Despite standardisation initiatives, the modern financial landscape continues to be characterised by heterogeneous payment systems. This issue persists even with the emergence of distributed ledger technology in the market. Independent groups of developers are producing their own permissioned blockchain solutions without clear directions for standardisation that could be associated to the lack of a clear position from central banks and regulatory organisations regarding these technologies. The unresolved problem of transaction finality in distributed ledgers adds to the difficulty of reconciling separate distributed platforms. One potential solution is the implementation of cross-chain bridges, which can establish connections between platforms and potentially enable seamless experiences for end users and applications. The paper discusses the advantages and issues associated with these bridges.
Blockchain technology has emerged as a potential solution to improve healthcare services by offering secure, transparent and efficient management of healthcare data. The paper presents an overview of the applications, benefits, challenges and recommendations for using Blockchain technology in healthcare. A systematic literature review has been conducted to identify relevant articles published in recent years, using the PRISMA framework to ensure methodological rigor. The review has revealed that Blockchain technology can be used in various healthcare applications, including data management, drug supply chain management, clinical trials, medical record keeping, and telemedicine. Blockchain technology offers several benefits in healthcare, such as secure and efficient data sharing, real-time access to patient data, improved patient outcomes, and lower costs. However, the adoption of Blockchain technology in healthcare also presents some challenges, such as regulatory barriers, interoperability issues, data privacy and security concerns, and technical limitations. To overcome these challenges, recommendations are provided, including developing a regulatory framework, addressing interoperability issues, implementing robust data privacy and security measures, and investing in Blockchain technology research and development.
Given an initial set of planar nodes, the problem is to build a minimum spanning tree connecting the maximum possible number of nodes by not exceeding the maximum edge length. To obtain a set of edges, a Delaunay triangulation is performed over the initial set of nodes. Distances between every pair of the nodes in respective edges are calculated used as graph weights. The edges whose length exceeds the maximum edge length are removed. A minimum spanning tree is built over every disconnected graph. The minimum spanning trees covering a maximum of nodes are selected, among which the tree whose length is minimal is the solution. It is 1.17 % shorter on average for 10 to 80 nodes compared to a nonselected tree.
Deep neural networks are widely used in computer vision for image classification, segmentation and generation. They are also often criticised as “black boxes” because their decision-making process is often not interpretable by humans. However, learning explainable representations that explicitly disentangle the underlying mechanisms that structure observational data is still a challenge. To further explore the latent space and achieve generic processing, we propose a pipeline for discovering the explainable directions in the latent space of generative models. Since the latent space contains semantically meaningful directions and can be explained, we propose a pipeline to fully resolve the representation of the latent space. It consists of a Dirichlet encoder, conditional deterministic diffusion, a group-swap and a latent traversal module. We believe that this study provides an insight into the advancement of research explaining the disentanglement of neural networks in the community.
To select optimal solutions in multicriteria decision-making (MCDM) problems, many practical approaches have been developed. In almost all of these approaches, it is necessary to assess the importance of individual criteria for decision makers. Subjective assessments of importance are transformed into numerical assessments of decision weights by applying appropriate computational procedures. A large number of methods for determining the weights of the criteria have been proposed. These methods differ in their operating principles and in the calculation procedures underlying each method. The paper presents the most well-known methods and provides a brief comparative analysis.
The paper describes a method for predicting genes associated with the development of cancer. The method applies the convolutional neural network for the purpose of predicting disease driver genes. Distinctive features of the method are the use of gene expression data to determine the topological structure of the network, the efficiency of prediction with limited information about genes associated with the disease, and the possibility of jointly including information on mutations and similarity of gene expression profiles to improve the accuracy of prediction.
The analysis of alternative decisions and the choice of the optimal – in a given sense – decision is an integral part of people’s purposeful activity in all areas of their social life. Many formal approaches have been proposed to solve these problems. One such approach is expected utility theory, which correctly models individuals’ subjective preferences and attitudes to risk. For a very long time this theory was the leading approach for decision making under conditions of risk. However, numerous practical studies have shown its weakness: the theory did not explicitly use subjective perceptions of decision outcome probabilities in optimal decision-making processes. This research has led to the creation and development of approaches to explicitly consider the probabilities of outcomes in decision making. This paper provides a critical analysis of the descriptive properties of expected utility theory and presents various forms of probability weighting functions.
