Effective integration and implementation of
systems require commitment, responsibility, and a focus on sustainability from
both Biological and Non-Biological Systems. High-level design integration
demands that Systems Owners take responsibility for social and environmental
impacts, especially in Biological Systems. Systems owners must provide ethical
strategies and sustainable social security measures to maintain the quality and
performance of these systems.
System Owners are tasked with designing and
implementing a Surveillance Component in Non-Biological Systems to monitor
quality, performance, and productivity. Data collection inputs are closely
monitored before accessing the entire system platform. The system evaluates
input data through accountable sensors and resource allocation layers.
Satisfactory inputs are passed through the
output channel, with profile data saved for insights to end-users. Conversely,
dissatisfactory inputs are halted, and critical discrepancies are flagged for
assessment using diagnostic tools. Complex, unresolved issues are stored for
further analysis in the Component Controller. Optimized parameters are fed back
into the system's input channel, allowing automated processes to handle
repetitive tasks efficiently. (Figures 1, 2,3)
Intellectual integration requires designing an
ethical surveillance system when integrating with Biological Systems. The
system owners are responsible for maintaining a harmonic balance within these
Biological Systems. Social Input, processed through advanced algorithms and
global variables, is filtered through input sensors to assess its alignment
with performance goals. (Figures 1, 2, 3)
Satisfactory inputs are stored for future
reference, while dissatisfactory inputs are halted at the output component.
Biological Systems, operating in real-life modes, can produce open loops or
vicious cycles, depending on the quality of Input. When implemented within the
system framework, global variables must prioritize more than just business
owner profits. Improper implementation of these variables in Biological Systems
can lead to system breakdowns. Therefore, system owners must carefully monitor
and balance these inputs to prevent failure and ensure sustainable integration.
System Dynamics Modeling and Control Mechanisms in Biological and
Non-Biological Systems
System Owners employ three fundamental dynamics modeling methods:
1. Probation Domain
2. Rehabilitation Process
3. Elimination
According to observational studies, while the probation domain and
rehabilitation processes can enhance and develop Biological Systems, these
methods often create an automated, integrated vicious cycle leading to system
breakdowns. On the other hand, the Elimination model applied within the system
framework introduces severe consequences for Biological Systems, including
complex post-traumatic stress disorders, capital punishment, imprisonment (both
short and long-term), and early retirement programs.
However, adding these three dynamic sub-control structures does not
resolve social complexity or enhance cost-effectiveness. Instead, they impose adverse
side effects on the evolutionary trajectory of Biological Systems, making
Biological Systems more vulnerable than Non-Biological Systems.
In the case of an aircraft accident, saved performance parameters from
Input and Output Components (such as data from the Flight Data Recorder) are
investigated. Global air safety standards and engine performance are analyzed
to identify the causes and prevent future accidents. Notably, the aircraft
itself is not blamed because it is an industrial design object that adheres to
flight safety protocols and security regulations (Figure 1)
This scenario illustrates a metaphorical connection between the role of
control mechanisms in Biological and Non-Biological Systems. Just as an
aircraft must follow safety standards, Biological Systems must function
according to Global Variables. Failures in these systems, especially when
Global Variables are improperly implemented, can result in incidents and
tragedies in the social contexts. However, unlike in aviation, where the focus
is on the root causes of accidents, public attention in Biological Systems
tends to shift towards ethical dilemmas and sensationalized scenarios, often
overlooking the underlying causes of breakdowns.
The complexity behind these failures is often hidden, as multiple factors
within the Global Variable Structure are intertwined to obscure the true
causes, leaving the reality of these breakdowns unseen. Global Variables'
mystery remains invisible, with the more profound truth concealed behind the
surface-level narrative.
Observation:
Designing a higher level of integration demands increased
responsibility from the hierarchy layer. In Biological Systems, once caught in
a vicious cycle, they gradually progress toward the elimination process.
Enterprises driven by ambitious economic goals often operate beyond the
limitations of sub-control structures, accelerating this cycle. (Figure 3)
