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Recent advances in deep neural networks in terms of convolutional neural networks (CNNs) have enabled researchers to significantly improve the accuracy and speed of object recognition systems and their application to plant disease and pest detection and diagnosis. This paper presents the first comprehensive review and analysis of deep learning approaches for disease and pest detection in tomato plants, using self-collected field-based and benchmarking datasets extracted from real agricultural scenarios. The review shows that only a few studies available in the literature used data from real agricultural fields such as the PlantDoc dataset. The paper also reveals overoptimistic results of the huge number of studies in the literature that used the PlantVillage dataset collected under (controlled) laboratory conditions. This finding is consistent with the characteristics of the dataset, which consists of leaf images with a uniform background. The uniformity of the background images facilitates object detection and classification, resulting in higher performance-metric values for the models. However, such models are not very useful in agricultural practice, and it remains desirable to establish large datasets of plant diseases under real conditions. With some of the self-generated datasets from real agricultural fields reviewed in this paper, high performance values above 90% can be achieved by applying different (improved) CNN architectures such as Faster R-CNN and YOLO.
In the transition from traditional electrical energy generation with mainly linear sources to increasing inverter-based distributed generation, electrical power systems’ power quality requires new monitoring methods. Integrating a high penetration of distributed generation, which is typically located in medium- or low-voltage grids, shifts the monitoring tasks from the transmission to distribution layers. Compared to high-voltage grids, distribution grids feature a higher level of complexity. Monitoring all relevant nodes is operationally infeasible and costly. State estimation methods provide knowledge about unmeasured locations by learning a physical system’s non-linear relationships. This article examines a new flexible, close-to-real-time concept of harmonic state estimation using synchronized measurements processed in a neural network. A physics-aware approach enhances a data-driven model, taking into account the structure of the electrical network. An OpenDSS simulation generates data for model training and validation. Different load profiles for both training and testing were utilized to increase the variance in the data. The results of the presented concept demonstrate high accuracy compared to other methods for harmonic orders 1 to 20.
Scientific writing builds upon already published papers. Manual identification of publications to read, cite or consider as related papers relies on a researcher’s ability to identify fitting keywords or initial papers from which a literature search can be started. The rapidly increasing amount of papers has called for automatic measures to find the desired relevant publications, so-called paper recommendation systems. As the number of publications increases so does the amount of paper recommendation systems. Former literature reviews focused on discussing the general landscape of approaches throughout the years and highlight the main directions. We refrain from this perspective, instead we only consider a comparatively small time frame but analyse it fully. In this literature review we discuss used methods, datasets, evaluations and open challenges encountered in all works first released between January 2019 and October 2021. The goal of this survey is to provide a comprehensive and complete overview of current paper recommendation systems.
Rupture discs, also known as bursting discs, are indispensable components in fluid-operated systems providing effective protection against hazardous over-pressure or partial vacuum. They belong to a special class of safety devices and are found in a variety of technical applications including pressure vessels, piping systems, reactors and boilers. In all application scenarios, rupture discs act as sacrificial parts that have to fail precisely at a predetermined differential pressure, opening a relief flow path for the working fluid. The membrane employed within rupture discs is usually made out of specific metal alloys or different material layers depending on the particular application. However, for many manufacturers of rupture discs, the production process is characterized by a lack of systematic procedures, relying instead on trial and error as well as empirical values. By means of thorough finite-element-based modeling and simulation of the bulge-forming process of rupture discs, including an elastic–plastic material law, large deformation, as well as contact mechanics, it is possible to accurately predict the resulting stress–strain behavior. All simulation results are rigorously validated through corresponding experiments conducted during the bulge-forming process. Therefore, this contribution provides a reliable basis for the parameter set-up during the manufacturing process of rupture discs.
Onshore wind energy, the highest share of electricity production and the cheapest form of electricity in Germany has become an essential part of Germany’s aim to reach greenhouse gas neutrality in 2045. Still, its development faces social rejection due to perceived health problems that are as a result of wind turbines construction in close proximity to settlements. Forest, the second largest land use in Germany provides consistent wind resources for further expansion of wind energy generation but the social perception about wind energy in the forest is not great as forest is identified as the second largest natural carbon sink and provides some ecological benefits to humans and animals. This thesis sought to provide a quantitative comparison of the climate impact of wind turbines and forests.
Forest carbon stock of German forests was extracted from the German National Forest Inventory (NFI) database on the seven tree species and 2 species classification. The data available were the tree size and tree age. A multiple linear regression was used to estimate the influence of tree age and tree size on the carbon stock on a tree species. The model was performed using R language. The emission saving of Vestas V162-6.0 wind turbine was estimated and the results were compared to those of the results of the multiple linear regression.
