bluuebunny/crossref_metadata_2025 huggingface dataset:
Model-based flight phase suppression safety design and evaluation process for crew warnings
Authors: Wu Lina , Liu Kai
Published: June 2022

The crew warning information provides the crew with status indication information that they need to know during normal or abnormal flight, accurately and effectively inform the crew of the aircraft status, and guide the crew to take corresponding measures or establish situational awareness to reduce the impact of failure.However, in some critical flight stages, the flight crew needs to concentrate on manipulating the aircraft. The appearance of inappropriate warning messages will cause interference to the flight crew and affect flight safety. Therefore, in the warning design of civil aircraft, a flight phase suppression plan for warnings is generally formulated to suppress part of the crew warning information in some specific flight phases.The design of the flight phase suppression of the crew warning information will lead to the failure of the crew warning information during the flight phase of the crew warning information suppression when the warning function is normal. notice".Therefore, the flight phase suppression plan for the crew warning information should be evaluated by the safety engineer to ensure that the suppressed warning information will not affect the pilot's current flight control and meet the safety requirements.In the process of safety assessment, the analysis of the impact of "unannounced" failures is to consider that the crew is not aware of the failure during the entire flight phase, and the crew is unable to perform mitigation procedures or establish situational awareness, resulting in the impact of "unannounced" failures than "announced" failures. Bigger. However, the flight phase suppression of the crew warning information does not mean that the flight phase does not fail. The crew can know the failure after the suppression phase; therefore, the "unannounced" effect of the flight phase suppression phase may be the same as the "unannounced" effect during the entire flight phase. It's not the same. Used directly, the "unannounced" failure impact level of the entire phase is used as the "unannounced" impact level of the flight phase suppression phase, which may be too conservative.This paper presents a safety assessment method for the suppression of civil aircraft crew warning information during flight phase. Through determining the establishment of a list of factors affecting the failure; establishing one by one the corresponding relationship matrix between the factors and the failure impact levels and the relationship matrix of the factors changing with time; finally establishing the relationship matrix of the civil aircraft's various failure impact levels with time. As a criterion for the safety of civil aircraft crew warning information during flight phase suppression.
Paradoxes in Typeface Design for creating new Digital Design Experience
Authors: Ho Amic
Published: June 2022

Generally speaking, the paradox of design elements brought about by visual conflict can attract people's attention. Many designers apply similar ideas to their work. However, seldom do designers investigate the reasons behind the paradox of typography, as well as the approaches to creating this contradiction. The research question of this study refers to how the paradox of typography work for communication. The function of typography is not only to deliver messages but also to serve as the foundation for compositional communication. Letterforms were regarded as one of the most valuable graphic elements as they worked as the representations of language and organising type to serve multiple roles within a composition. This paper focuses on the experience of emotions created by typographic design, in particular, it also suggested an example to demonstrate how the experience of typographic designing related to the paradox of typography, built a hypothesis of why these may impact one another. Thus, the finding of this study provide a richer understanding of the design and the development of design projects.
Won’t you see my neighbor? User predictions, mental models, and similarity-based explanations of AI classifiers
Authors: Glasgow Kimberly , Kopecky Jonathon , Gersh John , Crego Adam
Published: June 2022

Humans should be able work more effectively with artificial intelligence-based systems when they can predict likely failures and form useful mental models of how the systems work (Johnson, et al. 2014, Klein, at al. 2005, Bansal et al. 2019, Tomsett, et al. 2020). We conducted a study of people’s mental models of artificial intelligence systems using a high-performing image classifier, focusing on participants’ ability to predict the classification result for a particular image. Participants viewed images in one of two classes and then predicted whether the classifier would label them correctly and indicated their confidence in their predictions. Participants also provided their own assessment of the correct class. In this experiment, we explored the effect of giving participants additional information. We showed them an array of the image’s nearest neighbors in a space representing the otherwise uninterpretable features extracted by the lower layers of the classifier’s neural network, using t-distributed stochastic neighbor embedding. We found that providing this neighborhood information did increase participants’ prediction performance, and that the performance improvement could be related to the neighbor images’ similarity to the target image. We also found indications that the presentation of this information may influence people’s own classification of the target image; in some cases after viewing the image’s neighbors, participants’ accuracy in identifying the actual class of the image was significantly worse when given the additional information, particularly when the set of neighbor images included images from the incorrect class. They became “mechanomorphized” in their own judgements, rather than anthropomorphizing the classifier’s process. There was also a significant relationship between reported confidence in predictions and accuracy, indicating that at a given level of confidence, participants in a control condition were significantly less accurate than experimental participants. In addition to the differences in mental models suggested by prediction accuracy and confidence, participants in the control and experimental conditions differed in how the described their mental models in comments on the image stimuli. Participants with less information tended to discuss image details, whereas those with more seemingly tried to find a pattern across the images and so focused more on the classifier itself and less on the image.
Robust AI for Accident Diagnosis of Nuclear Power Plants Using Meta-Learning
Authors: Lee Deail , Lee Heejae , Kim Jonghyun
Published: June 2022