Successful property management, which can be done by individual owners or by professional property managers, has numerous interrelated complex business processes: tenant attraction, screening, leasing, tenant move-in, property maintenance, retention/lease renewals, tenant move-out, and reputation management. Property managers must be highly skilled communicators and thoroughly understand the legal implications of housing and contract law in their operating environments. The lack of affordable solutions available to small and mid-sized property managers drives their use of fragmented technology solutions provided by numerous technology vendors that employ redundant data-gathering methods, putting them at a competitive disadvantage. This research paper explores the information systems needs of property managers in contrast to the solutions available to them in the marketplace.
The paper describes a method for constructing a hybrid classification model that allows combining several sources of biological information in order to build a classifier to identify subtypes of complex diseases. The distinctive feature of the method is its adaptive nature, i.e. the ability to build efficient classifiers regardless of data types, as well as a multi-criteria approach to evaluate the effectiveness of a classification. The testing results on real biomedical data showed the advantages of the proposed hybrid model in comparison with individual classifiers.
Established manufacturing corporates are facing major challenges today, as more and more technology-based startups are disrupting existing market competitors and are striving to gain foothold in new markets. Therefore, it can be observed that corporates and startups are increasingly seeking collaborations in order to gain advantageous access to resources, markets or even technologies from the respective partner. However, the majority of these collaborations fail for two reasons: first, the opportunistic choice of a collaboration type and, second, a poor suitability of established types of collaboration for technology-based startups. Consequently, the solution developed in this paper aims at addressing these problems by initially deriving a suitable collaboration framework based on strategic success potentials. Starting from identified requirements, a characteristic space for types of collaboration is determined. Based on this investigation, the paper shows which of the newly determined characteristics help fulfil strategically relevant success potentials of collaboration and, thus, enable a well-founded typification of collaboration types.
This study compares the performance of Logistic Regression and Classification and Regression Tree model implementations in predicting chronic kidney disease outcomes from predictor variables, given insufficient training data. Imputation of missing data was performed using a technique based on k-nearest neighbours. The dataset was arbitrarily split into 10 % training set and 90 % test set to simulate a dearth of training data. Accuracy was mainly considered for the quantitative performance assessment together with ROC curves, area under the ROC curve values and confusion matrix pairs. Validation of the results was done using a shuffled 5-fold cross-validation procedure. Logistic regression produced an average accuracy of about 99 % compared to about 97 % the decision tree produced.
In recent years, systemic and society-changing technological innovations (Deep Tech or DT innovations) have emerged primarily in the USA and Asia, while Europe is technologically dependent in many application fields. The development of DT is characterised by high financial capital needs. Additionally, intellectual property (IP) management plays a major role. To reduce the technological dependency for many areas in Europe, an adjustment of the government’s role as an actor in the innovation system appears beneficial. Targeted measures can improve the development and transfer of DT and, thus, contribute to securing long-term competitiveness of European nations. The aim of this contribution is therefore to identify support options within the technology transfer of DT innovations by conducting a structured literature analysis. In total, 27 applicable options are identified and structured into derived fields of action within innovation systems.
In today’s dynamically changing environment, we need to be able to respond in a timely manner to changes in supply chain processes. Software agents are successfully used in supply chain management tasks for a variety of purposes. The behaviour of agents is determined by the purpose of their development, and the effectiveness of the use of agents is considered in accordance with the purpose of their development. The paper presents research on the development of a multi-agent system for supply chain management, focusing on the steps of developing a multi-agent system. The choice of each algorithm for agents is analysed and argued. The application of the developed multi-agent system for supply chain management is also described in the paper. The efficiency of application of the developed multi-agent system is presented as well.
Blockchain is being promoted as the platform to disrupt business as usual in many transaction-heavy sectors. Questions remain about the future standards of blockchain, their feature set, functionality, and the willingness of organisations to disrupt their existing revenue streams via blockchain. The most significant near-term promise that affords industry is the cost and complexity savings via the standardization of their technology infrastructure stacks. This paper explores the benefits available of reduced costs and complexity via the adoption of blockchain.
