This interdisciplinary research seeks
to exploit instance domains of the Conscious Component by identifying
pattern-recognition algorithms and creating an analogical model through
Biological and Non-Biological Systems. The analysis and exploration of an
analogical model of Non-Biological Systems is more concrete and tangible than
the examination model in Biological Systems because a source/target domain is
transparent and represents allocative algorithmic codes. Analogical reasoning
can pave the way for detecting unknown target domains in Biological Structures.
The Black Box Testing Method is
exploited to identify a guideline for analyzing, developing, and illustrating a
particular analogical model between Biological and Non-Biological Systems.
The term analogy refers to the
relationship and similarity between the source and target domains. Analogy is a
Cognitive Process of transferring information from an understandable source
domain to a target domain. A source domain can identify a complex phenomenon of
a target domain. A source domain metaphorically provides a general notion of
the target domain. A source domain can also deduce and justify information
about a target domain.
Analogical reasoning is a mapping
between two systems belonging to similar or dissimilar domains. The Analogical
Reasoning Model develops from a known source domain and moves to an unknown
target domain. This study compares Biological and Non-Biological Inferences and
contributes to the instance domain of the Conscious Component by showing
correlations between abstract and concrete domains.
An analogical model represents a
phenomenon of the world that considers the source domain and seeks unknown
phenomena. Cultivating an optimal Analogical Model can create a sense that the
hypothesis can be true to a certain extent. Furthermore, researchers try to
focus and find more details about an abstract instance of a concrete domain.
A real-life structural model for a
Non-Biological System considers and uses in this study a physical enterprise
for the source domain. The cloud computing service provider is the target
domain. A tangible part of a structural model for the Biological System is the
Human or the source domain, an abstract is the Conscious Component, and
Supernatural Forces are the target domain. (Fig 1,2,5)
According to the observational study,
the general procedure for analogical inferences compares similar attributes,
common-control functions, and algorithmic mechanisms between Non-Biological and
Biological Systems in three main stages as follows:
Identification of Source and Target Domains:
1-Establishing the known and unknown domains.
2-Recognizing attributes in Biological and
Non-Biological Systems.
Mapping and Correlation Analysis:
1- Identifying similarities and differences between
systems.
2- Exploring standard control functions and algorithmic
mechanisms.
Model Development and Hypothesis Testing:
1- Creating an analogical model based on findings.
2-Testing an analogical proportion between
the source and target domain in the seven layers conceptual framework of
Biological and Non-Biological Systems through Black Box Testing and pattern
recognition in surroundings and interactions.
By systematically following these
stages, this research aims to bridge gaps between abstract and tangible
knowledge domains, refining our understanding of the Conscious Component and
its instance domains.
1. Perceptual Stimuli
Bottom-up processing involves perceptual stimuli derived from the real physical
world. In this study, an enterprise is selected as a non-biological system.
Internal data is stimulated through interactions between internal database
resources and external contexts. Aggregated data is categorized and interpreted
based on procedural rules and business workflow processes within the system
platform.
Humans, as biological systems, are explored in
this research. Data is collected through interactions between external
resources in social contexts and the brain’s sensory frameworks. Aggregated
source data is categorized and interpreted according to procedural models
within internal resources, such as modules in the conscious/subconscious
components and algorithmic codes in the decision-making map. (Fig 1,2,5)
2. Data Availability
Cloud network computing ensures affordable, real-time data accessibility and
accountability. It provides end users with a reliable and feasible data
process, offering a logical data structure within enterprises and delivering
aggregated data in vast online storage. Authorized users can share reliable
data through cloud networks, ensuring real-time information availability. (Fig
1,2,5)
Conversely, as an instance of the Biological System,
the Conscious Component encapsulates a logical data structure that transmits
information to the brain via electromagnetic frequencies and vibrations. It
delivers and executes aggregated data from the memory component to the
decision-making map. (Fig 1,2,5)
3. Control System
A centralized control system operates as a distributed control structure with a
central operator and supervisory control. Cloud computing providers enable
resource sharing, software accessibility, and information distribution over a
network. The service and infrastructure components function through the
internet on system platforms, subsystems, and interface domains. Infrastructure
components include servers, data storage for various devices, data monitoring,
and storage resiliency. A hierarchical component model governs operations
management and activities, ensuring accountability and real-time availability
of the logical data structure.