Application with artificial intelligence (AI) techniques is considered for nuclear power plants (NPPs) that seem to be the last industry of the technology. The application includes accident diagnosis, automatic control, and decision support to reduce the operator’s burden. The most critical problem in their application is the lack of actual plant data to train and validate the AI algorithms. It is very difficult to collect the data from operating NPPs and even more to obtain the data about accidents in NPPs because those situations are very rare. For this reason, most of the studies on the AI applications to NPPs rely on the simulator that is software to mimic NPPs. However, it is highly uncertain that an AI algorithm that is trained by using a simulator can still work well for the actual NPP. This study suggests a Robust AI algorithm for diagnosing accidents in NPPs. The Robust AI is trained by the data collected in an environment (e.g., simulator) and can work under a similar but not exactly the same environment (e.g., actual NPP). Robust AI algorithm applies the Prototypical Network (PN), which is a kind of Meta-learning to extract major features from a few datasets and learn by these features. The PN learns a metric space in which classification can be performed by computing distances to prototype representations of each class. With the PN, the Robust AI algorithm extracts symptoms from the training data in the accident and uses these symptoms in the training of diagnosing accidents. The symptoms of accidents are almost identical between the simulator and the actual NPP, although the parametric values can be different. The suggested Robust AI algorithm is trained using a simulator and tested using another simulator of a different plant type, which is considered an actual plant. The experiment result shows that the Robust AI algorithm can properly diagnose accidents in different environments.
Artificial vision system to detect the mood of an Alzheimer's patient
Authors: Ricardo Castillo Salazar David , Lanzarini Laura , Gómez Alvarado Héctor , Varela-Aldás José
Published: June 2022

Dementia is a brain disorder that affects older individuals in their ability to carry out their daily activities, such as in the case of neurological diseases. The main objective of this study is to automatically classify the mood of an Alzheimer's patient into one of the following categories: wandering, nervous, depressed, disoriented, bored or normal i.e. in Alzheimer's patients from videos obtained in nursing homes for the elderly in the canton of Ambato, Ecuador. We worked with a population of 39 people from both sexes who were diagnosed with Alzheimer's and whose ages ranged between 75 and 89 years of age. The methods used are pose detection, feature extraction, and pose classification. This was achieved with the usage of neural networks, the walk classifier, and the Levenshtein Distance metric. As a result, a sequence of moods is generated, which determine a relationship between the software and the human expert for the expected effect. It is concluded that artificial vision software allows us to recognize the mood states of the Alzheimer patients during pose changes over time.
Generating a Multimodal Dataset Using a Feature Extraction Toolkit for Wearable and Machine Learning: A pilot study
Authors: Marte Zorrilla Edwin , Villanueva Idalis , Husman Jenefer , Graham Matthew
Published: June 2022

Studies for stress and student performance with multimodal sensor measurements have been a recent topic of discussion among research educators. With the advances in computational hardware and the use of Machine learning strategies, scholars can now deal with data of high dimensionality and provide a way to predict new estimates for future research designs. In this paper, the process to generate and obtain a multimodal dataset including physiological measurements (e.g., electrodermal activity- EDA) from wearable devices is presented. Through the use of a Feature Generation Toolkit for Wearable Data, the time to extract clean and generate the data was reduced. A machine learning model from an openly available multimodal dataset was developed and results were compared against previous studies to evaluate the utility of these approaches and tools. Keywords: Engineering Education, Physiological Sensing, Student Performance, Machine Learning, Multimodal, FLIRT, WESAD
Hepatitis predictive analysis model through deep learning using neural networks based on patient history
Authors: Pizarro Jorge , Vásquez Byron , Steven Mendieta Molina Willan , Hurtado Remigio
Published: June 2022

