Kant’s Moral Theory Meets Evolutionary Theory

This paper delves into the intersection between Kant’s moral theory and evolutionary perspectives on _personhood_. It explores how Kant’s emphasis on rationality in moral agency aligns with evolutionary studies on the development of moral behaviors. By examining the transcendental implications of Kant’s _Categorical Imperative_ (CI) and the evolutionary origins of moral agency, this study aims to illuminate the link between Kant’s conception of moral agency and personhood. Additionally, it investigates how Kant’s call for CI resonates with evolutionary insights on the adaptive nature of social cooperation in human societies. Through this analysis, we seek to deepen our understanding of the cognitive, social dimensions of moral agency and moral status within the framework of Kant’s moral theory and evolutionary perspectives on personhood

Fuzzy-rough set models and fuzzy-rough data reduction

Rough set theory is a powerful tool to analysis the information systems. Fuzzy rough set is introduced as a fuzzy generalization of rough sets. This paper reviewed the most important contributions to the rough set theory, fuzzy rough set theory and their applications. In many real world situations, some of the attribute values for an object may be in the set-valued form. In this paper, to handle this problem, we present a more general approach to the fuzzification of rough sets. Specially, we define a broad family of fuzzy rough sets. This paper presents a new development for the rough set theory by incorporating the classical rough set theory and the interval-valued fuzzy sets. The proposed methods are illustrated by an numerical example on the real case

Named entity recognition using a new fuzzy support vector machine.

Recognizing and extracting exact name entities, like Persons, Locations, Organizations, Dates and Times are very useful to mining information from electronics resources and text. Learning to extract these types of data is called Named Entity Recognition(NER) task. Proper named entity recognition and extraction is important to solve most problems in hot research area such as Question Answering and Summarization Systems, Information Retrieval and Information Extraction, Machine Translation, Video Annotation, Semantic Web Search and Bioinformatics, especially Gene identification, proteins and DNAs names. Nowadays more researchers use three type of approaches namely, Rule-base NER, Machine Learning-base NER and Hybrid NER to identify names. Machine learning method is more famous and applicable than others, because it’s more portable and domain independent. Some of the Machine learning algorithms used in NER methods are, support vector machine(SVM), Hidden Markov Model, Maximum Entropy Model (MEM) and Decision Tree. In this paper, we review these methods and compare them based on precision in recognition and also portability using the Message Understanding Conference(MUC) named entity definition and its standard data set to find their strength and weakness of each these methods. We have improved the precision in NER from text using the new proposed method that calls FSVM for NER. In our method we have employed Support Vector Machine as one of the best machine learning algorithm for classification and we contribute a new fuzzy membership function thus removing the Support Vector Machine’s weakness points in NER precision and multi classification. The design of our method is a kind of One-Against-All multi classification technique to solve the traditional binary classifier in SVM

The Impact of Coronavirus on the Ecosystem of Rationality

The recent pandemic is a reminder of several important lessons from Popper's philosophy. My aim in this paper is to address some of these lessons. By making use of Popper's theory of three worlds, I explain how coronavirus has a far-reaching impact on the ecosystem of rationality, and how the viruses that threaten humans could also be a threat to the whole life on Earth. Applying the epistemological distinction between science and technology, I go on to explain the pivotal role of science in preventing further crises. This, I argue, is done by putting technology in the sphere of rationality; through both criticizing technologies and inspiring the invention of clean technologies, and also technologies that serve us as alerting systems. I shall argue that critical rationalism helps us to understand the ‘pandemic problem situation’ in a more informed manner and thus helps us to find out about the vulnerable points of our ecosystem of rationality in a more efficient way. In the latter part of the paper, I shall develop the thesis that while during the recent pandemic, science did it best to warn us about its dangers, the policy-makers, who are technologists of a sort, in many countries did not take those warnings seriously. Even when the crisis turned into a global catastrophe, the three types of technologies (health-care, lock-down, and diagnosis and treatment) were not fully efficient in controlling the pandemic. Drawing on Popper’s ideas I shall argue that in the face of the current emergency, our best chance to improve our situation is to apply the method of piecemeal social engineering to alleviate people’s suffering

