Press Release
WarRin Protocol: A point-to-point anonymous privacy communication system
Dr.WarRin
Summary
This white paper provides an explanation of the WarRin protocol and related blockchain, point-to-point, network value, transport protocol, and encryption algorithms. The limited space will highlight the WRC allocation scheme and purpose of the WarRin Protocol Token, which is important for achieving the WRC’s stated objectives. This white paper is for informational purposes only and is not a promise of final implementation details. Some details may change during the development and testing phases.
1. Introduction
Traditional centralized communication systems such as WeChat,WhatsApp, FacebookMessage,Google Allo,Skype face a range of problems, including government surveillance, privacy breaches, and inadequate security, and the WarRin protocol proposes apoint-to-pointencrypted communications system that leveragesblockchain technology, combined with Double Ratc het algorithms, pre-keys, and extended X3DH handshakes. The WarRin Protocol uses The Generalized Directional Acyclic Graph and Curve25519,AES-256, and HMAC-SHA256 as the pronamor, allowing each account to have its own unique account chain, providing unlimited instant communication between points and unlimited scalability, anonymity, integrity, consistency, and asynchronousness.
2. WarRin Protocol communication system
2.1 Two types of communication
The Waring Protocol communication system divides chat channels into two types.
Two modes of communication
- General Chat mode: Using point-to-point encrypted communication, the service side has access to the key and can log in via multiple devices.
- Secret Chat mode: Encrypted communication using point-to-point can only be accessed through two specific devices.
The design combines some of the advantages of raiBlocks multi-chain construction with IOTA/Byteball DAG, which we call the Waring protocol. With improvements, we have given the WarRin protocol greater throughput and faster processing power while ensuring the security of the ledger, and network nodes can store the ledger in less space and search their communications accounts quickly in the ledger. When two users communicate, third parties contain content that neither manager can access. When a user is chatting in secret, the message contains multimedia that can be designated as a self-destruct message, and when the message is read by the user, the message is automatically destroyed within the specified time. Once the message expires, it disappears on the user’s device.
2.2 How chat history is encrypted
2.2.1 MTProto Transport Protocol
MTProto transport protocol
The WarRin communication system draws on RaiBlocks’ multi-chain structure for point-to-point communication. Each account has its own chain that records the sending and receiving behavior of the account. For example, in Figure 1, there are 7 accounts, each with 7 chain records of the account sending and receiving communications. On the graph, horizontal coordinates represent the timeline, and portrait coordinates represent the index of the account.
Transferring information from one account to another requires two transactions: one to send a communication from the sender’s transfer content, and one to receive information to add that content to the content of the receiving account. Whether in a send-side account or a receiving account, a PoW proof of work with the previous communication content Hash is required to add new communications to the account. In the account chain, poWwork proves to be an anti-spam communication tool that can be done in seconds. In a single account chain, the Hash field of the previous block is known to pre-generate the PoW required for subsequent blocks. Therefore, as long as the time between the two communications is greater than the time required to generate the PoW, the user’s transaction will be completed instantaneously.
In such a design, only the receiving end of the communication is required for settlement. The receiving end places the received communication signature on the account chain, which is called accepted communication. Once accepted, the receiving end then broadcasts the communication to the ledger of the other nodes. However, there may be situations where the receiving end is not online or is subject to a DoS attack, which prevents the receiving end from putting the receiving side communication on the account chain, which we call uncommoted transactions. The X symbol in Figure 1 represents an open transaction sent from Account 2 to Account 5.
Obviously, because only the sending and receiving sides of the communication are required to settle, such communication is very lightweight, all traffic can be transmitted in a UDP package and processed very quickly. At the same time, all communications in an account are kept in one chain, with great integrity, and the ledger can be trimmed to a minimum. Some nodes are not interested in spending resources to store the full communication history of the account; They are only interested in the current communications for each account. When an account communicates, its accumulated information is encoded, and these nodes only need to keep track of the latest blocks so that historical data can be discarded while maintaining correctness. Such communication is only possible if the sending and receiving sides trust each other and are not the final settlement of the entire network consensus. There is a security risk in the absence of trust on the sending and receiving ends, or in situations where the receiving end is attacked by DoS without the sender’s knowledge.
We have observed that although each account has a separate chain, the entire ledger can be expressed in the form of a WarRin object. As shown in Figure 2, this is represented by the WarRin astros trading on all accounts in Figure 1.
