed”, never “interpolated”. The vertical axis spans 78,694–79,040 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)
Subscribers
Change
21 Aug 2026, 09:14
78,734
-53
20 Aug 2026, 12:09
78,787
-14
19 Aug 2026, 15:04
78,801
-18
18 Aug 2026, 12:34
78,819
-10
17 Aug 2026, 10:27
78,829
-23
15 Aug 2026, 20:12
78,852
-5
14 Aug 2026, 11:48
78,857
-4
13 Aug 2026, 04:08
78,861
-9
12 Aug 2026, 00:42
78,870
-26
11 Aug 2026, 01:05
78,896
-19
10 Aug 2026, 01:51
78,915
-24
9 Aug 2026, 01:51
78,939
-24
7 Aug 2026, 22:31
78,963
-16
6 Aug 2026, 22:20
78,979
-21
6 Aug 2026, 01:34
79,000
no change
5 Aug 2026, 22:45
79,000
first reading
Engagement
23 posts held, back to 20 June 2026 — the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 35 pagesof Telegram’s post history, 20 posts per page.
ERR · 30 days
6.81%
avg views ÷ 78,734 subscribers
Avg views / post
5,360
13 posts measured
Reaction rate
0.389%
reactions ÷ views · ER floor
Posts in window
13
of 23 held
ERR is average views per post over the last 30 days divided by subscribers, the definition TGStat uses, so this figure is comparable with the one you will see elsewhere. It falls structurally as a channel grows: a high ERR on a small channel and a low one on a large channel describe reach mathematics, not quality. We publish the figure and the sample it came from and pass no verdict on it.
ER is defined industry-wide as (forwards + reactions + comments) ÷ views— note the denominator is views, not subscribers. Telegram’s public web preview carries views and reactions but not forward or comment counts, so the reaction rate above is the reactions term only and is therefore a floor: the true ER for this channel is higher by an amount we have not measured and will not estimate.
What these figures were computed from
Window
Rolling 30 days · latest post in window 22 August 2026
Posts held
23 (20 June 2026 – 22 August 2026)
Views total
69,660
Reactions total
271
Forwards / comments
not exposed by the public surface — not measured, not estimated
Readings taken
22 Aug 2026, 12:24 UTC
Views are a single reading per post, taken at the time above. A post published in the last day or two is still accumulating views, which pulls the 30-day average down slightly. That is a property of the standard definition rather than a fault in it, so we keep the definition rather than “correcting” the number into something nobody can reproduce.
Precision. Telegram publishes view counts on its public widget in short form — 8.12K, 3.7M — so any reading at or above 1,000 reaches us rounded to three significant figures, and only counts below 1,000 are exact. Averages and rates derived from them are shown to the same precision rather than to the unit: a figure like 3,701,250 would assert digits nobody measured.
Reaction counts are published per emoji and rounded the same way, so a total below 1,000 is exact and a larger one is a sum that may carry a rounded component from each emoji above 1,000. Because it is a sum, it does not look rounded — read a large reaction total as three significant figures per contributing emoji rather than as the figure it prints.
What this channel posts
Photos
≈1,050
Videos
≈615
Links
≈1,740
Lifetime counters from Telegram’s own channel header, read 22 August 2026 — not the date at the top of this page, which is when the subscriber count was last read. A count marked ≈ was rounded by Telegram before we ever saw it — t.me prints these counters in full below 1,000 and to three significant figures above, so ≈142,000 means somewhere between 141,500 and 142,499.
Video runtime
6m 39s
Average length
50s
Measured directly from 8 videos with a duration reading, out of the posts we hold for this channel — not this channel’s whole posting history, only the sample this register has actually read. An exact reading to the second, taken from the post itself rather than from Telegram’s own rounded chrome, so it carries no ≈ mark.
