A comprehensive comparison of the performance of metaheuristic algorithms in neural network training for nonlinear system identification

Many problems in daily life exhibit nonlinear behavior. Therefore, it is important to solve nonlinear problems. These problems are complex and difficult due to their nonlinear nature. It is seen in the literature that different artificial intelligence techniques are used to solve these problems. One of the most important of these techniques is artificial neural networks. Obtaining successful results with an artificial neural network depends on its training process. In other words, it should be trained with a good training algorithm. Especially, metaheuristic algorithms are frequently used in artificial neural network training due to their advantages. In this study, for the first time, the performance of sixteen metaheuristic algorithms in artificial neural network training for the identification of nonlinear systems is analyzed. It is aimed to determine the most effective metaheuristic neural network training algorithms. The metaheuristic algorithms are examined in terms of solution quality and convergence speed. In the applications, six nonlinear systems are used. The mean-squared error (MSE) is utilized as the error metric. The best mean training error values obtained for six nonlinear systems were 3.5×10−4, 4.7×10−4, 5.6×10−5, 4.8×10−4, 5.2×10−4, and 2.4×10−3, respectively. In addition, the best mean test error values found for all systems were successful. When the results were examined, it was observed that biogeography-based optimization, moth–flame optimization, the artificial bee colony algorithm, teaching–learning-based optimization, and the multi-verse optimizer were generally more effective than other metaheuristic algorithms in the identification of nonlinear systems

Composite alginate-hyaluronan sponges for the delivery of tranexamic acid in post-extractive alveolar wounds

The management of wounds in patients on anticoagulant therapy who require oral surgical procedures is problematic and often results in a non-satisfactory healing process. Here we report a method to prepare an advanced dressing able to avoid uncontrolled bleeding by occluding the post-extractive alveolar wounds, and simultaneously, capable of a fast release of tranexamic acid (TA). Composite alginate/hyaluronan (ALG/HA) sponge dressings loaded with TA were prepared by a straightforward internal gelation method followed by a freeze-drying step. Both blank and drug-loaded sponges were soft, flexible, elegant in appearance and non-brittle in nature. SEM analysis confirmed the porous nature of these dressings. The integration of HA influenced the microstructure, reducing the porosity, modifying the water uptake kinetic and increasing the resistance to compression. TA release from ALG/HA sponges showed a controlled release up to 3h and it was faster in the presence of HA. Finally, an in vitro clotting test performed on human whole blood confirmed that the TA-loaded sponges significantly reduce the blood clotting index (BCI) by 30% compared to ALG/HA20 sponges. These results suggest that, if placed in a socket cavity, these dressings could give a relevant help to the blood hemostasis after dental extractions, especially in patients with coagulation disorders

Public Private Partnership in Italian Health Care Management An Organizational Maturity Assessment Model

This work aims to analyze a specific phenomenon of innovation in health management: public private partnerships within the Italian healthcare sector. The object of the study is to measure the degree of organizational maturity (OM) of the forms of public-private partnerships (PPP) analyzing and measuring key managerial processes, in terms of innovation in meeting the partnership‘s goals/targets. The analysis is based on the identification of key processes relevant to the management of partnerships, to check which systems of governance are able to meet different stakeholder interests. We therefore built a conceptual standard for analysis of the OM through a field survey based on visits, participant observation, analysis of documents and semi-structured interviews with the management

A framework for distributed interaction in intelligent environments

Ubiquitous computing is extending its applications to an increasing number of domains. "Monolithic" approaches use centralised systems, controlling devices and users' requests. A different solution can be found in works proposing "distributed" intelligent devices that communicate, without a central reasoner, creating little communities to support the user. If the former approach uses all the available sensors being more easily context-aware, the latter is scalable and naturally supports multiple users. In this work we introduce a model for a distributed network of entities in Intelligent Environments. Each node satisfies users' requests through Natural User Interfaces. If a node cannot produce the expected output, it communicates with others in the network, generating paths where the final target is undetermined and intermediate nodes do not understand the request; this is the focus of our work. The system learns parameters and connections in the initial topology. We tested the system in two scenarios. Our approach finds paths close to the optimum with reasonable connections

