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Knowledge Graph Question Answering (KGQA) has gained attention from both industry and academia over the past decade. Researchers proposed a substantial amount of benchmarking datasets with different properties, pushing the development in this field forward. Many of these benchmarks depend on Freebase, DBpedia, or Wikidata. However, KGQA benchmarks that depend on Freebase and DBpedia are gradually less studied and used, because Freebase is defunct and DBpedia lacks the structural validity of Wikidata. Therefore, research is gravitating toward Wikidata-based benchmarks. That is, new KGQA benchmarks are created on the basis of Wikidata and existing ones are migrated. We present a new, multilingual, complex KGQA benchmarking dataset as the 10th part of the Question Answering over Linked Data (QALD) benchmark series. This corpus formerly depended on DBpedia. Since QALD serves as a base for many machine-generated benchmarks, we increased the size and adjusted the benchmark to Wikidata and its ranking mechanism of properties. These measures foster novel KGQA developments by more demanding benchmarks. Creating a benchmark from scratch or migrating it from DBpedia to Wikidata is non-trivial due to the complexity of the Wikidata knowledge graph, mapping issues between different languages, and the ranking mechanism of properties using qualifiers. We present our creation strategy and the challenges we faced that will assist other researchers in their future work. Our case study, in the form of a conference challenge, is accompanied by an in-depth analysis of the created benchmark.

The use of image analysis has often been suggested as a practical way to monitor the soiling accumulated on the surfaces of solar energy conversion devices. Indeed, the deposited soiling particles can be counted and characterized to calculate the area they cover, and this area can be converted into an energy loss. However, several particle counting methodologies exist and can lead to dissimilar results. This work focuses on the role of thresholding, an essential step where particles are distinguished from a background based on the pixel brightness. Sixteen automatic thresholding methods are assessed using 13200 micrographs of glass coupons soiled at nine locations globally. In low‐to‐intermediate soiling conditions, the “Triangle” method is found to return the minimum coefficient of variation and a mean deviation closer to zero. On the other hand, methods assuming a bimodal distribution of pixel brightness underestimate the area coverage. In addition, since soiling can be unevenly distributed over a surface, different loss estimations can be returned when the same image analysis process is employed on different spots on a sample’s surface. For these reasons, image analysis should be repeated at multiple locations on each investigated surface. This article is protected by copyright. All rights reserved.

Background As the use of internet memes as a form of communication has grown in recent years, it is important to understand their impact on society, particularly in relation to discrimination. This paper examines the impact of internet memes containing weight-stigmatizing content, called Fat People Memes (FPMs). The aim of the study was to examine whether individuals with lower levels of fat acceptance have a higher entertainability, higher shareability, and stronger emotions when viewing these types of memes. Methods A one-month (15 May–15 June, 2021) online questionnaire-based case-control study was conducted with a sample size of 147 participants aged 25–35 years. Participants were categorized into case (lower fat acceptance, AFA-Score < 55) and control (higher fat acceptance, AFA-Score ≥ 55) groups using a German Anti-Fat Attitudes Questionnaire, which had scores ranging from 13 to 117, and had to be fully answered. Participants were asked to assess the entertainability and shareability of selected FPMs, as well as their expression of emotions when viewing different FPMs. Results Subjects in the case group were significantly more likely to rate the seen FPMs as highly entertaining (77.6% vs. 59.6%; p = 0.023; x2(1) = 5.140, φ = 0.023); however, no significant difference was found in the shareability of FPMs between the two groups (36.2% vs. 27.0%; p = 0.235; x2(1) = 1.412, φ = 0.235). The case group expressed significantly greater emotions of disgust (p = 0.004), shame (p = 0.001), and surprise (p = 0.044) when viewing the FPMs. A two-sided significance level of 0.05 was set (95% confidence intervals). Conclusions Significant differences were observed in the entertainability of FPMs, but not in their shareability. The findings indicate that weight stigma persists, and is often justified, particularly as it is perceived as less severe due to its portrayal as humorous.