The first unit in the WarRin object is the Genesis unit, the next six cells represent the allocation of the initial token, and the other units correspond to the communication transactions between the account chains. We use the symbol a/b to represent a communication transaction, where the sender is a andthe recipient is b. The last 4/1 unit in Figure 2 is the last communication corresponding to Figure 1 – sending communication from account 4 to account 1. A transaction in Figure 1 is a confirmation of the latest block or the latest communication on the account chains of both parties to the communication, reflected in Figure 2 as a reference to the latest units of the account chains of both parties to the communication. Take unit 4/1, for example, where the latest block on account 4 was the receiving block for 2/4 trades and the newest block on account 1 was the send block for 1/5 trade. So on the DAG, the 4/1 cell refers to the 2/4 cell and the 1/5 cell.
The WarRin protocol uses triangular shrapned storage technology to crack impossible triangles in the blockchain through the shrapghine technology, with extensive node engagement and decontalination while maintaining high throughput and security:
- Complete shraping of blockchain status;
- Secure and low-cost cross-synth trading;
- Completely random witness selection;
- Flexible and efficient configuration
Complete decentralization ensures absolute security and scalability of the standard chain.
(Figures above show seven Ling-shaped objects:2/1 one;3/2 one… ）
2.2.2 Curve25519 Elliptic Curve Encryption Algorithm
Curve25519, proposed by Daniel Bernstein, is anelliptic curve algorithm for the exchange of The Montgomery Curve’s Difi Herman keys.
Montgomery Curve Curve Mathematical Expression:
Curve25519 Curve Mathematical Expression:
Curve25519 encryption algorithms are 
Generate a one-time random secret 
3. Incentives
On the basis of the WarRin agreement, by adding the incentive layer, we can effectively avoid the whole network being attacked and eliminate spam. As long as honest nodes control most of the calculations, for an attacker, the network is robust because of its simplicity of structure, and nodes need little coordination to work at the same time. They do not need to be authenticated because information is not sent to a location.
3.1 WRC Certificate
WRC issued a total of 2,500,000 pieces and continued to increment according to the WoRin gain function.
3.1.1 WoRin Gain Function
3.1.2 WoRin gain function control table
| The WoRin gain function is compared to the table | ||
| Number of layers /F | Growth factor /I | WRC circulation |
| [1,50] | 0.002 | 334918.8057 |
| [51,100] | 0.002 | 780024.2108 |
| [101,150] | 0.004 | 1177129.617 |
| [151,200] | 0.006 | 1487860.923 |
| [201,250] | 0.01 | 1722637 |
| [251,300] | 0.016 | 1894309.216 |
| [301,400] | 0.03 | 2101623.789 |
| [401,500] | 0.06 | 2217555.464 |
| [501,1000] | 0.1 | 2450712.257 |
| [1001,2000] | 0.12 | 2557457.3 |
According 
3.2 Allocation
WarRin protocol node distribution
3.2.1 Node allocation
Set the initial price 
For example, the number of tiers in which the 98th node is located is Tier 13, and the price of Tier 13 is 0.214,the tokens assigned by Tier 98 are
3.2.2 Total number of address assignments
Each node occupies one address, and the total number of 
4. The use
WRC is the native pass-through of the WarRin protocol, andWRC will assign to Genesis nodes according to the above allocation scheme, which together form the entire network, andWRC can be used in the following scenarios, including but not limited to:
Pay the network’s gas charges, i.e. for transferring money and invoking smart contracts;
System Staking tokens, used for node elections and token issues;
The capital is lent to the validator in exchange for the amount of the reward;
Voting rights for system proposals;
The means of payment for apps developed on WoRin Services;
WoRin Storage is a means of payment on the decentralization storage;
WoRin DNS domain name and WoRin WWW website means of payment;
WoRin Proxy agents hide the means of payment for body and IP addresses;
WoRin Proxy penetrates payment methods reviewed by local ISPs
……
5. Conclusions
Metcalfe’s Law states that thevalue of a network is equal to the square of the number of nodes within the network, and that the value of the network is directly related to the square of the number of connected users. That is 
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About Author
Disclaimer: The views, suggestions, and opinions expressed here are the sole responsibility of the experts. No Digi Observer journalist was involved in the writing and production of this article.
Press Release
Salience Clinical Introduces S2G Framework Pairing AI Screening with Physician-Executive GestaltSubmissions
New methodology provides biopharma and medtech sponsors an auditable path from multimodal clinical data entropy to regulatory-ready narratives.
