Coins of the Lower Danube Region in a Hoard of the Late 14th Century — a Source for the Initial History of Moldovan Statehood (Preliminary Report)

The scientific literature has been enriched with new very interesting information about the hoards of the late 14th century found in the Republic of Moldova. The purpose of this preliminary communication is to offer for scientific discussion several types of Juchid coins found in a large treasure hoard in the south of Moldova. One type of coins is dirhams of Kilia 770/1368—1369, the second type is dirhams without indication of a mint and anonymous, with the year 1371. The article contains photos of coins, their catalog description, as well as classification. In addition, the authors offer a brief historical reference and a retrospective of the finds of treasures from the end of the 14th century on the territory of Moldova and in neighboring lands.

What has time to do with risk? A preliminary communication

The reliability of risk techniques is of concern to academics and practitioners: if techniques are not reliable in their design, they cannot give reliable results. This paper briefly discusses risk velocity, which is a way of providing specificity to an understanding of risk through applying time as a lens. The research is a preliminary communication from initial Masters research. Risk velocity has been identified in the limited literature as being divided into three sections: time to cause, time to impact, and time to recover; each of which can assist an organisation to better understand their risk landscape and how risks link with business continuity planning. However, risk velocity has been the subject of limited research to validate the concept and reliability in practice, suggesting this a ‘white space’ meriting investigation (Cherry, 2010).

From Bubbles to Nanobubbles

Nanobubbles are classified into surface and bulk. The main difference between them is that the former is immobile, whereas the latter is mobile. The existence of sNBs has already been proven by atomic force microscopy, but the existence of bNBs is still open to discussion; there are strong indications, however, of its existence. The longevity of NBs is a long-standing problem. Theories as to the stability of sNBs reside on their immobile nature, whereas for bNBs, the landscape is not clear at the moment. In this preliminary communication, we explore the possibility of stabilizing a bNB by Brownian motion. It is shown that a fractal walk under specific conditions may leave the size of the bubble invariant.

DECIMER 1.0: deep learning for chemical image recognition using transformers

The amount of data available on chemical structures and their properties has increased steadily over the past decades. In particular, articles published before the mid-1990 are available only in printed or scanned form. The extraction and storage of data from those articles in a publicly accessible database are desirable, but doing this manually is a slow and error-prone process. In order to extract chemical structure depictions and convert them into a computer-readable format, Optical Chemical Structure Recognition (OCSR) tools were developed where the best performing OCSR tools are mostly rule-based. The DECIMER (Deep lEarning for Chemical ImagE Recognition) project was launched to address the OCSR problem with the latest computational intelligence methods to provide an automated open-source software solution. Various current deep learning approaches were explored to seek a best-fitting solution to the problem. In a preliminary communication, we outlined the prospect of being able to predict SMILES encodings of chemical structure depictions with about 90% accuracy using a dataset of 50–100 million molecules. In this article, the new DECIMER model is presented, a transformer-based network, which can predict SMILES with above 96% accuracy from depictions of chemical structures without stereochemical information and above 89% accuracy for depictions with stereochemical information.

DECIMER 1.0: Deep Learning for Chemical Image Recognition using Transformers

<p>The amount of data available on chemical structures and their properties has increased exponentially over the past decades. In particular, articles published before the mid-1990 are available only in printed or scanned form. The extraction and storage of data from those articles in a publicly accessible database are desirable, but doing this manually is a slow and error-prone process. In order to extract chemical structure depictions and convert them into a computer-readable format, optical chemical structure recognition (OCSR) tools were developed where the best performing OCSR tools are mostly rule-based.</p><p> </p><p>The DECIMER (Deep lEarning for Chemical ImagE Recognition) project was launched to address the OCSR problem with the latest computational intelligence methods to provide an automated open-source software solution. Various current deep learning approaches were explored to seek a best-fitting solution to the problem. In a preliminary communication, we outlined the prospect of being able to predict SMILES encodings of chemical structure depictions with about 90% accuracy using a dataset of 50-100 million molecules. In this article, the new DECIMER model is presented, a transformer-based network, which can predict SMILES with above 96% accuracy from depictions of chemical structures without stereochemical information and above 89% accuracy for depictions with stereochemical information.</p><p><br></p>

DECIMER 1.0: Deep Learning for Chemical Image Recognition using Transformers

<p>The amount of data available on chemical structures and their properties has increased exponentially over the past decades. In particular, articles published before the mid-1990 are available only in printed or scanned form. The extraction and storage of data from those articles in a publicly accessible database are desirable, but doing this manually is a slow and error-prone process. In order to extract chemical structure depictions and convert them into a computer-readable format, optical chemical structure recognition (OCSR) tools were developed where the best performing OCSR tools are mostly rule-based.</p><p> </p><p>The DECIMER (Deep lEarning for Chemical ImagE Recognition) project was launched to address the OCSR problem with the latest computational intelligence methods to provide an automated open-source software solution. Various current deep learning approaches were explored to seek a best-fitting solution to the problem. In a preliminary communication, we outlined the prospect of being able to predict SMILES encodings of chemical structure depictions with about 90% accuracy using a dataset of 50-100 million molecules. In this article, the new DECIMER model is presented, a transformer-based network, which can predict SMILES with above 96% accuracy from depictions of chemical structures without stereochemical information and above 89% accuracy for depictions with stereochemical information.</p><p><br></p>

Determining Air Traffic Complexity – Challenges and Future Development

Air traffic complexity is one of the main drivers of the air traffic controllers’ workload. With the forecasted increase of air traffic, the impact of complexity on the controllers' workload will be even more pronounced in the coming years. The existing models and methods for determining air traffic complexity have drawbacks and issues which are still an unsolved challenge. In this paper, an overview is given of the most relevant literature on air traffic complexity and improvements that can be done in this field. The existing issues have been tackled and new solutions have been given on how to improve the determination of air traffic complexity. A preliminary communication is given on the future development of a novel method for determining air traffic complexity with the aim of designing a new air traffic complexity model based on air traffic controller tasks. The novel method uses new solutions, such as air traffic controller tasks defined on pre-conflict resolution parameters, experiment design, static images of traffic situations and generic airspace to improve the existing air traffic complexity models.

Preliminary Communication and research in Humanistic Sociology Series

New technology is referred as any set of productive techniques which offers a significant improvement. What is seen as new’ is obviously subject to continual redefinition, as successive changes in technology are undertaken. The social and political impact of new technologies is complex being subject to variations in managerial strategies, worker resistance, and a host of other cultural and political circumstances. User adoption of new technologies and the models explaining their behaviors is an ongoing research problem. Identifying the factors that affect the adoption of new technologies is understood by developing technology adoption models and theories with different theoretical insights, variables and measurements. To recognize the needs and acceptance of individuals is to realize the factors that drive user acceptance or rejection of technologies. Researchers can conceptualize underlying technology models and theories that may affect the previous, current and future application of technology adoption. As a case study, 50 users of new technology in Croatia were questioned regarding pros and cons and was determined majority will benefit from the use.