European Dataset Research Articles

Investigating the Factors That Determine the ESG Disclosure Practices in Europe

The increased focus on environmental (E), social (S), and governance (G) (ESG) disclosure has become a necessary step toward the integration of sustainability practices into firms’ culture to meet the expectations of stakeholders. The social and environmental implications of firm activities on the environment and surrounding communities have led to the growing demand for useful non-financial information. This paper investigates the impacts of the board’s corporate social responsibility (CSR) strategy and CSR orientation, GRI, and the country–cultural dimensions, based on Hofstede’s measures of ESG disclosure practices within Europe. Using a European dataset from Bloomberg and Refinitiv Eikon, this paper adopts a quantitative research methodology to test the research hypotheses through a statistical analysis of 7840 observations from European companies to analyze the extent of the relationship between micro- and macro-variables and the disclosure of company ESG. Our findings suggest that both board CSR orientation and strategy and the GRI have positively and significantly affected the overall disclosure of ESG practices within Europe. When examining country–cultural dimensions, we find that individualism and feminine cultures are positively associated with increased levels of ESG disclosure. Our findings shed light on factors affecting ESG disclosure practices within Europe and could be of interest to companies, policy makers, and other stakeholders.

A Novel Machine learning Algorithms used to Detect Credit Card Fraud Transactions

During the Covid-19 pandemic, the world was under lockdown, and everyone was inside their home. There are so many restrictions for going out, so many companies introduced online shopping, and this online shopping helped more people; the e-commerce platform also increased their revenue; at the same time, online fraud has also risen worldwide. Everyone adopted online shopping during the pandemic. In 2019 India's 2019 credit/debit card fraud rate was 365, according to the National Crime Record Bureau. The developed countries are the highest rate of credit card fraud in 2020 compared to India; for that reason, we have to implement mechanisms that can detect credit theft. The machine learning algorithm with the R program will play an essential role in credit card fraud detection. The following machine learning algorithm will have used for credit card fraud, Random Forest, Logistic regression, Decision trees, and Gradient Boosting Classifiers. The European bank dataset used in our research and the dataset size is 284808. Here we used two classes, the first one is called the positive class (fraud transactions), and the second one is the negative class (genuine transactions). The final result will show us the outperforms of our proposed system.

Exposure to Phthalates in European Children, Adolescents and Adults since 2005: A Harmonized Approach Based on Existing HBM Data in the HBM4EU Initiative.

Phthalates are mainly used as plasticizers and are associated inter alia with adverse effects on reproductive functions. While more and more national programs in Europe have started monitoring internal exposure to phthalates and its substitute 1,2-Cyclohexanedicarboxylic acid (DINCH), the comparability of results from such existing human biomonitoring (HBM) studies across Europe is challenging. They differ widely in time periods, study samples, degree of geographical coverage, design, analytical methodology, biomarker selection, and analytical quality assurance level. The HBM4EU initiative has gathered existing HBM data of 29 studies from participating countries, covering all European regions and Israel. The data were prepared and aggregated by a harmonized procedure with the aim to describe-as comparably as possible-the EU-wide general population's internal exposure to phthalates from the years 2005 to 2019. Most data were available from Northern (up to 6 studies and up to 13 time points), Western (11; 19), and Eastern Europe (9; 12), e.g., allowing for the investigation of time patterns. While the bandwidth of exposure was generally similar, we still observed regional differences for Butyl benzyl phthalate (BBzP), Di(2-ethylhexyl) phthalate (DEHP), Di-isononyl phthalate (DiNP), and Di-isobutyl phthalate (DiBP) with pronounced decreases over time in Northern and Western Europe, and to a lesser degree in Eastern Europe. Differences between age groups were visible for Di-n-butyl phthalate (DnBP), where children (3 to 5-year olds and 6 to 11-year olds) had lower urinary concentrations than adolescents (12 to 19-year-olds), who in turn had lower urinary concentrations than adults (20 to 39-year-olds). This study is a step towards making internal exposures to phthalates comparable across countries, although standardized data were not available, targeting European data sets harmonized with respect to data formatting and calculation of aggregated data (such as developed within HBM4EU), and highlights further suggestions for improved harmonization in future studies.

