Romanian Space Agency

The problem of human systems is a difficult and fascinating one. The author believes that in future cybernetics will play an important part in helping to unravel this problem. The aim of this work is to survey some possibilities for a cybernetics of society, and this has been done principally through the investigation of a model offered by the theory of fuzzy systems.

This paper brings a solution for bridging the gap between the results of state-of-the-art automatic classification algorithms and high semantic human-defined manually created terminology of cartographic data. Using a recent pure-spectral rule-based fully automatic classifier to define the basic 'vocabulary', we provide a hybrid method to automatically understand and describe semantic rules that link existent mapping data according to different specifications with the end-results of unsupervised computer information mining methods. Following an agreement between the learning model and the cartographic scale implied, we exploit Latent Dirichlet Allocation model (LDA) to map heterogeneous pixels with similar intermediate-level semantic meaning into land cover classes of various mapping products. By discovering the set of rules that explain semantic classes in existent vector systems, we introduce the prototype of an interactive learning loop that uses the concept of direct semantics applied on satellite imagery. We solve a big problem in generating cartographic information layers from a fully automatic classification map and demonstrate it for the typical case of Landsat images.

Concentrations of hexachlorobenzene (HCB), alpha-, beta- and gamma-hexachlorocyclohexane (HCH) isomers, 6 o,p'-and p,p'-isomers of DDT and 28 PCB congeners have been measured in eleven soil samples and one lichen collected on the Eastern coast of Antarctica from 5 Russian stations. For samples with low concentrations of PCBs (range 0.20-0.41 ng g(-1) dry weight) and pesticides (0.86-4.69 ng g(-1) and 0.11-1.22 ng g(-1) dry weight for HCHs and DDTs, respectively), atmospheric long-range transport from Africa, South America or Australia was suggested as the sole source of contamination. The profile of PCB congeners was dominated by the more volatile tri-, tetra- and penta-PCBs congeners, thus supporting long-range transport hypothesis. Four samples contained moderate levels of PCBs (range 1.98-6.94 ng g(-1) dry weight) and variable concentrations of pesticides (gamma-HCH, p,p'-DDT and o,p'-DDT being the main contaminants). For samples with high concentrations of PCBs (range 90.26-157.45 ng g(-1)) and high concentrations of pesticides, the presence of high molecular weight PCB congeners such as: 153, 180, 187, 170 etc, strongly suggest a local source (biotic) of PCBs rather than atmospheric transport. It is likely that on a local scale, biotic focussing of pollutants, due to bird activities (nesting and excrement) can cause high contamination levels and become more significant than contaminant input via abiotic pathways.

Abstract Aims Understanding fine‐grain diversity patterns across large spatial extents is fundamental for macroecological research and biodiversity conservation. Using the GrassPlot database, we provide benchmarks of fine‐grain richness values of Palaearctic open habitats for vascular plants, bryophytes, lichens and complete vegetation (i.e., the sum of the former three groups). Location Palaearctic biogeographic realm. Methods We used 126,524 plots of eight standard grain sizes from the GrassPlot database: 0.0001, 0.001, 0.01, 0.1, 1, 10, 100 and 1,000 m 2 and calculated the mean richness and standard deviations, as well as maximum, minimum, median, and first and third quartiles for each combination of grain size, taxonomic group, biome, region, vegetation type and phytosociological class. Results Patterns of plant diversity in vegetation types and biomes differ across grain sizes and taxonomic groups. Overall, secondary (mostly semi‐natural) grasslands and natural grasslands are the richest vegetation type. The open‐access file ”GrassPlot Diversity Benchmarks” and the web tool “GrassPlot Diversity Explorer” are now available online ( https://edgg.org/databases/GrasslandDiversityExplorer ) and provide more insights into species richness patterns in the Palaearctic open habitats. Conclusions The GrassPlot Diversity Benchmarks provide high‐quality data on species richness in open habitat types across the Palaearctic. These benchmark data can be used in vegetation ecology, macroecology, biodiversity conservation and data quality checking. While the amount of data in the underlying GrassPlot database and their spatial coverage are smaller than in other extensive vegetation‐plot databases, species recordings in GrassPlot are on average more complete, making it a valuable complementary data source in macroecology.

