Computational Science and Its Applications – ICCSA 2022

Breast cancer is the most common cancer in women worldwide. While the early diagnosis and treatment can significantly reduce the mortality rate, it is a challenging task for pathologists to accurately estimate the cancerous cells and tissues. Therefore, machine learning techniques are playing a significant role in assisting pathologists and improving the diagnosis results. This paper proposes a hybrid architecture that combines: three of the most recent deep learning techniques for feature extraction (DenseNet_201, Inception_V3, and MobileNet_V2) and random forest to classify breast cancer histological images over the BreakHis dataset with its four magnification factors: 40X, 100X, 200X and 400X. The study evaluated and compared: (1) the developed random forest models with their base learners, (2) the designed random forest models with the same architecture but with a different number of trees, (3) the decision tree classifiers with the best random forest models and (4) the best random forest models of each feature extractor. The empirical evaluations used: four classification performance criteria (accuracy, sensitivity, precision and F1-score), 5-fold cross-validation, Scott Knott statistical test, and Borda Count voting method. The best random forest model achieved an accuracy mean value of 85.88%, and was constructed using 9 trees, 200X as a magnification factor, and Inception_V3 as a feature extractor. The experimental results demonstrated that combining random forest with deep learning models is effective for the automatic classification of malignant and benign tumors using histopathological images of breast cancer.

This paper aims to analyze space-time cubes for visualizing and processing multi-temporal spatial monitoring data. The proposed case study is the Cathedral of Milan (Duomo di Milano), which has a set of monitoring time series spanning more than half a century. Differential vertical movements are periodically measured for the cathedral columns, constituting a continuous spatio-temporal dataset for structural health monitoring. More specifically, the space time pattern mining toolbox in ArcGIS Pro was used to (i) create a space-time cube and (ii) perform advanced analysis using the monitoring dataset, including time-series clustering and forecasting operations.

The paper proves that as a result of the experience accumulation in using rigidly deterministic models, real opportunities have been created for the successful application of a more advanced methodology promoting modeling economic processes to consider stochastic and uncertainty: carrying out multivariate calculations and model experiments with a variation of the model design and its initial data; studying stability and reliability of the obtained solutions, identifying the zone of uncertainty, including reserves in the model: the use of techniques increasing the adaptability of the economic decisions in probable and unforeseen situations. The study outlines the general classification of economic and mathematical models including more than ten main features with the development of economic and mathematical research. The process of integration of different types models into more complex model constructions is carried out. It has also been proven that the formulated mathematical problem of the economic analysis can be solved by one of the most developed mathematical methods. It is emphasized that economic cybernetics makes it possible to analyze economic phenomena and processes as very complex systems from the point of view of the laws and mechanisms of control and the movement of information in them. It has been proven that many economic processes are studied by using econometric models, including ARMA models (mixed process of auto regression and moving average), GARCH (regressive models with conditional heteroscedastic errors), ECM (deviation correction models) and VAR (vector auto regression models). Accordingly, one of the important achievements is the ability to illustrate, using the constructed model, medium-term periodic fluctuations in technological changes and resource utilization due to external shocks associated with the development of technologies.

In the age of the global pandemic and during the war in Eastern Europe, inflation is increasing daily, affecting not only the socio-economic situation of developed but also developing and small open economy countries. Among the factors causing inflation are often indicated rising prices for petroleum products. Therefore, the aim of the paper is to make a correlation-regression analysis of the impact of changes in oil prices at the level of inflation. The majority of the empirical data covers the years 2000 to 2021. Quantitative data was analyzed in line with the objectives, resulting in the identification of particular issue tendencies. The paper examines the methodology of world prices for oil products and current factors. The characteristics of price formation in the global market and the indicators of the relationship between oil prices and the level of inflation are studied. The analysis of the impact of the dynamics of oil prices on inflation is established. The applicable conclusion was produced based on the examination of the information acquired through the characteristic and quantitative research methodologies.