PRINCETON, N.J., United States, 15th Sep 2026 – Salience Clinical, LLC, a clinical development and regulatory advisory consultancy, today announced the launch of the Salience Signal-to-Gestalt (S2G) Framework. The methodology addresses a major industry pain point: clinical programs produce massive amounts of digital biomarkers, sensor data, and real-world evidence (RWE) that create an unmanageable flood of false signals using conventional statistical tools, while opaque machine learning algorithms fail to provide regulators the biological plausibility and audit trail they demand.
This problem is addressed by the S2G Framework, which employs a four-tier, Human-in-the-Loop (HITL) pipeline connecting computational screening directly to senior physician judgment:
- Tier 1 – Multi-Source Ingestion & Harmonization: Pulls together high-entropy datasets including clinical trial safety databases, EHRs, continuous sensor streams, and biomarker panels normalizing data syntax and removing background noise.
- Tier 2 – Explainable AI & Signal Detection: Uses auditable algorithms aligned with FDA Good Machine Learning Practice (GMLP), cross-center AI credibility assessment frameworks, and lifecycle management standards (including Predetermined Change Control Plans [PCCP] when applicable for digital endpoints and SaMD) to detect real safety clusters and efficacy trends with end-to-end data provenance.
- Tier 3 – Mechanistic & Pharmacodynamic Triangulation: Cross-references statistical flags with target pharmacology, receptor binding profiles, and biological pathways to identify true pharmacodynamic effects vs. computational artifacts.
- Tier 4 – The Salience Gestalt Synthesis: The output filters transition directly to Denis Katz, M.D., Founder and Medical Director of Salience Clinical. Drawing on senior leadership across clinical neuroscience, advanced biologics, Phase III global trials, and BLA/NDA strategy, Dr. Katz synthesizes disparate findings into a unified clinical gestalt—the integrated benefit-risk assessment, target product profile alignment, and scientific justification needed to meet FDA and Advisory Committee scrutiny.
Caption: Figure 1: The Salience S2G Framework — A four-tier pipeline connecting computational data screening to physician-executive clinical gestalt for FDA dossiers.
Preserving Clinical Judgment in an Algorithmic Era
“Sponsors are rightfully hesitant to bring AI-derived claims to health authorities because regulators do not accept black boxes, nor do they approve algorithmic correlations without a validated Context of Use,” said Denis Katz, M.D. “The S2G Framework was built around that reality. We deploy explainable computational filters to isolate signals within high-dimensional noise, but the regulatory deliverable is driven entirely by physician-led mechanistic adjudication and clinical gestalt. Sponsors gain the operational speed of advanced analytics without inheriting regulatory audit vulnerability.”
Strategic Applications
- Agency Briefing Packages: Isolating primary therapeutic signals and biomarker-enriched responder cohorts for Type B, Pre-IND, and Pre-BLA/NDA briefing documents.
- Safety Signal Adjudication: Distinguishing drug-related adverse events from disease progression and background noise to prevent unmerited clinical holds or excessive post-market warnings.
- Digital Endpoints and SaMD: Structuring continuous physiological data and software-as-a-medical-device metrics into defensible clinical validation dossiers that meet CDER, CBER, and CDRH standards.
The S2G Framework is immediately available for active client engagements across biotechnology, pharmaceuticals, and medical devices. Sponsors preparing for upcoming agency interactions can contact Salience Clinical for a confidential regulatory readiness consultation.
About Salience Clinical, LLC
Salience Clinical, LLC is a focused consulting firm for clinical development, founded by Denis Katz, M.D. Salience helps biopharmaceutical and medical device sponsors with trial design, translational biomarker implementation, safety signal assessment, and critical FDA interactions. Visit salienceclinical.com to learn more.
Media Contact
Organization: Salience Clinical, LLC
Contact Person: Denis Katz, M.D.
Website: https://salienceclinical.com/
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State: N.J.
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The post Salience Clinical Introduces S2G Framework Pairing AI Screening with Physician-Executive GestaltSubmissions appeared first on King Newswire. This content is provided by a third-party source.. King Newswire makes no warranties or representations in connection with it. King Newswire is a press release distribution agency and does not endorse or verify the claims made in this release. If you have any complaints or copyright concerns related to this article, please contact the company listed in the ‘Media Contact’ section
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Liberty Partners Wealth Management has launched its first VIP membership program, integrating professional research with its MegaOS analytical system to support core investors.