There is increasing recognition of the global burden related to mental and neurological conditions greatly surpassing many health conditions such as cardiovascular disease and cancer. Recently, partnership among leading funders and academics has given rise to the grand challenges in global mental health initiative, aiming to reduce the global burden associated with mental and neurological conditions [Collins et al., 2011]. Among the actions of this initiative was a priority-setting exercise to articulate the most pressing challenges research in this area needs to address. Representing a diverse group of researchers and practitioners, we collectively considered progress and barriers in these priorities as they apply to autism research. In this editorial, we describe, based on our knowledge of and direct experience with autism in low- and middle-income countries (LMICs), the state of the science corresponding to the grand challenges and offer suggestions for how a truly global approach to autism research can bridge knowledge gaps leading to substantive improvements in quality of life for those affected wherever they may be. Our knowledge of the phenotypes and aetiology of autism is almost entirely limited to a small proportion of the world's population [Elsabbagh et al., 2012]. To understand the nature of this complex disorder, a global approach offers opportunities to examine the impact of a much wider range of biological and social risk factors directly related to autism. For example, a growing number of genetic and genomic studies are exploiting the large population size of China [Xu et al., 2012]. Other studies from LMICs have informed the search for inherited risk factors through tracing shared ancestry in consanguineous families [Morrow et al., 2008]. Moreover, links between autism and a range of environmental risk factors have been proposed in diverse geographical settings [reviewed in Elsabbagh et al., 2012]. A major barrier to this line of investigation is that it relies on expensive infrastructure unfit with research capacity in most LMICs. Furthermore, when such projects are led by researchers in high-income countries, people affected may be reluctant to participate because of mistrust resulting from negative experiences with research and/or lack of awareness of research benefits. This suggests that while research in LMICs can accelerate modelling of causal pathways underlying autism, this approach must go hand in hand with capacity building and public engagement in the target communities [see relevant discussion in Grinker et al., 2012]. It is widely acknowledged that early identification and intervention for a range of neurodevelopmental disorders including autism is critical in reducing the long-term negative impact of the condition. In view of this, a number of autism screening instruments have been translated for use across different cultural settings [see Elsabbagh et al., 2012, for a review]. An alternative approach has been to validate screeners such as the "Ten Questions Plus," previously used for childhood disabilities in LMICs [Durkin et al., 1994] to screen for a wider range of developmental conditions [Wu et al., 2011]. In a similar vein, translation of diagnostic tools commonly used in autism research and often described as the “gold standard” has been pursued [Wallace et al., 2012], but Progress in this area has been slow. Although such tools rely on the best available evidence and focus on comprehensive evaluation, they tend to be highly resource-intensive and reliant on specialist training. Overall, there is a demand to critically review the use of resource-intensive diagnostic instruments, as these may be barrier to research progress and capacity building in most low-resource settings. In the complementary area of early intervention, there is a striking lack of studies emerging from LMICs [Hastings et al., 2012]. It is commonly thought that effectiveness of early intervention in autism is limited to costly, highly intensive programs delivered by specialized professionals [Myers, Johnson, & American Academy of Pediatrics Council on Children With Disabilities, 2007], limiting generalizability to most real-world settings, including LMICs. However, this landscape is beginning to change, with growing excitement around a wider range of innovative early intervention strategies. These include parent-mediated approaches that have demonstrated a positive impact in reducing disabling consequences and improving cognitive function [Warren et al., 2011]. Such interventions are currently being tested across a range of settings including in LMIC. It is critical to note that the landscape of supportive interventions in LMIC has not been void of alternative approaches to those developed in high-income countries, and these approaches provide equally viable targets for research. There is a need for these culturally sensitive and community-based models to become integrated into the range of approaches being evaluated internationally. This may inspire more diverse and creative approaches in meeting current challenges facing intervention research in autism, including its generalizability to low-resource settings. Expanding the knowledge base in complementary areas of screening, diagnosis, and implementation of community-based services to support early identification and intervention is thought to yield benefits for those affected across the life-span. Common barriers currently hinder access to evidence-based treatments for those affected in childhood and adulthood. The state of science in relation to autism intervention coupled with little formal consideration of the generalizability of existing approaches to real-world settings clearly illustrates both knowledge and resource gaps even within high-income