First of all, one of the applications of artificial intelligence is the prediction of diseases, including hepatitis. Hepatitis has been a recurring disease over the years as it seriously affects the population, increasing by 125,000 deaths per year. This process of inflammation and damage to the organ affects its performance, as well as the functioning of the other organs in the body. In this work, an analysis of variables and their influence on the objective variable is made, in addition, results are presented from a predictive model.We propose a predictive analysis model that incorporates artificial neural networks and we have compared this prediction method with other classification-oriented models such as support vector machines (SVM) and genetic algorithms. We have conducted our method as a classification problem. This method requires a prior process of data processing and exploratory analysis to identify the variables or factors that directly influence this type of disease. In this way, we will be able to identify the variables that intervene in the development of this disease and that affect the liver or the correct functioning of this organ, presenting discomfort to the human body, as well as complications such as liver failure or liver cancer. Our model is structured in the following steps: first, data extraction is performed, which was collected from the machine learning repository of the University of California at Irvine (UCI). Then these data go through a variable transformation process. Subsequently, it is processed with learning and optimization through a neural network. The optimization (fine-tuning) is performed in three phases: complication hyperparameter optimization, neural network layer density optimization, and finally dropout regularization optimization. Finally, the visualization and analysis of results is carried out. We have used a data set of patient medical records, among the variables are: age, sex, gender, hemoglobin, etc. We have found factors related either indirectly or directly to the disease. The results of the model are presented according to the quality measures: Recall, Precision and MAE.We can say that this research leaves the doors open to new challenges such as new implementations within the field of medicine, not only focused on the liver, but also being able to extend the development environment to other applications and organs of the human body in order to avoid risks possible, or future complications. It should be noted that the future of applications with the use of artificial neural networks is constantly evolving, the application of improved models such as the use of random forests, assembly algorithms show a great capacity for application both in biomedical engineering and in focused areas to the analysis of different types of medical images.
Artificial Intelligence in aviation decision making process.The transition from extended Minimum Crew Operations to Single Pilot Operations (SiPO)
Authors: Ziakkas Dimitrios , Plioutsias Anastasios , Pechlivanis Konstantinos
Published: June 2022

Innovation, management of change, and human factors implementation in-flight operations portray the aviation industry. The International Air Transportation Authority (IATA) Technology Roadmap (IATA, 2019) and European Aviation Safety Agency (EASA) Artificial Intelligence (A.I.) roadmap propose an outline and assessment of ongoing technology prospects, which change the aviation environment with the implementation of A.I. and introduction of extended Minimum Crew Operations (eMCO) and Single Pilot Operations (SiPO). Changes in the workload will affect human performance and the decision-making process. The research accepted the universally established definition in the A.I. approach of “any technology that appears to emulate the performance of a human” (EASA, 2020). A review of the existing literature on Direct Voice Inputs (DVI) applications structured A.I. aviation decision-making research themes in cockpit design and users’ perception - experience. Interviews with Subject Matter Experts (Human Factors analysts, A.I. analysts, airline managers, examiners, instructors, qualified pilots, pilots under training) and questionnaires (disseminated to a group of professional pilots and pilots under training) examined A.I. implementation in cockpit design and operations. Results were analyzed and evaluated the suitability and significant differences of e-MCO and SiPO under the decision-making aspect.Keywords: Artificial Intelligence (A.I.), Extended Minimum Crew Operations (e-MCO), Single Pilot Operations (SiPO), cockpit design, ergonomics, decision making.
Artificial intelligence in B2B sales: Impact on the sales process
Authors: Fischer Heiko , Seidenstricker Sven , Berger Thomas , Holopainen Timo
Published: June 2022

Digitalization is a driving force for innovation in the business-to-business (B2B) environment and profoundly changes the way companies do business. It affects the entire value chain of a company and can be used for automating human tasks. For instance, previous research indicates that 40% of all sales tasks can be automated. Thus the digital transformation in sales has the potential to improve a firm’s performance. Depending on its development level, digitalization in sales can assist or even replace numerous sales tasks. Therefore, using digital solutions in sales can be seen as an essential trigger to competitive advantage. Recent developments in research and practice have revealed that especially artificial intelligence (AI) has gained increasing attention in the sales domain. A challenging issue in this domain is how AI affects the sales process and how it can be applied meaningfully in B2B sales. Thus, our paper aims to investigate how AI can be used along the sales process and how it can improve sales practices.To explore this, we conduct systematic literature research in scientific databases such as Business Source Premier, Science Direct, Emerald, Springer Online Library, Wiley Online Library, and Google Scholar, supplementing the findings with a qualitative research approach. Analyzing this literature focused on digital transformation in sales, we find that the application and benefits of AI depend on the sales process step. For this reason, we conduct research on B2B sales process models, compare them, and choose a reference model for the evaluation of AI in B2B sales. Moreover, we present common definitions of AI and show how this technology is usually applied in B2B sales. Afterward, we combine the sales process with use cases of AI. For each step, we present use cases in detail and explain their benefits for sales. For instance, we find that especially tasks with traditionally high human involvement are challenging to automate. In particular, in complex sales situations, the human salesperson may not be entirely replaced by digital technologies, while routine tasks can be carried out with the help of digital technologies. Our paper closes with a discussion and conclusion. Summing up, the proposed paper analyzes different viewpoints of the sales process in the digital sales literature. We can conclude that the main focus of our paper will be presenting the application of AI along the sales cycle. Our research closes with a discussion and conclusion and gives recommendations for practice and academia.
Automatic Labeling of Human Actions by Skeleton Clustering and Fuzzy Similarity
Authors: Yang Chao-Lung , Hsu Shang-Che , Wang Si-Hao , Nian Jing-Feng
Published: June 2022

