Researchers have concluded on human abilities during the recent decades that beside two general intelligence (IQ) and excitement intelligence (EQ), there is another aspect of intelligence which is completely limited to human. In fact, it is said that IQ and EQ are not accountable for everything and not really effective. However, human needs a third factor, which is called spiritual intelligence (SQ). Accepting multi intelligences and existence of different approaches on this matter, it seems that not only the common fields but also spiritual intelligence being unique could be explained briefly comparing to other types of intelligences and its abilities. Spiritual intelligence could be openly recognized from other intelligences by some inherent abilities, features such as ability for spiritual state, metaphorical mental process, and create a general viewpoint to life experiences.

We are... So, to reframe the inquiry: who are we on a metaphysical level? Which aspects of ourselves are the most universally representative of who we are? How do we fare in the face of the passage of time? For decades, philosophers have debated the concept of diachronic personal identity, which focuses on the question of what keeps us alive. An intricate debate has developed between those who hold the body view (animalism) and those who hold the psychological view (memory) on the question of who we are. The two groups will eventually become so firmly set in their views that they will be unable to compromise. Hylomorphic animalism, or the view that humans are rational animals, living bodies made of prime matter and a rational soul, is an alternative answer to this divisive question that I propose in this study, following in the footsteps of Aristotle and Aquinas. We only survive if matter and rational souls do.

Background: Ameloblastoma an odontogenic tumor of ectodermal origin has been reported to arise, on rare occasions, in a primordial or dentigerous cyst of a young person. Numerous authors have suggested differing nomenclatures for these ameloblastomas and have sought to describe and classify the clinical and histopathologic features. These tumors have been characterized as a distinct variant exhibiting less aggressive behavior and a lower rate of recurrence than conventional ameloblastoma.Aims: This study was designed to look at the Human papilloma virus as a caurse for Ameloblastoma.Materials and Methods: Samples of Ameloblastoma tumors were traced from the filing system in path lab Beheshti Dental School. Using PCR technique a search was carried out to dated HPV in DNA of samples.Results: There was a clear relationship between the rates of HPV in Ameloblastoma cases.Conclusion: HPV could be a preparing factor for Ameloblastoma.

Human Identification has been widely implemented to enhance the efficiency of surveillance systems, however, systems based on common CCTV (closed-circuit television) cameras are mostly incompatible with the advanced identification algorithms which aim to extract the facial features or speech of an individual for identification. Gait (i. e., an individual’s unique walking pattern/style) is a leading exponent when compared to first-generation biometric modalities as it is unobtrusive (i. e., it requires no contact with the individual), hence proving gait to be an optimal solution to human identification at a distance. This paper proposes an automatic identification system that analyzes gait to identify humans at a distance and predicts the strength of the match (i. e., probability of the match being positive) between two gait profiles. This is achieved by incorporating computer vision, digital image processing, vectorization, artificial intelligence, and multi-threading. The proposed model extracts gait profiles (from low-resolution camera feeds) by breaking down the complete gait cycle into four quarter-cycles using the variations in the width of the region-of-interest and then saves the gait profile in the form of four distinct projections (i. e., vectors) of length 20 units each, thus, summing up to 80 features for each individual’s gait profile. The focus of this study revolved around the speed-accuracy tradeoff of the proposed model where, with a limited dataset and training, the model runs at a speed of 30Hz and yields 85% accurate results on average. A Receiver Operating Characteristic Curve (ROC) is obtained for comparison of the proposed model with other machine learning models to better understand the efficiency of the system

Machine learning technologies are expected to increasingly affect people's daily lives. The next generation of children will be most affected by this technology; because they will be born and raised in an era of big data and machine systems that will affect their life decisions from education, access to credit and job opportunities, and many other things. In this article, the effects of artificial intelligence on children's human rights have been investigated and solutions have been tried to protect children's rights in the dynamic world of technology and artificial intelligence. In addition, the impact and interaction with human rights and especially children's right to education and respect for privacy, freedom of expression, and prohibition of discrimination are important issues that the main guardians such as companies, parents, and governments should address.