Reaction mix
590 reactions across 23 posts, in 8 distinct kinds. The most used accounts for 64.4% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
❤
380
64.4%
👍
119
20.2%
👌
36
6.10%
🙏
31
5.25%
💯
13
2.20%
💔
7
1.19%
🥰
3
0.508%
⚡
1
0.169%
No sentiment is inferred, and none should be read in. This table is ordered by count and by nothing else. Emoji do not carry stable meaning across languages or communities — 🙏 is thanks in one channel and mourning in another — so we publish which ones were pressed and how often, and pass no judgement on what an audience meant by them.
Precision. Telegram publishes reaction counts per emoji and short-forms each one — 4.34K, 1.2M — so any single kind at or above 1,000 reaches us at three significant figures, and only counts below 1,000 are exact. The shares above are ratios of those figures and carry the same error. This is also why the total here can differ slightly from a reaction total printed elsewhere on the page: both are sums of the same rounded parts, taken over samples with different edges.
Coverage. Reactions were read on 23 of the 23 sampled posts in this sample. Summed by Telegram’s own count on each post — not by adding up the per-emoji breakdown above — those same posts carry 590reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 23 most recent posts we hold, published 20 June 2026 to 22 August 2026, using the newest reading held for each. Telegram Stars are excluded: they are a payment, not a reaction, and they have their own section.
📚 انواع مرور ادبیات در پژوهش
🖋 کدام روش برای شما مناسب است؟
مرور ادبیات فقط یک روش ندارد بسته به هدف پژوهش، نوع سؤال، دامنه موضوع و نوع مطالعات، میتوان از روشهای مختلفی برای مرور منابع استفاده کرد.
در ادامه ۸ نوع مهم مرور ادبیات را ببینید 👇
🔹 ۱. مرور روایی (سنتی) | Narrative Review
📌 خلاصهای گسترده و توصیفی از ادبیات موجود درباره یک موضوع، بدون پروتکل جستوجوی سختگیرانه و قابل تکرار.
🎯 مناسب برای: زمینهسازی …
🔶 کارگاه جامع پروپوزال، پایاننامه و رسالهنویسی
👈 آموزش به دو روش سنتی و با هوش مصنوعی
اگر میخواهید پروپوزال، پایاننامه یا رسالهتان را اصولی و حرفهای پیش ببرید، این دوره مسیر پژوهش را از آموزش روشهای سنتی تا استفاده کاربردی از هوش مصنوعی پوشش میدهد. 🎓
🔹 ۲۰ ساعت آموزش آفلاین
آموزش پروپوزال، پایاننامه و رسالهنویسی با روشهای کمی و کیفی
🔹 ۳ ساعت آموزش آنلاین با هوش مصنوعی
آموزش کاربردی استفاده از هوش مصنوعی …
دو اقتصاددان به طور ریاضی اثبات کردند که هوش مصنوعی اقتصاد را نابود خواهد کرد.
محققان از دانشگاه وارتون و دانشگاه بوستون مقالهای وحشتناک با عنوان «تله اخراج هوش مصنوعی» منتشر کردند.
آنها نقشه پایانی اقتصادی گذار به هوش مصنوعی را ترسیم کردند، و این نقشه نقص مرگباری را در سرمایهداری رقابتی آشکار میکند.
وقتی شرکتی یک کارگر را با هوش مصنوعی جایگزین میکند، ۱۰۰٪ صرفهجویی در دستمزد را تصاحب میکند.
اما آن کارگر اخر…
✅رتبه بندی ابزارهای هوش مصنوعی بر اساس راحتی/سختی یادگیری آنها
🟢 ChatGPT — بسیار آسان
🟢 Gemini — بسیار آسان
🟢 Claude — آسان
🟢 Perplexity — آسان
🟢 GitHub Copilot — آسان
🔵 Cursor — متوسط
🔵 Lovable — متوسط
🔵 Replit — متوسط
🔵 v0 — متوسط
🔵 Bolt — متوسط
🟠 LangChain — سخت
🟠 LlamaIndex — سخت
🟠 CrewAI — سخت
🟠 AutoGen — سخت
🟠 RAG Systems — سخت
🔴 AI Agents — بسیار سخت
🔴 Model Fine Tuning — بسیار سخت
🔴 LLM Training — بسیار س…
📌چگونه متن نوشته شده توسط هوش مصنوعی را در عرض چند ثانیه انسانیتر کنیم؟
1. به https://humanizethis.io مراجعه کنید.
