Psychological, Physical, and Heat Stress Indicators Prior to and after a 15-Minute Structural Firefighting Task

Firefighters work in strenuous conditions for prolonged periods wearing up to 20 kg of personal protective equipment. This often contributes to significant heat and cardiovascular strain. This study examined the relationships between psychological and physical measures taken prior to undertaking a 15 min firefighting task, and the occurrence of heat stress and high levels of fatigue following the task. Nine qualified firefighters completed a 15 min “live burn” scenario designed to mimic a fire started by a two-seater couch in a lounge room and completed simulated tasks throughout the duration. Logical reasoning, speed and accuracy, general motivation and fatigue, and physical and mental effort were recorded pre-scenario, and at 0- and 20-min post-scenario. General motivation and fatigue scores at 0- and 20-min post-scenario were highly correlated with each other (rs = 0.90; p = 0.001). The general motivation and fatigue scores, at 0- and 20-min post-scenario, were also strongly related to pre-task logic/reasoning test scores (Post 0 rs = −0.77, p = 0.016; Post 20 rs = −0.87, p = 0.002). Firefighters with lower logical reasoning and speed and accuracy scores were more susceptible to fatigue and impaired cognition when exposed to rises in core temperature and heat stress.

Algorithm of axial compressor stall warning based on BP neural network and fuzzy logic

Stall warning of axial compressor is very challenging and the existing warning margin is not enough. A algorithm based on BP neural network fusion fuzzy logic is proposed. Firstly, BP neural network is used for training recognition, next the identification results are fused with fuzzy logic reasoning to form the result judgment of time sequence, finally the stall early warning of axial compressor is realized. The simulation results of the experimental data show that the stall data at all speeds are at least 0.1s in advance of the early warning. Compared with other methods, this method has a better surge early warning margin performance and engineering practicability.

Granule-Based-Classifier (GbC): A Lattice Computing Scheme Applied on Tree Data Structures

Social robots keep proliferating. A critical challenge remains their sensible interaction with humans, especially in real world applications. Hence, computing with real world semantics is instrumental. Recently, the Lattice Computing (LC) paradigm has been proposed with a capacity to compute with semantics represented by partial order in a mathematical lattice data domain. In the aforementioned context, this work proposes a parametric LC classifier, namely a Granule-based-Classifier (GbC), applicable in a mathematical lattice (T,⊑) of tree data structures, each of which represents a human face. A tree data structure here emerges from 68 facial landmarks (points) computed in a data preprocessing step by the OpenFace software. The proposed (tree) representation retains human anonymity during data processing. Extensive computational experiments regarding three different pattern recognition problems, namely (1) head orientation, (2) facial expressions, and (3) human face recognition, demonstrate GbC capacities, including good classification results, and a common human face representation in different pattern recognition problems, as well as data induced granular rules in (T,⊑) that allow for (a) explainable decision-making, (b) tunable generalization enabled also by formal logic/reasoning techniques, and (c) an inherent capacity for modular data fusion extensions. The potential of the proposed techniques is discussed.

Structured argumentation dynamics

This paper develops a logical theory that unifies all three standard types of argumentative attack in AI, namely rebutting, undercutting and undermining attacks. We build on default justification logic that already represents undercutting and rebutting attacks, and we add undermining attacks. Intuitively, undermining does not target default inference, as undercutting, or default conclusion, as rebutting, but rather attacks an argument’s premise as a starting point for default reasoning. In default justification logic, reasoning starts from a set of premises, which is then extended by conclusions that hold by default. We argue that modeling undermining defeaters in the view of default theories requires changing the set of premises upon receiving new information. To model changes to premises, we give a dynamic aspect to default justification logic by using the techniques from the logic of belief revision. More specifically, undermining is modeled with belief revision operations that include contracting a set of premises, that is, removing some information from it. The novel combination of default reasoning and belief revision in justification logic enriches both approaches to reasoning under uncertainty. By the end of the paper, we show some important aspects of defeasible argumentation in which our logic compares favorably to structured argumentation frameworks.