countries. In some communities, intervention remains limited to practices such as the use of medications and excludes the family and the wider community. In several countries, approaches already dismissed by research such as psychoanalytic treatments still thrive. Private, unregulated, and usually expensive services for autism are abundant. Such services often employ or directly apply “packaged” models used in high-income countries, with little consideration of cultural or contextual validity. Rather than adopting a “one-size-fits-all” approach, research in this area should be motivated by the needs of diverse and especially under-resourced communities. Cross-cutting questions include the effectiveness of more efficient screening, diagnostic, and intervention procedures as well as the delivery of evidence-based care by nonspecialized community health workers, an approach known as “task-shifting” [Patel, Singh Goel, & Desai, 2009]. Research specifically targeting use of affordable technology to support implementation of evidence-based care will undoubtedly enhance positive impact. Communities who combat stigma and misconceptions against disabled people benefit from their skills and competencies instead of marginalizing them as a burden to society. It is acknowledged that advocacy and awareness efforts in North America, and some parts of Europe have had a global impact welcomed by those affected around the world and reinforced by local action. Today, there is a flourishing number of autism advocacy and awareness groups around the world. In parallel, there is increasing focus in autism research on estimating the economic burden of the condition to support evidence-based policy. Adopting this approach across a wider range of communities around the world will broaden the impact of these findings from national to global policy. A critical move forward will be to produce cross-national evidence or standardized global data systems for collecting surveillance data on the prevalence, treatment patterns, and availability of human resources and services of autism. Our experience across several LMIC suggests improvements in the availability of training among researchers, specialist practitioners, community health workers, and parents. However, none of these training efforts are at an adequate scale to address the needs of their wider populations. In some countries, there has been a focus in recent years on training professionals on standardized instruments (delivered in high-income countries or by regional providers). This is a costly and challenging approach because it is detached from the specific research or practice context [see discussion in Wallace et al., 2012]. Effectiveness of training approaches on capacity building and service development has rarely been considered in autism research, and a global perspective can enrich and support this area. There is a need for innovative models to increase the number of culturally and ethnically diverse specialist and nonspecialist providers to deliver evidence-based services. In most communities where policy change has taken place, it was the families of those affected who spearheaded changes through tireless activism, fundraising, and lobbying. Individuals with a wide range of neurodevelopmental challenges including autism have benefited from policy responses ensuring the inclusion of individuals with autism in health, social, and educational systems. Nevertheless, these policies are yet to have a wide and far-reaching impact on the lived experience of those affected. There is now growing momentum for leveraging these global community efforts towards substantive transformations in health systems and in policy. Autism research can support policy transformation by systematically creating and prioritizing policy-relevant evidence in health services, education, vocational training, and family support both nationally and globally. Although the majority of autism research remains concentrated in a handful of high-income countries, advances in our understanding of this condition have no geographical boundaries. A number of principles cutting across the challenges considered earlier initially developed for a wide range of neurodevelopmental and mental health conditions resonate very clearly in autism research today. First, while some of the recommendations for future research may target specific age groups, it is necessary to adopt a life-course approach in considering the impact of autism on an individual. Second, autism does not only affect individuals but affects entire communities, and therefore, system-wide perspectives are crucial. Finally, an evidence-based approach is necessary for a wide range of knowledge users including families, practitioners, and policymakers to support informed decisions. To meet the global challenges, autism research will need to characterize genotypes, phenotypes, and risk factors in autism across diverse geographical settings; develop culturally appropriate, valid, and comparable diagnostic instruments; and design affordable care packages for use by community health workers applicable to a range of neurodevelopmental disabilities. A recent rise in national, regional, and global research and practice networks has given new impetus to this area and created promising opportunities for research. It will be critical for these autism initiatives to forge alliance with global networks already achieving these goals for a wider range of disabilities. In view of the increased awareness of autism worldwide and the growing interest from a wide range of stakeholders, research in this relatively narrow field may be viewed as a potential vehicle for improvements in evidence and practice standards not only in autism but more generally in child mental health.