2. متن خود را کپی و پیست کنید یا فایل را آپلود کنید.
3. HumanizeThis متن شما را در عرض چند ثانیه انسانیتر میکند.
4. پس از این، آن را از طریق 8 سیستم تشخیص هوش مصنوعی بررسی میکند:
- TurnItIn
- GPTZero
- Originality AI
- CopyLeaks و غیره
5. این سیستمها تأیید میکنند که متن انسانی شده است.
6. شما هم…
💥کارگاه آنلاین هوش مصنوعی پیشرفته در پژوهش یک قدم فراتر از استفاده معمولی از AI
اگر در پژوهش از هوش مصنوعی استفاده میکنید، احتمالاً با ابزارهای مختلف آشنا شدهاید
اما مرحله بعد این است که یاد بگیرید چطور حرفهایتر از AI برای انجام کارهای پژوهشی استفاده کنید.
در کارگاه هوش مصنوعی پیشرفته در پژوهش، در یک جلسه فشرده و عملی با این مباحث آشنا میشویم:
🔹 Claude در پژوهش
استفاده حرفهای از Claude برای کارهای پژوهشی، تحل…
✔️هوش مصنوعی الزویر هم اومد! 🔥
الزویر، ناشر بزرگ علمی، حالا یک ابزار هوش مصنوعی برای پژوهشگرها معرفی کرده که فقط به مقالات خودش محدود نیست!
میتونه در کارهایی مثل:
🔹 جستوجوی ادبیات علمی
🔸 Deep Research
🔹تحلیل و مقایسه مقالات
🔸 پیدا کردن شکاف پژوهشی
🔹کمک به نوشتن و بررسی ادعاهای علمی
🔸پیدا کردن فرصتهای فاند
🔹 پیدا کردن پژوهشگران و همکاران احتمالی
⭕️حتما فیلم رو ببینید و با دیگران به اشتراک بذارید 14 روز رایگانه!…
📌نحوه افزودن skill ها به claude
این ویدیو به صورت گامبهگام روش افزودن اسکیلها به پلتفرم Claude را تشریح مینماید.
در این ویدیو، ده مورد از برترین مهارتها معرفی شدهاند که قادرند بهرهوری شما را افزایش داده و قابلیتهای Claude را در حوزههای بژوهش و.. . گسترش دهند.
✨✨برای مشاهده آموزشهای بیشتر کانال/ صفحه ما را دنبال نمایید.
#منابع_پارس_پژوهه
👁🗨 @tephd
🎓 مگادوره جامع «تربیت پژوهشگر برتر»
📌 این مگادوره شما را از شرکت در دهها دوره پراکنده و گرانقیمت بینیاز میکند! چون تمام ابزارهای سنتی و مدرن (هوش مصنوعی) را یکجا به شما آموزش داده و شمارو تبدیل به یک پژوهشگر حرفه ای می کند.
✨ ویژگیهای منحصربهفرد این مگادوره:
⏱️ ۷۰ ساعت آموزش جامع و تخصصی (به صورت آفلاین و همیشگی)
📜 ارائه مدرک معتبر (معادل ۷۰ ساعت آموزش آکادمیک جهت تقویت رزومه)
🤖 آموزش کاملاً کاربردی و مجهز ب…
📌دکتری چی هست و چی نیست؟
۱. دکتری به چه معناست؟ (✅)
۱.۱ - مسیری پژوهشی است، نه صرفاً گذراندن دروس.
