Pedagogical Justifications That Zero Factorial Equals One

Zero factorial, defined to be one, is often counterintuitive to students but nonetheless an interesting concept to convey in a classroom environment. The challenge is to delineate the concept in a simple and effective way. This article reveals and makes contribution to much simpler justifications on the notion of zero factorial to be one when compared to previous studies in the area. We note that the connection of zero factorial to the definition of the gamma function provides a first-hand conceptual understanding of the concept of zero factorial. But for the purpose of teaching, it is not particularly helpful from the pedagogical point of view in early years of study, as it is quite challenging to explain the rationale behind the origin of the definite integral that defines the gamma function. In this regard two algebraic and one statistical justification are presented. The "squeeze theorem" plays a pivotal role in this article. To assess the effectiveness of the justifications pedagogically, an online survey was conducted at a Canadian university.Comment: 22 page

Donation of Human Organs and Transplantation in the Light of Great Religions

Donation of Human Organs and Transplantation are very important issues of the current era. It is being processed in different manners. The medical field is providing new kinds of treatments as well as the transplant of organs for the benefit of humanity. It is, no doubt, a very difficult and intricate part of recent medication. It is a substitution of original but defective organ of a human being by an alternative but perfect organ through operation. It has a variety of methods. Inserting and organ transplantation is a significant issue currently. The medical field is presenting new sources of treatments together with the human organs transplant for human welfare at large. In Christianity and Judaism organ donation is permissible but in Islam there are a small number of religious scholars of the Indo-Pak who are in conflict with the matter of organ donation. But the middle-of-the-road of religious scholars of Islamic states approve and support that organ donation is permissible in Islamic Shariah. From the Islamic point of view, Organ Donation and Transplantation is only allowed if the goals of Islamic Sharia are pursued in letter and spirit. It will be helpful, under the direction of a certified transplant team. In this article, the legitimacy of Donation of Human Organs and Transplantation in the light of great Religions has been deliberated

Detection and Localization of Firearm Carriers in Complex Scenes for Improved Safety Measures

Detecting firearms and accurately localizing individuals carrying them in images or videos is of paramount importance in security, surveillance, and content customization. However, this task presents significant challenges in complex environments due to clutter and the diverse shapes of firearms. To address this problem, we propose a novel approach that leverages human-firearm interaction information, which provides valuable clues for localizing firearm carriers. Our approach incorporates an attention mechanism that effectively distinguishes humans and firearms from the background by focusing on relevant areas. Additionally, we introduce a saliency-driven locality-preserving constraint to learn essential features while preserving foreground information in the input image. By combining these components, our approach achieves exceptional results on a newly proposed dataset. To handle inputs of varying sizes, we pass paired human-firearm instances with attention masks as channels through a deep network for feature computation, utilizing an adaptive average pooling layer. We extensively evaluate our approach against existing methods in human-object interaction detection and achieve significant results (AP=77.8\%) compared to the baseline approach (AP=63.1\%). This demonstrates the effectiveness of leveraging attention mechanisms and saliency-driven locality preservation for accurate human-firearm interaction detection. Our findings contribute to advancing the fields of security and surveillance, enabling more efficient firearm localization and identification in diverse scenarios.Comment: This paper is accepted in IEEE Transactions on Computational Social System

Reliability Assessment of the Iraqi National Communication Network

The design of communication networks continues to progress rapidly in more suitable forms to meet the challenges of the present era. This paper presents a tentative study of the design of a reliable Iraqi National Communication Network (INCN). The INCN connects all major cities in Iraq with Baghdad as the central node. The followed subjected procedure is a general method which can be applied to all similar problems concerning any Wide Area Network design.  An optimal backbone is first designed by Prim’s algorithm which has distances between cities as input data. The INCN is subjected to reliability improvement by adding links to the initial backbone. An improved algorithm based on tie-sets method is developed for network reliability computation. Three chosen scenarios (Net1, Net2, and Net3) for the INCN are presented and underwent reliability estimation. Evaluation results show a successive improvement of the network reliability to yield to an optimal solution recognized as being Net3. The adopted topology for the INCN is based on two clusters having Baghdad as the common head cluster. Clustering will simplify the reliability evaluation by decreasing the number of tie-sets, and hence the computation complexity.