Abstract Diabetes is a chronic and progressive disease with continuously increasing prevalence, rising financial pressure on the worldwide healthcare systems. Recently, the insulin resistance, hallmark of type 2 diabetes, was cured in mice treated with NAD + precursor β-nicotinamide mononucleotide (NMN), no toxic effects being reported. However, NMN has a high price tag, more cost effective production methods are needed. This study proposes a biotechnological NMN production method in Escherichia coli . We show that bicistronic expression of recombinant nicotinamide phosphoribosyl transferase (Nampt) and phosphoribosyl pyrophosphate (PRPP) synthetase in the presence of nicotinamide (NAM) and lactose may be a successful strategy for cost effective NMN production. Protein expression vectors carrying NAMPT gene from Haemophilus ducreyi and PRPP synthetase from Bacillus amyloliquefaciens with L135I mutation were transformed in Escherichia coli BL21(DE3)pLysS. NMN production reached a maximum of 15.42 mg per L of bacterial culture (or 17.26 mg per gram of protein) in these cells grown in PYA8 medium supplemented with 0.1% NAM and 1% lactose.

A spatiotemporal machine learning framework for automated prediction and analysis of long-term Land Use/Land Cover dynamics is presented. The framework includes: (1) harmonization and preprocessing of spatial and spatiotemporal input datasets (GLAD Landsat, NPP/VIIRS) including five million harmonized LUCAS and CORINE Land Cover-derived training samples, (2) model building based on spatial k-fold cross-validation and hyper-parameter optimization, (3) prediction of the most probable class, class probabilities and model variance of predicted probabilities per pixel, (4) LULC change analysis on time-series of produced maps. The spatiotemporal ensemble model consists of a random forest, gradient boosted tree classifier, and an artificial neural network, with a logistic regressor as meta-learner. The results show that the most important variables for mapping LULC in Europe are: seasonal aggregates of Landsat green and near-infrared bands, multiple Landsat-derived spectral indices, long-term surface water probability, and elevation. Spatial cross-validation of the model indicates consistent performance across multiple years with overall accuracy (a weighted F1-score) of 0.49, 0.63, and 0.83 when predicting 43 (level-3), 14 (level-2), and five classes (level-1). Additional experiments show that spatiotemporal models generalize better to unknown years, outperforming single-year models on known-year classification by 2.7% and unknown-year classification by 3.5%. Results of the accuracy assessment using 48,365 independent test samples shows 87% match with the validation points. Results of time-series analysis (time-series of LULC probabilities and NDVI images) suggest forest loss in large parts of Sweden, the Alps, and Scotland. Positive and negative trends in NDVI in general match the land degradation and land restoration classes, with “urbanization” showing the most negative NDVI trend. An advantage of using spatiotemporal ML is that the fitted model can be used to predict LULC in years that were not included in its training dataset, allowing generalization to past and future periods, e.g. to predict LULC for years prior to 2000 and beyond 2020. The generated LULC time-series data stack (ODSE-LULC), including the training points, is publicly available via the ODSE Viewer. Functions used to prepare data and run modeling are available via the eumap library for Python.