The role of advertising through social networks to promote brand equity

Social networks are online gathering places for people who would like to share their interests and activities. In this context, advertising through social networks is one of the most important topics in the field of marketing and brand that has been considered only in few studies. This study examines the impact of advertising on brand equity through social networks in the beverage industry (PepsiCo). This research study was to survey and collect data from the a standard questionnaire. PepsiCo brand, which is a well known beverage industry worldwide is selected for the proposed study of this paper. Thus, all customers of Pepsi products in city of Tehran are considered as statistical research community and a sample size of 385 people is selected for the proposed study. In order to analyze the data, we use structural equations method and certified factor analysis. The results of our survey indicate that advertisement on social networks has a positive impact in this industry. Based on the results of our survey, we realize that there are some positive relationship between social network advertisement and quality perception, brand loyalty, brand awareness and brand association when the level of significance is one percent

Determining Surgical Candidacy in Temporal Lobe Epilepsy

Temporal lobe epilepsy (TLE) is the most common form of adult epilepsy that is amenable to surgical treatment. In the carefully selected patient, excellent seizure outcome can be achieved with minimal or no side effects from surgery. This may result in improved psychosocial functioning, achieving higher education, and maintaining or gaining employment. The objective of this paper is to discuss the surgical selection process of a patient with TLE. We define what constitutes a patient that has medically refractory TLE, describe the typical history and physical examination, and distinguish between mesial TLE and neocortical TLE. We then review the role of routine (ambulatory/sleep-deprived electroencephalography (EEG), video EEG, magnetic resonance imaging (MRI), neuropsychological testing, and Wada testing) and ancillary preoperative testing (positron emission tomography, single-photon emission computed tomography (SPECT), subtraction ictal SPECT correlated to MRI (SISCOM), magnetoencephalography, magnetic resonance spectroscopy, and functional MRI) in selecting surgical candidates. We describe the surgical options for resective epilepsy surgery in TLE and its commonly associated risks while highlighting some of the controversies. Lastly, we present teaching cases to illustrate the presurgical workup of patients with medically refractory TLE

The relationship between spiritual intelligence and self-management in patients with diabetes

BACKGROUND: Type 1 diabetes is one of the most common chronic diseases in children. Metabolic control and following diet therapy in teenagers with type 1 diabetes are weaker than children before the adolescence stage. One of the most important factors influencing self-management seems to be spiritual intelligence. The aim of this study was to investigate the relationship between spiritual intelligence and self-management in patients with diabetes.METHODS: The population of this descriptive cross-sectional study consisted of all adolescents with type 1 diabetes referring to the clinic of Tohid Hospital in Sanandaj, Iran, which were 194 people. Data were collected by interview and using a questionnaire. Sampling method was available or simple sampling. The collected data were analyzed using descriptive statistics and SPSS software.RESULTS: The majority of people were in the middle period of adolescence. More than half (88.5%) of them had a moderate and good economic situation and the majority of them (62.5%) had a history of diabetes in the family. Most of the people (56.5%) had an average duration of diabetes. More than half of the subjects were the first and second children of the family.CONCLUSION: The results showed that self-management increased with increasing spiritual intelligence of individuals, and with decreasing spiritual intelligence, self-management decreased; in other words, there was a positive and significant correlation between spiritual intelligence and self-management.

Coordinated Deep Neural Networks: A Versatile Edge Offloading Algorithm

As artificial intelligence (AI) applications continue to expand, there is a growing need for deep neural network (DNN) models. Although DNN models deployed at the edge are promising to provide AI as a service with low latency, their cooperation is yet to be explored. In this paper, we consider the DNN service providers share their computing resources as well as their models' parameters and allow other DNNs to offload their computations without mirroring. We propose a novel algorithm called coordinated DNNs on edge (\textbf{CoDE}) that facilitates coordination among DNN services by creating multi-task DNNs out of individual models. CoDE aims to find the optimal path that results in the lowest possible cost, where the cost reflects the inference delay, model accuracy, and local computation workload. With CoDE, DNN models can make new paths for inference by using their own or other models' parameters. We then evaluate the performance of CoDE through numerical experiments. The results demonstrate a $75\%$ reduction in the local service computation workload while degrading the accuracy by only $2\%$ and having the same inference time in a balanced load condition. Under heavy load, CoDE can further decrease the inference time by $30\%$ while the accuracy is reduced by only $4\%$