Network science and explainable AI-based life cycle management of sustainability models.

Model-based assessment of the potential impacts of variables on the Sustainable Development Goals (SDGs) can bring great additional information about possible policy intervention points. In the context of sustainability planning, machine learning techniques can provide data-driven solutions throughout the modeling life cycle. In a changing environment, existing models must be continuously reviewed and developed for effective decision support. Thus, we propose to use the Machine Learning Operations (MLOps) life cycle framework. A novel approach for model identification and development is introduced, which involves utilizing the Shapley value to determine the individual direct and indirect contributions of each variable towards the output, as well as network analysis to identify key drivers and support the identification and validation of possible policy intervention points. The applicability of the methods is demonstrated through a case study of the Hungarian water model developed by the Global Green Growth Institute. Based on the model exploration of the case of water efficiency and water stress (in the examined period for the SDG 6.4.1 & 6.4.2) SDG indicators, water reuse and water circularity offer a more effective intervention option than pricing and the use of internal or external renewable water resources.

Reconstructing the post-glacial spread of the sand fly Phlebotomus mascittii Grassi, 1908 (Diptera: Psychodidae) in Europe.

Phlebotomine sand flies (Diptera: Phlebotominae) are the principal vectors of Leishmania spp. (Kinetoplastida: Trypanosomatidae). In Central Europe, Phlebotomus mascittii is the predominant species, but largely understudied. To better understand factors driving its current distribution, we infer patterns of genetic diversity by testing for signals of population expansion based on two mitochondrial genes and model current and past climate and habitat suitability for seven post-glacial maximum periods, taking 19 climatic variables into account. Consequently, we elucidate their connections by environmental-geographical network analysis. Most analyzed populations share a main haplotype tracing back to a single glacial maximum refuge area on the Mediterranean coasts of South France, which is supported by network analysis. The rapid range expansion of Ph. mascittii likely started in the early mid-Holocene epoch until today and its spread possibly followed two routes. The first one was through northern France to Germany and then Belgium, and the second across the Ligurian coast through present-day Slovenia to Austria, toward the northern Balkans. Here we present a combined approach to reveal glacial refugia and post-glacial spread of Ph. mascittii and observed discrepancies between the modelled and the current known distribution might reveal yet overlooked populations and potential further spread.

Indicators for climate change-driven urban health impact assessment.

Climate change can cause multiply potential health issues in urban areas, which is the most susceptible environment in terms of the presently increasing climate volatility. Urban greening strategies make an important part of the adaptation strategies which can ameliorate the negative impacts of climate change. It was aimed to study the potential impacts of different kinds of greenings against the adverse effects of climate change, including waterborne, vector-borne diseases, heat-related mortality, and surface ozone concentration in a medium-sized Hungarian city. As greening strategies, large and pocket parks were considered, based on our novel location identifier algorithm for climate risk minimization. A method based on publicly available data sources including satellite pictures, climate scenarios and urban macrostructure has been developed to evaluate the health-related indicator patterns in cities. The modelled future- and current patterns of the indicators have been compared. The results can help the understanding of the possible future state of the studied indicators and the development of adequate greening strategies. Another outcome of the study is that it is not the type of health indicator but its climate sensitivity that determines the extent to which it responds to temperature rises and how effective greening strategies are in addressing the expected problem posed by the factor.

Scandium Recovery Methods from Mining, Metallurgical Extractive Industries, and Industrial Wastes.

The recovery of scandium (Sc) from wastes and various resources using solvent extraction (SX) was discussed in detail. Moreover, the metallurgical extractive procedures for Sc recovery were presented. Acidic and neutral organophosphorus (OPCs) extractants are the most extensively used in industrial activities, considering that they provide the highest extraction efficiency of any of the valuable components. Due to the chemical and physical similarities of the rare earth metals, the separation and purification processes of Sc are difficult tasks. Sc has also been extracted from acidic solutions using carboxylic acids, amines, and acidic ß-diketone, among other solvents and chemicals. For improving the extraction efficiencies, the development of mixed extractants or synergistic systems for the SX of Sc has been carried out in recent years. Different operational parameters play an important role in the extraction process, such as the type of the aqueous phase and its acidity, the aqueous (A) to organic (O) and solid (S) to liquid (L) phase ratios, as well as the type of the diluents. Sc recovery is now implemented in industrial production using a combination of hydrometallurgical and pyrometallurgical techniques, such as ore pre-treatment, leaching, SX, precipitation, and calcination. The hydrometallurgical methods (acid leaching and SX) were effective for Sc recovery. Furthermore, the OPCs bis(2-ethylhexyl) phosphoric acid (D2EHPA/P204) and tributyl phosphate (TBP) showed interesting potential taking into consideration some co-extracted metals such as Fe(III) and Ti(IV).