BACKGROUND AND AIMS: Despite advances in our knowledge of effective services for people who use drugs over the last decades globally, coverage remains poor in most countries, while quality is often unknown. This paper aims to discuss the historical development of successful epidemiological indicators and to present a framework for extending them with additional indicators of coverage and quality of harm reduction services, for monitoring and evaluation at international, national or subnational levels. The ultimate aim is to improve these services in order to reduce health and social problems among people who use drugs, such as human immunodeficiency virus (HIV) and hepatitis C virus (HCV) infection, crime and legal problems, overdose (death) and other morbidity and mortality. METHODS AND RESULTS: The framework was developed collaboratively using consensus methods involving nominal group meetings, review of existing quality standards, repeated email commenting rounds and qualitative analysis of opinions/experiences from a broad range of professionals/experts, including members of civil society and organisations representing people who use drugs. Twelve priority candidate indicators are proposed for opioid agonist therapy (OAT), needle and syringe programmes (NSP) and generic cross-cutting aspects of harm reduction (and potentially other drug) services. Under the specific OAT indicators, priority indicators included 'coverage', 'waiting list time', 'dosage' and 'availability in prisons'. For the specific NSP indicators, the priority indicators included 'coverage', 'number of needles/syringes distributed/collected', 'provision of other drug use paraphernalia' and 'availability in prisons'. Among the generic or cross-cutting indicators the priority indicators were 'infectious diseases counselling and care', 'take away naloxone', 'information on safe use/sex' and 'condoms'. We discuss conditions for the successful development of the suggested indicators and constraints (e.g. funding, ideology). We propose conducting a pilot study to test the feasibility and applicability of the proposed indicators before their scaling up and routine implementation, to evaluate their effectiveness in comparing service coverage and quality across countries. CONCLUSIONS: The establishment of an improved set of validated and internationally agreed upon best practice indicators for monitoring harm reduction service will provide a structural basis for public health and epidemiological studies and support evidence and human rights-based health policies, services and interventions.

is an ideal lepidopteran species representative of many scientific studies, a model of studies for medicine and a significant insect from an ecological standpoint. This review was performed to summarize the fatty acids (FA) composition of silkworm pupae (SP) that are associated with other important compounds that could add value to SP, diversifying the ways of valorization. The proposal to complete plant-based feeds with insect-based feeds represents a viable option to beneficially impact human and animal health and the environment. The quality and quantity of fats consumed significantly impact the aetiology of certain diseases. The key compounds of fat named essential FA (EFA) substantially influence the prevention and treatment of several diseases through their nutraceutical functions. Due to its excellent profile in nutrients such as protein and fat, amino acids and fatty acids composition, SP has become an important alternative feed ingredient and source of EFA. SP is a by-product that was discarded in large quantities. Following the need to act to improve human health and reduce climate change impact, many researchers focused on studying SP applications in the medical and agricultural industries. Several authors noticed an improvement in the health markers by using SP. The feed cost for the animal was reduced with economic implications. Minimization of environmental impact was recorded. Few precautions were recommended regarding SP use, although they should not be ignored. The composition of SP and its potential for use in various industries provides us with persuasive arguments for continuing to develop the sericulture industry.

Various effects of microgravity on prokaryotes have been recognized in recent years, with the focus on studies of pathogenic bacteria. No archaea have been investigated yet with respect to their responses to microgravity. For exposure experiments on spacecrafts or on the International Space Station, halophilic archaea (haloarchaea) are usually embedded in halite, where they accumulate in fluid inclusions. In a liquid environment, these cells will experience microgravity in space, which might influence their viability and survival. Two haloarchaeal strains, Haloferax mediterranei and Halococcus dombrowskii, were grown in simulated microgravity (SMG) with the rotary cell culture system (RCCS, Synthecon). Initially, salt precipitation and detachment of the porous aeration membranes in the RCCS were observed, but they were avoided in the remainder of the experiment by using disposable instead of reusable vessels. Several effects were detected, which were ascribed to growth in SMG: Hfx. mediterranei's resistance to the antibiotics bacitracin, erythromycin, and rifampicin increased markedly; differences in pigmentation and whole cell protein composition (proteome) of both strains were noted; cell aggregation of Hcc. dombrowskii was notably reduced. The results suggest profound effects of SMG on haloarchaeal physiology and cellular processes, some of which were easily observable and measurable. This is the first report of archaeal responses to SMG. The molecular mechanisms of the effects induced by SMG on prokaryotes are largely unknown; haloarchaea could be used as nonpathogenic model systems for their elucidation and in addition could provide information about survival during lithopanspermia (interplanetary transport of microbes inside meteorites).