This paper presents a novel approach to optimizing Application Layer Multicast (ALM) using the Non-dominated Sorting Genetic Algorithm II (NSGA-II) for multiple network metrics, including round-trip time (RTT), available bandwidth, and other relevant parameters. ALM offers the possibility of using multicast in global networks, a scenario where Layer 4 multicast is unusable due to known challenges. To achieve this, we have incorporated the advanced capabilities of the RMDT protocol for efficient data transfer. Our proposed method focuses on optimizing the multicast tree structure to provide a more robust and efficient data dissemination across the network. By employing NSGA-II, we simultaneously optimize multiple objectives, ensuring a well-rounded and adaptable solution for various network conditions. Importantly, our approach is fast, scalable, and does not require extensive information about the channel, thus setting it apart from machine learning approaches. This adaptability and efficiency highlight the necessity and value of the proposed method in a dynamic network environment. To validate our approach, we conducted extensive experiments on the Internet using the Amazon Web Services (AWS) infrastructure. Through these experiments and simulations, we demonstrate the effectiveness of our approach in minimizing the overall multicast tree cost and maximizing the network resource utilization. Our results show that our proposed method provides a promising solution for large-scale and resource-intensive applications.

With the increasing number of Internet of Things (IoT) devices and rapid diversification of IoT applications, the need for wide coverage by keeping very efficient power consumption by end devices and their scalability are increasing. The study analyzes in detail the power consumption of the Long Range Wide Area Network (LoRaWAN) technology. It seeks to highlight the limitations and areas of improvement with regards to energy saving potential. In order to achieve this, two LoRaWAN end devices and a gateway were used as an experimental setup. The received signal strength indicator, uplink, delay, downlink, sleep and round-trip time periods were tested in a laboratory environment. The technology was tested in different scenarios with different network parameters and their impact was observed. Static and dynamic experiments have been done in urban and free field environments. Tests have been done in order to find the maximum possible range in which a stable communication is possible between an end device and a gateway in different surrounding areas. Special attention has been paid to the power consumption and battery life of an end device operating with the help of the LoRaWAN technologies in order to identify weak spots and places for improvement. The needed processing power of the microcontroller and the power needed by the transceiver and their impact on the battery life of an end device has been tested according to the used transmission power, packet size and spreading factor. Methods and ideas of increasing the life of a battery are discussed.

This paper addresses the problem of processing large volumes of satellite data and compares different cloud platforms for potential solutions. Existing cloud platforms like Google Earth Engine, Amazon Web Services (AWS), and CREODIAS have been used to tackle this challenge. However, this study proposes an optimal pipeline for satellite data processing, taking into account the advantages and limitations of each platform. The specific focus is on solving machine learning problems using satellite data. In the experiment conducted, the effectiveness of each cloud platform was analyzed. It was found that cloud platforms offer benefits such as flexibility, access to computing resources, and parallel processing architectures, leading to increased productivity and cost reduction. CREODIAS, in particular, stands out due to its specialization in satellite data and easy access to various data types, along with tools for data searching and visualization. The experiment demonstrated that tasks, from data loading to classification, were executed fastest on CREODIAS resources. However, AWS performed data classification faster. The availability of its own internal data bucket was a significant advantage of CREODIAS, especially when considering ARD data. These findings contribute to the advancement of AI methodologies and have practical implications for solving satellite monitoring applications.

Advanced data analysis tools and bioinformatics are essential for uncovering the nature of breast cancer, which is the leading cause of cancer death among women. The goal of this study is to identify potential genomic biomarkers that have a significant impact on four prognostic factors, including tumour size, lymph node involvement, metastasis, and overall survival status. The Random Forest algorithm has been trained on data from The Cancer Genome Atlas Breast Cancer, which contains the expression values of 19,737 genes. In order to obtain the optimal learning model, the process has been repeated 20 times for each indicator, and only the genes with a p-value < 0.05 were taken into further consideration. Several performance metrics (e.g., F1 score) were calculated to check the algorithm's reliability. As a result, 97 and 7 genes were included in the extended and final databases, respectively. The chosen genes have been proven to play a critical role in cancer-related pathways, such as Toll-like receptor and NF-κB, and have effects on cell proliferation, tumour formation, and angiogenesis. Thus, this study demonstrates the potential of machine learning analyses for biomedical purposes and provides machine-generated insights into breast cancer development, setting the groundwork for further in vitro examinations to validate the prognostic potential of these biomarkers.