New York, United States, 15th Sep 2026 – Liberty Partners Wealth Management has officially launched its first VIP membership recruitment program, marking a deliberate step in the firm’s ongoing strategy to expand its specialized investor service infrastructure. Designed specifically to support core community participants, the program combines the firm’s multidisciplinary research capabilities with its MegaOS intelligent analysis system to provide members with structured market information and research support.
The introduction of the VIP membership framework comes at a time when financial institutions globally are accelerating the adoption of data analytics and computational tools within market research. As the volume of market information continues to grow, financial institutions are placing greater emphasis on information screening, data processing and research efficiency. Liberty Partners Wealth Management said the combination of professional research capabilities and technology-supported analysis is intended to improve information-processing efficiency within its member services.
MegaOS Enhances Data Analytics and Research Processing
As a core technical component of this service initiative, the technology division at Liberty Partners Wealth Management is actively incorporating the MegaOS intelligent analysis system into daily research workflows. The software platform assists analysts by organizing, categorizing, and processing extensive volumes of publicly available financial and corporate information.
The system assists researchers in organizing information from publicly available sources, including corporate disclosures, industry developments, market conditions and macroeconomic policy updates. By supporting information organization and preliminary analysis, MegaOS allows researchers to spend more time evaluating the context and relevance of the information reviewed.
Company representatives emphasized that MegaOS functions strictly as a technology support tool designed to improve data processing speed and assist researchers in identifying notable market developments that warrant deeper qualitative examination. The system forms part of the technology support used within the VIP service framework.
VIP Program Focuses on Structured Investor Support
In contrast to prior operational phases that emphasized broader brand awareness and general audience engagement, the current VIP recruitment program centers specifically on delivering structured analytical resource allocations to core participants. Under the current service framework, VIP members will have access to market updates, industry research, monitoring of selected companies and MegaOS-assisted analysis. The scope and frequency of these services may be adjusted based on the firm’s research capacity and changing market conditions.
The specific composition of research deliverables will be periodically evaluated and adjusted to reflect changing macroeconomic conditions and evolving member requirements. By placing the VIP program at the forefront of its operational agenda, Liberty Partners Wealth Management is strengthening its commitment to providing structured research and information resources for participating members.
The firm indicated that as financial market environments become increasingly intricate, combining qualitative institutional research with automated data analytics will remain a long-term priority in refining its broader investor management framework.
Industry Focus on Technology-Enabled Wealth Analytics
The use of artificial intelligence and data analytics in financial research has received increasing attention across the asset management and wealth management industries.
Liberty Partners Wealth Management’s integration of MegaOS into its service model reflects a broader industry movement toward technology-assisted decision support structures.The launch of the VIP membership program represents the firm’s latest effort to incorporate technology-supported research into its member service framework.
Enrollment for the initial cohort of VIP members is currently underway. According to the firm, onboarding will be managed in phases based on service capacity and operational requirements.
About Liberty Partners Wealth Management
Liberty Partners Wealth Management is a wealth management and financial research firm based in New York. The firm focuses on combining professional investment expertise with technological infrastructure to deliver structured research and asset management services.
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The post Liberty Partners Wealth Management Launches First VIP Program with Data Analytics appeared first on King Newswire. This content is provided by a third-party source.. King Newswire makes no warranties or representations in connection with it. King Newswire is a press release distribution agency and does not endorse or verify the claims made in this release. If you have any complaints or copyright concerns related to this article, please contact the company listed in the ‘Media Contact’ section
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Press Release
Guggenheim Introduces AI-Powered Financial Analytics System in Indonesia Under Prof. Wong Woon Thiam
Guggenheim deploys an AI-assisted financial analytics infrastructure in Indonesia under the guidance of Prof. Wong Woon Thiam, focusing on multi-asset risk monitoring and predictive market research.
United States, 15th Sep 2026 – Guggenheim has formally introduced an advanced artificial intelligence-driven financial analytical system in Indonesia as part of its ongoing operational infrastructure expansion across Southeast Asia. Overseen by financial scholar and regional operational advisor Prof. Wong Woon Thiam, the technological deployment incorporates machine learning algorithms, natural language processing for regional data ingestion, predictive market modeling, and real-time risk evaluation protocols.
The system is engineered to assist institutional participants, risk managers, and investment professionals in processing complex market datasets, evaluating macroeconomic trends across Emerging Asian asset classes, and maintaining quantitative risk governance standards.