Similarly, the hierarchical domain of the Conscious
Component is an aggregate data model responsible for managing its operations.
This domain monitors and enforces the accountability and availability of the
logical data structure within the Conscious Component. Such an unknown
hierarchical domain may manifest as an analogy-based expectation profile of
supernatural forces. (Fig 1,2,5)
Common Analogous Patterns
for Source/Target Domains
This concept refers to cohesive
patterns in default operations and functional pre-existing value processes
within Biological and Non-Biological Systems. These patterns are represented by
symbols A and B, which describe the contribution of algorithmic code within the
source/target domains through system development along the evolutionary path of
life.
1. First Layer: Communication and
Visibility
A-Source Domain in Non-Biological
Systems: The structural subsystems model within an enterprise operates
with transparency and accountability across multiple IT platforms. (Fig. 3)
B-Source Domain in Biological Systems:
Community portals share data, controversial debate topics in mainstream media,
and information on the evolutionary path of life. Algorithmic codes within
social contexts significantly influence human actions and values. (Fig. 3)
2. Second Layer: Active Connection
Mechanism
A:
Enterprises communicate transparent data with partners, suppliers, and
subsystems through algorithmic processes embedded in IT blueprints.
B: Humans
interact with environmental contexts via social life cycle events, generating
experimental logical data within the brain framework. (Fig. 3)
3. Third Layer: Transparency in
Physical Entities
A:
Functional mechanisms beyond enterprises trigger algorithmic codes to influence
physical structures and devices, generating positive business outcomes. (Fig.
3)
B:
Knowledge and experiences within the Superego Adjuster challenge individuals to
behave positively in environmental contexts, transforming aspirations into
social realities. (Fig. 3)
4. Fourth Layer: Data Transformation
& Connection Mechanism
A:
Algorithmic codes extend beyond internet connections, enabling enterprises to
conduct data transactions via cloud networking. (Fig. 3)
B: The
brain’s receptor mechanisms transmit external data as electrical signals to the
conscious component. (Fig. 3)
5. Fifth Layer: Data Availability,
Reliability, and Consistency
A:
Transparent enterprise data storage within cloud computing signifies a
contractual agreement with service providers, ensuring data maintenance and
control. (Fig. 3)
B:
Sensory neurons within the brain manage and control logical data through the
designer’s structure and the conscious component’s algorithmic properties.
(Fig. 3)
6. Sixth Layer: Active Access Control
Mechanism
A: Under
contractual agreements, cloud computing service providers safeguard enterprise
data through an active access control mechanism in local databases. (Fig. 3)
B: Human
data storage and algorithmic mechanisms within the Conscious Component are
regulated and protected by their inherent design. (Fig. 3)
7. Seventh Layer: System Extinction
and Data Transfer
When a system reaches extinction,
algorithmic codes from the source domain in Biological and Non-Biological Systems
transfer to the target domain.
A: A
cloud computing service provider manages aggregated data and functions within
the global cloud network. When an enterprise ceases operations, critical,
logical data may persist in the cloud, and the enterprise’s physical elements
dissolve into different environments. (Fig. 4)
B: The Conscious
Component retains essential logical parameters and abstract data structures
from the brain framework even after physical death. While the human body
decomposes, stored information remains within the Conscious Component. (Fig. 4)
Common Analogous Patterns for
Source/Target Domains (appendix)
Layer 1: Isolated Mode
A.