Recently, a lot of data with variety factors and indicators of cognitive diseases is available for clinical research. Although the transformation of information to particular data model is straight-forward, a lot of challenges arise if data from different repositories is integrated. Since each data source keeps entities with different names and relationships at different levels of granularity, the information can be partially lost or not properly presented. It is therefore important to have a common data model that provides a unified description of different factors and indicators related to cognitive diseases. This paper proposes a hierarchical data model of patients with cognitive disorders, which keeps the semantics of the data in a human-readable format and accelerates the interoperability of clinical datasets. It defines data entities, their attributes and relationships related to diagnosis and treatment. The data model covers four main aspects of the patient’s profile, including personal profile, anamnestic profile, related to social status, everyday habits, and head trauma history, clinical profile, describing medical investigations and assessments, comorbidities and the most likely diagnose, and treatment profile with prescribed medications. It provides a native vocabulary, improving data availability, saving efforts, accelerating clinical data interoperability, and standardizing data to minimize risk of rework and misunderstandings. The data model enables the application of machine learning algorithms by helping scientists to understand the semantics of information through a holistic view of patient. It is intended to be used by researchers in the field of Biostatistics, Bioinformatics, Neuroscience, etc. supporting them in content mapping and data integration from different datasets.

The study of cavities and their areas of influence for purposes of environmental intervention has gained increasing relevance in Brazilian legislation. Serra do Gandarela is a geomorphological site, located in the State of Minas Gerais, which concentrates a large amount of speleological features, in addition to mineral resources. This article seeks to present multi-criteria analysis techniques in a GIS environment to measure the potential for the occurrence of cavities in the region in order to arrive at a map of the speleological potential of the site. The result will then be superimposed on a geological model developed for the region, in order to be able to observe transversal profiles in order to correlate the results considering the imputed geological and geomorphological factors. The importance of the analysis carried out is to expose the areas with the highest concentration of factors favorable to the development of cavities in regions close to mining activities, with the objective of guiding mining activities in a less harmful way to the environment. The suggestion of presentation in 3D platform is made to allow a more dynamic observation in addition to highlighting topographic and slope factors.

The paper presents a method for calculating frequency-dependent rays, which allows effective approximation of a broadband signal propagation. We provide comparative analysis of this method with the standard ray method and the finite-difference method. The developed algorithm is promising for modelling and inverse problems. We performed the numerical examples for the realistic Sigsbee model.

Nowadays, many technologies are changing our way of life, including those related to extended reality. One of the most interesting is Augmented Reality (AR). Today, even if this technology seems to be discovered yet, it is widely applied in several contexts, including the fruition and conservation of cultural heritage. Such spread is mainly offered by the new and more powerful mobile devices, allowing museums and art exhibitions to use AR to offer new experiences to visitors. In this paper, we present an augmented reality mobile system based on content-based image analysis techniques and Linked Open Data to improve the user knowledge about cultural heritage. We use different image analysis techniques, and we present several experimental results to show the performance of our system.

In the current era of big data, huge volumes of valuable data have been generated and collected at a rapid velocity from a wide variety of rich data sources. In recent years, the willingness of many government, researchers, and organizations are led by the initiates of open data to share their data and make them publicly accessible. Healthcare, disease, and epidemiological data, such as privacy-preserving statistics on patients who suffered from epidemic diseases such as Coronavirus disease 2019 (COVID-19), are examples of open big data. Analyzing these open big data can be for social good. For instance, people get a better understanding of the disease by analyzing and mining the disease statistics, which may inspire them to take part in preventing, detecting, controlling and combating the disease. Having a pictorial representation further enhances the understanding of the data and corresponding results for analysis and mining because a picture is worth a thousand words. Hence, in this paper, we present a visual data science solution for the visualization and visual analytics of big sequential data. The visualization and visual analytics of sequences of real-life COVID-19 epidemiological data illustrate the ideas. Through our solution, we enable users to visualize the COVID-19 epidemiological data over time. It also allows people to visually analyze the data and discover relationships among popular features associated with the COVID-19 cases. The effectiveness of our visual data science solution in enhancing user experience in the visualization and visual analytics of big sequential data are demonstrated by evaluation of these real-life sequential COVID-19 epidemiological data.