Leucine-rich-repeats (LRRs) belong to an archaic procaryal protein architecture that is widely involved in protein-protein interactions. In eukaryotes, LRR domains developed into key recognition modules in many innate immune receptor classes. Due to the high sequence variability imposed by recognition specificity, precise repeat delineation is often difficult especially in plant NOD-like Receptors (NLRs) notorious for showing far larger irregularities. To address this problem, we introduce here LRRpredictor, a method based on an ensemble of estimators designed to better identify LRR motifs in general but particularly adapted for handling more irregular LRR environments, thus allowing to compensate for the scarcity of structural data on NLR proteins. The extrapolation capacity tested on a set of annotated LRR domains from six immune receptor classes shows the ability of LRRpredictor to recover all previously defined specific motif consensuses and to extend the LRR motif coverage over annotated LRR domains. This analysis confirms the increased variability of LRR motifs in plant and vertebrate NLRs when compared to extracellular receptors, consistent with previous studies. Hence, LRRpredictor is able to provide novel insights into the diversification of LRR domains and a robust support for structure-informed analyses of LRRs in immune receptor functioning.

In recent times, satellite-based remote sensing has a growing role in archaeology and inherently in the cultural heritage management process. This paper demonstrates the potential and usefulness of satellite imagery for the documentation, mapping, monitoring, and in-depth analysis of cultural heritage and the archaeological sites located in urban landscapes. The study focuses on the assessment and monitoring of Alba Iulia, which is one of the Romanian cities with the richest historical past. Multitemporal analysis was performed to identify the land use/land cover changes that might contribute to an increased cultural heritage vulnerability to natural disasters. A special emphasis was dedicated to the assessment of the built-up area growth and consequently of the urbanization trend over a large time interval (30 years). Next, the urbanization and urban area expansion impact was further analyzed by concentrating on the urban heat island within Alba Iulia city and Alba Iulia Fortress (located in the center of the city). As temperature change represents a key element of climate change, the temperature trend within the same temporal framework and its impact on cultural heritage were determined. In the end, with regard to the cultural heritage condition assessment, the research was complemented with an assessment of the urban ground and individual building stability, using persistent scatterer interferometry. The results contribute to the detailed depiction of the cultural heritage site in such a manner that the site is monitored over an extensive timeframe, its current state of conservation is accurately determined, and the future trends can be identified. In conclusion, the present study offers reliable results regarding the main factors that might endanger the cultural heritage site as a basis for future preservation measures.

Unemployment has risen as the economy has shrunk. The coronavirus crisis has affected many sectors in Romania, some companies diminishing or even ceasing their activity. Making forecasts of the unemployment rate has a fundamental impact and importance on future social policy strategies. The aim of the paper is to comparatively analyze the forecast performances of different univariate time series methods with the purpose of providing future predictions of unemployment rate. In order to do that, several forecasting models (seasonal model autoregressive integrated moving average (SARIMA), self-exciting threshold autoregressive (SETAR), Holt-Winters, ETS (error, trend, seasonal), and NNAR (neural network autoregression)) have been applied, and their forecast performances have been evaluated on both the in-sample data covering the period January 2000-December 2017 used for the model identification and estimation and the out-of-sample data covering the last three years, 2018-2020. The forecast of unemployment rate relies on the next two years, 2021-2022. Based on the in-sample forecast assessment of different methods, the forecast measures root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percent error (MAPE) suggested that the multiplicative Holt-Winters model outperforms the other models. For the out-of-sample forecasting performance of models, RMSE and MAE values revealed that the NNAR model has better forecasting performance, while according to MAPE, the SARIMA model registers higher forecast accuracy. The empirical results of the Diebold-Mariano test at one forecast horizon for out-of-sample methods revealed differences in the forecasting performance between SARIMA and NNAR, of which the best model of modeling and forecasting unemployment rate was considered to be the NNAR model.