Structural mechanisms, functional processes, and threading modules between
subsystems and enterprises are eliminated. Interruptions trigger invisibility
in subsystems and disable Instance Parameters. This result can cause system
failure and the isolation of subsystems from external environments. (Fig 4)
B. Humans
become isolated from social contexts due to a deadlock in the Instinct
Component beyond the Subconscious Component. Starvation in algorithmic codes,
beyond old Open-loop instinct cycles, prevents human communication in social
communities and leads to isolation. (Fig 4)
Layer 2: Inactive Communication
A.
Invisible transactions between enterprises and subsystems arise due to
deadlocks when multiple processes request resources. The primary system fails
to allocate essential resources within subsystems.
B. A
death state induces a deadlock between humans and social environments. (Fig 4)
Layer 3: Death Domains
External forces can alter Biological and Non-Biological
Systems, leading to disappearances or system extinction.
A. In Non-Biological
Systems, the Death Domain signifies the cessation of operational activities in
enterprises and physical devices. Enterprises go bankrupt, and fundamental
configuration data become invisible to external environments. Physical
components of an enterprise may transition into hidden environments, vanishing
from social contexts. (Fig 4)
B. In Biological
Systems, the Death Domain refers to the cessation of brain function,
transferring memory data to the Conscious Component. Brain data become
invisible to external environments, and the human body may transition into
concealed environments through waste disposal systems, disappearing from social
contexts. (Fig 4)
Layer 4: Obstacle Course of Data
A.
Internet disconnection obstructs data transactions between enterprises and
cloud service providers. Essential data remain stored and protected under
business contracts with cloud service providers. (Fig 4)
B. The
brain’s death receptor signals the Conscious Component to upload stored data
upon oxygen depletion in the brain. Once data is transferred, communication
between the brain and the Conscious Component is permanently blocked. (Fig 4)
Layer 5: Data Availability
A.
Portions of enterprise data storage may be made publicly accessible under
business contracts. Cloud computing service providers can manage entire
enterprise data storage under contractual agreements.
B.
Supernatural forces can access available reposited data within the second
memory component and preserved framework in the Conscious Component. (Fig 4)
Layer 6: Active Access Control Mechanism
A. Cloud
computing providers protect enterprise data storage through active access
control mechanisms. Under business contracts, encrypted data can be preserved
in local databases within cloud networks.
B.
Supernatural forces protect encrypted data and algorithmic patterns within the
Conscious Component through an active access control mechanism. (Fig 4)
Layer 7: Control of Aggregated Data and
Guidance Functions
A. Cloud
computing service providers monitor aggregated data and guidance functions
within cloud networks (global storage). (Fig 4)
B.
Supernatural forces control aggregated data and guidance functions within the
Conscious Component (global storage). (Fig 4)
Conclusion
This study hypothesizes that an
explicit representation of analogical reasoning can reveal fundamental
similarities in data collection processes throughout the lifespan of both
Biological and Non-Biological Systems. Specifically, during the end-of-life phase,
a system framework can activate functional mechanisms that encode and transfer
algorithmic patterns from the source domain to the target domain when the
system performs unresponsive to external stimuli.
Observational studies suggest that
analogical inferences between Biological and Non-Biological Systems emphasize
the similarity of Layer 5, Layer 6, and Layer 7, collectively
contributing to a Model of Harmony Perception. Within this framework, the
target domain approximates control function values, reflecting the pattern
regularities identified in the source domain.
Observation:
General insights into functional mechanisms within the Conscious Component have
been explored in previous research.
Observation:
Analogical reasoning involves mapping relationships between two distinct
systems within similar or divergent domains.
Observation:
Prior research suggests that logical inference algorithms within the Conscious
Component and harmonic balance in Iceberg Cells play a role in validating
decision-making patterns.
Observation:
Global Variables shaping social contexts are manifestations of the Conscious
Component. Influential decision-makers generate algorithmic codes that extend
beyond these Global Variables through their Conscious Components.