On the modern stage the economic relationships in Ukraine, which constant by years, the characterized it stage of development of the state regulation, organically connect with right ownership into economics object, and beforehand, land. Here determined of through property relationships is legal regulation the state into transfer, accepted, appropriation by different of physical and juridical persons depend from fields of their activity. And existing innovation of this research to lie down in that, what objects of the right ownership, as forms of legal regulation of public relationships, clearly interconnected by territory borders, especially at the time reforming of local self-government and territorial organization of authority in Ukraine. And by this example is creation and open of transparent land market. The purpose of the article is to study the stages of development of state regulation of relations to ensure, provide and protect property rights by their types and forms, depending on the characteristics of territorial entities of Ukraine, which, of course, contain objects of different possessions. The article used methods of comparison and description, the method of decomposition, methods of observation and grouping. It should be concluded that the right of ownership in Ukraine is particularly subject to regulation by public authorities and local governments and is evidenced by the legal fact that establishes common relations in society and has a relationship with the territorial location of objects according to their purpose or benefits. And providing large sections of the population with property rights to objects such as property and land resources is directly related to the processes of zoning the territory of Ukraine.

This contribution presents an approach for generating and modelling spatially referenced, multi-dimensional urban models from remote sensing data for regions where no reliable reference data are available. The focus is on a modular service-oriented process chain covering the entire process from data acquisition to effective web-based visualization, addressing raw data, preprocessing, management, spatial analyses, visualisation and applications. The use case, investigated in the framework of a EU-funded multidisciplinary R+D project, is the cityspace of East Jerusalem (about 190 about 190 sqkm). Due to the controversial geopolitical situation, usable and high-resolution reference data of the complex, granular urban fabric is unavailable. Hence very high-resolution stereo aerial images (571 pcs. à 10 cm GSD) were acquired and photogrammetrically processed to build a high-resolution 3D reference point cloud. Corresponding semantic data from various sources and household surveys (3,500 pcs.) specially conducted were integrated into the data and managed within a unified, fully-referenced 3D database (open-source). The resulting virtual urban 3D model includes functionalities for geospatial exploration and analysis, allowing spatio-temporal applications, such as land use/management, social/technical infrastructure, or tourism for documentation, information, education as well as decision-making. It addresses both the local civil society and, through specific functions, urban planners and decision makers. Analysis and interaction functionalities are provided in a user-oriented and application-related framework, taking into account effective visualisation (cartographic and visual gestalt principles). The independent spatial reference of a multiscale grid is used for spatial analyses and visualisation purposes. The generic construction of the dedicated virtual cityspace allows for the transferability of results.

Noise emissions are a relevant externality, which cause negative effects on affected population. Due to the topographic and physical characteristics of the geographical context, road transport in mountain areas is a main source of noise emissions. Adequate solutions have to be foreseen, able to reduce the impacts on affected population. First, this contribution describes alternative solutions to reduce the noise pressure along mountain roads. Then, it provides a method to evaluate their effectiveness based on a cost-effectiveness ratio and hedonic pricing. Two case studies located along a mountain road in South Tyrol (Italy) show the performances of different solutions in various contexts, suggesting the importance of evaluations that consider the territorial and settlement specificities. These results can be helpful for policy makers to define the most effective solutions to reduce transport externalities and to make mountain areas more liveable.

In recent years, the usage of electronic health records (EHR), wearable devices, and health applications has expanded in popularity. Because of the abundance of data that has been accumulated and integrated, health self-management is becoming more practicable. Some of the difficulties that the current healthcare system is facing include smart homes and smart workplaces enabled by the internet of things. The Web of Things (WoT) is a subset of the Internet of Things that aims to connect everyday things to the Internet and manage interoperability. Furthermore, collaboration of health data with data from various devices at home and at work, as well as open data on the Internet, is critical for successful and accessible health self-management. Unfortunately, shared health data may be untrustworthy for a variety of reasons. Uncertainty can be caused by heterogeneity, incompleteness, unavailability, and data inconsistency. To address the problem of health data uncertainty, we provide a probabilistic approach for composing uncertain Health Connected Data and computing the probabilities to deliver the final degree of uncertainty. We also present a method for parsing typical WoT objects into a new programmatic form that mimics the uncertainty of health data. Using a health care use case, we show how our technique successfully integrates uncertain health data with home, work, and sport environment data for the WoT domain.