V2O5, CeO2 and Their MWCNTs Nanocomposites Modified for the Removal of Kerosene from Water.

In this paper, the application of multiwalled carbon nanotubes (MWCNTs) based on metal oxide nanocomposites as adsorbents for the removal of hydrocarbons such as kerosene from water was investigated. Functionalized MWCNTs were obtained by chemical oxidation using concentrated sulfuric and nitric acids. V2O5, CeO2, and V2O5:CeO2 nanocomposites were prepared using the hydrothermal method followed by deposition of these oxides over MWCNTs. Individual and mixed metal oxides, fresh MWCNTs, and metal oxide nanoparticle-doped MWCNTs using different analysis techniques were characterized. XRD, TEM, SEM, EDX, AFM, Raman, TG/DTA, and BET techniques were used to determine the structure as well as chemical and morphological properties of the newly prepared adsorbents. Fresh MWCNTs, Ce/MWCNTs, V/MWCNTs, and V:Ce/MWCNTs were applied for the removal of kerosene from a model solution of water. GC analysis indicated that high kerosene removal efficiency (85%) and adsorption capacity (4270 mg/g) after 60 min of treatment were obtained over V:Ce/MWCNTs in comparison with fresh MWCNTs, Ce/MWCNTs and V/MWCNTs. The kinetic data were analyzed using the pseudo-first order, pseudo-second order, and intra-particle diffusion rate equations.

Identification of sampling points for the detection of SARS-CoV-2 in the sewage system.

A suitable tool for monitoring the spread of SARS-CoV-2 is to identify potential sampling points in the wastewater collection system that can be used to monitor the distribution of COVID-19 disease affected clusters within a city. The applicability of the developed methodology is presented through the description of the 72,837 population equivalent wastewater collection system of the city of Nagykanizsa, Hungary and the results of the analytical and epidemiological measurements of the wastewater samples. The wastewater sampling was conducted during the 3rd wave of the COVID-19 epidemic. It was found that the overlap between the road system and the wastewater network is high, it is 82 %. It was showed that the proposed methodological approach, using the tools of network science, determines confidently the zones of the wastewater collection system and provides the ideal monitoring points in order to provide the best sampling resolution in urban areas. The strength of the presented approach is that it estimates the network based on publicly available information. It was concluded that the number of zones or sampling points can be chosen based on relevant epidemiological intervention and mitigation strategies. The algorithm allows for continuous effective monitoring of the population infected by SARS-CoV-2 in small-sized cities.

Polystyrene-Fe3O4-MWCNTs Nanocomposites for Toluene Removal from Water.

In this research, multi-walled carbon nanotubes (MWCNTs) were functionalized by oxidation with strong acids HNO3, H2SO4, and H2O2. Then, magnetite/MWCNTs nanocomposites were prepared and polystyrene was added to prepare polystyrene/MWCNTs/magnetite (PS:MWCNTs:Fe) nanocomposites. The magnetic property of the prepared nano-adsorbent PS:MWCNTs:Fe was successfully checked. For characterization, X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), Fourier transform infrared spectroscopy (FTIR), Raman spectroscopy, and BET surface area were used to determine the structure, morphology, chemical nature, functional groups, and surface area with pore volume of the prepared nano-adsorbents. The adsorption procedures were carried out for fresh MWCNTs, oxidized MWCNTs, MWCNTs-Fe, and PS:MWCNTs:Fe nanocomposites in batch experiments. Toluene standard was used to develop the calibration curve. The results of toluene adsorption experiments exhibited that the PS:MWCNTs:Fe nonabsorbent achieved the highest removal efficiency and adsorption capacity of toluene removal. The optimum parameters for toluene removal from water were found to be 60 min, 2 mg nano-sorbent dose, pH of 5, solution temperature of 35 °C at 50 mL volume, toluene concentration of 50 mg/L, and shaking speed of 240 rpm. The adsorption kinetic study of toluene followed the pseudo-second-order kinetics, with the best correlation (R2) value of 0.998, while the equilibrium adsorption study showed that the Langmuir isotherm was obeyed, which suggested that the adsorption is a monolayer and homogenous.