Multi-label classification aims at assigning more than one label to each instance. Many real-world multi-label classification tasks are high dimensional, leading to reduced performance of traditional classifiers. Feature selection is a common approach to tackle this issue by choosing prominent features. Multi-label feature selection is an NP-hard approach, and so far, some swarm intelligence-based strategies and have been proposed to find a near optimal solution within a reasonable time. In this paper, a hybrid intelligence algorithm based on the binary algorithm of particle swarm optimization and a novel local search strategy has been proposed to select a set of prominent features. To this aim, features are divided into two categories based on the extension rate and the relationship between the output and the local search strategy to increase the convergence speed. The first group features have more similarity to class and less similarity to other features, and the second is redundant and less relevant features. Accordingly, a local operator is added to the particle swarm optimization algorithm to reduce redundant features and keep relevant ones among each solution. The aim of this operator leads to enhance the convergence speed of the proposed algorithm compared to other algorithms presented in this field. Evaluation of the proposed solution and the proposed statistical test shows that the proposed approach improves different classification criteria of multi-label classification and outperforms other methods in most cases. Also in cases where achieving higher accuracy is more important than time, it is more appropriate to use this method.

Purpose: The current research was conducted to imaging governance intelligence and the result of the co-creation of human and machine intelligence in the governance laboratory. In this research, governance intelligence is first introduced, and then its place in the governance laboratory is explained. Not only has the concept of governance recently received the attention of scientific and experimental actors in the scientific literature, but the governance laboratory has also been introduced to identify the system of governance issues and the evidence-based and scientific solution to these issues. In the meantime, due to the high complexity of governance, governance intelligence will help on the one hand to understand and on the other hand to solve problems in the governance laboratory that may occur.Design/ methodology/ approach:  Sandelowski and Barroso's (2007) meta-synthesis qualitative method was used for this research, and during it, 250 related documents were initially examined from among various scientific documents in domestic and foreign reliable scientific databases, and finally, 60 documents were coded as samples. Considering the new and emerging concept of governance, governance laboratory and governance intelligence, with the analysis of scientific texts that refer to various aspects of these phenomena and between the lines of research, the model of governance intelligence has been designed to provide a clearer picture of the concept and reality presented governance intelligence for researchers and practitioners.Research Findings: The results showed that governance intelligence has 10 aspects and dimensions, which indicate its complexity; These funds include rational and problem-solving intelligence, policy-making and regulatory intelligence; Foresight and strategic intelligence, holistic and systemic intelligence, management and leadership intelligence, political and network intelligence, spiritual and moral intelligence, cultural and social intelligence, financial and economic intelligence, emotional and emotional intelligence. Together, these dimensions create a suitable intelligence capacity for governance. The gathering of such dimensions and components of governance intelligence shows the multiplicity of factors and elements of governance, resulting in the need for multiple intelligences in governance. As the complexity of a phenomenon increases, the intelligence required to understand and solve its problems will also become more complex. Human and machine intelligence together can overcome these complexities and provide more suitable solutions for governance issues, which have been able to understand the complexity of governance and provide deeper analyses of it. Finally, governance laboratories provide an approach for the co-creation of human and machine intelligence, in 10 dimensions to identify, understand and solve governance issues, and it is expected that with the continuous development of governance intelligence, it will be possible to provide scientific and reasonable answers for the system of governance issues in the country. He gradually witnessed the maturity of governance intelligence, and as a result, more accurate answers to governance issues.Limitations & Consequences: The most important limitation of the current research is the lack of specific and clear definitions and concepts of the research topic, strengthening the necessity of conducting the research. It is expected that such researches will have scientific and experimental consequences in the thinking of experts and scientists as well as the actions of governance activists who consider governance to be complex and require deep and wide analysis and always seek to develop intellectual and practical capacities in understanding and solving governance issues.Practical Consequences: The present article emphasizes the creation of a governance laboratory in understanding and solving problems and introduces governance intelligence (human and machine intelligence) as a necessity and one of the most important capacities for understanding and solving governance problems, especially in the governance laboratory.Innovation or value of the Article: The introduction of the governance laboratory, governance intelligence, and the perspective of experimental governance that emphasizes evidence-based and scientific methodology for solving governance issues is the initiative and innovation of this article.Paper Type: Original Paper