Nowadays, human action recognition (HAR) has been applied in multiple fields with the rapid growth of artificial intelligence and machine learning. Applying HAR onto industrial production lines can help on visualizing and analyzing the correlation between human operators and machine utilization to improve overall productivity. However, to train HAR model, the manual labeling of certain actions in a large amount of the collected video data is required and very costly. How to label a large amount of video automatically is an emerging practical problem in HAR research domain. This research proposed an automatic labeling framework by integrating Dynamic Time Warping (DTW), human skeleton clustering, and Fuzzy similarity to assign the labels based on the pre-defined human actions. First, the skeleton estimation method such as OpenPose was used to jointly detect key points of the human operator’s skeleton. Then, the skeleton data was converted to spatial-temporal data for calculating the DTW distance between skeletons. The groups of human skeletons can be clustered based on DTW distance among skeletons. Within a group of skeletons, the undefined skeletons will be compared with the pre-defined skeletons, considered as the references, and the labels are assigned according to the similarity against the references. The experimental dataset was created by simulating the human actions of manual drilling operations. By comparing with the manual labeled data, the results show that all of accuracy, precision, recall, and F1 of the proposed labeling model can achieve up to 95% with 40% saving time.
Proposal for the Generation of Profiles using a Synthetic Database
Authors: Viscaino - Quito Andres , Serpa-Andrade Luis
Published: June 2022

The lack of data to perform various models that feed an artificial intelligence with which you can get or discover various patterns of behavior in a set of data. Therefore, due to this lack of data, the systems are not well nourished with data large enough to fulfill its learning function, presenting a synthetic database which is parameterized with restrictions on the characteristics of graphomotor and language elements, which develops a set of combinations that will be the model for the AI. As effect to all this gave a commensurable amount of 777,600 combinations at the moment of applying the first filter with the respective restrictions, when taking the valid combinations that are 77304 a second filter is applied with the remaining restrictions that gave 57,672 valid combinations for the generation of the synthetic database that will feed the AI. It is concluded that the generation of synthetic data helps to create, according to its importance, more or less similar to real data and in this way ensures a quantity and no dependence on real or original data.
Pattern noise prediction using Artificial Neural Network
Authors: Kwon Lee Sang
Published: June 2022

In early design stage of tire pattern, it is very useful to predict noise level associated with tire pattern. Artificial neural network (ANN) was used for development of the model for the prediction of tire pattern noise recently. The ANN used supervised training method which extracts the feature applying Gaussian curve fitting to the tread profile spectrum of tire pattern and used it as the input of ANN. This method requests laser scanning for tire pattern of a real tire. In early design, there is no real tire. In this study, the convolutional neural network (CNN) to predict tire pattern noise was developed based on non-supervised training method. Two Learning algorithms such as stochastic gradient descent (SGD) and RMSProp were studied in the CNN model for the comparison of their learning performance. RMSProp algorithm was suggested for the CNN model. In this case, a pattern image of a tire to be designed was used as the input of CNN. The CNN to predict tire pattern noise was developed and its utility in the early design stage of tire was discussed. In the study, pattern noise for 28 tires were measured in the anechoic chamber and their pattern images were scanned. For the training of ANN and CNN, pattern noise for 24 tires and their pattern images were used. The trained ANN and CNN were validated respectively with 4 tires which were not used for the training of two neural networks. Finally, two networks were successfully developed and validated for the prediction of tire pattern noise. The trained CNN can be used for the prediction of pattern noise for a tire to be designed in early design stage using the only drawing image of tire whilst ANN can be used for the prediction of pattern noise for a real tire in development stage.
Three-degree graph and design of an optimal routing algorithm
Authors: Seong Bo-Ok , Ahn Jimin , Son Myeongjun , Lee Hyeongok
Published: June 2022