Impacts of cultural factors and mode of administration on item nonresponse for political questions in the European context

ABSTRACT We tested the impacts of Hofstede’s cultural factors and mode of administration on item nonresponse (INR) for political questions in the European Values Study (EVS). We worked with the integrated European Values Study dataset, using descriptive analysis and multilevel binary logistic regression models. We concluded that (1) modes of administration with an interviewer lead to an INR lower than those without an interviewer. In terms of Hofstede’s cultural factors, we concluded that (2) the higher the power distance index in the country, the lower the INR, (3) the higher the rate of individualism in the country, the higher the INR, and (4) the higher the uncertainty avoidance index, the lower the INR. These findings stress the importance of sensitive work with cross-national data, especially with increasingly abundant data from mixed-mode surveys.

Spring phenology is advancing at a faster rate than arrival times of Common Starling

Climate change poses significant impact on most migratory species, altering their breeding and non-breeding distributions, migration distances, and phenology. The ability of a population to track and adjust to the changing environmental conditions is a vital prerequisite for its persistence. In this study, we describe changes in spring arrival phenology of a short-distance migrant, the Common Starling Sturnus vulgaris, in Northeastern Europe and their relation to changes in the onset of meteorological spring during the last century. Phenological data were obtained from an open access database Chronicles of Nature Calendar that held 4291 usable records of the 1st spring observation date of Starlings from 171 phenological network sites between 1922 and 2017. Air temperature data were downloaded from the European Climate Assessment and Dataset database. We found that spring arrival time of Starlings was positively correlated with the onset of spring on a local and broad scale. Spring arrival progression followed a northeast gradient at an average rate of 1.6 days per one degree of latitude (ca. 71 km day−1) and 0.4 days per one degree of longitude (152 km day−1). During the 96-year long period, Starlings on average advanced their spring arrival time by 4.7 days at a rate of 0.05 days per year. However, changes in the onset of spring occurred more rapidly with an average rate of change of 0.18 days per year over the study period. These findings imply broad-scale adjustments in migratory patterns of a short-distance migrant that result from climate change-induced shifts in spring phenology. However, adaptations in bird migration phenology are not occurring fast enough to track and compensate for the rapid increase in air temperature, thus disrupting the earlier matching between bird arrival and spring onset.

Uncovering the Combined Impact of Process Characteristics and Reward Types on Employees’ Job Satisfaction: A European Quantitative Study

Organizations should constantly improve their business processes to increase performance while keeping employees satisfied. Therefore, human actors are considered a success factor in business process management (BPM) projects. This fact amplifies the impact of employees’ satisfaction on business process performance. Although several reward approaches exist, it remains unclear how they affect job satisfaction specifically in combination with certain process characteristics. To address this gap, we conducted a statistical analysis of survey data based on a representative European working conditions dataset. We applied two-way analysis of variance (ANOVA) and analysis of covariance (ANCOVA, i.e., controlled for organization size and sector) to explore the interaction effects. By looking at all possible combinations, we uncover how the presence or absence of specific pay modes and process-related aspects influence job satisfaction. Additionally, we reveal and discuss the joint effect of process characteristics and pay-for-performance types on job satisfaction. The results argue for a differentiated approach in pay-for-performance to obtain optimal reward solutions. Moreover, we advise for better strategic planning and facilitating successful BPM implementation.

The causal effect of hot beverage temperature preference on esophageal cancer risk: a Mendelian randomization study.