It was determined that the emissions payback period of the wind turbine was seven (7) months, and the emissions avoided per year by the wind turbine was equivalent to the carbon stock of about 28 hectares of a matured forest. The research showed that both forest and wind energy are gains to climate change but in different ways and to be able to meet the climate goals, there should be a harmonious use of both resources.
Lake Sevan in Armenia is a unique, large, alpine lake given its surface, volume, and geographic location. The lake suffered from progressing eutrophication and, since 2018, massive cyanobacterial blooms repeatedly occurred. Although the lake is comparatively intensely monitored, the feasibility to reliably detect the algal bloom events appeared to be limited by the established in situ monitoring, mostly because algal bloom dynamics are far more dynamic than the realized monitoring frequency of monthly samplings. This mismatch of monitoring frequency and ecosystem dynamics is a notorious problem in lakes, where plankton dynamics often work at relatively short time scales. Satellite-based monitoring with higher overpass frequency, e.g., by Sentinel-3 OLCI with its daily overcasts, are expected to fill this gap. The goal of our study was therefore the establishment of a fast detection of algal blooms in Lake Sevan that operates at the time scale of days instead of months. We found that algal bloom detection in Lake Sevan failed, however, when it was only based on chlorophyll due to complications with optical water properties and atmospheric corrections. Instead, we obtained good results when true-color RGB images were analyzed or a specifically designed satellite-based HAB indicator was applied. These methods provide reliable and very fast bloom detection at a scale of days. At the same time, our results indicated that there are still considerable limitations for the use of remote sensing when it comes to a fully quantitative assessment of algal dynamics in Lake Sevan. The observations made so far indicate that algal blooms are a regular feature in Lake Sevan and occur almost always when water temperatures surpass approximately 20 °C. Our satellite-based method effectively allowed for bloom detection at short time scales and identified blooms over several years where classical sampling failed to do so, simply because of the unfortunate timing of sampling dates and blooming phases. The extension of classical in situ sampling by satellite-based methods is therefore a step towards a more reliable, faster, and more cost-effective detection of algal blooms in this valuable lake.
This study investigated the perception of drought by cocoa farmers and explored the effectiveness of adaptive strategies (ASs) used in smallholding farms in the transboundary region between Ghana and Togo. Drought significantly threatens cocoa production in this region, affecting farmers’ livelihoods and cocoa supply chains. This study used a multistage sampling approach, which involved surveys with questionnaires administered to 330 cocoa farmers throughout the study area, along with on-site observations. Statistical analysis included binary logistic and Poisson regression models to explore the relationship between farmer socioeconomic characteristics and adaptation practices. The findings revealed that cocoa farmers in the region have a nuanced understanding of drought, attributed to changing climatic patterns and unsustainable land management practices such as deforestation. To mitigate its impacts, farmers employ a variety of ASs, including investment in farm management, soil management, and intercropping with crop diversification. Furthermore, socioeconomic factors, including age, formal education, household size, land tenure right, adaptation cost assessment, and an underestimation of self-efficacy, were shown to affect the choice in the AS. Among the ASs adopted, only farm management practices (weeding, pruning, fertilizer application, etc.) significantly improved the cocoa yield. This study contributes to understanding drought as a critical issue for cocoa farmers and the adaptation practices used by smallholder cocoa farmers. Given that among the strategies adopted, only farm management practices, also known as good agricultural practices (GAPs), significantly improves yield, this study recommends well-designed and innovative packages of sustainable farm management based on farm and owner characteristics. These include irrigation schemes, timely soil fertilizer monitoring and supply, and the provision of drought-resistant varieties along with technical itineraries. Additional interventions require drought emergency responses, with other factors such as education and financial support mechanisms expected to improve farmers’ timely decision-making to adapt and improve cocoa production resilience to drought episodes in international transboundary regions with complex governance structures.
Hintergrund: Bei Notrufen liegen integrierten Leitstellen (ILS) durch Systeme wie E‑Call oder Advanced Mobile Location (AML) häufig die Geokoordinaten des Anrufers vor. Diese können jedoch ohne Datenschnittstelle schwer an die Rettungskräfte kommuniziert werden. Die Software what3words (w3w) verkürzt Geokoordinaten zu sogenannten Dreiwortadressen, die an Einsatzkräfte problemlos weitergegeben werden können. Im Rahmen der vorliegenden Studie wird untersucht, für welche Arten von Einsatzstellen das System genutzt und wie häufig es angewandt wird.
