An Intelligent Mechanism to Automatically Discover Emerging Technology Trends: Exploring Regulatory Technology

Technology trend analysis uses data relevant to historical performance and extrapolates it to estimate and assess the future potential of technology. Such analysis is used to analyze emerging technologies or predict the growing markets that influence the resulting social or economic development to assist in effective decision-making. Traditional trend analysis methods are time-consuming and require considerable labor. Moreover, the implemented processes may largely rely on the specific knowledge of the domain experts. With the advancement in the areas of science and technology, emerging cross-domain trends have received growing attention for its considerable influence on society and the economy. Consequently, emerging cross-domain predictions that combine or complement various technologies or integrate with diverse disciplines may be more critical than other tools and applications in the same domain. This study uses a design science research methodology, a text mining technique, and social network analysis (SNA) to analyze the development trends concerning the presentation of the product or service information on a company's website. This study applies regulatory technology (RegTech) as a case to analyze and justify the emerging cross-disciplinary trend. Furthermore, an experimental study is conducted using the Google search engine to verify and validate the proposed research mechanism at the end of this study. The study results reveal that, compared with Google Trends and Google Correlate, the research mechanism proposed in this study is more illustrative, feasible, and promising because it reduces noise and avoids the additional time and effort required to perform a further in-depth exploration to obtain the information.

Duplex Aging and Gas Nitriding Process as a Method of Surface Modification of Titanium Alloys for Aircraft Applications

This study discusses the effect of a duplex aging + nitriding process on the wear resistance of an aged double-phase titanium alloy, BT22. Nitriding was applied simultaneously with the heat treatment of the alloy, which is advantageous over the conventional heat and surface treatment methods applied to titanium alloys. According to the results, the thickness of the case depth of the nitrided samples was 40–50 μm. Moreover, nitrogen was uniformly dispersed in the substrate, which was indicated by the hardness tests. The average microhardness of the substrate material was 300 HV0.01, while the hardness of the top layer was 1190 HV0.01, which is an almost four-fold increase. The applied duplex treatment substantially affected the wear performance of the tested alloy. For the untreated alloy, the maximum coefficient of friction was 0.8, while in the surface-modified sample, the maximum fluctuations reached 0.6. The abrasive wear process was dominant in the nitrided samples, while delamination and adhesive wear were observed for the untreated specimens. The nitrided alloy exhibited double the wear resistance of the untreated samples. The proposed treatment does not require additional time or energy consumption, providing a substantial technological advantage over conventional methods. Though the alpha case reduces the mechanical performance of titanium, the nitriding of only the component sections intended to withstand friction will have a positive effect.

Introduction to teaching science with three-dimensional images of dinosaur footprints from Cristo Rey, New Mexico

In this paper we discuss the use of three-dimensional (3-D) imagery and virtual field trips to teach pre-university and non-major university geoscience courses. In particular, 3-D PDF (Portable Document Format) files can be used to either prepare students for or completely replace a field trip when logistical problems make the actual trip too difficult to be effective or when some students need an alternative accommodation. Three-dimensional images can replace or supplement classroom activities, such as the identification of rocks and minerals from hand samples or the identification of geologic structures from 2-D photographs and limited field observations. Students can also become involved in data collection and processing to further their understanding of photogrammetry and visualization. The use of 3-D imagery can make additional time available to instructors to cover more advanced topics and teach students more about the role of science in geologic research. We use an example from Cristo Rey, New Mexico, where dinosaur footprints and tracks are present but difficult to see in many cases, and they are often in places that are hard to access for many people. At this site, approximately 10 000 photographs were collected and processed as 3-D images to show one approximately 72 m2 area of known footprints. However, we also conducted some very simple digital manipulations of the images that allowed us to identify new footprints and tracks that were not apparent when viewed in the field. The photographs and 3-D images have been donated to the Insights El Paso Science Center (denoted Insights Museum herein) that owns the fossil site, and they are now being used to develop educational materials and lessons for the nearby communities.

