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We can learn more about how our reality is made and what non-ordinary states of consciousness are by studying the Quantum Hologram Theory of Physics and Consciousness (QHTC). The QHTC says that consciousness is not local and that altered states of consciousness can help us understand how our minds work in more than one way. That's what Schrödinger thought. He thought that the quantum mechanical wave function was a field of consciousness. QHTC is based on holographic theories for human consciousness. These theories say that the brain works like a hologram and that it processes images into interference patterns that are then turned into virtual images, just like a laser hologram. These quantum waves can store a lot of information, which our brains use to make our three-dimensional world. This article says that this last theory should be the main framework for research on altered states of consciousness, and it talks about how to get data for analysis and how to get into an altered state for possible experiments.

According to the survival hypothesis, a person's personality and consciousness survive the death of the physical body. Ontology is a well-established theoretical branch of philosophy concerned with representations of reality. This research proposes the use of computer natural language processing (NLP), databases, structured query language and near-death experiences (NDEs) narratives to develop a quantum ontology based on the quantum hologram theory of physics and consciousness. This research proposes the use of a quantum ontology to represent the incomprehensible aspects of near-death experiences. The research demonstrates how to validate ontology constructs within a quantum ontology, demonstrating the methodology's potential for the development of a consciousness model based on the quantum paradigm.

Our study deals with detecting the causal structure of risk in industrial systems. We focus on the prioritization of risks in the form of correlated events sequences. To improve existing prioritization methods, we propose a new methodology using Dynamic Bayesian networks (DBN). We explore a new user interface for industrial control systems and data acquisition, known as Supervisory Control and Data Acquisition (SCADA), to demonstrate the analysis method of risk causal structure. Our results show that: (1) the network of variables before and after the failure is represented by a limited and distinct number of factors;(2) the network of variables before and after the failure can be graphically represented dynamically in a user interface to assist in fault prevention and diagnosis;(3) variables related to the sequence of events at the time of failure can be used as a model to predict its occurrence, and find the main cause of it, thus making it possible to prioritize the requirements of the production system on the right variables to be monitored and manage in the event of a breakdown

Credit card fraud, a significant and growing problem in commerce that costs the global economy billions of dollars each year, has kept up with technological advancements as criminals devise new and innovative methods to defraud account holders, merchants, and financial institutions. While traditional fraudulent methods involved card cloning, skimming, and counterfeiting during transactional processes, the rapid adoption and evolution of Internet technologies aimed at facilitating trade has given rise to new digitally initiated illegitimate transactions, with online credit card fraud beginning to outpace physical world transactions. According to the literature, the financial industry has used statistical methods and Artificial Intelligence (AI) to keep up with fraudulent card patterns, but there appears to be little effort to provide neural network architectures with proven results that can be adapted to financial legacy systems. The paper examines the feasibility and practicality of implementing a proof-of-concept Perceptron-based Artificial Neural Network (ANN) architecture that can be directly plugged into a legacy paradigm financial system platform that has been trained on specific fraudulent patterns. When using a credit checking subscription service, such a system could act as a backup.

In a photovoltaic (PV) control system, the application of a maximum power point tracking (MPPT) method is the key factor that enables PV modules to operate efficiently under shading conditions. However, dealing with technical parameters of the MPPT-based controller requires expertise’s knowledge about MPPT methods. PV planning tools overlook to provide system design parameters needed for the control system. Ontologies, as knowledge-based models, provide improved representation, sharing and re-use of the relevant information that facilitate the process of decision-making. In this work, we propose a knowledge-based model representing key concepts associated with an MPPT-based control device. The ontology model is featured with Semantic Web Rule Language (SWRL) allowing the system planner to extract information about MPPT classifications and select an appropriate MPPT method. Moreover, technical recommendations and design information needed for the control system is delivered as well. Using the proposed ontology helps nontechnical practitioners and end-users to define design-related parameters correctly and plan efficient PV systems.

The pandemic declared in many countries in 2020 due to COVID-19 led to the freezing of economies and the introduction of distance learning in both schools and universities. This unusual situation has affected the mental state of citizens, which has the potential to lead to the development of post-traumatic stress and depression. This study aimed to assess the level of stress in dental students in the context of the outbreak of the SARS-CoV-2 virus pandemic. A survey on the PSS-10 scale was prepared to measure the level of perceived stress. The study included 164 dental students at the Faculty of Medical Sciences of the Medical University of Silesia in Katowice, Poland. The results showed the impact of COVID-19 on the stress of students, with 67.7% reporting high levels of stress. The study also revealed that stress was higher among older female students. This paper recommends that the university provide more intensive psychological care as psychological first aid strategies in epidemics or natural disasters and to consider telemedicine in order to deliver services due to the limitations of the pandemic.