Previous research has found a link between the temperature of food and beverages and the risk of esophageal cancer (EC). A causal relationship between the two has not been well established. Herein, we used Mendelian randomization (MR) analysis to assess the causal effect of temperature preference for hot beverages on EC risk. Genome-wide association studies (GWAS) data for hot beverage temperature preference were obtained from the UK biobank. There were 457,873 European and 2,617 East Asian participants included. GWAS data for EC were obtained from the Integrative Epidemiology Unit (IEU) project database. Two datasets from the European population and two datasets from the East Asian population were included. Totally, 4,426 EC cases and 1,202,270 control subjects were included. The "TwoSampleMR" R package was used to conduct a two-sample MR analysis. A random-effect inverse variance weighted (IVW) was used as the main analytical method to estimate the causal effect, and various sensitivity analyses, including MR Egger, weighted median, simple mode, and weighted mode, were used to examine the potential violation of the second and third MR assumptions. Meta-analyses were performed to further confirm the results. Sixty-eight single nucleotide polymorphisms (SNPs) from the European population and 11 SNPs from the East Asian population were used for MR analysis. No significant causal effect was found between hot beverage temperature preference and EC risk in the European population {for the ieu-b-4960 dataset, inverse variance weighted odds ratio (ORIVW) =1.00 [95% confidence interval (CI): 0.99-1.00], P=0.54; for the ebi-a-GCST90018841 dataset, ORIVW =0.35 (95% CI: 0.10-1.29), P=0.12} or in the East Asian population [for the bbj-a-117 dataset, ORIVW =1.09 (95% CI: 0.80-1.48), P=0.59; for the ebi-a-GCST90018621 dataset, ORIVW =0.11 (95% CI: 0.82-1.50), P=0.49]. Meta-analyses of the European population datasets and the Asian population datasets showed consistent results. The current MR analysis provides new genetic evidence for a null causal relationship between hot beverage temperature preference and EC, both in the European population and the East Asian population. Evidence to prevent EC by reducing the intake of hot beverages is insufficient.

Ellenberg‐type indicator values for European vascular plant species

AbstractAimsEllenberg‐type indicator values are expert‐based rankings of plant species according to their ecological optima on main environmental gradients. Here we extend the indicator‐value system proposed by Heinz Ellenberg and co‐authors for Central Europe by incorporating other systems of Ellenberg‐type indicator values (i.e., those using scales compatible with Ellenberg values) developed for other European regions. Our aim is to create a harmonized data set of Ellenberg‐type indicator values applicable at the European scale.MethodsWe collected European data sets of indicator values for vascular plants and selected 13 data sets that used the nine‐, ten‐ or twelve‐degree scales defined by Ellenberg for light, temperature, moisture, reaction, nutrients and salinity. We compared these values with the original Ellenberg values and used those that showed consistent trends in regression slope and coefficient of determination. We calculated the average value for each combination of species and indicator values from these data sets. Based on species’ co‐occurrences in European vegetation plots, we also calculated new values for species that were not assigned an indicator value.ResultsWe provide a new data set of Ellenberg‐type indicator values for 8908 European vascular plant species (8168 for light, 7400 for temperature, 8030 for moisture, 7282 for reaction, 7193 for nutrients, and 7507 for salinity), of which 398 species have been newly assigned to at least one indicator value.ConclusionsThe newly introduced indicator values are compatible with the original Ellenberg values. They can be used for large‐scale studies of the European flora and vegetation or for gap‐filling in regional data sets. The European indicator values and the original and taxonomically harmonized regional data sets of Ellenberg‐type indicator values are available in the Supporting Information and the Zenodo repository.

