CS-102 DS-class04a Lectures DS Class.pdf

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This document discusses different components of space complexity, including instruction space for storing compiled program code, data space for storing variables and constants, and environment stack space for saving function call information. It provides examples of calculating space complexity for programs using arrays, recursion, and dynamic memory allocation, noting space complexities of O(1), O(n), O(n^2), and O(log n).

Space Complexity
• Components of Space Complexity
– Instruction space
– Data Space
– Environmental Stack Space

Instruction space
• Space needed to store the compiled version of
the program instructions.
• The compiler used to compile the program
into machine code
– Suppose we have an expression a+b+c*d, the
compiler computes this as (c*d)+a+b and generate
the shorter and more time-efficient code.

Data Space
• The space needed to store all the constants
and variables.
• Data space has two components
1. Space needed by constants and simple variables.
2. Space needed by dynamically allocated objects such
as arrays and class instances

Environment Stack Space
• The environment stack is used to save information
needed to resume execution of partially completed
functions and methods.
• Beginning performance analysts often ignore the
space needed by the environment stack because
they don’t understand how functions are invoked
and what happens on termination.
• Each time a function is invoked the following data
are saved on the environment stack:
– The return address
– The values of all local variables and formal parameters in
the functions being invoked (necessary for recursive
functions only).

#include <iostream.h>
void easy(int N)
{
if (N < 1) return;
easy(N-2);
cout<<N;
easy(N-3);
cout<<N;
}
void main()
{
easy(4);
}

Examples
int main()
{ int i, sum=0;
int n;
cin>>n;
for (i=0; i<n; i++)
sum+=i;
}
Time Complexity - O(n)
Space complexity – O(1)

Examples
int main()
{ int n;
cin>>n;
int *arr;
arr=new int [n];
for (int i=0; i<n; i++)
for (int j=0; j<i; j++)
{
some statements;
}
return 0;
}}
Time Complexity - O(n2)
Space complexity – O(n)

const int n=100;
int main()
{
for (int i=0;i<n;i++)
f();
return 0;
}
void f()
{ int a[n];
for (j=0;j<n;j++)
{
some statements;
}
}
Time Complexity - O(n2)
Space complexity – O(n)

int n=64;
steps=0;
for (int i=1; i<=n;i*=2)
steps++;
cout<<steps;
int* A;
A=new int[steps];
….
….
Time Complexity - O(log n)
Space complexity – O(log n)

Data Structures
Linear
(Lists, Arrays,
Linked lists,
Stacks)
Non-Linear
(Trees,
Graphs…)

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Newly poured concrete opposing hot and windy conditions is considerably susceptible to plastic shrinkage cracking. Crack-free concrete structures are essential in ensuring high level of durability and functionality as cracks allow harmful instances or water to penetrate in the concrete resulting in structural damages, e.g. reinforcement corrosion or pressure application on the crack sides due to water freezing effect. Among other factors influencing plastic shrinkage, an important one is the concrete surface humidity evaporation rate. The evaporation rate is currently calculated in practice by using a quite complex Nomograph, a process rather tedious, time consuming and prone to inaccuracies. In response to such limitations, three analytical models for estimating the evaporation rate are developed and evaluated in this paper on the basis of the ACI 305R-10 Nomograph for “Hot Weather Concreting”. In this direction, several methods and techniques are employed including curve fitting via Genetic Algorithm optimization and Artificial Neural Networks techniques. The models are developed and tested upon datasets from two different countries and compared to the results of a previous similar study. The outcomes of this study indicate that such models can effectively re-develop the Nomograph output and estimate the concrete evaporation rate with high accuracy compared to typical curve-fitting statistical models or models from the literature. Among the proposed methods, the optimization via Genetic Algorithms, individually applied at each estimation process step, provides the best fitting result.