The climate crisis challenges architects, designers, and engineers to explore alternative opportunities for more sustainable fabrication processes. Biopolymers have emerged as a potential material to replace petroleum-based plastics used in building and construction processes. This research aims to re-evaluate the production of non-standard building elements and introduce bio-based and biodegradable materials for formworks in architecture. This research paper investigates the production of thermoplastic starch (TPS) pellets and connected digital fabrication techniques. It studies the effects of varying ratios of the plasticizer on the behavior and properties of the material. TPS pellets are further processed using a large-scale robotic 3D printing setup, utilizing the Fused Deposition Modeling (FDM) method. The initial printed results using a robotic pellet extrusion system are presented, analyzed, and evaluated. The advantages and challenges of this approach are discussed within the scope of the architectural research field. This paper focuses on the production and digital fabrication techniques of TPS pellets, with the primary goal of developing a sustainable, bio-based, and bio-compostable system for concrete formwork in architecture.

Photovoltaic devices degrade during their lifetime, just like any other technology. Degradation rates, and thus the lifetime of products, are currently not well understood. This lack of understanding results in stakeholders being exposed to unquantified risks and subsequently insufficient securities being set aside. It is shown that one cannot rely on a singular degradation rate of module's datasheet when it comes to assessing potential failure rates or warranty risks. Aging rates will vary in the field due to normal production variability. The existing approach that accepts a module type as reliable when a single certification test is passed cannot explain failures seen in the field nor provide any quantitative prediction. To address this problem, a methodology is developed that, in principle, predicts the likelihood of warranty claims in different operating environments. The method generates probability distributions of module performance for 25 years with typical degradation patterns seen for different degradation modes. The risk of failure is calculated from these distributions according to warranty conditions typical in the industry. The results show that over a quarter of the modules may be at risk of warranty failure at the end of warranty period. Furthermore, when the calculation is made considering that the degradation is exponential rather than linear, there is a risk of observing roughly five times more warranty cases five years after installation. Unlike current practices, this probabilistic warranty risk calculation approach can provide quantitative results, and help stakeholders better assess likely performance reduction of photovoltaic (PV) assets.

Analysis of how semantic concepts are represented within Convolutional Neural Networks (CNNs) is a widely used approach in Explainable Artificial Intelligence (XAI) for interpreting CNNs. A motivation is the need for transparency in safety-critical AI-based systems, as mandated in various domains like automated driving. However, to use the concept representations for safety-relevant purposes, like inspection or error retrieval, these must be of high quality and, in particular, stable. This paper focuses on two stability goals when working with concept representations in computer vision CNNs: stability of concept retrieval and of concept attribution. The guiding use-case is a post-hoc explainability framework for object detection (OD) CNNs, towards which existing concept analysis (CA) methods are successfully adapted. To address concept retrieval stability, we propose a novel metric that considers both concept separation and consistency, and is agnostic to layer and concept representation dimensionality. We then investigate impacts of concept abstraction level, number of concept training samples, CNN size, and concept representation dimensionality on stability. For concept attribution stability we explore the effect of gradient instability on gradient-based explainability methods. The results on various CNNs for classification and object detection yield the main findings that (1) the stability of concept retrieval can be enhanced through dimensionality reduction via data aggregation, and (2) in shallow layers where gradient instability is more pronounced, gradient smoothing techniques are advised. Finally, our approach provides valuable insights into selecting the appropriate layer and concept representation dimensionality, paving the way towards CA in safety-critical XAI applications.

The existence of pharmaceutically active compounds (PhACs) in the water is a major concern for environmentalists due to their deleterious effects on living organisms even at minuscule concentrations. This review focuses on PhACs such as analgesics and anti-inflammatory compounds, which are massively excreted in urine and account for the majority of pharmaceutical pollution. Furthermore, other PhACs such as anti-epileptics, beta-blockers and antibiotics are discussed because they also contribute significantly to pharmaceutical pollution in the aquatic environment. This review is divided into two parts. In the first part, different classes of PhACs and their fate in the wastewater environment are presented. In the second part, recent advances in the removal of PhACs by conventional wastewater treatment plants, including membrane bioreactors (MBRs), activated carbon adsorption and bench-scale studies concerning a broad range of advanced oxidation processes (AOPs) that render practical and appropriate strategies for the complete mineralization and degradation of pharmaceutical drugs, are reviewed. This review indicates that drugs like diclofenac, naproxen, paracetamol and aspirin are removed efficiently by conventional systems. Activated carbon adsorption is suitable for the removal of diclofenac and carbamazepine, whereas AOPs are leading water treatment strategies for the effective removal of reviewed PhACs.