Spatial pattern identification (SPI) for ecological modelling.

There is a growing interest to understand the static and dynamic components of population ranges. In general, the frequently used environmental forecasting and evaluating methods of occurrences like niche-based statistical processes are based on the static evaluation of the causative environmental variables. These techniques do not consider that natural populations of species form the systems of complex, connected networks. The aim of this study was to suggest a possible solution to this methodological problem. The proposed variable pattern comparison tool (Spatial pattern identification (SPI) for ecological modelling) provides an opportunity of deep examination of spatial connections between environmental variables and occurrence data in GIS models. The idea of the developed method is, that the network characteristic of the primary point-like occurrence data provides statistically evaluable new and valuable information about the nature and reasons for the interconnections of populations. In technical sense, the approach is based on which the key variables of the models can be identified, thus establishing the targeted variable selection and possible solutions for model reduction.•Exploring the relationships between variables of a GIS model.•Static and pattern similarity-based comparison of the model variables.•Identification of key variables of the model and model reduction.•The network allows the understanding intra- and interspecific population connections.

Data describing the relationship between world news and sustainable development goals.

The data article presents a dataset and a tool for news-based monitoring of sustainable development goals defined by the United Nations. The presented dataset was created by structured queries of the GDELT database based on the categories of the World Bank taxonomy matched to sustainable development goals. The Google BigQuery SQL scripts and the results of the related network analysis are attached to the data to provide a toolset for the strategic management of sustainability issues. The article demonstrates the dataset on the 6th sustainable development goal (Clean Water and Sanitation). The network formed based on how countries appear in the same news can be used to explore the potential international cooperation. The network formed based on how topics of World Bank taxonomy appear in the same news can be used to explore how the problems and solutions of sustainability issues are interlinked.

The intertwining of world news with Sustainable Development Goals: An effective monitoring tool.

This study aims to bring about a novel approach to the analysis of Sustainable Development Goals (SDGs) based solely on the appearance of news. Our purpose is to provide a monitoring tool that enables world news to be detected in an SDG-oriented manner, by considering multilingual as well as wide geographic coverage. The association of the goals with news basis the World Bank Group Topical Taxonomy, from which the selection of search words approximates the 17 development goals. News is extracted from The GDELT Project (Global Database of Events, Language and Tone) which gathers both printed as well as online news from around the world. 60 851 572 relevant news stories were identified in 2019. The intertwining of world news with SDGs as well as connections between countries are interpreted and highlight that even in the most SDG-sensitive countries, only 2.5% of the news can be attributed to the goals. Most of the news about sustainability appears in Africa as well as East and Southeast Asia, moreover typically the most negative tone of news can be observed in Africa. In the case of climate change (SDG 13), the United States plays a key role in both the share of news and the negative tone. Using the tools of network science, it can be verified that SDGs can be characterized on the basis of world news. This news-centred network analysis of SDGs identifies global partnerships as well as national stages of implementation towards a sustainable socio-environmental ecosystem. In the field of sustainability, it is vital to form the attitudes and environmental awareness of people, which strategic plans cannot address but can be measured well through the news.

Multilayer network based comparative document analysis (MUNCoDA).

The proposed multilayer network-based comparative document analysis (MUNCoDA) method supports the identification of the common points of a set of documents, which deal with the same subject area. As documents are transformed into networks of informative word-pairs, the collection of documents form a multilayer network that allows the comparative evaluation of the texts. The multilayer network can be visualized and analyzed to highlight how the texts are structured. The topics of the documents can be clustered based on the developed similarity measures. By exploring the network centralities, topic importance values can be assigned. The method is fully automated by KNIME preprocessing tools and MATLAB/Octave code.•Networks can be formed based on informative word pairs of a multiple documents•The analysis of the proposed multilayer networks provides information for multi-document summarization•Words and documents can be clustered based on node similarity and edge overlap measures.

Environmental assessment of physical-chemical features of Lake Nasser, Egypt.