Currently, there are a large number of articles describing the theoretical aspects of development in the field of machine learning. However, the experience of their practical application in real systems is described much less often. Basically, authors describe the efficiency, accuracy, and other performance metrics of the resulting solution, but everything stops at the prototype stage. At the same time, how the trained model will behave not on test data, but in real conditions, can be very different from the indicators obtained at the development stage. This article describes the experience of the implementation and real use of a classification service based on machine learning techniques.
The study proposes a smart restaurant system and analyses its benefits to be able to determine system potential advantages in restaurants. Service time is one of the main criteria that can be improved to enhance the speed of the customer service as well as to increase the number of restaurant visitors. To develop the system, solutions found in scientific literature, software and their different architectures are analysed. It has been found out that it is possible to decrease the average restaurant service load time by 52.76 %. Two hypotheses have been proposed for further research in order to determine how a smart restaurant service system can increase chef’s efficiency and how the use of different algorithms can decrease chef’s workload during peak hours.
The paper proposes a 2D-hybrid system of computational intelligence, which is based on the generalized neo-fuzzy neuron. The system is characterised by high approximate abilities, simple computational implementation, and high learning speed. The characteristic property of the proposed system is that on its input the signal is fed not in the traditional vector form, but in the image-matrix form. Such an approach allows getting rid of additional convolution-pooling layers that are used in deep neural networks as an encoder. The main elements of the proposed system are a fuzzified multidimensional bilinear model, additional softmax layer, and multidimensional generalized neo-fuzzy neuron tuning with cross-entropy criterion. Compared to deep neural systems, the proposed matrix neo-fuzzy system contains gradually fewer tuning parameters – synaptic weights. The usage of the time-optimal algorithm for tuning synaptic weights allows implementing learning in an online mode.
In a business-to-consumer (B2C) context, customers order more frequently and in smaller quantities, resulting in a high number of consignments. Moreover, online shoppers expect a fast and accurate delivery at low cost or even free. To survive in such a market, companies can no longer optimise individual supply chain processes, but need to integrate several activities. In this article, the integrated order picking-vehicle routing problem is analysed in an e-commerce environment. In previous research, a mathematical programming formulation has been formulated in literature but only small-size instances can be solved to optimality. Two picking policies are studied: discrete order picking and batch order picking. The influence of various problem contexts on the value of integration is investigated: a small picking time period, outsourcing to 3PL service providers, and a dynamic environment context.
Vaccine requirements are becoming more mandatory in several countries as public health experts and governments become more concerned about the COVID-19 pandemic and its variants. In the meantime, as the number of vaccine requirements grows, so does the counterfeiting of vaccination documents. Fake vaccination certificates are steadily growing, being sold online and on the dark web. Due to the nature of the COVID-19 pandemic, there is a need of robust authentication mechanisms that support touch-less technologies like Near Field Communication (NFC). Thus, in this paper, a blockchain-NFC based COVID-19 Digital Immunity Certificate (DIC) system is proposed. The vaccination data are first encrypted by the Advanced Encryption Standard (AES) algorithm on Hadoop Distributed File System (HDFS) and then uploaded to the blockchain. The proposed system is based on the amalgamation of NCF and blockchain technologies which can mitigate the issue of fake vaccination certificates. Furthermore, the emerging issues of employing the proposed system are discussed with future directions.
Choice and decision making are an integral part of the purposeful activities of people in all areas of public and private life. Tasks of multi-criteria decision making are characterised by the fact that alternative decisions are evaluated by a set of criteria and the concept of a decision and its outcome coincide. The defining concept in such problems is the concept of a set of Pareto optimal decisions (Pareto set). This set forms alternative decisions that are not comparable in terms of the set of evaluation criteria. The choice of the optimal decision in the Pareto set can be performed only on the basis of the subjective preferences of the decision maker. In recent decades, extensions of traditional methods of multi-criteria decision making to a fuzzy environment have been proposed. One of the well-known approaches to multi-criteria decision making is the TOPSIS method. In the paper, a fuzzy version of this method is considered in situations where the values of evaluation criteria are set in the form of fuzzy numbers.