Stem cells are essential for the development and organ regeneration of multicellular organisms, so their infection by pathogenic viruses must be prevented. Accordingly, mammalian stem cells are highly resistant to viral infection due to dedicated antiviral pathways including RNA interference (RNAi). In plants, a small group of stem cells harbored within the shoot apical meristem generate all postembryonic above-ground tissues, including the germline cells. Many viruses do not proliferate in these cells, yet the molecular bases of this exclusion remain only partially understood. Here, we show that a plant-encoded RNA-dependent RNA polymerase, after activation by the plant hormone salicylic acid, amplifies antiviral RNAi in infected tissues. This provides stem cells with RNA-based virus sequence information, which prevents virus proliferation. Furthermore, we find RNAi to be necessary for stem cell exclusion of several unrelated RNA viruses, despite their ability to efficiently suppress RNAi in the rest of the plant. This work elucidates a molecular pathway of great biological and economic relevance and lays the foundations for our future understanding of the unique systems underlying stem cell immunity.

An asset of organic farming systems with diversified crop rotations, next to a cut in pesticide and mineral fertilizer application, is the built-up of organic carbon stocks in the long-term. In addition, the inclusion of deep-rooting legumes like alfalfa is known to improve soil structure and increase particulate organic matter contents in the subsoil. These views have been challenged recently ascribing limited potential for legume-based crop rotations to increase carbon stocks in Chernozems (Mollisols) due to limited accrual of mineral-associated organic carbon. Likewise, the direct impact of these legumes on soil structure and associated soil properties like water retention and transport has been questioned and linked to indirect effects instead. The objective of this study was to investigate the impact of legume-based crop rotations on carbon stocks and soil structure properties in the Flurweg II long-term farming systems trial (26 years) established on a Chernozem soil in Germany. We compared one conventional (INT) and two organic farming systems, with (O + M) and without (O-M) integrated livestock management. The three farming systems differ in biomass return via plant residues or farmyard manure as well as share and type of legumes in the eight-year crop rotation (INT: pea, O-M: faba bean and pea, O + M: biannual alfalfa). The comparison included yields, carbon stocks, soil physical properties and microstructure properties based on X-ray computed tomography of soil within and beneath the plow horizon. All farming systems underwent conventional plowing. In addition, we compared carbon stocks and microstructure properties with those from the nearby Westerfeld tillage trial with conventional and reduced tillage in a legume-free crop rotation. The carbon stocks in the plowed topsoil of the Flurweg II systems trial increased significantly with organic farming including livestock (INT: 53 ± 2 vs. O + M: 61 ± 2 t ha − 1), but not without (O-M: 53 ± 4 t ha − 1), likely because of farmyard manure application in the O + M system. This increase in topsoil carbon stocks is only moderate compared to the increase in topsoil carbon stocks in the Westerfeld tillage trial by switching from conventional to reduced tillage (53 vs. 70 t ha − 1). The carbon stocks of the whole soil profile (down to 48 cm) in the Flurweg II systems trial tended to increase with organic farming irrespective of livestock integration (INT: 72 ± 5 t ha − 1 vs. O ± M: 82-83 ± 7 t ha − 1). Particulate organic matter (POM) contents and biopore diameter below the plow layer tended to increase with alfalfa in the crop rotation of the O + M farming system. However, the legacy effect four years after the presence or absence of alfalfa was only in the range of natural, spatial variability. As a result of similar soil microstructure there was also hardly any difference in hydraulic conductivity and no difference in soil mechanical properties between farming systems. This study shows that Chernozems in this region still have the capacity to increase POM contents and carbon stocks with climate-smart, regenerative agricultural management, but also demonstrate that this has limited effects on structural properties.