A new statistical test is presented to detect the monotonicity of uncertainty (F is DURL) based on derivative criteria and the histogram method. Some properties such as consistency and asymptotic normality are discussed. Simulation by the Monte-Carlo method gives the critical values of the statistics. We compare the power estimate with that of the test based on the criteria of monotonicity of residual entropy. Finally, we show on real survival data that the two tests lead to the same conclusions.

This contribution describes a new approach to formulation of ODE and PDE critical points using implicit formulation as t-variant scalar function using the Taylor expansion. A general condition for the critical points is derived and specified for t invariant case. It is expected, that the given new formulae lead to more reliable detection of critical points especially for large 3D fluid flow data acquisition, which enable high 3D vector compression and their representation using radial basis functions (RBF).In the case of vector field visualization, e.g. fluid flow, electromagnetic fields, etc., the critical points of ODE are critical for physical phenomena behavior.

Agile software development is an iterative software development methodology that aims at maximizing productivity, effectiveness, and speed of delivery. There are a lot of benefits of Agile Software Development. However, there are still some challenges. For example, non-functional requirements are not treated as first-class artifacts during the development lifecycle, which causes many problems such as customer dissatisfaction and much rework which therefore affects time and cost. This paper explains different solutions that have handled non-functional requirements issues in Agile. The paper shows the strength and weakness of each solution, however, there is no single solution that handles all main activities of requirements engineering such as elicitation, analysis, validation and management in regards of non-functional requirements.

We develop a numerical method to approximate the adjusted value of a European contingent claim in a market model where the underlying’s price is correlated with the stochastic default intensities of the two parties of the contract.When the close-out value of the contract is chosen as a fraction of the adjusted value, the latter verifies a non linear, not explicitly solvable BSDE. In a Markovian setting, this adjusted value is a deterministic function of the state variable verifying a non-linear PDE.We develop here a numerical method to approximate the PDE solution, as an alternative choice to the commonly used Monte Carlo simulations, which require large computational times, especially when the number of the state variables grows.We construct the approximated solution by the simple method of lines and we show the method to be accurate and efficient in a simplified cases. We show numerical results in the case of both constant intensities and the situation where only one is diffusive.

Bicubic parametric plates are essential for many geometric applications, especially for CAD/CAM systems used in the automotive industry, mechanical and civil engineering applications. Usually the Hermite, Bézier, Coons or NURBS plates are used. There is always a problem to explain how the Hermit bicubic plate is constructed. This contribution describes a novel formal approach to Hermite bi-cubic plate construction using the tensor product.

The robustness of two important digital image forensic tasks (i.e. SIFT-based copy-move forgery detection and PRNU-based camera sensor identification) against four different lossy compression techniques is investigated (while typically, only JPEG compression is considered) to identify the best suited technique for this application scenario. Overall, we find that the accuracy of forensic tasks is reduced for increasing compression strength as expected, however, the relative performance of the compression schemes is different for the two tasks. While JPEG is superior for realistic application settings (where accuracy is in an acceptable range) in SIFT-based copy-move forgery detection, JPEG XR and BPG provide the best option for PRNU-based camera sensor identification, whereas JPEG is clearly impacting this forensic application most severely.

Cardiovascular disease (CVD) has quickly grown in prevalence over the previous decade, becoming the major cause of human morbidity on a global scale. Due to the massive number of ECG data, manual analysis is regarded as a time-consuming, costly and prone to human error task. In the other hand, computational systems based on biomedical signal processing and machine learning techniques might be suited for supporting arrhythmia diagnostic processes, while solving some of those issues. In general, such systems involve five stages: acquisition, preprocessing, segmentation, characterization, and classification. Yet numerous fundamental aspects remain unresolved, including sensitivity to signal fluctuation, accuracy, computing cost, generalizability, and interpretability. In this context, the present study offers a comparative analysis of ECG signal classification using two artificial neural networks created by different machine learning frameworks. The neural nets were built into a pipeline that aims to strike an appropriate balance among signal robustness, variability, and accuracy. The proposed approach reaches up to 99% of overall accuracy for each register while keeping the computational cost low.