Learning, as well as the importance of a high-performance computer is significantly emerging. In parallel computing, we denote the concept of interconnection between the single memory and multiple processors as multi-processor. In a similar context, multi-computing signifies the connection of memory-loaded processors through the communication link. The relationship between the performance of multi-computing and the processor’s linkage structure is extremely proximate. Let the connection structure of the processor be an interconnection network. The interconnection network can be modeled through a classical graph consisting of node and edge. In this regard, a multi-computing processor is expressed as a node, communication link as an edge. When categorizing the suggested interconnection network through the criteria of the number of nodes, it can be classified as follows: Mesh class type consisted of the n×k nodes (Torus, Toroidal mesh, Diagonal mesh, Honeycomb mesh), Hypercube class type with 2^n nodes (Hypercube, Folded hypercube, Twisted cube, de Breijin), and Star graph class type (Star graph, Bubblesort star, Alternating group, Macro-star, Transposition) with n! nodes. The mesh type structure is a planar graph that is widely being utilized in the domains such as VLSI circuit design and base station installing (covering) problems in a mobile communication network. Mesh class types are comparatively easier to design and could potentially be implemented in algorithmic domains in a practical manner. Therefore, it is considered as a classical measure that is extensively preferred when designing a parallel computing network system. This study suggests the novel mesh structure De3 with the degree of three and designs an optimal routing algorithm as well as a parallel route algorithm (병렬경로알고리즘) based on the diameter analysis. The address of the node in the De3 graph is expressed with n-bit binary digits, and the edge is noted with the operator %. We built the interval function (구간 함수) that computes the locational property of the corresponding nodes to derive an optimal routing path from node u to node v among the De3 graph structure. We represent the optimal routing algorithm based on the interval function, calculating and validating the diameter of the De3 graph. Furthermore, we propose the algorithm that establishes the node disjoint parallel path which addresses a non-overlap path from node u to v. The outcome of this study proposes a novel interconnection network structure that is applicable in the routing algorithm optimization by limiting the communication links to three while the number of nodes These results implicate the viable operation among the high-performance edge computing system in a cost-efficient and effective manner.
Real-Time Breath Pattern Detection from Helmet
Authors: Cai Yang , Vancura Tomas , Zheng Haocheng , Weiss Lenny
Published: June 2022

In this paper, we developed a remote breath pattern from a helmet-mounted thermal sensor while providing real-time feedback from the head-up display on the helmet. We use Lucas-Kanade Tracking and the Fast Fourier Transform to estimate and display a subject’s breaths per minute and breathing waveform in an embedded systems environment. In addition to respiration rate (RR), our visualization shows the waveform of the subject’s breathing pattern, which provides real-time diagnostic information. Our system was able to predict respiration rate with high accuracy and stability in all trials of subjects wearing face masks, due to the heat-trapping effect of facial coverings. In the unmasked cases, the error rates are higher than the masked cases, due to the higher signal-to-noise ratio and other causes. In future work, we would like to focus on unmasked RR detection to improve accuracy and robustness with better color mapping from the raw data to pixel colors, improve the tracking accuracy, improve the thermal resolution, apply signal filters, and reduce the sampling region of interest that is more sensitively tracked and centered around the nostrils.
Designing wearables devices to build emotional relationships
Authors: Sethi Suresh
Published: June 2022

The focus of this paper is designing wearable devices to support the emotional relationship between users. The research reflects that any attempt by designers to produce outcomes aimed at helping design wearable devices must be grounded in an understanding of emotional engagement, convenient access, and intimate interaction. Emotionally positive experiences can inspire attempts to develop rich experiential devices. The case is made through several student projects that deal with particulars rooted in human relationships.
Using Inertial Measurement Units (IMU) and Comparative Trajectory Analysis for Modeling Micro-level Human Motion Dysfunction
Authors: Gutierrez Robert , Hart Joe , Barnes Laura , Boukhechba Mehdi
Published: June 2022

Ubiquitous sensing from smartphones and wearable devices has proven to be useful for applications ranging from sports to modern medicine. The aim of this paper is to propose a visualization framework to illustrate the points in time when a query trajectory is deviating the most from a reference trajectory. Validation is performed through the use of a novel post ACL reconstruction dataset. Validation is performed through wearable sensing data collected from 11 patients recovering from ACL reconstruction and 10 healthy participants. Results provide promising insights about how this method can be used to visualize anomalies in motion trajectories and to detect abnormal motion patterns.
Sensory Fusion for Remote Multi-body Temperature Monitoring
Authors: Hackett Sean , Alber Florian , Zheng Haocheng , Cai Yang
Published: June 2022

