Digital design computing and modelling for 3-D concrete printing

Limited investigations evaluated the synergistic effects between ceramic waste powder (CWP) and blast furnace slag (BFS) on the pozzolanic activity and thermal properties of cementitious ma...

Use of Ceramic-Waste Powder as Value-Added Pozzolanic Material with Improved Thermal Properties

Bio-Inspired optimization algorithms are inspired from principles of natural biological evolution and distributed collective of a living organism such as (insects, animal, …. etc.) for obtaining the optimal possible solutions for hard and complex optimization problems. In computer science Bio-Inspired optimization algorithms have been broadly used because of their exhibits extremely diverse, robust, dynamic, complex and fascinating phenomenon as compared to other existing classical techniques.This paper presents an overview study on the taxonomy of bio-inspired optimization algorithms according to the biological field that are inspired from and the areas where these algorithms have been successfully applied

/pdf/taxonomy-of-bio-inspired-optimization-algorithms-ant65hbuyw.pdf

Taxonomy of bio-inspired optimization algorithms

Metaheuristic algorithms are optimization algorithms that are used to address complicated issues that cannot be solved using standard approaches. These algorithms are inspired by natural processes such as genetics, swarm behavior, and evolution, and they are used to explore a broad search space to identify the global optimum of a problem. Genetic algorithms, particle swarm optimization, ant colony optimization, simulated annealing, and tabu search are examples of popular metaheuristic algorithms. These algorithms have been widely utilized to address complicated issues in domains like as engineering, finance, and computer science. In general, the history of metaheuristic algorithms spans several decades and involves the development of various optimization algorithms that are inspired by natural systems. Metaheuristic algorithms have become a valuable tool in solving complex optimization problems in various fields, and they are likely to continue to play an important role in the development of new technologies and applications.

Overview of Metaheuristic Algorithms

Flying Ad hoc Network (FANET) is a self-organizing wireless network that constitutes swarms of flying nodes, namely Unmanned Aerial Vehicles (UAV), and communicates in close proximity. It has various distinguishing characteristics that set it apart from other ad hoc networks, posing some issues, particularly in routing. UAVs are highly dynamic and have frequent topology changes. Hence, the network urges an efficient routing technique to coordinate the node swarms and enhance the evaluation metrics of the network. The biological behavior of various living organisms, such as animals, insects, microbes, and humans, inspires researchers to solve various routing problems in ad hoc networks. Decentralized self-organized swarms of UAVs closely resemble the biological system. Therefore, the Bio-Inspired Algorithms (BIA) resolve a wide range of routing challenges in FANET. A Systematic Literature Review (SLR) is adopted to survey FANET routing methods based on non-hybrid and hybrid BIAs to properly comprehend the existing bio-inspired strategies used in FANET routing. The review will be beneficial for the researchers in the specified area. To our knowledge, no SLR has been conducted about the FANET routing protocol that employs BIA. This paper examines (1) the characteristics and features of existing routing algorithms, (2) the need of both non-hybrid and hybrid BIA for effective and optimal routing, (3) an analysis of the method’s simulation tools, evaluation metrics, and mobility models, (4) the current issues and scope of the study related to the specified method. 

/pdf/optimized-routing-of-uavs-using-bio-inspired-algorithm-in-1d3opx5b.pdf

Optimized Routing of UAVs Using Bio-Inspired Algorithm in FANET: A Systematic Review

Whenever characterization of a new DNA sequence takes place then, database search is carried out to find whether homolog’s of gene is present or not. Various evolutions in this field have marked the shift from exact matching to a completely different concept, parameterized matching. Parameterized matching is detected by consistent renaming of text and pattern using bijective mapping. While finding matches between pattern and text, the PBMH-Hash algorithm results in frequent occurrence of false matches with large number of character comparison. This paper presents a new algorithm to detect similarity in biological sequences. The proposed algorithm is based on the concept of Berry-Ravindran algorithm and q-Gram. Analysis shows that our algorithm outperforms existing PBMH-Hash algorithm.

Similarity Detection in Biological Sequences using Parameterized Matching and Q-gram

Finding a pattern P[0...m-1] in a large body of text T[0...n-1], m≤n, is called pattern matching. One of its variation is parameterized string matching. Two strings are said to parameterized match, if one can be transformed into other via some bijective mapping. The parameterized matching has important applications in software maintenance and plagiarism detection. In this paper, we propose a new algorithm for multi-pattern parameterized string matching problem. This is an extension of quick multiple matching algorithm for exact string matching. Experimental results show that the new algorithm is very fast in practice.