The impact of urbanization is determined by the amount of land taken and the intensity with which it is used, such as soil sealing and population density. Land take can be referred to the loss of agricultural, forest, and other semi-natural and natural land to urban and other artificial land development. It is closely linked to urban expansion. City centers play an important role to curb such land take issues in allocating the growing population through urban densification. In order to assess how built-up, environmental, and socio-economic factors impacts zero net land take, this paper aims at using Multinomial regression model (MLR) to evaluate the built-up densification. This model is built, calibrated, and validated for the area of Brussels Capital region and its peripheral Brabant’s using cadastral data. Three 100 × 100 m built-up maps are created for 2000, 2010 and 2020 where the map for 1990–2000 were used for calibration and was further validated using 2000–2010 maps. The causative factors are calibrated using MLR and validated using ROC curve and goodness of fit. The results show that areas at closer periphery of the city center with high densities have high probability for allocating further growth as they provide a broad range of facilities and local services along with an established connectivity infrastructure. This can be observed as a pragmatic solution for the policy makers and urban planners to achieve the intended policy of “zero net land take”.

The new European standards and directives on land take raise critical issues concerning the techniques for measuring and monitoring the phenomenon in order to achieve the targets fixed. The directive “No Net Land Take by 2050”, makes it necessary to homogenize both the terminology used to define land take or consumption and the standardization of a computational methodology for its quantification. In order to achieve the goals, set by the EU regarding land take and soil sealing, it is necessary for EU member states to produce comparable data. It is essential to use the same data sources with standardized coding and to share the same meaning of the concept of land take. Therefore, with the aim of highlighting the criticalities and inconsistencies arising from the use of different techniques and datasets for monitoring land take, we will analyze, first, different definitions of land take derived from institutional sources including the European Environment Agency (EEA); then to each definition we will associate the corresponding land cover classes derived from the Copernicus Corine Land Cover (CLC) project. For the quantitative analysis we will use continuous and discontinuous datasets (raster and vectors) whose results will be compared with the data of the annual report on land take of the Superior Institute for environmental protection and research of Italy (ISPRA 2020).

In the last 50 years, the phenomenon of urban land occupation in Europe has become increasingly relevant, leading to the development of low-density and highly dispersed settlements. The shapes and extensions of urban settlements have moved away from the more traditional and recognized dynamics of urban expansion, acquiring different forms and very low values of settlement density. These are models of unsustainable development because they tend to consume a limited resource, converting those soils intended for other uses or with a different natural vocation into artificial soils. In addition to the direct negative effects related to the amount of land consumed, the indirect effects are related to the total dependence on private cars for daily commuting, resulting in increased pollution, overall economic and social inefficiency, dependence on fossil fuels and minerals and services deficiency.The shape of the urban settlement inevitably influences the quality of life of men and women, and if we consider that, in Italy, there are still important differences in terms of gender equality, we propose the first developments of a research project in which we want to investigate whether and how the dispersion of urban settlement affects and influences the quality of life of women.

Green roofs experience an evolution over the years of physical and chemical properties of the substrate layer that may lead to substantial changes in their hydrological behavior. This study, benefiting from a 5-years monitoring period, aims at assessing changes in the retention capacity of two experimental green roofs (GR1 and GR2), located in Southern Italy and different in drainage layer, by comparing pairs of similar rainfall-runoff events which occurred respectively in 2018 and early 2019, one year after installation, and in 2022. To this end, once identified the retention capacity of each, differences among similar events occurred four years apart were assessed and compared for the two green roof configurations to detect: possible changes in their hydrological performance, the configuration most affected by aging and the evolution of their differences over time. The results obtained so far suggest a general decay of the green roofs retention capacity with some differences according to the drainage characteristics. GR1, with a drainage layer made by 5-cm depth expanded clay, reported a 79% reduction of retention capacity while GR2, with a drainage layer made by MODI’ plastic panel filled with expanded clay, experienced a reduction of about 53%. Differences between the two green roof configurations also increase due to aging effects because the substrate, initially crucial in the retention dynamics, seems affected by a progressive performance loss. Conversely, the drainage layer, that appears to play a secondary role in the early operational period, becomes determinant in the medium observation period.