Nowadays Automatic Speech Recognition (ASR) applications are increasingly successful in the air traffic (ATC) domain. Paramount to achieving this is collecting enough data for speech recognition model training. Thousands of hours of ATC communication are recorded every day. However, the transcription of these data sets is resource intense, i.e. writing down the sequence of spoken words, and more importantly, interpreting the relevant semantics. Many different approaches including CPDLC (Controller Pilot Data Link Communications) currently exist in the ATC community for command transcription, a fact that e.g. complicates exchange of transcriptions. The partners of the SESAR funded solution PJ.16-04 are currently developing on a common ontology for transcription of controller-pilot communications, which will harmonize integration of ASR into controller working positions. The resulting ontology is presented in this paper.

The main concerns and challenges of raising silkworms include economic value, mulberry management, biodiversity conservation of genetic resources, and developing highly productive breeds for genetic variety. This study investigated the relationship between the economic relevance of the products generated throughout the value chain, limitations, and opportunities to generate incomes for sericulture farmers, trends, and perspectives worldwide, particularly in Romania. Seventy-seven publications were considered from online databases. The diversification of products generated at each level of the value chain of silkworm rearing and their multipurpose applications impact social and economic life. Hence, silk is well known as a valuable biomaterial for industry, suitable for textile and medicine. There are several arguments to use silkworms in human food even though they are not yet authorized as edible insects at the European level. Thus, as a nutrient-rich by-product, silkworm pupae (extract, cakes, and oil) have medicinal properties and can be used for human and animal nutrition. Sericin, silk fibroin, and chitin are bioactive compounds in cocoons and pupae with pharmacological implications and drug composition, while biomass is suitable for biodiesel and excreta for compost. The farmers’ attitudes and mentality associated with political circumstances influence the perspectives for the sericulture field. Due to the high likelihood of using their products, small-medium-scale farmers might benefit sericulture by identifying new sales marketplaces and finding new beneficiaries for directing their multiple products. The funds allotted by government subventions for supporting this fascinating activity and opportunities for jobs may aid in encouraging to start of a new sericulture business or to contribute developing the existing one.

Monitoring large built structures, landslides, volcanoes, or glaciers implies relative height measurements and line-of-sight (LOS) displacement measurements, which are typically performed with monostatic spaceborne synthetic aperture radar (SAR) sensors. In this case, for each satellite that illuminates a given location, we can usually exploit only the information available from one ascending/descending orbit, and if the satellite uses multiple subswaths, the information is obtained only from one subswath per orbit. A bistatic configuration with spaceborne transmitter and fixed ground-based (GB) receiver opens the possibility to exploit the information available from more than one orbit and one subswath per orbit. Additionally, such a configuration offers new perspectives for target tracking and characterization (e.g., multiple LOSs and bistatic scattering signatures). This article presents an opportunistic C-band bistatic SAR differential interferometry architecture that uses a multichannel GB stationary receiver and a separate transmitter, which can be either the Sentinel-1A/B satellite, used opportunistically, or a specially designed GB sliding transmitter. The combination of the operation modes based on spaceborne and GB transmitter allows monitoring a small critical area of interest with many acquisitions triggered by the GB transmitter and surveying of the whole surrounding area with a small number of acquisitions corresponding to the satellites passes. The hardware platforms are presented along with the bistatic interferometric processing flow, and the potential of the proposed architecture is assessed in the context of monitoring large built structures.

By fuzzy optimization we here mean optimization in a fuzzy environment, i.e., optimization with fuzzy constraints. Such a problem can be reduced to a family of ordinary optimization problems by using the representation theorem which states that a fuzzy set is a family of ordinary sets. Since it is difficult to work with a family of sets, in this paper a fuzzy set is approximated by an ordinary set. The Chebyshev norm is introduced into the set of all fuzzy sets, and a set is said to approximate a fuzzy set if the norm of a difference of its characteristic functions is smaller than a given number.

