Resolution and Quality Issues in 3D Analysis of Inscribed Signs: An Example from Cypro-Minoan Inscriptions

A recurrent demand in many archaeological digital documentation systems is the need for an accurate as possible registration of data. Somehow, contrary to this request, are efforts led by various computer science groups dealing with 3D documentation and focusing on developing fast and cheap solutions to record 3D models of archaeological assets. The aim of the article is to highlight the importance of aligning the 3D documentation strategy to the archaeological aims, by detailing all factors to be considered when deciding on one documentation strategy over another. The archaeological question discussed here, part of the PhD thesis of one of the co-authors (MP), relates to the Cypro-Minoan signatory and its diachronic variability. The 3D geometric characterization of signs and subsequent shape analysis is the method chosen to reach this goal. A major effort to be invested in correctly determining the shape and variability of each sign, is in assuring that the 3D captured shape is as close as possible to the archaeological reality, which is a common problem not only in palaeographical analysis but also in other fields, where features of interest are in the sub-millimetre range. The paper will illustrate how different data acquisition approaches and post-processing steps such as alignment methods and error treatment may distort the visualised result and thus have a negative impact on planned analysis. Thus, it will argue for the importance of more detailed paradata to allow an informed assessment of the reliability of 3D models and it proposes a list of values and decision-making steps that help make the 3D digitization process more robust and verifiable.

Adaptive backstepping control for ship nonlinear active fin system based on disturbance observer and neural network

<span>Adaptive backstepping control based on disturbance observer and neural network for ship nonlinear active fin system is proposed. One disturbance observer is given to observe the disturbances of the system, by this way, the response time is shorten and the negative impact of disturbance and uncertain elements of the system is reduced. In addition, radial basic function neural network (RBFNN) is proposed to approach the unknown elements in the ship nonlinear active fin system, therefor the system can obtain good roll reduction effectiveness and overcome the uncertainties of the model, the designed controller can maintain the ship roll angle at desired value. Finally, the simulation results are given for a supply vessel to verify the successfulness of the proposed controller.</span>

Impact of the COVID-19 Outbreak on the Currency Exchanges of Selected Countries

The paper aims to analyse the impact of the COVID outbreak on the currency market. The study considers spot rates of seven major currencies (i.e., EUR/USD, USD/JPY, GBP/USD, AUD/USD, USD/CAD, USD/CHF, and CHF/JPY). To capture the impact of the outbreak on returns and the volatility of returns of seven currencies during pandemic, the study has segregated in two window periods (i.e., pre- [1st Jan 2019 to 31st Dec, 2019] and post-outbreak of COVID-19 [1st Jan, 2020 to 22nd Dec, 2020]). The study has applied various methods and models (i.e., econometric-based compounded annual growth rate [CAGR], dummy variable regression, and generalized autoregressive conditional heteroskedasticity [GARCH]). The result of the study captures the negative impact of the COVID-19 pandemic on three currencies—USD/JPY, AUD/USD, and USD/CHF—and positive significant impact on EUR/USD, GBP/USD, USD/CAD, and CHF/JPY. Investors can take short position in these while having long position in other currencies. The inferences drawn from the analysis are providing insight to investors and hedgers.

The Aquaponic Ecosystem Using IoT and IA Solutions

For solving the negative impact of the human evolution in earth, water, pollution and quality of feed. A system of aquaponic is proposed to manage gardening and recover up to 90% of water used for plants. Aquaponic is a system that combines two names: aquaculture which is the farming of fish and hydroponic which is the cultivation of plants (off-soil). On the other hand, the possibility of using the phytotron system. The objective of this solution is to collect performance measures, to control the watering conditions of plants (water level, temperature, humidity, ...) With a cloud support and other possibilities offered by the internet of things (IoT). The paper at hand aim to provide a smart solution integrates the phytotron solution in order to control the first part wish is the hydroponic and the second part concerning the aquaculture in order to offer a smart environment for the cycle of fish’s life.

Impact of Anthropomorphic Robot Design on Trust and Attention in Industrial Human-Robot Interaction

The application of anthropomorphic features to robots is generally considered beneficial for human-robot interaction (HRI ). Although previous research has mainly focused on social robots, the phenomenon gains increasing attention in industrial human-Robot interaction as well. In this study, the impact of anthropomorphic design of a collaborative industrial robot on the dynamics of trust and visual attention allocation was examined. Participants interacted with a robot, which was either anthropomorphically or non-anthropomorphically designed. Unexpectedly, attribute-based trust measures revealed no beneficial effect of anthropomorphism but even a negative impact on the perceived reliability of the robot. Trust behavior was not significantly affected by an anthropomorphic robot design during faultless interactions, but showed a relatively steeper decrease after participants experienced a failure of the robot. With regard to attention allocation, the study clearly reveals a distracting effect of anthropomorphic robot design. The results emphasize that anthropomorphism might not be an appropriate feature in industrial HRI as it not only failed to reveal positive effects on trust, but distracted participants from relevant task areas which might be a significant drawback with regard to occupational safety in HRI.