IEEE Access Special Section Editorial: Data Mining for Internet of Things

It is an irrefutable fact that the Internet of Things (IoT) will eventually change our daily lives because its applications and relevant technologies have been or will be penetrating our daily lives. Also, the IoT is aimed to connect all the things (e.g., devices and systems) together via the Internet, thus making it easy to collect the data of users or environments and to find out useful information from the gathered data by using data mining technologies. As a consequence, how intelligent systems are developed for the IoT has become a critical research topic today. This means that artificial intelligence (AI) technologies (e.g., supervised learning, unsupervised learning, and semi-supervised learning) were used in the development of intelligent systems for analyzing the data captured from IoT devices or making decisions for IoT systems. It can be easily seen that AI can make an IoT system more intelligent and thus more accurate. For example, various sensors can be used for a smart home system to pinpoint the location and analyze the behavior of a human; however, with AI technologies, a more accurate prediction can be provided on the two pieces of information of a human. One of the most important uses for AI technologies is to make IoT systems more intelligent in order to provide a more convenient environment for users; thus, how to use existing AI technologies or develop new AI technologies to construct a better IoT system has attracted the attention of researchers from different disciplines in recent years. That is why, besides using existing supervised, unsupervised, semi-supervised learning algorithms, data mining algorithms, and machine learning algorithms, several recent studies have also attempted to develop new intelligent methods for the devices or systems for the IoT. All these approaches for making an IoT system more intelligent can also be found in the articles of this Special Section

Investigating business process elements: a journey from the field of Business Process Management to ontological analysis, and back

Business process modelling languages (BPMLs) typically enable the representation of business processes via the creation of process models, which are constructed using the elements and graphical symbols of the BPML itself. Despite the wide literature on business process modelling languages, on the comparison between graphical components of different languages, on the development and enrichment of new and existing notations, and the numerous definitions of what a business process is, the BPM community still lacks a robust (ontological) characterisation of the elements involved in business process models and, even more importantly, of the very notion of business process. While some efforts have been done towards this direction, the majority of works in this area focuses on the analysis of the behavioural (control flow) aspects of process models only, thus neglecting other central modelling elements, such as those denoting process participants (e.g., data objects, actors), relationships among activities, goals, values, and so on. The overall purpose of this PhD thesis is to provide a systematic study of the elements that constitute a business process, based on ontological analysis, and to apply these results back to the Business Process Management field. The major contributions that were achieved in pursuing our overall purpose are: (i) a first comprehensive and systematic investigation of what constitutes a business process meta-model in literature, and a definition of what we call a literature-based business process meta-model starting from the different business process meta-models proposed in the literature; (ii) the ontological analysis of four business process elements (event, participant, relationship among activities, and goal), which were identified as missing or problematic in the literature and in the literature-based meta-model; (iii) the revision of the literature-based business process meta-model that incorporates the analysis of the four investigated business process elements - event, participant, relationship among activities and goal; and (iv) the definition and evaluation of a notation that enriches the relationships between activities by including the notions of occurrence dependences and rationales

Limits of inclusion: multimodal action-nets and the challenge of communication technologies for disability

This paper investigates the effects that the extensive use of communication technologies, fostered by the pandemic, has on organizational inclusion. It is an explorative study that offers theoretical reflections supported by analysis of interviews and journalistic reports of disabled people’s experience with communication technologies and assistive devices. We argue that such technology, although able to foster unexpected changes in work activities, is not inclusive in itself, as it can also produce errors, malfunctions, frustrations, misnarration. Therefore, we propose a relational approach that sees inclusion not in terms of the adoption of single accessibility devices, nor of specific policies in HR management, but rather as a dynamic process characterized by multimodal action-nets, composed of multiple socio-material agents and nodes, both human and non-human, and complex interdependencies between individuals, public and private organizations, technological artifacts, design, IT services and data processing, hiring policies, knowledge and narratives. Such an approach highlights the fruitful connection between inclusion and resilience