Virology in the Classroom: Current Approaches and Challenges to Undergraduate- and Graduate-Level Virology Education

The pervasive effects of the current coronavirus disease 2019 pandemic are but one reason for educators to refocus their efforts on virology teaching. Additionally, it is critical to understand how viruses function and to elucidate the relationship between virus and host. An understanding of current virology education may improve pedagogical approaches for educating our students and trainees. Faculty who teach undergraduate microbiology indicate that approximately 10% of the course content features viruses; stand-alone virology courses are infrequently offered to undergraduates. Fortunately, virology taught to undergraduates includes foundational material; several approaches for delivery of lecture- and lab-based content exist. At the graduate education level, there is growing appreciation that an emphasis on logic, reasoning, inference, and statistics must be reintroduced into the curriculum to create a generation of scientists who have a greater capacity for creativity and innovation. Educators also need to remove barriers to student success, at all levels of education. Expected final online publication date for the Annual Review of Virology, Volume 8 is September 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

A Breakdown of System of Systems Needs Using Architecture Frameworks, Ontologies and Description Logic Reasoning

Aerospace systems are connected with the operational environment and other systems in general. The focus in aerospace product development is consequently shifting from a singular system perspective to a System-of-Systems (SoS) perspective. This increasing complexity gives rise to new levels of uncertainty that must be understood and managed to produce aerospace solutions for an ever-changing future. This paper presents an approach to using architecture frameworks, and ontologies with description logic reasoning capabilities, to break down SoS needs into required capabilities and functions. The intention of this approach is to provide a consistent way of obtaining the functions to be realized in order to meet the overarching capabilities and needs of an SoS. The breakdown with an architecture framework results in an initial design space representation of functions to be performed. The captured knowledge is then represented in an ontology with description logic reasoning capabilities, which provides a more flexible way to expand and process the initial design space representation obtained from the architecture framework. The proposed approach is ultimately tested in a search and rescue case study, partly based on the operations of the Swedish Maritime Administration. The results show that it is possible to break down SoS needs in a consistent way and that ontology with description logic reasoning can be used to process the captured knowledge to both expand and reduce an available design space representation.

Design of Multi-Information Fusion Based Intelligent Electrical Fire Detection System for Green Buildings

Building management systems are costly for small- to medium-sized buildings. A massive volume of data is collected on different building contexts by the Internet of Things (IoT), which is then further monitored. This intelligence is integrated into building management systems (BMSs) for energy consumption management in a cost-effective manner. Electric fire safety is paramount in buildings, especially in hospitals. Facility managers focus on fire protection strategies and identify where system upgrades are needed to maintain existing technologies. Furthermore, BMSs in hospitals should minimize patient disruption and be immune to nuisance alarms. This paper proposes an intelligent detection technology for electric fires based on multi-information fusion for green buildings. The system model was established by using fuzzy logic reasoning. The extracted multi-information fusion was used to detect the arc fault, which often causes electrical fires in the low-voltage distribution system of green buildings. The reliability of the established multi-information fusion model was verified by simulation. Using fuzzy logic reasoning and the membership function in fuzzy set theory to solve the uncertain relationship between faults and symptoms is a widely applied method. In order to realize the early prediction and precise diagnosis of faults, a fuzzy reasoning system was applied to analyze the arcs causing electrical fires in the lines. In order to accurately identify the fault arcs that easily cause electrical fires in low-voltage distribution systems for building management, this paper introduces in detail a fault identification method based on multi-information fusion, which can consolidate the complementary advantages of different types of judgment. The results demonstrate that the multi-information fusion method reduces the deficiency of a single criterion in fault arc detection and prevents electrical fires in green buildings more comprehensively and accurately. For the real-time dataset, the data results are presented, showing disagreements among the testing methods.

Analisis Tingkat Kepuasan Penumpang Terhadap Jasa Pelayanan Penyeberangan Menggunakan Metode Logika Fuzzy Servqual Pada PT. ASDP Indonesia Ferry Cabang Sibolga

Service can be said to be good, if the service can meet passenger satisfaction, satisfaction is achieved if the service received by passengers can exceed what is expected. In Fuzzy Logic, decision making is done by using an inference system called the fuzzy inference system which is a process of making conclusions based on Fuzzy Logic reasoning. Among the various models for measuring service quality (servqual), servqual is the most widely used method because of its high frequency of use. The criteria for measuring passenger satisfaction are physical form, reliability, response, empathy, assurance. From the calculation of the Servqual value (gap) per dimension, the first rank is the Reliability dimension of 7.65, as is the case with the calculation per criterion where the three priority criteria are part of the Reliability dimension. For the second order, Tangibles is 0.17, the third order is the Assurance dimension -0.05, the fourth order is empathy at 0.20, and the last or fifth order is Responsiveness of -11.48. This system is designed to analyze the level of passenger satisfaction at PT. ASDP Sibolga Branch using the PHP programming language. Data management using the MySQL database.