Lake Nasser is one of the largest man-made lakes on earth. It has a vital importance to Egypt for several decades because of the safe water supply of the country. Therefore, the water quality of the Lake Nasser must be profoundly investigated, and physico-chemical parameter changes of the water of the Lake Nasser should be continuously monitored and assessed. This work describes the present state of the physico-chemical (nitrate-nitrogen, nitrite-nitrogen, orthophosphate, total phosphate content, dissolved oxygen content, chemical oxygen demand, and biological oxygen demand) water parameters of Lake Nasser in Egypt at nine measurement sites along the Lake Nasser. The algorithm was devised at the University of Pannonia, Hungary, for the evaluation of the water quality. The aquatic environmental indices determined alongside the Lake Nasser fall into the category of "good" water quality at seven sampling sites and exhibited "excellent" water quality at two sampling sites according to Egyptian Governmental Decree No. 92/2013. In light of the tremendous demand for safe and healthy water supply in Egypt and international requirements, the water quality assessment is a very important tool for providing reliable information on the water quality. The protocol for water quality assessment could significantly contribute to the provision of high-quality water supply in Egypt. In conclusion, it can be stated that the parameters under investigation in different regions of Lake Nasser fall within the permissible ranges and the water of the Lake has good quality for drinking, irrigation, and fish cultures according to Egyptian standards; however, according to European specifications, there are steps to be accomplished for future water quality improvement.

Focal points for sustainable development strategies-Text mining-based comparative analysis of voluntary national reviews.

Countries have to work out and follow tailored strategies for the achievement of their Sustainable Development Goals. At the end of 2018, more than 100 voluntary national reviews were published. The reviews are transformed by text mining algorithms into networks of keywords to identify country-specific thematic areas of the strategies and cluster countries that face similar problems and follow similar development strategies. The analysis of the 75 VNRs has shown that SDG5 (gender equality) is the most discussed goal worldwide, as it is discussed in 77% of the analysed Voluntary National Reviews. The SDG8 (decent work and economic growth) is the second most studied goal, With 76 %, while the SDG1 (no poverty) is the least focused goal, it is mentioned only in 48 % of documents and the SDG10 (reduced inequalities) in 49 %. The results demonstrate that the proposed benchmark tool is capable of highlighting what kind of activities can make significant contributions to achieve sustainable developments.

The changing distribution of Leishmania infantum Nicolle, 1908 and its Mediterranean sandfly vectors in the last 140 kys.

The understanding of the effects of past climatic changes on the distribution of vector arthropods can strongly support the understanding of the future potential impact of anthropogenic climatic change on the geographical risk of vector-borne diseases. The zoogeographical patterns of the European sandfly vectors may suffer the continuously changing climate of the last 140 kys. The former range of L. infantum and six Phlebotomus species were modelled for the Last Interglacial, the Last Glacial Maximum and the Mid-Holocene Periods. It was found that the potential distribution of the parasite was much smaller in the Last Glacial Period L. infantum mainly could persist in the western shelves of the Mediterranean Sea. West and East Mediterranean sandfly species inhabited partly distinct refugia. The Apennine Peninsula, Sicily and the Iberian refugium formed a habitat chain along with the coastal areas of the West Mediterranean Basin. There was no direct connection between the Eastern and the Western sandfly refugia in the last 140 kys. The modelled distribution of sandfly taxa for the Middle Holocene Period can explain the relict populations of sandfly taxa in such Central European countries. The former genetic studies strongly confirm the existence of the modelled glacial refugees.

Data-driven multilayer complex networks of sustainable development goals.

This data article presents the formulation of multilayer network for modelling the interconnections among the sustainable development goals (SDGs), targets and includes the correlation based linking of the sustainable development indicators with the available long-term datasets of The World Bank, 2018 [1]. The spatial distribution of the time series data allows creating country-specific sustainability assessments. In the related research article "Network Model-Based Analysis of the Goals, Targets and Indicators of Sustainable Development for Strategic Environmental Assessment" [2] the similarities of SDGs for ten regions have been modelled in order to improve the quality of strategic environmental assessments. The datasets of the multilayer networks are available on Mendeley [3].

Network model-based analysis of the goals, targets and indicators of sustainable development for strategic environmental assessment.