Development of Deep Learning based Intelligent Approach for Credit Card Fraud Detection

Credit card fraud (CCF) has long been a major concern of institutions of financial groups and business partners, and it is also a global interest to researchers due to its growing popularity. In order to predict and detect the CCF, machine learning (ML) has proven to be one of the most promising techniques. But, class inequality is one of the main and recurring challenges when dealing with CCF tasks that hinder model performance. To overcome this challenges, a Deep Learning (DL) techniques are used by the researchers. In this research work, an efficient CCF detection (CCFD) system is developed by proposing a hybrid model called Convolutional Neural Network with Recurrent Neural Network (CNN-RNN). In this model, CNN acts as feature extraction for extracting the valuable information of CCF data and long-term dependency features are studied by RNN model. An imbalance problem is solved by Synthetic Minority Over Sampling Technique (SMOTE) technique. An experiment is conducted on European Dataset to validate the performance of CNN-RNN model with existing CNN and RNN model in terms of major parameters. The results proved that CNN-RNN model achieved 95.83% of precision, where CNN achieved 93.63% of precision and RNN achieved 88.50% of precision.

AN EFFECTIVE DEEP LEARNING BASED APPROACH TO MEASURE INTERDEPENDENCE OF HEART RATE VARIABILITY ANALYSIS AND R-PEAKS


 
 
 Heart rate variability (HRV) is the physiological phenomenon to measure variations between consecutive heartbeats. HRV analysis has been used to analyse and detect different cardiac diseases that rely on R-peaks' reliability and accurate detection. Myocardial infarction (MI) is a main cardiac disease that causes irregularity in heartbeats and some non-specific abnormalities occur in the recorded ECG, including ST changes and T-wave alternans. These abnormalities may be ignored by considering minor changes that can lead to delayed diagnosis. Therefore, the analysis of these abnormalities is a challenging task. This study investigated these non-specific abnormalities to recognize MI patterns based on HRV analysis highlighting the importance of R-peaks detection accuracy. Short-term HRV analysis of two publicly available datasets, MIT-BIH ST Changes and European ST-T changes was performed based on R-peaks detected from ECG signals. R-peaks detection achieved an averaged 99.77% sensitivity, 99.49% positive predictability and 99.32% accuracy for MIT-BIH ST changes dataset whereas for European ST-T changes dataset, 99.85% sensitivity, 99.41% positive predictability, and 99.28% accuracy was achieved. Subsequently, HRV parameters were computed from both datasets and data fusion was performed followed by a deep learning model to find out the most accurate pattern recognition results. For pattern recognition, a finely tuned artificial neural network was applied to HRV parameters, in two scenarios. For both scenarios, an accuracy greater than 99% was achieved. Furthermore, linear regression was also performed on computed HRV measures in both scenarios that observed a similarity of 97% between both datasets. The importance of reliable and accurate R-peaks detection for HRV analysis to analyse and detect different cardiovascular diseases was elaborated in the end.
 
 

Sources of copper into the European aquatic environment.

Chemical contamination from point source discharges in developed (resource-rich) countries has been widely regulated and studied for decades; however, diffuse sources are largely unregulated and widespread. In the European Union (EU), large dischargers report releases of some chemicals, yet little is known of total emissions (point and diffuse) and their relative significance. We estimated copper loadings from all significant sources including industry, sewage treatment plants, surface runoff (from traffic, architecture, and atmospheric deposition), septic tanks, agriculture, mariculture, marine transport (antifoulant leaching), and natural processes. A combination of European datasets, literature, and industry data were used to generate export coefficients. These were then multiplied by activity rates to derive loads. A total of approximately 8 kt of copper per annum (ktpa) is estimated to enter freshwaters in the EU, and another 3.5 ktpa enters transitional and coastal waters. The main inputs to freshwater are natural processes (3.7 ktpa), agriculture (1.8 ktpa), and runoff (1.8 ktpa). Agricultural emissions are dominated by copper-based plant protection products and farmyard manure. Urban runoff is influenced by copper use in architecture and by vehicle brake linings. Antifoulant leaching from boats (3.2 ktpa) dominates saline water loads of copper. It is noteworthy that most of the emissions originate in a limited number of copper uses where environmental exposure and pathways exist, compared with the bulk of copper use within electrical and electronic equipment and infrastructure that has no environmental pathway during its use. A sensitivity analysis indicated significant uncertainty in data from abandoned mines and urban runoff load estimates. This study provided for the first time a methodology and comprehensive metal load apportionment to European aquatic systems, identifying data gaps and uncertainties, which may be refined over time. Source apportionments using this methodology can inform more cost-effective environmental risk assessment and management. Integr Environ Assess Manag 2023;19:1031-1047. © 2022 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Observational evidence of intensified nocturnal urban heat island during heatwaves in European cities