Detection of bacterial load in drinking water samples by 16s rRNA ribotyping and RAPD analysis

Background: Safe and healthy drinking water is inaccessible to more than 20% of the world population. Among some major risks to safety of potable water, contamination with pathogenic microorganisms is the most alarming and harmful Therefore, it is needed to develop and implement fast and accurate methods for the detection of bacterial contamination in water. Methods: Biological analysis of drinking water samples obtained from nine different collection points of Lahore city was carried out and total of six different bacterial strains were isolated. Biochemical characterization was done under standard laboratory conditions. Molecular identification of these isolates was done by using random amplified polymorphic DNA (RAPD) analysis. Results: The drinking water sample collected from Punjab University showed highest bacterial count 1066/0.5 ml of drinking water while residential area of University of the Punjab contained least number of bacterial counts i.e., 38/0.5 ml of drinking water. Amplification patterns of isolates SZ1, SZ3, SZ4 and SZ6 obtained by RAPD were found similar to genus Bacillus. While, SZ2 and SZ5 had unique amplification patterns identical to Bacillus megaterium. All the six bacterial strains were tested for the presence of protease, lipase, cellulase, and amylase. Strain SZ2 gave positive result for all of them except amylase.Conclusion: Tube well water of Punjab University area of Lahore is safe for drinking purpose except admin block tube. It is recommended to monitor the bacteriological load of drinking water at regular intervals in order to control water borne bacterial diseases

Evolutionary and Reinforcement Fuzzy Control

Many modern and classical techniques exist for the design of control systems. However, many real world applications are inherently complex and the application of traditional design and control techniques is limited. In addition, no single design method exists which can be applied to all types of system. Due to this 'deficiency', recent years have seen an exponential increase in the use of methods loosely termed 'computational intelligent techniques' or 'soft- computing techniques'. Such techniques tend to solve problems using a population of individual elements or potential solutions or the flexibility of a network as opposed to using a rigid, single point of computing. Through use of computational redundancies, soft-computing allows unmatched tractability in practical problem solving. The intelligent paradigm most successfully applied to control engineering, is that of fuzzy logic in the form of fuzzy control. The motivation of using fuzzy control is twofold. First, it allows one to incorporate heuristics into the control strategy, such as the model operator actions. Second, it allows nonlinearities to be defined in an intuitive way using rules and interpolations. Although it is an attractive tool, there still exist many problems to be solved in fuzzy control. To date most applications have been limited to relatively simple problems of low dimensionality. This is primarily due to the fact that the design process is very much a trial and error one and is heavily dependent on the quality of expert knowledge provided by the operator. In addition, fuzzy control design is virtually ad hoc, lacking a systematic design procedure. Other problems include those associated with the curse of dimensionality and the inability to learn and improve from experience. While much work has been carried out to alleviate most of these difficulties, there exists a lack of drive and exploration in the last of these points. The objective of this thesis is to develop an automated, systematic procedure for optimally learning fuzzy logic controllers (FLCs), which provides for autonomous and simple implementations. In pursuit of this goal, a hybrid method is to combine the advantages artificial neural networks (ANNs), evolutionary algorithms (EA) and reinforcement learning (RL). This overcomes the deficiencies of conventional EAs that may omit representation of the region within a variable's operating range and that do not in practice achieve fine learning. This method also allows backpropagation when necessary or feasible. It is termed an Evolutionary NeuroFuzzy Learning Intelligent Control technique (ENFLICT) model. Unlike other hybrids, ENFLICT permits globally structural learning and local offline or online learning. The global EA and local neural learning processes should not be separated. Here, the EA learns and optimises the ENFLICT structure while ENFLICT learns the network parameters. The EA used here is an improved version of a technique known as the messy genetic algorithm (mGA), which utilises flexible cellular chromosomes for structural optimisation. The properties of the mGA as compared with other flexible length EAs, are that it enables the addressing of issues such as the curse of dimensionality and redundant genetic information. Enhancements to the algorithm are in the coding and decoding of the genetic information to represent a growing and shrinking network; the defining of the network properties such as neuron activation type and network connectivity; and that all of this information is represented in a single gene. Another step forward taken in this thesis on neurofuzzy learning is that of learning online. Online in this case refers to learning unsupervised and adapting to real time system parameter changes. It is much more attractive because the alternative (supervised offline learning) demands quality learning data which is often expensive to obtain, and unrepresentative of and inaccurate about the real environment. First, the learning algorithm is developed for the case of a given model of the system where the system dynamics are available or can be obtained through, for example, system identification. This naturally leads to the development of a method for learning by directly interacting with the environment. The motivation for this is that usually real world applications tend to be large and complex, and obtaining a mathematical model of the plant is not always possible. For this purpose the reinforcement learning paradigm is utilised, which is the primary learning method of biological systems, systems that can adapt to their environment and experiences, in this thesis, the reinforcement learning algorithm is based on the advantage learning method and has been extended to deal with continuous time systems and online implementations, and which does not use a lookup table. This means that large databases containing the system behaviour need not be constructed, and the procedure can work online where the information available is that of the immediate situation. For complex systems of higher order dimensions, and where identifying the system model is difficult, a hierarchical method has been developed and is based on a hybrid of all the other methods developed. In particular, the procedure makes use of a method developed to work directly with plant step response, thus avoiding the need for mathematical model fitting which may be time-consuming and inaccurate. All techniques developed and contributions in the thesis are illustrated by several case studies, and are validated through simulations