Automation of an automobile internal combustion engine bench calibration tests

Introduction (problem statement and relevance). The article presents the work on the automation of an internal combustion engine (ICE) calibration tests results on a motor stand. The relevance of the article is due to the high labor intensity of such tests, the complexity of documentation and decisionmaking based on the results of the work.Purpose of the study. This work is part of a comprehensive methodology, the purpose of which is to reduce the duration of tests and improve the calibration results quality of the vehicle’s power plant. The entire methodology description as a whole is also given in the publication.Methodology and research methods. The achievement of this goal is ensured with the help of special systems – INCA-FLOW (test automation) and ASCMO (processing results and optimization), produced by Bosch/ETAS. The approbation of the technique was carried out on a motor stand in the MADI training box in relation to the problem of forming an ignition timing map.Scientific novelty and results. As a result of the methodology application, a 4.8 times reduction in the motor tests duration takes place if 2 people work in manual mode at the test bench without interruption.At the same time, the variance of the adequacy of Sad of the torque empirical model Mk turned out to be, on average, 1.5 times less if the model was built according to the automated tests results. The obtained data indicated an improvement in the quality of measurements in the transition to automated test methods.From a scientific point of view, the most original part of the work is the application of the “Gaussian process” method to build empirical models. This method provides more accurate results than, for example, the traditional method of least squares.The practical significance of the work lies in the ability to considerably reduce routine actions on a motor stand, and the additional time spent on developing and testing a test scenario (program) is compensated for by the fact that scenario models can be used in the future for other similar tests. The proposed methodology makes it possible to cover a significant part of the internal combustion engine calibration tests. For example, you can apply it if you possess the preliminary information about the test object (basing on which you can draw up an experiment plan) and the engine is to be prepared either for a car road tests or tests under special conditions.

Effects of stress during commercial hatching on growth, egg production and feather pecking in laying hens

Every year, billions of egg layer chicks around the world are hatched under highly stressful, industrial circumstances. Here, it is investigated how the stressful procedure in the commercial hatchery, including incubation, hatching, processing, and transport affects the chicks with regards to traits relevant for the egg production industry. These traits were compared to those of a control group hatched in a small incubator and handled gently och quietly in a quiet room without any processing and transport. The chicks were weighed at hatch and at eight additional time points: 4 days, 1 week (w), 2 w, 3 w, 5 w, 8 w, 20 w and 25 w of age. Feather pecking was studied at 15 w of age and damages to the feathers and injuries on the comb and wattle were assessed at 25 w of age. From 19 w of age, eggs were collected on three days per week, counted and weighed. Chicks from a commercial hatchery had a lower hatch weight than control chicks (p<0.001). At 20 w of age, the weight of the commercial hatched chicks was still numerically lower, although this did not reach statistical significance. Commercially hatched chicks tended to show more feather pecking behaviour at 15 w of age compared to control chicks (p<0.1), although feather condition at 25 w of age showed the opposite pattern. Regarding production, commercially hatched chickens laid fewer (p<0.05) and smaller (p<0.05) eggs than chicks hatched and handled under calm circumstances. From this experiment, it is concluded that the stressful experience in the commercial hatchery has an overall negative effect on traits relevant for the industry.

How to Manage an Effective Laboratory for Science Learning in Schools?

The role of school laboratories is vital in helping junior high school students learn science. Many schools have laboratory facilities but are underutilized. This study aims to optimize the role of school laboratories through action research. In three cycles, the writer carried out the SMP Negeri 2 Metro research and SMP Negeri 6 Metro. The first cycle carried out technical guidance to the laboratory manager. Still, the results were not as expected due to communication problems to understand the principal's importance of incomplete laboratory improvement. The second cycle is carried out by mentoring the laboratory management. This includes the making of the laboratory management, inventory, and labeling, and creating work programs. This cycle two treatment also has not shown the results as expected. The reflection results found that the root of the problem was that the teachers and the laboratory manager believed that practicum activities needed time. So, it had to be carried out outside of class hours to require additional time, effort, and cost. To overcome this problem, in the third cycle, a workshop on making a practicum guide and Standard Operating Procedure (SOP) of practicum was developed according to the lesson schedule, then a trial was conducted. Data collection uses documentary, interview, and Focus Group Discussion (FGD) methods to explore the root of the problem and the solution. Data processing uses a qualitative approach with steps: data collection, selection, and grouping, tabulation, description, interpretation, and conclusions. Based on the results of action research data analysis, it can be concluded: First, management improvement and inventory of laboratory tools and materials as well as making practical SOPs, which are effective in optimizing the role of science laboratories in supporting the learning process; Second, the use of SOP practicum according to the lesson schedule is quite efficient in saving time, effort, and costs. According to the results of this study, it is recommended that for the laboratory to be optimal in supporting learning, laboratory management must be orderly equipped with SOPs, and inventory of tools and materials must be good.