A heatwave (HW) is a large-scale meteorological event characterised by persistent and extremely high-temperature condition. At the local scale, the urban heat island (UHI) is another thermal-related phenomenon defined as an urban area warmer than its surrounding regions due to different surfaces’ capabilities to absorb and store heat. However, the assessment about the effect produced on UHI by HW events is not homogeneous. Indeed, regarding the capability of HWs to influence the urban-rural temperature difference, several studies report different conclusions describing both an exacerbation and a reduction of UHI during HW events. In this context, the present study analyses in situ long records of temperature measurements (20 years) to provide observational shreds of evidence of UHI modification under HW conditions. We examine data from the European Climate Assessment & Dataset and World Meteorological Organization computing the UHI index (UHII) to quantify the UHI effect intensity in 37 European cities during the last 20 summers. The results show an UHII intensification for 28 of the 32 cities affected by positive UHI during extremely high temperatures at night, while substantial variations are not observed during the daytime. The time evolution of UHI during a HW highlights that a more significant and persistent urban-rural temperature gradient explains the UHI intensification. Finally, the relationship between the large and local-scale temperature phenomena reveals that continental high-temperature periods are often associated with prominent temperature differences between small-scale urban and rural environments, assessing the impact of large-scale features on thermal stress at the local scale.

(Healthy) Aging Patterns in Europe: A Multistate Health Transition Approach

We explore patterns and determinants of health transition probabilities by combining outcomes of morbidity and mortality to examine different aging patterns across Europe, and to ascertain how individual socio-demographic characteristics modify these patterns. We use panel data from the Survey of Health, Ageing and Retirement in Europe (2004– 2017) for 76,536 individuals aged 50 + in 20 European countries who participated in at least two waves. All transition probabilities were calculated applying a multistate analytical approach. Our findings show significant gender, education, and cohort differences in health transition probabilities and marked cross-country group differences. Central and Northern European countries present lower probabilities of health deterioration than Southern and, especially, Eastern European countries. Having a high level of education, living in Central Europe, and being younger are associated with lower probabilities of health deterioration and, if any, a higher probability of being restored to good health. We found less evidence of differences when transitions end in death. Our study contributes to this line of research by implementing a multistate approach using European harmonized panel dataset, to examine the effects of birth cohort, educational attainment and gender differences on health transitions. Our findings point to the need to consider the specific influence of individual factors in the aging process in different transitions according to the context and with reference to specific vulnerable groups. In the context of aging societies, such a consideration is both essential and policy relevant.

On the basis of sex: outcomes in myelodysplastic syndromes

Sex has long been relevant in patients with myelodysplastic syndromes given there is a higher prevalence1 and poorer prognosis2 among men compared with women. However, this relationship has been traditionally viewed as one that is confounded by disease genetics or caused by differential responses to therapy,3 and not necessarily as an independent prognostic factor. In The Lancet Haematology, the GenoMed4All consortium4 used large European population datasets to challenge this dogma. The authors' data show that male sex is associated with a poorer prognosis in myelodysplastic syndromes than female sex, particularly in patients with low-risk prognostic scores according to the Revised International Prognostic Scoring System (IPSS-R).