Strategic environmental assessment is a decision support technique that evaluates policies, plans and programs in addition to identifying the most appropriate interventions in different scenarios. This work develops a network-based model to study interlinked ecological, economic, environmental and social problems to highlight the synergies between policies, plans, and programs in environmental strategic planning. Our primary goal is to propose a methodology for the data-driven verification and extension of expert knowledge concerning the interconnectedness of the sustainable development goals and their related targets. A multilayer network model based on the time-series indicators of the World Bank open data over the last 55 years was assembled. The results illustrate that by providing an objective and data-driven view of the correlated variables of the World Bank, the proposed layered multipartite network model highlights the previously not discussed interconnections, node centrality measures evaluate the importance of the targets, and network community detection algorithms reveal their strongly connected groups. The results confirm that the proposed methodology can serve as a data-driven decision support tool for the preparation and monitoring of long-term environmental policies. The developed new data-driven network model enables multi-level analysis of the sustainability (goals, targets, indicators) and will make it possible to plan long-term environmental strategic planning. Through relationships among indicators, relationships among targets and goals can be modelled. The results show that sustainable development goals are strongly interconnected, while the 5th goal (gender equality) is linked mostly to 17th (partnerships for the goals) goal. The analysis has also highlighted the importance of the 4th (quality education).

Modified Jordanian zeolitic tuff in hydrocarbon removal from surface water.

This work aims to investigate the potential of Jordanian raw zeolitic tuff (RZT) as oil adsorbent for oil-contaminated water. As hydrophobic properties are the primary determinants of effective oil adsorbents, the hydrophobicity of RZT was enhanced by dealumination process; since the degree of hydrophobicity of zeolites is directly dependent on their aluminum content. The microemulsion modification of the dealuminated zeolitic tuff (TZT) was also applied to increase its hydrophobicity. The raw and modified tuffs were characterized in terms of the surface area and porosity (BET), mineral composition (XRD), microstructure and morphology using scanning electron microscopy (SEM) and Fourier-transform infrared spectroscopy (FTIR). In this work, a mixture of water and kerosene was used to examine the hydrophobic/organophilic character of raw and modified zeolitic tuff. Water/dodecane and water/octane mixtures were used to study the kinetics of the adsorption over zeolitic tuff. The results revealed that the sorption capacity using kerosene as a mixed model (water-oil) was enhanced by three- and four-fold for TZT and micro-emulsified zeolitic (MeTZT) tuff respectively. The adsorption capacity of modified zeolitic was compared with that of activated carbon adsorbents.

Methodology development on aquatic environmental assessment.

The Water Framework Directive aims at reaching the good ecological status of the surface and ground water bodies (László et al. Microchem J 85(1):65-71, 2007). The paper deals with quality evaluation of waters with special focus on the water chemistry parameters as defined in the Water Framework Directive and pertaining legal regulations. The purpose of this paper is to devise a quantitative type of water quality assessment method which could provide rapid, accurate, and reliable information on the quality of the surface waters by using water chemistry parameters. Quality classes have been defined for every water chemistry parameter in light of the legal limit values of the water parameters. In addition to this, weight indices were calculated on the basis of the outcome of the paired comparison of water chemistry parameters and normalized matrix. This was followed by the parametric level analysis of the water chemistry parameters, and finally, the aquatic environment index (AEI) was calculated, which provided general information on the quality of water regarding the water chemistry parameters. The method was illustrated on Lake Balaton, Hungary in which case water samples taken from Balatonfüred City lake area were analyzed and evaluated with the method devised.

Digital microscopy for boosting database integration and analysis in TMA studies.

The enormous amount of clinical, pathological, and staining data to be linked, analyzed, and correlated in a tissue microarray (TMA) project makes digital slides ideal to be integrated into TMA database systems. With the help of a computer and dedicated software tools, digital slides offer dynamic access to microscopic information at any magnification with easy navigation, annotation, measurement, and archiving features. Advanced slide scanners work both in transmitted light and fluorescent modes to support biomarker testing with immunohistochemistry, immunofluorescence or fluorescence in situ hybridization (FISH). Currently, computer-driven integrated systems are available for creating TMAs, digitalizing TMA slides, linking sample and staining data, and analyzing their results. Digital signals permit image segmentation along color, intensity, and size for automated object quantification where digital slides offer superior imaging features and batch processing. In this chapter, the workflow and the advantages of digital TMA projects are demonstrated through the project-based MIRAX system developed by 3DHISTECH and supported by Zeiss.The enhanced features of digital slides compared with those of still images can boost integration and intelligence in TMA database management systems, offering essential support for high-throughput biomarker testing, for example, in tumor progression/prognosis, drug discovery, and target therapy research.