We here present a database of evidence on the impact of agricultural management practices on biodiversity and yield. This database is the result of a systematic literature review, that aimed to identify meta-analyses that use as their response variables any measure of biodiversity and yield. After screening more than 1,086 titles and abstracts, we identified 33 relevant meta-analyses, from which we extracted the overall estimates, the subgroup estimates as well as all information related to them (effect size metric, taxonomic group, crop type etc.). We also extracted information relative to the empirical studies used for each meta-analysis and recorded the countries in which they took place and assessed the quality of each meta-analysis. Our dataset is publicly accessible and can be used for conducting second-order meta-analyses on the effect of management measures on species richness, taxon abundance, biomass and yields. It can also be used to create evidence maps on agriculture-related questions.

The current focus on renewable energy in global policy highlights the importance of methane production from biomass through anaerobic digestion (AD). To improve biomass digestion while ensuring overall process stability, microbiome-based management strategies become more important. In this study, metagenomes and metaproteomes were used for metagenomically assembled genome (MAG)-centric analyses to investigate a full-scale biogas plant consisting of three differentially operated digesters. Microbial communities were analyzed regarding their taxonomic composition, functional potential, as well as functions expressed on the proteome level. Different abundances of genes and enzymes related to the biogas process could be mostly attributed to different process parameters. Individual MAGs exhibiting different abundances in the digesters were studied in detail, and their roles in the hydrolysis, acidogenesis and acetogenesis steps of anaerobic digestion could be assigned. Methanoculleus thermohydrogenotrophicum was an active hydrogenotrophic methanogen in all three digesters, whereas Methanothermobacter wolfeii was more prevalent at higher process temperatures. Further analysis focused on MAGs, which were abundant in all digesters, indicating their potential to ensure biogas process stability. The most prevalent MAG belonged to the class Limnochordia; this MAG was ubiquitous in all three digesters and exhibited activity in numerous pathways related to different steps of AD.

Context Landscape composition and configuration, as well as seasonal landscape dynamics shape the behaviour, movement and energy expenditure of animals, i.e. foraging, hiding or fleeing, and ultimately survival. Especially in highly modified agricultural systems, it is crucial to understand how animal behaviour is influenced by landscape context to develop sustainable land management concepts. Objectives We show how landscape composition and configuration, together with seasonal dynamics affect animal behavioural types, accounting for the different life-history events in both sexes. Methods We investigated 34 European hares in two contrasting agricultural landscapes (a simple and a complex landscape) by using tri-axial accelerometer data to classify the animals’ behaviour into five categories: resting, foraging, moving, grooming and standing upright (i.e. vigilance behaviour). We tested whether the amount of behaviours per category changed with landscape composition and configuration, season and sex. Results During peak breeding, hares in areas of high habitat diversity rested more, moved less and spent less time searching for resources. During winter, hares moved more and rested less. Females rested less and foraged more in areas with large agricultural fields. Conclusions A complex landscape is particularly important during the breeding season, allowing animals to allocate enough energy into reproduction. In winter, hares in areas of low habitat diversity may not find enough thermal and anti-predator shelter to move as much as they would need to meet their requirements. Hence, high habitat diversity and small field sizes guarantee species persistence in human-altered agricultural areas throughout the year.

With the growing popularity of Li-ion batteries in large-scale applications, building a safer battery has become a common goal of the battery community. Although the small errors inside the cells trigger catastrophic failures, tracing them and distinguishing cell failure modes without knowledge of cell anatomy can be challenging using conventional methods. In this study, a real-time, non-invasive magnetic field imaging (MFI) analysis that can signal the battery current-induced magnetic field and visualize the current flow within Li-ion cells is developed. A high-speed, spatially resolved MFI scan is used to derive the current distribution pattern from cells with different tab positions at a current load. Current maps are collected to determine possible cell failures using fault-simulated batteries that intentionally possess manufacturing faults such as lead-tab connection failures, electrode misalignment, and stacking faults (electrode folding). A modified MFI analysis exploiting the magnetic field interference with the countercurrent-carrying plate enables the direct identification of defect spots where abnormal current flow occurs within the pouch cells.