Methode: Als Datenbasis dienten 201 Einsätze eines siebenmonatigen Pilotprojekts des Rettungsdiensts im Rettungsdienstbereich der ILS-Region Ingolstadt. Die zugehörigen Dreiwortadressen wurden retrospektiv mit digitalem Kartenmaterial manuell gesichtet und nach ihrer räumlichen Lage klassifiziert. Anschließend erfolgte eine quantitative Auswertung dieser Klassifizierung und der Anwendungshäufigkeit des Systems.
Ergebnisse: Die Auswertung zeigt, dass w3w nahezu zu gleichen Teilen innerhalb und außerhalb geschlossener Ortschaften verwendet wurde. Die Anwendungsfälle wurden in 18 Klassen bzw. Unterklassen unterteilt. Innerorts fand das System besonders häufig auf Straßen, Sportanlagen oder in Parks, außerorts meist bei Einsätzen auf Landstraßen oder Wirtschaftswegen Anwendung. Es kam im Mittel zu 0,97 Verwendungen pro Tag, dies entspricht 0,48 % aller Einsätze des Rettungsdiensts im Untersuchungszeitraum.
Schlussfolgerung: Die regelmäßige Anwendung von w3w zeigt nach Ansicht der Autoren den Nutzen von leicht zu kommunizierenden Geokoordinatensystemen insbesondere für Einsatzstellen, die außerhalb postalischer Adressen liegen. Eine Implementierung kann eine hilfreiche Ergänzung im Werkzeugkasten von ILS und Einsatzkräften darstellen.
Transformative disaster resilience in times of climate change underscores the importance of reflexive governance, facilitation of socio-technical advancement, co-creation of knowledge, and innovative and bottom-up approaches. However, implementing these capacity-building processes by relying on census-based datasets and nomothetic (or top-down) approaches remains challenging for many jurisdictions. Web 2.0 knowledge sharing via online social networks, whereas, provides a unique opportunity and valuable data sources to complement existing approaches, understand dynamics within large communities of individuals, and incorporate collective intelligence into disaster resilience studies. Using Twitter data (passive crowdsourcing) and an online survey, this study draws on the wisdom of crowds and public judgment in near-real-time disaster phases when the flood disaster hit Germany in July 2021. Latent Dirichlet Allocation, an unsupervised machine learning technique for Topic Modeling, was applied to the corpora of two data sources to identify topics associated with different disaster phases. In addition to semantic (textual) analysis, spatiotemporal patterns of online disaster communication were analyzed to determine the contribution patterns associated with the affected areas. Finally, the extracted topics discussed online were compiled into five themes related to disaster resilience capacities (preventive, anticipative, absorptive, adaptive, and transformative). The near-real-time collective sensing approach reflected optimized diversity and a spectrum of people’s experiences and knowledge regarding flooding disasters and highlighted communities’ sociocultural characteristics. This bottom-up approach could be an innovative alternative to traditional participatory techniques of organizing meetings and workshops for situational analysis and timely unfolding of such events at a fraction of the cost to inform disaster resilience initiatives.
We have shown that in murine cardiomyopathy caused by overexpression of the β1-adrenoceptor, Gαi2-deficiency is detrimental. Given the growing evidence for isoform-specific Gαi-functions, we now examined the consequences of Gαi3 deficiency in the same heart-failure model. Mice overexpressing cardiac β1-adrenoceptors with (β1-tg) or without Gαi3-expression (β1-tg/Gαi3−/−) were compared to C57BL/6 wildtypes and global Gαi3-knockouts (Gαi3−/−). The life span of β1-tg mice was significantly shortened but improved when Gαi3 was lacking (95% CI: 592–655 vs. 644–747 days). At 300 days of age, left-ventricular function and survival rate were similar in all groups. At 550 days of age, β1-tg but not β1-tg/Gαi3−/− mice displayed impaired ejection fraction (35 ± 18% vs. 52 ± 16%) compared to wildtype (59 ± 4%) and Gαi3−/− mice (60 ± 5%). Diastolic dysfunction of β1-tg mice was prevented by Gαi3 deficiency, too. The increase of ANP mRNA levels and ventricular fibrosis observed in β1-tg hearts was significantly attenuated in β1-tg/Gαi3−/− mice. Transcript levels of phospholamban, ryanodine receptor 2, and cardiac troponin I were similar in all groups. However, Western blots and phospho-proteomic analyses showed that in β1-tg, but not β1-tg/Gαi3−/− ventricles, phospholamban protein was reduced while its phosphorylation increased. Here, we show that in mice overexpressing the cardiac β1-adrenoceptor, Gαi3 deficiency slows or even prevents cardiomyopathy and increases shortened life span. Previously, we found Gαi2 deficiency to aggravate cardiac dysfunction and mortality in the same heart-failure model. Our findings indicate isoform-specific interventions into Gi-dependent signaling to be promising cardio-protective strategies.