An offensive algorithm for multi-pattern parameterized string matching

Information retrieval systems developed during last 2–3 decades have marked the existence of web search engines. These search engines have become an important role player in the field of information seeking. This increasing importance of search engines in the field of information retrieval has compelled the search engine companies to put their best for the improvement of the search results. Therefore the measurement of the search efficiency has become an important issue. Information retrieval is basically used for identifying the activities which makes us capable to extract the required documents from a document repository. Information retrieval today is done on the basis of numerous textual and geographical queries having both the textual and spatial components. The textual queries of any IRS are resolved using indexes and an inversion list. This paper mainly concentrates on the indexing part and the analysis of the algorithm. Several structures are in existence for implementing these indexes. Hash tables, B-trees, sorted arrays, wavelet trees are few to name. For an efficient data structure there are different deciding parameters that are to be taken into account. Some important parameters considered in this paper are index creation time, storage required by inverted file and retrieval time. This paper provides a detailed comparative study of different data structures for the implementation of inverted files.

Efficient Methods to Generate Inverted Indexes for IR

Aim: To evaluate multiple clinical complications and management of chronic suppurative otitis media Study design: A retrospective study Place and Duration: This study was conducted at Jinnah Post Graduate Medical Centre Karachi Pakistan from June 2020 to June 2021. Methodology All patients of chronic suppurative otitis media with intra or extra cranial complications who were admitted to the ENT Department were included in this study. Clinical data, related complications, care, and follow-up were all analyzed. In all patients, pure tone audiometry was used to assess their hearing. Every patient had a high-resolution computer tomography (HRCT) temporal bone scan and all other relevant investigations including aural pus swab for culture and sensitivity done . Every patient with CSOM-related intracranial problems had a Magnetic Resonance Imaging (MRI) brain scan. Results: During the trial of 280 individuals, 42 patients with CSOM complications were observed. A total of 19 of them had intracranial complications, while 23 had extra cranial complications. The youngest patient was five years old, while the oldest was 58 years. The majority of the cases were observed in 11 to 20 years age groups. Only 6 of the 42 individuals had bilateral CSOM, whereas the remaining 36 had unilateral CSOM. Conclusions: CSOM-related problems are still widespread, despite the availability of broad spectrum antibiotics. Patients should be given higher doses of intravenous antibiotics (that breach the blood-brain barrier) followed by mastoid surgery. Early detection of concomitant intracranial complications using HRCT and MRI and adequate antibiotic treatment, abscess drainage, and mastoid surgery as soon as possible are all essential to prevent mortality from CSOM complications. Keywords: Acute otitis media, CSOM, intravenous antibiotics, intracranial complication, extracranial complication.

Evaluation of Complications and Management of Chronic Suppurative Otitis Media: A Retrospective Study

Maintenance of the software is concerned with the changes and modifications implemented to the software. It needs to be validated that the modifications has not led to the degradation in the quality of the software. Software regression testing is required to instill confidence that changes are valid. Regression testing is very expensive. It requires the optimization of regression test cases. Recently, evolutionary and meta- heuristic algorithms are coming into existence as search and optimization tools. This paper proposes a regression test case optimization technique based on honey bee mating optimization and fuzzy rule base, which reduces the size of the test suite by selecting the test cases from the existing test suite. The test cases which are necessary for validating the recent changes in the software and have the ability to find the faults and cover maximum coding under testing in minimum time are selected. An algorithm is designed which takes as input the test suite containing several test cases and based on their execution time and fault coverage, reduces the number of test cases. The test suite is reduced by 50% which optimizes the overall regression testing process. The proposed algorithm reduces the test suite data by approximately 50%. Although the percentage reduction is found little bit low in comparison to existing algorithm but the overall algorithm is found efficient as it provides an authentic way of selecting test cases using fuzzy rule base.

/pdf/regression-test-case-optimization-using-honey-bee-mating-5ga21yuxsg.pdf

Regression Test Case Optimization Using Honey Bee Mating Optimization Algorithm with Fuzzy Rule Base

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