We present a prototype of the remote multi-body temperature screening system that uses an RGB-D camera for face tracking and distance measurement and a thermal imaging camera for temperature detection. An automated calibration algorithm is implemented based on the distance to the target, the ambient temperature, and a reference object. Our field tests include outdoor and in-vehicle body temperature monitoring. We found that face tracking and distance measurements help to improve the dynamic automatic remote body temperature monitoring. Wearing a face mask would impair face tracking, with sensor fusion of thermal, RGB, and depth data, we have reduced the false detection of faces. In addition to the indoor environment, we evaluated the system in extreme conditions, including multiple types of face masks, outdoor, vehicle checkpoints, and under a canopy. We found that the vehicle drive-through and canopy offer improved performance over outdoors.
Measurement of Spine Curvature using Flexicurve Integrated with Machine Vision
Authors: M Ramnaath , D Mounika , Subramaniyam Murali , Nam Min Seung
Published: June 2022

Many Spinal disorders require medication and prolonged rehabilitation, which consist of physical exercise and posture control. During restoration, frequent monitoring of spine curvature is necessary. The professionals do not suggest the radiography method more frequently since it has harmful effects on our bodies. Flexicurve has been widely used in much research to measure spine curvature in the sagittal plane. However, the data extraction from Flexicurve are not standardized or the current methodology adopted is difficult and time-consuming. Researchers use a graph (by hand) based method to extract curvature data from Flexicurve. Changes in spine curvature measurements can lead to the wrong methodology for correcting it. So, it is necessary to automate the data collection while measuring spine curvature with Flexicurve. This study proposes a method to automatically record the spine curvature data using the machine vision technique.
Design and Research of Flexible Wearable Medical Testing Equipment for Pregnant Women
Authors: Fu Luoyu , Yi Peiqi , Gao Zikun , Gan Yan
Published: June 2022

Pregnant women, as a special group, bear the mission of nurturing and continuing human life. Pregnant women need to experience psychological and physiological changes in the tenth month of pregnancy. In the special "post-epidemic era", it is hard and unsafe for pregnant women to go to the hospital regularly for birth check-up. In order to make pregnant women have a better prenatal experience, our team wants to design a wearable device, which can monitor the fetal heart rate and the frequency of fetal movement, so that pregnant women can also realize routine detection of the fetal condition at home, and protect the growth health and safety of the fetus. In this design, questionnaire interview, literature search and collaborative story telling are used to deeply understand the pain points of pregnant women's antenatal examination, the development status of wearable devices for pregnant women at home and abroad, pregnant women's preferences and so on. Then, determine the product use process, product functional structure and product packaging. This design adopts cutting-edge technologies such as flexible sensors, and combines ergonomics and kansei engineering. The product obtains the data and information of pregnant women and fetuses, and then through sorting and analysis, the results are intuitively transmitted to pregnant women, pregnant women's relatives or doctors in the matching APP, so that users can clearly obtain the health data of pregnant women in real time. Realize early warning of physical abnormalities of infants and mothers, early warning and early treatment, so as to better protect the safety and health of pregnant women and fetuses during pregnancy. After the usability test, the interviewed pregnant women thought that the design had a certain effect.
Hypoglycemia Vehicle Detection System Using Non-Invasive Sensors Applying Both EEG And HRV Real Time Measures: Neuroergonomics Theoretical Design
Authors: Aljuaid Awad
Published: June 2022

During COVID 19 pandemic the global needs for online shopping, ride-sharing transportation, and food delivery services have been dramatically increased. The drivers who suffer from diabetes especially low blood sugar level (Hypoglycemia) are more likely at risk than others. Earlier literature has revealed that hypoglycemic issues in patients with diabetes are correlated with significant changes in scalp electroencephalography (EEG); signals amplitude (time domain) or power spectral density (frequency domain). In addition, Haret rate variability HRV which reflects the balance between the sympathetic and parasympathetic nervous system has been proven as one of the indicators of Hypoglycemia. The aim of this paper to propose a conceptual design of a Vehicle detection system using both EEG and HRV measures at the same time in real time feed using non-invasive sensors to reduce the potential of driver’s cognitive dysfunction.
Human Gait Recognition Using Bag of Words Feature-Representation Method
Authors: Bayat Nasrin , Rastegari Elham , Li Qifeng
Published: June 2022

In this paper, we propose a novel gait recognition method based on a bag-of-words feature representation method. The algorithm is trained, tested and evaluated on a unique human gait data consisting of 93 individuals who walked with comfortable pace between two end-points during two different sessions. To evaluate the effectiveness of the proposed model, the results are compared with the outputs of the classification using extracted features. As it is presented, the proposed method results in significant improvement accuracy compared to using common statistical features, in all the used classifiers.
Toward a Systems Framework Coupling Safety Culture, Risk Perception, and Hazard Recognition for the Mining Industry
Authors: Brown Leonard , Pham Ngan , Burgess Jefferey
Published: June 2022