Measuring Patient Similarity on Multiple Diseases by Joint Learning via a Convolutional Neural Network

Patient similarity research is one of the most fundamental tasks in healthcare, helping to make decisions without incurring additional time and costs in clinical practices. Patient similarity can also apply to various medical fields, such as cohort analysis and personalized treatment recommendations. Because of this importance, patient similarity measurement studies are actively being conducted. However, medical data have complex, irregular, and sequential characteristics, making it challenging to measure similarity. Therefore, measuring accurate similarity is a significant problem. Existing similarity measurement studies use supervised learning to calculate the similarity between patients, with similarity measurement studies conducted only on one specific disease. However, it is not realistic to consider only one kind of disease, because other conditions usually accompany it; a study to measure similarity with multiple diseases is needed. This research proposes a convolution neural network-based model that jointly combines feature learning and similarity learning to define similarity in patients with multiple diseases. We used the cohort data from the National Health Insurance Sharing Service of Korea for the experiment. Experimental results verify that the proposed model has outstanding performance when compared to other existing models for measuring multiple-disease patient similarity.

Digital Storytelling as a Meaningful Learning Strategy in Online Learning

The purpose of this study was to ascertain students' perceptions of the use of digital storytelling as a meaningful learning strategy in an online environment at a senior high school in Indonesia. The case study method was used in this study, which included twenty-eleventh-grade students as participants. The data analyzed in this study come from a student questionnaire, a semi-structured interview, and five-week classroom observation. This qualitative study indicated that students viewed digital storytelling as an instructional method capable of involving them in an active, authentic, and purposeful learning environment. Additionally, digital storytelling enhances students' exposure to a variety of skills and collaborative work portfolios. The study's practical implications for teachers implementing digital storytelling are to allow additional time for content acquisition and comprehensive learning reflection.

TCAD simulation for capture / emission of carriers by traps in SiN: trap-assisted tunneling model extended for capture of carriers injected via Fowler-Nordheim tunneling

We propose the novel trap-assisted tunneling (TAT) model that incorporates the ability to calculate dissipation of the kinetic energy of carriers propagating in the conduction or valence band. The proposed model allows us to evaluate capture efficiency (or the capture cross section) of carriers injected into the SiN charge trap layer via Fowler-Nordheim tunneling. By applying our TAT model to large planar Metal-Oxide-Nitride-Oxide-Semiconductor (MONOS) capacitors, experimental data showing that electron capture efficiency depends on the tunnel oxide thickness are physically interpreted. Furthermore, 3-dimensional technology computer-aided design (TCAD) simulation using SiN trap parameters roughly extracted from planar MONOS data shows that the calculated incremental step pulse programming characteristics of the charge trap memory (CTM) prototype device are comparable with measured data. We have found that additional time to calculate SiN trap charges is less than only 5 % of all remaining calculation time.

The Gaia Explorer, a Powerful Search Platform

After two years of development, the GAIA Explorer is now ready to assist Geoscientists at Total! This knowledge platform works like a little Google, but with a focus solely on Geosciences - for the time being. The main goal of the GAIA Explorer is to save time finding the right information. Therefore, it is particularly useful for datarooms or after business acquisitions to quickly digest the knowledge, but also for feeding databases, exploration syntheses, reservoir studies, or even staff onboarding specially when remote working. With this additional time, Geoscientists can focus on tasks with added value, such as to synthesize, find analogies or propose alternative scenarios. This new companion automatically organizes and extracts knowledge from a large number of unstructured technical documents by using Machine Learning (ML). All the models relie on Google Cloud Platform (GCP) and have been trained on our own datasets, which cover main petroleum domains such as geosciences and operations. First, the layout of more than 75,000 document pages were analyzed for training a segmentation model, which extracts three types of content (text, images and tables). Secondly, the text content extracted from about 6,500 documents labelled amongst 30 classes was used to train a model for document classification. Thirdly, more than 55,000 images were categorized amongst 45 classes to customize a model of image classification covering a large panel of figures such as maps, logs, seismic sections, or core pictures. Finally, all the terms (n-grams) extracted from objects are compared with an inhouse thesaurus to automatically tag related topics such as basin, field, geological formation, acquisition, measure. All these elementary bricks are connected and used for feeding a knowledge database that can be quickly and exhaustively searched. Today, the GAIA Explorer searches within texts, images and tables from a corpus (document collection), which can be made up of both technical and operational reports, meeting presentations and academic publications. By combining queries (keywords or natural language) with a large array of filters (by classes and topics), the outcomes are easily refined and exploitable. Since the release of a production version in February 2021 at Total, about 180 users for 30 projects regularly use the tool for exploration and development purposes. This first version is following a continuous training cycle including active learning and, preliminary user feedback is good and admits that some information would have been difficult to locate without the GAIA Explorer. In the future, the GAIA Explorer could be significantly improved by implementing knowledge graph based on an ontology dedicated specific to petroleum domains. Along with the help of Specialists in related activities such as drilling, project or contract, the tool could cover the complete range of upstream topics and be useful for other business with time.