The territorial cohesion is one of the primary objectives for the European Union and it affects economic recovery pushing the role of Public Administration in promoting territorial development actions. The National Strategy for Inner Areas (SNAI) is a public policy promoting endogenous development processes in marginal territories with low settlement density. Specific contexts where rules and standards defined for the organization of large metropolitan aggregates lose their effectiveness whose identification represents a critical stage for policy efficacy and the actual map of SNAI target areas appears to be the results of a weak and simplified analytical approach. These considerations are the origin of the research question that underlies this work: identify the typical characteristics of Basilicata's marginal areas through machine learning techniques and, subsequently, reclassify the national territory using the trained model. However, outlining the boundary of this territories is only a preliminary task. The following step is the identification of the dynamics within the different territorial sub-systems that make up the inner peripheries. This paper presents the results of the local model-agnostic method for interpreting the obtained results. It emerges, thought cooperative game theory by Shapley values, the need to refine analytical methods that are sensitive to the measurement of the different context conditions. Future perspectives of the research regard the extensive deepening of the application on the basis of wider datasets able to make explicit spatial components of the distribution of the observed phenomena.

A computational model of interaction of a compressible fluid and deformable elastic solid is presented. The model is derived from the general solid-fluid two-phase mixture model and its derivation is based on the Symmetric Hyperbolic Thermodynamically Compatible (SHTC) systems theory. The governing equations form a symmetric hyperbolic system of partial differential equations of the first order, the solutions of which satisfy the thermodynamic law of conservation of energy. These properties allow the direct application of advanced high accuracy computational methods to solve model equations and ensure the reliability of numerically obtained solutions. Some preliminary results of numerical simulation are presented, showing the applicability of the model for studying complex problems of the solid-fluid interaction.

The capability of the Schiff’s base complex from the hydrazine group, which has azomethine (-NHN=CH-) as the main group, has been studied for its potential in the medical field, such as anticancer, antioxidant, and antibacterial. The synthesis method and its reaction condition for coordinating the first-row transition complex compound with base Schiff’s ligands SDPH (Salicylaldehyde-2,4-Dinitrophenylhydrazine) and VDPH (Vanillin-Dinitrophenylhydrazine) were still challenging to be applied as not all reaction conditions can succeed in the synthesis experiment. To determine the potential of first-row transition metal ions coordinating with SDPH and VDPH ligands, this research studies computationally the Extended Tight Binding Quantum Chemical method, called GFN2-xTB. Variations of the octahedral complex molecular formula used in computational calculations are [M(L)2XaYb] and [M(L)2XaYb]+ with M = (Cr, Mn, Fe, Co, Ni); L = SDPH and VDPH; X = NH3 (a = 0 or 1); and Y = H2O (b = 1 or 2). In addition, these complex compounds, which have metal ions with electron configuration d4-d7, were studied for their stability under high and low spin conditions. The data indicate the presence of stable complexes with more negative ∆G values were generally found in: (i) complexes with VDPH ligands, (ii) complexes containing the NH3 ligand, (iii) complexes with metal ions M+3, (iv) complexes having low spin magnetic properties. These results were also supported by the characteristics of global reactive descriptors, including a global electrophilicity index, electronegativity, chemical potential, chemical hardness, and chemical softness. It also included studies related to the HOMO-LUMO electronic energy to determine the reactivity and stability of the studied complex compounds.

Many use cases give the impression that fundamental spatial data themes could help to bridge the gap of data silos and even approach a common conscious dataspace. Starting from the fundamental data themes of UNGGIM, we evaluate the minimal consensus for spatial data quality and create evidence for a missing common spatial data space by pragmatic examples from a data provider viewpoint. By the hand of given examples, the main challenges to start a common conscious dataspace in terms of the geospatial perspective can be highlighted, which leads to the need for a foundational semantic structure and sustainable persistent information structures. One possible approach to establish the baseline for a common persistent information space are geographical grid systems.