The United States mining industry has made steady progress to improve worker safety and reduce injuries. Despite these gains, the industry remains largely reactive in its approach to health and safety. There remains a primary focus on lagging indicators, such as the numbers of injuries, hours lost, and hazards found at the worksite. To facilitate a more proactive approach, new methods are needed to evaluate hazardous conditions and unsafe behaviors. This work explores the relationships among mine workers’ hazard recognition abilities, the individual’s perception of risk, and the safety culture of the mining workplace. We have conducted a literature review to identify key factors and analytical models in industries where health and safety are a major consideration, including construction, manufacturing, mining, and transportation. Our analysis considered both process-oriented frameworks, such as Systems Thinking approaches, and statistical methods, including Structural Equation Modeling (SEM). A meta-model was then developed to aggregate and examine key factors and potential causal relationships. We discuss the creation of this meta-model, identifying notable structural characteristics and hypotheses for future confirmatory analysis. Use cases are then outlined, including descriptive, evaluative, and generative applications.
Optimizing Human Capital Performance: Influence of Simulation
Authors: Hodges Glenn , Patton Debbie , Napier Samantha
Published: June 2022

Simulations have been employed to train people and provide novel environments topractice and test new skills as well as experiment with new concepts and procedures.The US Department of Defense (DoD) spends millions of dollars each year to provideboth live and virtual training to military personnel. Realizing that simulations offer aplethora of opportunities, the DoD is now spending millions of dollars to design anddevelop what it believes will be the optimal versions of synthetic trainingenvironments to train its workforce. Each of the military services has a slightlydifferent view of how simulation will or should support them in the future. This paperaims to provide readers with insights about the needed human requirements and thepath that the services are on to achieve their future visions with respect to simulation.It will briefly discuss historical, functional, and future views of how simulations havebeen, are being, and are envisioned to support the optimizing of human performance.
Distinguishing Between Dynamic Altitude Breathing Threats to Improve Training
Authors: Fernandez Kylie , Tindall Mitchell , Atkinson Beth , Logsdon Daniel , Anania Emily
Published: June 2022

Breathing related adverse physiological conditions are a prominent Warfighter pilot problem (Inspector General 2020). As a result of an investigation citing multiple types of adverse physiological conditions with various causes and symptoms (DoN 2017), there have been changes to training requirements to broaden the focus to include Dynamic Altitude Breathing Threat Training (DoN 2020). However, there remain questions about symptom definitions, distinctiveness, and response procedures that influence the content of this new training. In order to investigate the effects of different breathing conditions, the authors propose a between subjects design with adjustments to breathing conditions (i.e., restricted oxygen, restricted inhalation, restricted exhalation) using a mask on breathing device. Dependent measures include physiological data and pilot symptomology. The objective of this investigation is to inform awareness training for dynamic altitude breathing threats by validating instructional strategies and standard operating procedures for training implementation.Authors Note. The views of the author expressed herein do not necessarily represent those of the U.S. Navy or Department of Defense (DoD). Presentation of this material does not constitute or imply its endorsement, recommendation, or favoring by the DoD. NAWCTSD Public Release 22-ORL021 Distribution Statement A. Approved for public release; distribution is unlimited.
An Analysis of Squad Communication Behaviors during a Field-Training Exercise to Support Tactical Decision Making
Authors: Saville Jason , Spain Randall , Johnston Joan , Lester James
Published: June 2022

Understanding how teams function in dynamic environments is critical for advancing theories of team development. In this paper, we compared communication behaviors of high and low performing U.S. Army squads that completed a field training event designed to assess tactical decision-making skills and performance under stress. Transcribed audio logs of U.S. Army squad communications were analyzed. A series of 2 (performance group) by 2 (time: Pre-Contact and Post-Contact) mixed-model ANOVAs were conducted to determine whether team communication behaviors changed for squads after coming under duress from hostile contact. Significant main effects for time were found for several communication labels indicating communication patterns differed as task complexity and stressors increased. Significant interaction effects were found between time and performance group for the number of commands given by squad leaders and overall speech frequency. Results highlight the value of examining communications at a granular level as adaptive patterns may otherwise be overlooked.
Creative practice applied in a higher education class
Authors: Springer Leonardo
Published: June 2022