۱.۲ - اشتیاق و علاقه، نسبت به هوش اهمیت بیشتری دارند.
۱.۳ - کنجکاوی، قدرتمندترین ابزار شماست.
۱.۴ - خلاقیت، راهحلی برای مسائل نوین است.
۱.۵ - نظم و انضباط، پیشرفت روزانه را تضمین میکند.
۱.۶ - پشتکار، در درازمدت به پیروزی منجر میشود.
۱.۷ - فرآیند یادگیری هرگز متوقف نمیگردد.
۱.۸ - استاد راهنما، یک مشورتدهنده است، ن…
چند وقت پیش گفتم دیگه پرامپت قدیمی شده همه لوپ مینویسن!
اما دنیای هوش مصنوعی با سرعتی باورنکردنی در حال تغییر است...
حالا اصطلاح جدیدی که زیاد درباره آن میشنوید Graph Engineering است.
در Graph Engineering، بهجای اینکه فقط یک مدل یا یک Agent همه کارها را انجام دهد، چند Agent تخصصی با هم همکاری میکنند، اطلاعات را بین هم ردوبدل میکنند و مرحلهبهمرحله یک مسئله را حل میکنند.
برای پژوهشگران، این یعنی آینده ابزارها…
✅ابزار Scinapse یک موتور جستجوی علمی مبتنی بر هوش مصنوعی است که دارای ویژگیهای استثنایی بوده و برای پژوهشگران بسیار سودمند میباشد.
🔶برجستهترین قابلیتهای آن به شرح زیر است:
🔹 ارائه فهرستی جامع از کلیه منابع و ارجاعات موجود در مقالات.
🔹 قابلیت «ثبت»: تاریخچه جستجوهای پیشین کاربر را نگهداری میکند.
https://scinapse.io
✨برای دیدن اموزشهای بیشتر کانال/ پیج ما را دنبال کنید.
#منابع_پارس_پژوهه
👁🗨 @tephd
👍19💯1
Showing the 12 most recent of 23 posts we hold for @tephd. View and reaction counts are the latest single reading for each post, not a live figure, and a recent post is still accumulating both. A view count marked ≈ was rounded by Telegram before we ever saw it — t.me prints views in full below 1,000 and to three significant figures above, so ≈1,200,000 means somewhere between 1,150,000 and 1,249,999. Unmarked counts are exact. Text is reproduced from the public post preview and truncated for length.
Citation-graph rank
Citation-graph rank — 52,406 of 1,583,249entries in the measured graph. A weighted position computed from the forward and mention edges below — republished posts weigh more than named mentions — and recomputed periodically, over the whole graph. Published only as this ordinal position, never as a score: a position is a fact, and a score printed beside one channel’s name would read as a verdict this register does not make. The two counts beneath stay separate for the same reason mentions are never summed with forwards anywhere else on this page — a named-by count costs nothing to manufacture. The top 100 by this measure, or how it is computed.
Forward network
Republished by
Channels on the register that have forwarded this channel's posts into their own feed.
Built only from forwarded posts we have actually read, on both sides. Coverage is early and deliberately incomplete: a missing link means we have not read the post that would prove it, never that the relationship does not exist. Counts are distinct forwarded posts observed, so they only ever go up as we read more.
Mentions
Named by 13 registered channels — every channel on the register whose own posts have named this one, by its current username or any other username it currently holds, merged from two separately captured readings of the same fact so a namer caught by only one of them is not missed and a namer both caught is not counted twice. A username this channel has since dropped is not matched — that handle may belong to someone else now, and crediting today’s namer to yesterday’s owner would misattribute it.
Named by
Channels on the register whose posts name this channel's handle.
A mention is a weaker signal than a forward and is counted separately for that reason — naming a channel is not republishing it, and a handle in a post body is easy to place deliberately. The post counts beside each row below are distinct posts in which the handle appeared, from posts we have read on both sides — the “Named by N registered channels” figure above is a different count, of distinct NAMING CHANNELS rather than posts, and is not the sum of the rows under it.