Operational Scope of AI Analytics in Regional Capital Markets
The rapid evolution of Southeast Asian financial markets has generated vast volumes of structured and unstructured market data, creating both opportunities and analytical challenges for institutional investors. Within Indonesia, domestic economic activity, cross-border trade dynamics, and currency fluctuations require continuous monitoring to maintain operational efficiency. Guggenheim’s AI-driven platform addresses these requirements by deploying localized data ingestion pipelines designed to evaluate real-time financial metrics with high processing efficiency.
Rather than functioning as an autonomous trading mechanism, the system serves as a quantitative research and analytical overlay. It aggregates real-time market feeds, macroeconomic indicators, and institutional order flows, transforming disparate data streams into structured analytical insights that assist portfolio management teams in evaluating market liquidity and sector exposure.
Integration of Predictive Research and Natural Language Processing
A core capability of the technological rollout is the integration of custom-trained natural language processing (NLP) models capable of parsing financial documentation in both Bahasa Indonesia and international business languages. The system continuously analyzes regional economic releases, regulatory announcements, corporate disclosure filings, and market news to extract relevant macroeconomic signals and sector-specific indicators.
By converting qualitative information into quantitative variables, the predictive research module assists analysts in identifying early shifts in market sentiment and sector volatility. These analytical capabilities enable risk management teams to evaluate potential stress scenarios across regional fixed-income, equity, and currency markets with direct, data-supported precision.
Real-Time Risk Monitoring and Multi-Asset Portfolio Governance
In institutional asset management, active risk monitoring is vital to maintaining capital stability during periods of market volatility. The upgraded financial system incorporates automated risk tracking protocols that continuously monitor portfolio parameters against pre-established tolerance limits and regulatory guidelines.
The risk analytics framework supports the monitoring of multi-asset exposures, liquidity conditions and changing market risks across ASEAN markets. If localized market anomalies or elevated volatility levels are detected, the system generates structured risk reports for portfolio managers, enabling timely and disciplined adjustments to exposure levels in full alignment with international risk governance benchmarks.
Methodological Insights from Prof. Wong Woon Thiam
Under the academic and quantitative guidance of Prof. Wong Woon Thiam, the deployment emphasizes empirical validation, mathematical rigor, and strict operational oversight. Prof. Wong’s research background in financial economics and quantitative modeling has informed the calibration of the underlying analytical algorithms, ensuring that technology serves to enhance, rather than replace, human expertise.
“Artificial intelligence in asset management functions as a quantitative complement to human judgment and analytical rigor,” stated Prof. Wong Woon Thiam during a seminar on financial technology in Jakarta. “By applying rigorous mathematical validation models to real-time market datasets, our objective is to provide institutional participants in Indonesia with clear, data-driven insights that support prudent risk management and long-term operational stability.”
Long-Term Strategy for Regional Financial Technology Development
The deployment of AI-driven financial analytics in Jakarta represents a key milestone in Guggenheim’s multi-phase strategy to strengthen regional capital market infrastructure across Southeast Asia.
Through continuous investment in advanced analytics, local technical talent, and robust governance frameworks, the firm remains committed to fostering financial innovation, market transparency, and sustainable institutional growth in Indonesia and the wider ASEAN economic corridor.
About Guggenheim
Guggenheim is a global financial services firm engaged in asset management, investment banking, and capital market services. The firm provides institutional investors, corporations, and high-net-worth clients with disciplined investment strategies, comprehensive market research, and tailored financial solutions. Guided by principles of analytical rigor, risk management, and client-centric service, Guggenheim operates across major global financial centers to deliver long-term value and institutional stability.
Media Contact
Organization: Guggenheim
Contact Person: Henry Johnny
Website: https://www.guggenheimpartners.com/
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Country: United States
Release id: 49082
The post Guggenheim Introduces AI-Powered Financial Analytics System in Indonesia Under Prof. Wong Woon Thiam appeared first on King Newswire. This content is provided by a third-party source.. King Newswire makes no warranties or representations in connection with it. King Newswire is a press release distribution agency and does not endorse or verify the claims made in this release. If you have any complaints or copyright concerns related to this article, please contact the company listed in the ‘Media Contact’ section
About Author
Disclaimer: The views, suggestions, and opinions expressed here are the sole responsibility of the experts. No Digi Observer journalist was involved in the writing and production of this article.
Liberty Partners Wealth Management Launches First VIP Program with Data Analytics
Salience Clinical Introduces S2G Framework Pairing AI Screening with Physician-Executive GestaltSubmissions
Guggenheim Introduces AI-Powered Financial Analytics System in Indonesia Under Prof. Wong Woon Thiam
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