Basilicata is composed by many small municipalities that offer poor accessibility to essential services. The key theme of this work is the evaluation of the endowment of these services, analyzing, in a GIS environment, their accessibility in terms of temporal distance. This work explores the specific issues and challenges for accessibility to internal areas and reflects on the future development prospects of the 131 Lucanian municipalities. The analysis was conducted on the basis of two types of information layers in relation to the totality of the municipalities: demographic structure of the population and provision of essential services, divided into 3 macro classes: education, health and mobility. Evaluations were thus extracted which provided a comparative-objective analysis of the presence of essential services. The result is a picture that shows serious difficulties linked to the socio-cultural and territorial fabric, the railway and motorway networks are profoundly lacking, showing a clear gap between the municipalities in terms of provision of services.

The agricultural areas abandonment has become one of phenomena that is most influencing the land transformation. This has been evident for some decades already, especially in inland mountainous and hilly areas of the Mediterranean where some types of agricultural activities have been abandoned as less profitable. The effects of this abandonment are not yet very clear but above all vary greatly in relation to morphological, geological and microclimatic characteristics of territory. The drivers of abandonment are also linked to these territorial characteristics with the addition of socio-economic factors. Therefore, studying this process from a spatial and geographical point of view is crucial. To do this, the integration of GIS and remote sensing in an open source environment represents a key approach as it allows to address the issues in a multi-temporal and multi-disciplinary way in an accurate way. In this paper, a case study of Southern Italy (Alto Bradano area - Basilicata region) has been chosen to quantify and qualify abandoned agricultural areas. First, a methodology for quantification of abandoned agricultural areas has been implemented through the time series analysis of spectral indices obtained starting from freely available satellite imagery, then a qualitative analysis has been carried out by relating abandoned agricultural areas to some spatial variables. In this way, we want to better define the drivers of change and contextualize them with respect to the local geography in order to have an overall view of the phenomenon of agricultural abandonment.

Soil is an essential, non-renewable natural resource that provides vital goods and services for ecosystems, human life, and the production of crops and fuels. The phenomena of land consumption and land use change have a considerable impact on ecosystems. In addition, the poor and confusing regulatory framework contributes to the spread of processes related to soil sealing, such as the wild installation of wind farms, resulting in an increasing fragmentation of the territory with related phenomena of soil degradation.The research work has proposed an innovative methodological approach on issues related to land consumption and land use change, based on a robust territorial and landscape study. The whole research has been focused on the use and integration between geographic information systems and remote sensing techniques for the study of the territory. The increasing availability of cartographic data and the evolution of satellite data is the basis of a system that provides a continuous phase of analysis of the phenomenon. The work defines the picture of the phenomenon in Basilicata, investigating various aspects of land consumption, going into specific detail of some sample areas. The objective was the application of remote sensing techniques and change detection analysis for the qualitative and quantitative estimation of land take related to degradation phenomena. The methodologies and data developed in this work, could be the basis for the creation of a regional database on soil consumption, which could be made up of a robust infrastructure of spatial data and could provide a service and a system of data collection and collect data and reports from citizens, companies, institutions, research organizations, would support public bodies in the definition of policies, strategies and actions aimed at the containment of the phenomenon and would implement, in addition, measures of limitation, prevention, monitoring and mitigation of the same.

