Background: Although soft tissue substitutes showed promising improvement in the past decade, epithelialized gingival graft (EGG) is still widely used in periodontal plastic surgery applications. Donor site management after EGG harvesting has been challenging for clinicians. This study aimed to compare the effectiveness of cyanoacrylate, hyaluronic acid, and their combination in palatal donor site management after EGG harvesting.Methods: Data from 89 patients were included and categorized as gelatin sponge (GS), gelatin sponge with either cyanoacrylate (GS + CY), hyaluronic acid (GS + HA), or both (GS + CY + HA). The data of pain perception (PP), quantity of analgesics (QA), secondary bleeding (SB), epithelization level (EL), and color match (CM) were assessed retrospectively. Results:The GS + CY and GS + HA + CY groups showed lower PP scores compared to the GS and GS + HA groups (p < 0.05). The QA was higher in the GS group compared to the GS + CY and GS + HA + CY groups (p < 0.001). All study groups showed greater EL than GS group on day 7 (p < 0.001). On day 14, full EL was present in 81% of the patients in the GS + HA + CY group, which was higher than the other groups (p < 0.001). All study groups reported lower SB in the first 3 days, which was lower compared to the GS group (p < 0.001) and showed higher CM scores than the GS group on days 7 and 14 (p < 0.001). Conclusions: CY application reduces pain and analgesic intake and HA may support the wound healing with increased EL. Using the CY-HA combination provides additional benefits for donor site management.

This article introduces the Special Issue on “Citizen Science and Geospatial Capacity Building” and briefly evaluates the future trends in this field. This Special Issue was initiated for emphasizing the importance of citizen science (CitSci) and volunteered geographic information (VGI) in various stages of geodata collection, processing, analysis and visualization; and for demonstrating the capabilities and advantages of both approaches. The topic falls well within the main focus areas of ISPRS Commission V on Education and Outreach. The articles collected in the issue have shown the enormously wide application fields of geospatial technologies, and the need of CitSci and VGI support for efficient information extraction and synthesizing. They also pointed out various problems encountered during these processes. The needs and future research directions in this subject can broadly be categorized as; (a) data quality issues especially in the light of big data; (b) ontology studies for geospatial data suited for diverse user backgrounds, data integration, and sharing; (c) development of machine learning and artificial intelligence based online tools for pattern recognition and object identification using existing repositories of CitSci and VGI projects; and (d) open science and open data practices for increasing the efficiency, decreasing the redundancy, and acknowledgement of all stakeholders.

Abstract. One sector that feels the effects of global warming and climate change on all levels is agriculture. In order to prepare for possible yield loss, as well as market, storage, and import planning challenges brought on by climate change, businesses can utilise agricultural decision support applications. Within the scope of this study, a crop yield prediction module has been developed that can provide in and end of season estimation of crop yields to be obtained from the determined regions. The Python programming language was used in the creation of the module as a QGIS plugin. The area for which crop yield predictions are to be made is covered by retrieving MODIS SR, MODIS LST, and Daymet data from the Google Earth Engine data catalogue. Histograms obtained from remotely sensed images are used as input data to two deep learning methods (CNN-LSTM and HistCNN). As a result, the HistCNN model outperformed CNN-LSTM for in season soybean yield prediction, with an R2 of 0.72, while the CNN-LSTM model outperformed it for in end of season soybean yield prediction, with an R2 of 0.67.

&lt;p&gt;Ionosphere plays an important role in radio communication, positioning and navigation as well as in various Earth observation techniques based on electromagnetic wave propagation. Thus, modeling, monitoring and forecasting of the ionosphere has been a rather active up-to-date research topic and various models have been proposed by different researchers. The availability of globally distributed dual frequency GNSS observations and ionosphere products delivered by the IGS and other institutions provided an unprecedented time series data source for developing ionosphere forecasting models. In addition, the availability of software tools for massively parallel numerical algorithms programmable into Graphical Processing Unit hardware have delivered a boosted computation power available to researchers.&amp;#160; In parallel, the application of machine learning and especially deep learning methods not only into the Ionosphere research but also to various research on Earth sciences have increased. In this work, an overview of recent developments in ionosphere forecasting research is presented with a spot on those which use especially machine learning and deep learning techniques. The opportunities and challenges are listed with a classification of different approaches in the literature. An outlook is provided for further research directions in the use of learning techniques for long and short term forecasting of Ionosphere. And finally, a potential interoperability in dissemination and the use of recently developed forecasting models are discussed.&lt;/p&gt;