Energy usage and human behavior modeling for residential bottom-up energy simulation

We introduce a probabilistic modeling for a disaggregated Bottom-up simulation of residential energy usage. Parametric probability distributions are modeled with parameters that have a natural explanation in terms of usage and appliance power. Human behavior such as sleep and home occupancy variables are considered too, with its corresponding trained probabilistic Models. Model parameters are adjusted by the minimization of the Kullback–Leibler divergence from known appliance and behavior usage data. Self-generated photovoltaic Energy is included in the simulation with a battery for storage and electric vehicle usage. Simulations match individual and aggregated usage load profiles in the European REMODECE and RSE Italian load data sets. Obtained Models are useful for residential disaggregated simulations allowing individual appliances to change from house to house. Probabilistic distributions can be used as prior knowledge for energy management systems, risk management, and grid failure prediction and can be adapted based on non-stationary real-time house behavior and appliance usage.

Credit Card Fraud Detection Based on Hyperparameters ‎Optimization Using the Differential Evolution

Due to the emigration of world business to the internet, credit ‎cards have become a tool for ‎payments for both online and outline purchases. However, fraudsters try ‎to attack those systems ‎using various techniques, and credit card fraud has become dangerous. To ‎secure credit cards, ‎different methods are proposed in the academic paper based on artificial ‎intelligence. The proposed ‎solution in this paper aims at combining the robustness of three methods: ‎the differential evolution ‎algorithm (DE) for selecting the best hyperparameters, a resampling ‎technique for handling ‎imbalanced data issues, and the XGBoost technique for classification. Finally, ‎the fraudulent ‎transactions are classified using the optimized XGBoost algorithm. The proposed ‎solution is ‎evaluated using two real-world datasets: the European dataset and the UCI dataset. The ‎evaluation ‎in terms of accuracy, sensitivity, specificity, precision, and F-measure shows the ability and ‎the ‎superiority of the proposed approach in comparison with the state-of-the-art machine learning ‎‎models.‎

Real-World Treatment Patterns of Patients Initiating Third-Line Therapy in Relapsed or Refractory Multiple Myeloma in Europe