In a society where information is widely available, being able to filter and under-stand facts is paramount to envision, devise and design practical solutions concerning a particular problem, and further consider its implications. In a higher education context, skills such as creativity and rational thinking are trained through-out a course, encouraging understanding and problem-solving skills, essential aptitudes in an everchanging world.Creativity can transform an idea of an existing domain into a new one, it is all about being curious, examining, connecting, experimenting, and playing with the surroundings, and thus incubating an entire new version or set of ideas, adding value to the process. It is the ability to reason and create connections with previously acquired knowledge that generate new ideas. The interest in creativity as a scientific area began in the 1960’s, with advertising result of interaction between the individual thoughts and sociocultural context, creativity is a systemic and non-individual process (Getzels et al., 1966), involving a cognitive and emotional dimensions.The purpose of any training is to stimulate awareness, develop skills, foster challenges, motivate, and attain accomplishments. In higher education institutions, students are encouraged to ask difficult questions, identifying the state-of-the-art, learning specific tools, applying methodologies, indulging in research and practice, developing soft skills, building character, and ultimately improving individual social-economic status.To face global challenges, it is necessary to understand its purposes to develop a global awareness, essential for present and future developments. The first step towards any (design) solution is to outline the problem, amongst all involved, correlating behavior, technology, and business in a reasonable manner. Most de-sign output depends on creativity, and reasoning, which can be trained to generate ideas and solve specific issues.This research, in a higher education context, regarding applied creativity in a classroom, aims to further develop the creative process design students, using creative methods such as brainstorming, registering ideas and thoughts, using visual references and metaphors from an array of circumstances, in a project-based learning environment, fostering a creative output.
A drawing system with an interface that can be used to impose limits and unleash creativity
Authors: Katsumata Hiromi , Liu Bo
Published: June 2022

In order to enjoy creativity, the creators need to be given environmental restrictions. For example, there are rules such as Japanese Haiku or Chinese Zeku for poetry, or musical grammar for music, and these rules are supposed to enhance the creativity of the creator. However, most existing drawing software focuses only on making the process of completing a work comfortable, and does not provide much support for the goal setting process that requires environmental restrictions. In this study, based on research on creativity, such as the idea method, we were able to show that developing a drawing system that imposes environmental restrictions, such as the appearance of obstacles on the canvas, causes changes in the creativity of the subjects. In conventional drawing software, it is common to start the creative process with a blank canvas so that the painter can paint freely. However, in our experiments, many of the participants said that they were able to come up with more ideas and enjoy painting more when obstacles appeared on the canvas. By creating obstacles, we succeeded in approaching the characteristics of human visual cognition and structuring the creative process. Digital creative activities give us more control than analog creative activities. This advantage can be used to make the first step of the creative process, the idea generation process, more convenient. In analog creation, you could not choose irreversible methods such as creating obstacles on the canvas. The drawing software of the future will emphasize the process of externalizing human images and make the creative process more enjoyable, without the need to stick to a blank canvas.
An ESG-SDGs alignment and execution model based on the Ocean Strategies transition in emerging markets
Authors: Markopoulos Evangelos , Barbara Ramonda Maria
Published: June 2022

Sustainable Development has been introduced in 1987, and it is underpinned by three equally essential pillars: economy, society, and the environment. The adoption of the Sustainable Development Goals in 2015 was a watershed moment in the campaign for sustainable growth. Since then, this set of 17 objectives has steered the private and governmental sectors toward more sustainable, inclusive, and responsible processes. In addition, the ESG Index has recently gained prominence primarily in the financial industry. While ESG Investment Markets in developed nations have proliferated, many emerging market businesses have been marginalized, owing to a lack of human and financial resources for ESG compliance. Considering that the appropriate ESG credentials may result in various advantages, ranging from attracting potential investors to establishing a good reputation and gaining a competitive edge, it is critical for firms in Emerging Markets to employ tactics that will assist them in achieving this.The research conducted involve an extensive literature review on this subject, a survey with 86 valid responses and four interviews with international experts. The results indicate that within different challenges regarding ESG compliance, the unbalanced approach that many businesses adopt is a major one. Societal issues seem to be the aspect that companies primarily focus on, while Environment issues appear to be neglected or forgotten. This is especially true in Emerging Markets because of a lack of data, a lack of interest and a lack of political pressure. Therefore mapping ESG and SDGs requirements can bring significant benefits to organizations that can improve their Corporate Sustainability without harming their economic growth, development and strategy.This research introduces an ESG – SDG mapping model and its execution process to achieve the maximum of this relationship. The prioritization of the SDGs has to be related to the ESG strategy an organization is committed to executing based on its capability and maturity. The proposed model is based on integrating the Blue, Pink and Green oceans and the gradual achievement of SDGs during the transition from one ocean to the other. This Pink Ocean and Green Ocean driven ESG framework is targeted primarily for the Emerging Markets where the need to adapt the SDGs is more significant than other regions. The paper indicates the initial limitations in the strategy and application of the proposed model and areas of further research that can overcome these limitations and wider its areas and regions of adaptation, and improve the scoring parameters and process. Local and international companies can use the proposed model to ignite and boost their Corporate Sustainability by complying with the ESG metrics and align themselves to the Sustainable Development Agenda.