Every system, when connected to a network, is susceptible to threat of being hacked. It is important to protect all systems of an organization in real-time in a cost-effective manner. This article presents a well-designed and integrated database for risk management data using a dashboard interface in real-time risk that makes it easy for risk managers to reach a understanding the level of threats to be able to apply right controls to mitigate them. In this article, a case study of a data center for a statistical management institute is presented that proposes the calculation of total risk at the organization level by using the proposed risk database. A digital dashboard is also designed for presenting the risk level results so that decision makers can apply counter measures. The risk level on a dashboard viewer makes it easy for decision maker to understand the overall risk level at the statistics data center and assists in the creation of a tool to follow-up risk management since the time a threat hits until the time of its mitigation.

The article examines the effect of e-supply chain management systems in the operations and business models in the North American electronics manufacturing services (EMS) industry. A causal and descriptive research study was conducted based on a survey applied to thirty-six individuals in EMS firms in order to determine the impact of e-SCM on their key supply chain operations and business models. Results of the research revealed that e-SCM had a positive effect in the operations and business models of the EMS industry as these showed that the profits of the firm increased and internal communications was improved due to the implementation of e-SCM. The research also showed that e-SCMs have many technical issues such as problems with process automation and transmission of supply chain data, e-procurement effectiveness, integration with existing systems and the monitoring of inventory systems and the purchasing process. The research also shows how social media and the internet has affected the business models of the EMS by improving communications and operations in the overall supply chain.

The information systems (IS) discipline is in an ongoing crisis because of several reasons. Learning institutions do not have a well-defined curriculum on what should be taught in the IS discipline. Recently, there are many organizations and researchers who have come up with different BoKs in the IS discipline as discussed in this article. Additionally, IS discipline does not have a universal body of knowledge like in other fields such as electrical engineering which makes it even more challenging to align the existing industry knowledge and academic curriculum. Therefore, there is a need to develop a unified knowledge framework in the IS discipline which offers a single source of reference in both professional sector and development of academic curriculum. The major challenge in the developing of a unified knowledge framework in the IS discipline is that it is changing and evolving rapidly.

This article proposes a framework to integrate IT workers' competencies within the business technology management (BTM) body of knowledge (BoK) to help discussions and reflection on current efforts within the Canadian BTM community. Using the CIO role as a reference, it starts with a review of recent literature to determine the requirements of BTM competencies to develop what elements are needed in the BTM BOK. The article concludes with a proposal for next steps to further develop this framework into an operational tool.

In the last decade, Artificial Intelligence (AI) have been applied overwhelmingly in various research domains in the context of smart grid. It has been one of the main streams of advanced technological approaches that the research community offered for developing smart grids. However, the broad scope of the subject matter has launched complexity for scholars to identify effective research approaches. In this paper, we present a literature review about utilizing AI in the key elements of smart grids including grid-connected vehicles, data-driven components, and the power system network. This will result in highlighting technical challenges of the integration of electric vehicles to the grid and the power network operation as well. Moreover, we discuss the four key research areas in the context of AI and its applications in intelligent power grids. The proposed research fields aid PhD candidates to consider these areas as the promising domains for investigation.

Photovoltaic (PV) installers and non-technical solar energy consumers use PV planning software for the system design and simulation. End-users rely on the designed system and power estimations provided by these tools. However, most planning software products fail to consider shading conditions. This problem affects energy forecasting for solar power plants located in cold climates. In this paper, we define the status of full shading for a snow-covered panel and the minimum depth of snow creating it. Using a case study, we design the project by the most reliable planning software, System Advisor Model (SAM). We show that the simulation overestimates power generations for snowy months. To identify shading conditions and the correlated performance reductions, we compare the SAM results with the measured data collected onsite. As a result, the minimum depth of snow that can create full shading and zero production is detected. Moreover, comparing the measured data with the simulated power helps us to define a rule-base system providing PV performance reduction factors. It assists solar sector practitioners to plan a PV project accurately, especially for the locations where snowfall is an important environmental factor for several months.

NeuroIS uses tools such as electroencephalogram (EEG) that can be used to measure high brainwave frequencies that can be linked to human anxiety. Past research showed that computer anxiety influences how users perceive ease of use of a learning management system (LMS). Although computer anxiety has been used successfully to evaluate the usability of LMS, the main data collection mechanisms proposed for its evaluation have been questionnaires. Questionnaires suffer from possible problems such as being inadequate to understand some forms of information such as emotions and honesty in the responses. Quantum-based approaches to consciousness have been very popular in the last years including the quantum model reduction in microtubules of Penrose and Hameroff (1995). The objective of the chapter is to propose an architecture based on a NeuroIS that collects data by using EEG from users and then use the collected data to perform analytics by using a quantum consciousness model proposed for computer anxiety measurements for the usability testing of a LMS.