Background: Despite advances, most patients with multiple myeloma (MM) eventually relapse and progress through multiple lines of therapy. To complement clinical trial data, real-world evidence on treatment patterns can provide valuable insights on clinical practice in different countries and help identify and address unmet medical needs. Here, we report real-world (RW) treatment use in patients from European data sets who initiated third-line (3L) treatment for relapsed or refractory (RR) MM. Methods: This retrospective, noninterventional analysis used claims data from the German AOK PLUS health insurance fund and Italian local health units (2012-2020) and applied an algorithm based on prescription and procedure codes. The algorithm defined the start of a new line of therapy as the introduction of a new agent not part of the prior line. Agents given within 30 days of start defined the regimen. Retreatment after >6 months of discontinuation were also considered a new line. Patients initiating 3L treatment from 2016 to 2020 (index date range) were identified. Baseline characteristics and treatment patterns were then reported. Results: Overall, 276 patients from Germany and 289 patients from Italy who met selection criteria were identified (Table). Patients in Germany and Italy had similar baseline characteristics in terms of sex, age, and time since diagnosis; 27% and 15% of patients, respectively, had prior stem cell transplant. Prior to 3L treatment, IMiD use was lower (56% vs 74%) and proteasome inhibitor (PI) use was higher (96% vs 52%) among patients in Germany vs those in Italy, respectively. At 3L, the proportion of double class-exposed patients in Germany was higher vs Italy (52% vs 33%). In Germany, common first-line (1L) regimens were bortezomib (BORT) + dexamethasone (DEX; Vd) (46%), melphalan (MEL) + prednisone (PRED) + BORT (14%), and Vd + cyclophosphamide (8%). The most common 2L treatment regimens were lenalidomide (LEN) + DEX (Rd; 30%), Vd (11%), and carfilzomib (CFZ) + Rd (8%). By 3L, 95% and 54% of patients had received prior BORT- and LEN-based regimens, respectively. In 3L, combinations were often based on LEN and/or CFZ with daratumumab (DARA) monotherapy and pomalidomide (POM) + DEX (Pd) also contributing about 4% each (Table). In Italy, common 1L regimens included MEL ± DEX ± PRED (22%), Vd (6%), and thalidomide (THAL) + DEX (5%). Rd (40%) and Vd (8%) were the most common 2L regimens in Italy; by 3L, 56% and 49% of patients had received prior LEN- and BORT-containing regimens, respectively. Overall, Pd was the second most common 3L treatment in Italy and its use was higher than in Germany (12% vs 4%), whereas 3L use was lower for regimens based on CFZ (10% vs 21%) and DARA (9% vs 29%). Patients in Italy also had higher 3L use of conventional therapies such as MEL compared with patients in Germany (15% vs 3%). Retreatment patterns in Germany and Italy showed that 60% and 82% of patients initiating 3L treatment, respectively, had prior exposure to the same agent class (IMiD, PI, monoclonal antibodies). Of 50 patients in Germany with BORT treatment in 3L, 47 (94%) also had prior BORT; of 135 patients with LEN treatment in 3L, 47 (35%) had prior LEN. Of 40 patients in Italy with BORT treatment in 3L, 31 (78%) had prior BORT; of 131 patients with LEN treatment in 3L, 82 (63%) had prior LEN. Additionally, of 13 patients in Italy that received prior THAL, all were retreated with THAL in 3L. Conclusions: This study of European claims datasets provides a perspective of RW clinical practice with a focus on patients initiating 3L treatment from 2016 to 2020 for comparison with the evolving treatment landscape for RRMM. LEN-based regimens were most commonly observed in 3L, with high proportions of CFZ- and POM-based regimens in Germany and Italy, respectively. This finding is likely the result of BORT- and LEN-based regimens being common in 1L and 2L, respectively, reflecting what was approved and/or used before and during 2016. Many 3L patients were retreated with the same agent class used in a prior line. BORT use in 3L was prominent for retreatment in Germany and Italy, which may reflect BORT's prior use in a fixed-duration regimen. Notably, retreatment with LEN in 3L was also prominent in Italy. Comparisons with more recent index groups could help elucidate treatment evolution over time. Additionally, it would be relevant to understand how European RW clinical practice compares with observations in the US setting. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Prevalence of Poisoned Google Search Results of Erectile Dysfunction Medications Redirecting to Illegal Internet Pharmacies: Data Analysis Study.

Illegal online pharmacies function as affiliate networks, in which search engine results pages (SERPs) are poisoned by several links redirecting site visitors to unlicensed drug distribution pages upon clicking on the link of a legitimate, yet irrelevant domain. This unfair online marketing practice is commonly referred to as search redirection attack, a most frequently used technique in the online illegal pharmaceutical marketplace. This study is meant to describe the mechanism of search redirection attacks in Google search results in relation to erectile dysfunction medications in European countries and also to determine the local and global scales of this problem. The search engine query results regarding 4 erectile dysfunction medications were documented using Google. The search expressions were "active ingredient" and "buy" in the language of 12 European countries, including Hungary. The final destination website legitimacy was checked at LegitScript, and the estimated number of monthly unique visitors was obtained from SEMrush traffic analytics. Compromised links leading to international illegal medicinal product vendors via redirection were analyzed using Gephi graph visualization software. Compromised links redirecting to active online pharmacies were present in search query results of all evaluated countries. The prevalence was highest in Spain (62/160, 38.8%), Hungary (52/160, 32.5%), Italy (46/160, 28.8%), and France (37/160, 23.1%), whereas the lowest was in Finland (12/160, 7.5%), Croatia (10/160, 6.3%), and Bulgaria (2/160, 1.3%), as per data recorded in November 2020. A decrease in the number of compromised sites linking visitors to illegitimate medicine sellers was observed in the Hungarian data set between 2019 and 2021, from 41% (33/80) to 5% (4/80), respectively. Out of 1920 search results in the international sample, 380 (19.79%) search query results were compromised, with the majority (n=342, 90%) of links redirecting individuals to 73 international illegal medicinal product vendors. Most of these illegal online pharmacies (41/73, 56%) received only 1 or 2 compromised links, whereas the top 3 domains with the highest in-degree link value received more than one-third of all incoming links. Traffic analysis of 35 pharmacy specific domains, accessible via compromised links in search engine queries, showed a total of 473,118 unique visitors in November 2020. Although the number of compromised links in SERPs has shown a decreasing tendency in Hungary, an analysis of the European search query data set points to the global significance of search engine poisoning. Our research illustrates that search engine poisoning is a constant threat, as illegitimate affiliate networks continue to flourish while uncoordinated interventions by authorities and individual stakeholders remain insufficient. Ultimately, without a dedicated and comprehensive effort on the part of search engine providers for effectively monitoring and moderating SERPs, they may never be entirely free of compromised links leading to illegal online pharmacy networks.