Pain is a complex phenomenon that arises from the interaction of multiple neuroanatomic and neurochemical systems with several cognitive and affective processes. Nowadays, the assessment of pain intensity still relies on the use of self-reports. However, recent research has shown a connection between the perception of pain and exacerbated stress response on the Autonomic Nervous System (ANS). The ANS, which is divided into the Parasympathetic Nervous System (PNS) and the Sympathetic Nervous System (SNS), functions as the subconscious regulator of the body. As a result, there has been increasing analysis of the autonomic reactivity with the objective to assess pain. The goal of this study was to explore and understand different responses in the electrocardiogram (ECG) signal when in the experience of pain. For this study, ECG was simultaneously recorded while a pain-inducing protocol (Cold Pressor Task - CPT) was implemented. Several features were extracted from the ECG to analyse differences related to pain induction tasks. The results obtained showed a statistically significant increase in the heart rate during the painful periods in comparison with non-painful periods. Additionally, heart rate variability features demonstrated a decrease in the PNS influence. These results are a step further in understanding the ECG response during the experience of pain, supporting the awareness and insights over physiological interactions within the pain experience.

The purpose of this paper is to provide a parallel acceleration of peer methods for the numerical solution of systems of Ordinary Differential Equations (ODEs) arising from the space discretization of Partial Differential Equations (PDEs) modeling the growth of vegetation in semi-arid climatic zones. The parallel algorithm is implemented by using the CUDA environment for Graphics Processing Units (GPUs) architectures. Numerical experiments, showing the performance gain of the proposed strategy, are provided.

Currently pain is mainly evaluated by resorting to self-reporting instruments, turning the objective evaluation of pain barely impossible. Besides the inherent subjectivity due to these reports, the perception of pain is influenced by several factors. Moreover, cognitive impairments and difficulties in expressing pose a burden difficulty in pain evaluation. Beyond less efficient pain management, the consequences of an incorrect pain assessment may result in over or under dosage of analgesics, with potentially harmful consequences due to the undesirable side-effects of wrong doses. Therefore, a quantitative and accurate assessment of pain is critical for the adaptation of healthcare strategies, providing a step further in personalized medicine. Thus, the analysis of Autonomic Nervous System (ANS) reactions, which can be assessed continuously with minimally invasive equipment, offers an excellent opportunity to monitor physiological indicators when in the experience of pain. The goal of the proposed work is to classify the presence of pain in postoperative records. The results show accuracy and precision of around 85%, and recall and $$F_{1}$$ F 1 -score of 92%, indicating that the experience of postoperative pain can be classified by relying on physiological data.

The ARMA models are used in econometric studies to predict the behavior of a time series. In case of non-stationarity of the initial data ARIMA models get time series to stationarity by differentiation. The problem is applying differentiation provide the loss of essential information. The paper is trying to prove that ARMA model based on the differences between non-stationarity initial data and trend line can provide the same with classic ARIMA approach level of prediction force. For this purpose, the comparison of the quality indicators of the model constructed according to the ARIMA model based on the initial data and the ARMA model based on trend line was carried out. The cryptocurrency market has been chosen as the sphere of research. It was found that the two approaches give approximately the same prediction error and variations from the initial data.

Violence, displaying a dynamic and multivariate nature with different biological, psychodynamic, social and individual factors, is reflected in media texts related to mental health and mental illness which embody numerous variables that are not simple to address and assess. The problematic issues around mental health are not only concerned with the medical aspect of mental problems, including the definition, diagnosis and treatment thereof, but also with respect to negative attitudes of people towards those with psychological problems, which may bring about cases of stigmatization. Media can play a significant role in criminalization of the mentally ill and be instrumental in shaping and changing attitudes towards mental illness. Accordingly, the current study aims to assess the trends in national discourse through media texts by looking into the volume and content of a sample of 496 news stories about mental illness, with a particular focus on bipolar disorder, from 2014 to 2019. Three national daily newspapers constitute the sample addressed in the study where frequency analysis has been performed on the media text dataset in accordance with the changes over the years. The frequency analyses based on time series conducted in the study demonstrate that violence and criminalization have been found to be the most frequently addressed topic among the ones mentioned across the study period. Based on the results derived from the analyses, it is seen that focus of the news media on violence is not proportional to actual rates of violence among the mentally ill. Thus, the research suggests that such a continued emphasis on violence and criminalization may intensify social stigmatization and hinder attempts to seek professional help. All these mental health-related considerations and raising public awareness point to ultimate strategies in public health domain regarding the socially constructed model which significantly affects the benefits of and barriers to action in health-promoting attitudes and behaviors.