Channels Telegram recommends alongside this one
Telegram’s own answer, not this register’s.When this register asks Telegram’s API what is similar to this channel, this is the list it returns, in the exact order Telegram returns it — never re-sorted by subscribers or by anything else this register measures. The relationship, and the order, are Telegram’s; we record them and date them, and make no claim of our own about which of these channels actually resemble this one.
Second Language Teacher Education @sltechannel · 1,180 (as read 19 August 2026)#87
Read from Telegram’s recommendation API, most recently 19 August 2026. Telegram holds a list like this for a small and growing share of the register — how this is measured, and why most channel pages show nothing here.
Appears in Telegram’s recommendations for other channels
The reverse of the list above, and a different kind of signal. This does not require this channel to have ever been asked about directly — each row below is a channel we DID ask Telegram about, whose Telegram-generated list happened to include this one. A channel can appear here with an empty list above it, because being named by someone else’s query is independent of having been queried itself.
آموزش مقاله و پایان نامه نویسی @dr_amaniiii · 62,427 Telegram ranks this channel #1 of 79 here — alongside 78 others — read 21 August 2026
منابع پروپوزال،مقاله،پایان نامه @PAPhd · 64,399 Telegram ranks this channel #2 of 76 here — alongside 75 others — read 21 August 2026
آموزش پایان نامه و مقاله نویسی پژوهه @pajoohehgroup · 61,178 Telegram ranks this channel #6 of 87 here — alongside 86 others — read 22 August 2026
جزوه گاه 📚 @JOZVEGAH · 97,682 Telegram ranks this channel #17 of 87 here — alongside 86 others — read 16 August 2026
سیویلیکا، مرجع مقالات علمی @civilicacom · 66,503 Telegram ranks this channel #20 of 90 here — alongside 89 others — read 21 August 2026
هوش مصنوعی در پژوهش @AI_in_Research · 249,739 Telegram ranks this channel #29 of 92 here — alongside 91 others — read 19 August 2026
آموزش فن بیان 🎤 @BahaminDadras · 81,435 Telegram ranks this channel #31 of 87 here — alongside 86 others — read 18 August 2026
کتابخانه دانشگاهی @Academic_Library · 144,383 Telegram ranks this channel #31 of 86 here — alongside 85 others — read 13 August 2026
FaraDars | فرادرس @faradars · 61,251 Telegram ranks this channel #40 of 87 here — alongside 86 others — read 22 August 2026
تولید محتوا |هوش مصنوعی| زنگ دانش @zangedanesh · 59,490 Telegram ranks this channel #61 of 85 here — alongside 84 others — read 22 August 2026
کافه مهندس @cofeeng · 113,746 Telegram ranks this channel #63 of 76 here — alongside 75 others — read 14 August 2026
آموزش انگلیسی به روش زبان مادری @hamidrezasotoudeh98 · 72,382 Telegram ranks this channel #64 of 89 here — alongside 88 others — read 20 August 2026
This channel appears in 12 seed channels' Telegram-generated recommendation lists in total. Each is Telegram’s list for THAT channel, not this one — see how this is measured.
Cite this entry
A live page changes as we take new readings, so a citation should name the measurement it is based on, not just the URL. The line below cites the subscriber count as measured 21 August 2026 — this
entry's latest reading, not the date you are reading this.
“آموزش نوین مقاله نویسی با هوش مصنوعی” (@tephd), 78,734 subscribers as measured 21 August 2026. Telegram Register, tgregister.com/channel/tephd.
Full measurement history, CC BY 4.0. Every reading this register holds for this entry, not just the latest one, as a dated, downloadable record: CSV · JSON. Free to use with attribution to tgregister.com. Each file carries its own generation timestamp, which is the figure to cite for exactly when the data was retrieved.