KRAN: Knowledge Refining Attention Network for Recommendation

Recommender algorithms combining knowledge graph and graph convolutional network are becoming more and more popular recently. Specifically, attributes describing the items to be recommended are often used as additional information. These attributes along with items are highly interconnected, intrinsically forming a Knowledge Graph (KG). These algorithms use KGs as an auxiliary data source to alleviate the negative impact of data sparsity. However, these graph convolutional network based algorithms do not distinguish the importance of different neighbors of entities in the KG, and according to Pareto’s principle, the important neighbors only account for a small proportion. These traditional algorithms can not fully mine the useful information in the KG. To fully release the power of KGs for building recommender systems, we propose in this article KRAN, a Knowledge Refining Attention Network, which can subtly capture the characteristics of the KG and thus boost recommendation performance. We first introduce a traditional attention mechanism into the KG processing, making the knowledge extraction more targeted, and then propose a refining mechanism to improve the traditional attention mechanism to extract the knowledge in the KG more effectively. More precisely, KRAN is designed to use our proposed knowledge-refining attention mechanism to aggregate and obtain the representations of the entities (both attributes and items) in the KG. Our knowledge-refining attention mechanism first measures the relevance between an entity and it’s neighbors in the KG by attention coefficients, and then further refines the attention coefficients using a “richer-get-richer” principle, in order to focus on highly relevant neighbors while eliminating less relevant neighbors for noise reduction. In addition, for the item cold start problem, we propose KRAN-CD, a variant of KRAN, which further incorporates pre-trained KG embeddings to handle cold start items. Experiments show that KRAN and KRAN-CD consistently outperform state-of-the-art baselines across different settings.

Does Rent-seeking Affect Environmental Regulation? Evidence from The Survey Data of Private Enterprises in China

Resource utilization not only meets the needs of economic development, but also has a far-reaching negative impact on the environment. Environmental regulation is regarded as the key measure to solve environmental pollution. However, the rent-seeking behavior of local enterprises will seriously weaken the implementation effect of environmental regulations. Under the background of the development of big data era, the massive micro enterprise data provided by China's private enterprise survey database provides favorable conditions for this paper to study its impact effect from the direction of big data. This paper uses OLS model and Tobit model to investigate the impact of rent-seeking on the implementation effect of environmental regulation. The results show that environmental regulation will make honest enterprises actively reduce output to control the emission level. However, rent-seeking enterprises will further expand their output to gain greater profits because they are sheltered by local governments.

The Aquaponic Ecosystem Using IoT and IA Solutions

For solving the negative impact of the human evolution in earth, water, pollution and quality of feed. A system of aquaponic is proposed to manage gardening and recover up to 90% of water used for plants. Aquaponic is a system that combines two names: aquaculture which is the farming of fish and hydroponic which is the cultivation of plants (off-soil). On the other hand, the possibility of using the phytotron system. The objective of this solution is to collect performance measures, to control the watering conditions of plants (water level, temperature, humidity, ...) With a cloud support and other possibilities offered by the internet of things (IoT). The paper at hand aim to provide a smart solution integrates the phytotron solution in order to control the first part wish is the hydroponic and the second part concerning the aquaculture in order to offer a smart environment for the cycle of fish’s life.

Evaluation of Economic Security of Water Resources and Analysis of Influencing Factors

To effectively evaluate the level of economic security of water resources (WES) in China and analyze its influencing factors, a comprehensive evaluation model of WES and a regression analysis model of influencing factors are established based on the panel data of 30 provinces in China from 2011 to 2017. It is found that, first, WES in China presents a fluctuating upward trend. Second, different regions have different economic security levels for their water resources in China, among which WES in the central region is the highest. Third, there is a U-shaped correlation between economic development and WES, and the population, pollution control level, technological innovation have negative impacts on WES. Moreover, this study also finds that with upgrades to the industrial structure and level of human capital, there will be improvements to WES. However, the external coefficient and the investment scale of fixed assets have a negative impact on the economic security of water resources. This is helpful to utilize the water resources, and improve the water resources safety management.

Social Network Evolution: The Case of UK Companies Before and After Brexit

The Brexit referendum has impacted both the UK and the EU economies in several ways. The uncertainty around Brexit highlighted the importance of a relationships network between directors of companies to access information and resources that are necessary for optimal decision making. It is difficult to develop informed business and economy policies without a deep understanding of the magnitude of Brexit on business-to-business relationships with EU-based firms. This study aims to analyze the impact of the passage of the Brexit referendum on the evolution of board interlock networks. The study uses network analysis to measure the evolution of UK-EU directors’ relationships over the Brexit period, predominantly between the 2010 and 2020 period. The study models the structural changes in dynamic networks by converting this evolving network into static graphs on yearly basis. The analysis indicates that links formation in the UK is affected negatively by the Brexit referendum. It also has a negative impact on forming a new link with potential companies’ directors in the EU, but it shows a rising tendency for shared affiliation bias analysis. Interestingly, the contradicted trend in 2007, the number of directors’ connection in consumer service and food & drug sectors was decreasing in the UK while rocketing in the EU. Doi: 10.28991/ESJ-2022-06-01-01 Full Text: PDF