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Using the Artificial Neural Network to Predict the Axial Strength and Strain ...Journal of Soft Computing in Civil Engineering

The main purpose of the current study was to formulate an empirical expression for predicting the axial compression capacity and axial strain of concrete-filled plastic tubular specimens (CFPT) using the artificial neural network (ANN). A total of seventy-two experimental test data of CFPT and unconfined concrete were used for training, testing, and validating the ANN models. The ANN axial strength and strain predictions were compared with the experimental data and predictions from several existing strength models for fiber-reinforced polymer (FRP)-confined concrete. Five statistical indices were used to determine the performance of all models considered in the present study. The statistical evaluation showed that the ANN model was more effective and precise than the other models in predicting the compressive strength, with 2.8% AA error, and strain at peak stress, with 6.58% AA error, of concrete-filled plastic tube tested under axial compression load. Similar lower values were obtained for the NRMSE index.

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OPTIMIZING DATA INTEROPERABILITY IN AGILE ORGANIZATIONS: INTEGRATING NONAKA’S...ijdmsjournal

Agile methodologies have transformed organizational management by prioritizing team autonomy and iterative learning cycles. However, these approaches often lack structured mechanisms for knowledge retention and interoperability, leading to fragmented decision-making, information silos, and strategic misalignment. This study proposes an alternative approach to knowledge management in Agile environments by integrating Ikujiro Nonaka and Hirotaka Takeuchi’s theory of knowledge creation— specifically the concept of Ba, a shared space where knowledge is created and validated—with Jürgen Habermas’s Theory of Communicative Action, which emphasizes deliberation as the foundation for trust and legitimacy in organizational decision-making. To operationalize this integration, we propose the Deliberative Permeability Metric (DPM), a diagnostic tool that evaluates knowledge flow and the deliberative foundation of organizational decisions, and the Communicative Rationality Cycle (CRC), a structured feedback model that extends the DPM, ensuring long-term adaptability and data governance. This model was applied at Livelo, a Brazilian loyalty program company, demonstrating that structured deliberation improves operational efficiency and reduces knowledge fragmentation. The findings indicate that institutionalizing deliberative processes strengthens knowledge interoperability, fostering a more resilient and adaptive approach to data governance in complex organizations.

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Modeling the Influence of Environmental Factors on Concrete Evaporation RateJournal of Soft Computing in Civil Engineering

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CS-102 DS-class04a Lectures DS Class.pdf

1. Space Complexity • Components of Space Complexity – Instruction space – Data Space – Environmental Stack Space

2. Instruction space • Space needed to store the compiled version of the program instructions. • The compiler used to compile the program into machine code – Suppose we have an expression a+b+c*d, the compiler computes this as (c*d)+a+b and generate the shorter and more time-efficient code.

3. Data Space • The space needed to store all the constants and variables. • Data space has two components 1. Space needed by constants and simple variables. 2. Space needed by dynamically allocated objects such as arrays and class instances

4. Environment Stack Space • The environment stack is used to save information needed to resume execution of partially completed functions and methods. • Beginning performance analysts often ignore the space needed by the environment stack because they don’t understand how functions are invoked and what happens on termination. • Each time a function is invoked the following data are saved on the environment stack: – The return address – The values of all local variables and formal parameters in the functions being invoked (necessary for recursive functions only).

5. #include <iostream.h> void easy(int N) { if (N < 1) return; easy(N-2); cout<<N; easy(N-3); cout<<N; } void main() { easy(4); }

6. Examples int main() { int i, sum=0; int n; cin>>n; for (i=0; i<n; i++) sum+=i; } Time Complexity - O(n) Space complexity – O(1)

7. Examples int main() { int n; cin>>n; int *arr; arr=new int [n]; for (int i=0; i<n; i++) for (int j=0; j<i; j++) { some statements; } return 0; }} Time Complexity - O(n2) Space complexity – O(n)

8. const int n=100; int main() { for (int i=0;i<n;i++) f(); return 0; } void f() { int a[n]; for (j=0;j<n;j++) { some statements; } } Time Complexity - O(n2) Space complexity – O(n)

9. int n=64; steps=0; for (int i=1; i<=n;i*=2) steps++; cout<<steps; int* A; A=new int[steps]; …. …. Time Complexity - O(log n) Space complexity – O(log n)

10. Data Structures Linear (Lists, Arrays, Linked lists, Stacks) Non-Linear (Trees, Graphs…)

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