AbstractIn this paper the current national legislations, the quality assurance approaches and the activities of impact analysis of three quality assurance agencies from Romania, Spain and Germany are described from a strategic perspective. The analysis shows that the general methodologies (comprising, for example, self-evaluation reports, peer reviews, on-site visits, assessment reports, follow-up measures) and main subjects of quality assurance in higher education (such as study programmes and institutional structures and processes) are very similar in the sample cases. However, up to now, impact evaluation of quality assurance has not been implemented systematically in the sample agencies (as in many others). This is the more relevant since the European standards of quality assurance in higher education oblige quality assurance agencies to analyse their general findings and observe the effects of their activities. Against that background, it is argued that methodologically sound impact analyses of quality assurance interventions in higher education institutions should be seen as an integral part of the agencies’ own quality assurance because it would make their work more transparent and easier to improve systematically. The paper identifies some professionalisation needs required for impact evaluation competences: staff and peers who are qualified by methodological knowledge but also by ‘soft’ skills such as project and conflict management.Keywords: legal foundation and procedures of quality assurance in Germany, Romania and Spainimpact evaluation of quality assurancestrategic perspective of quality assurance agenciesESG for agencies AcknowledgementsThe authors did the work on this paper in the context of a project on impact analysis of external quality assurance in higher education institutions, which is co-funded by the European Commission (Grant no. 539481-LLP-1-2013-1-DE-ERASMUS-EIGF). This publication reflects the views only of the authors and the Commission cannot be held responsible for any use that may be made of the information contained therein.Disclosure statementNo potential conflict of interest was reported by the authors.

Abstract. The Small Whiskbroom Imager for atmospheric compositioN monitorinG (SWING) is a compact remote sensing instrument dedicated to mapping trace gases from an unmanned aerial vehicle (UAV). SWING is based on a compact visible spectrometer and a scanning mirror to collect scattered sunlight. Its weight, size, and power consumption are respectively 920 g, 27 cm × 12 cm × 8 cm, and 6 W. SWING was developed in parallel with a 2.5 m flying-wing UAV. This unmanned aircraft is electrically powered, has a typical airspeed of 100 km h−1, and can operate at a maximum altitude of 3 km. We present SWING-UAV experiments performed in Romania on 11 September 2014 during the Airborne ROmanian Measurements of Aerosols and Trace gases (AROMAT) campaign, which was dedicated to test newly developed instruments in the context of air quality satellite validation. The UAV was operated up to 700 m above ground, in the vicinity of the large power plant of Turceni (44.67∘ N, 23.41∘ E; 116 ma.s.l.). These SWING-UAV flights were coincident with another airborne experiment using the Airborne imaging differential optical absorption spectroscopy (DOAS) instrument for Measurements of Atmospheric Pollution (AirMAP), and with ground-based DOAS, lidar, and balloon-borne in situ observations. The spectra recorded during the SWING-UAV flights are analysed with the DOAS technique. This analysis reveals NO2 differential slant column densities (DSCDs) up to 13±0.6×1016 molec cm−2. These NO2 DSCDs are converted to vertical column densities (VCDs) by estimating air mass factors. The resulting NO2 VCDs are up to 4.7±0.4×1016 molec cm−2. The water vapour DSCD measurements, up to 8±0.15×1022 molec cm−2, are used to estimate a volume mixing ratio of water vapour in the boundary layer of 0.013±0.002 mol mol−1. These geophysical quantities are validated with the coincident measurements.

Abstract Over 12% of the world’s health resources are spent on treating diabetes, as high blood glucose is the third cause of mortality worldwide. Insulin resistance is the basis of the most common form of diabetes: type 2 diabetes. Recent animal studies report successful attempts at reversing type 2 diabetes by the administering of the NAD + precursor nicotinamide mononucleotide (NMN). However, the current high price of this molecule urges for more efficient and cost-effective production methods. This work proposes a method for purifying NMN by Size Exclusion Chromatography (SEC) on silica with a covalently attached coating of poly(2-hydroxyethyl aspartamide) (PolyHEA) stationary phase using an isocratic elution with a denaturing mobile phase (50 mM formic acid) from a complex molecular mixture such as a fermentation broth. The eluted peaks were identified by UV-Vis analysis and confirmed with ESI+ mass spectrometry and a HPLC reversed-phase method. The proposed SEC method is simple, patent-free, directly applicable for industrial production with a minimum scale up effort. The need for multiple chromatographic steps is eliminated and the lysate filtration and clarification steps are simplified. Substantial reduction in NMN production costs and increased purity of NMN to the level suitable for usage in humans are expected.