A participatory tool for assessing land footprint in city-region food systems—A case study from Metropolitan Copenhagen

The ongoing COVID-19 pandemic has exposed the fragility of current food systems to feed populations around the world. Particularly in urban centers, consumers have been confronted with this vulnerability, highlighting reliance on just-in-time logistics, imports and distant primary production. Urban food demand, regional food supply, land use change, and transport strategies are considered key factors for reestablishing resilient landscapes as part of a sustainable food system. Improving the sustainability of food systems in such circumstances entails working on the interrelations between food supply and demand, rural and urban food commodity production sites, and groups of involved actors and consumers. Of special significance is the agricultural land in close proximity to urban centers. Calling for more holistic approaches in the sense of inclusiveness, food security, citizen involvement and ecological principles, this article describes the use of a new decision support tool, the Metropolitan Foodscape Planner (MFP). The MFP features up-to-date European datasets to assess the potential of current agricultural land use to provide food resources (with special attention to both plant- and animal-based products) and meet the demand of city dwellers, and help to empower citizens, innovators, companies, public authorities and other stakeholders of regional food systems to build a more regionalized food supply network. The tool was tested in the context of the food system of the Copenhagen City Region in two collaborative workshops, namely one workshop with stakeholders of the Copenhagen City Region representing food consultancies, local planning authorities and researchers, and one in-person workshop masterclass with MSc students from the University of Copenhagen. Workshop participants used the tool to learn about the impacts of the current food system at the regional and international level with regard to the demand-supply paradigm of city-regions. The ultimate goal was to develop a participatory mapping exercise and test three food system scenarios for a more regionalized and sustainable food system and, therefore, with increased resilience to crises. Results from this implementation also demonstrated the potential of the tool to identify food production sites at local level that are potentially able to feed the city region in a more sustainable, nutritious and way.

A large-scale dataset reveals taxonomic and functional specificities of wild bee communities in urban habitats of Western Europe

Wild bees are declining, mainly due to the expansion of urban habitats that have led to land-use changes. Effects of urbanization on wild bee communities are still unclear, as shown by contrasting reports on their species and functional diversities in urban habitats. To address this current controversy, we built a large dataset, merging 16 surveys carried out in 3 countries of Western Europe during the past decades, and tested whether urbanization influences local wild bee taxonomic and functional community composition. These surveys encompassed a range of urbanization levels, that were quantified using two complementary metrics: the proportion of impervious surfaces and the human population density. Urban expansion, when measured as a proportion of impervious surfaces, but not as human population density, was significantly and negatively correlated with wild bee community species richness. Taxonomic dissimilarity of the bee community was independent of both urbanization metrics. However, occurrence rates of functional traits revealed significant differences between lightly and highly urbanized communities, for both urbanization metrics. With higher human population density, probabilities of occurrence of above-ground nesters, generalist and small species increased. With higher soil sealing, probabilities of occurrence of above-ground nesters, generalists and social bees increased as well. Overall, these results, based on a large European dataset, suggest that urbanization can have negative impacts on wild bee diversity. They further identify some traits favored in urban environments, showing that several wild bee species can thrive in cities.