Search Results (75)

Bombus haematurus (Hymenoptera: Apidae), which arrived from the Balkan Peninsula, was first reported in Italy in 2020 in the Friuli Venezia Giulia region (FVG) (northeastern Italy) near the border with Slovenia. To study the spread and biology of the species, a survey was conducted at several sites of the FVG in the period 2023–2025. Bombus haematurus was recorded at 22 new sites across all four districts of the FVG (Trieste, Gorizia, Udine, and Pordenone), indicating its expansion towards the west. Bumblebees of this species were detected in plain and hilly areas at sites between 10 and 364 m a.s.l. They were observed more frequently at forest edges, undergrowth paths or clearings and meadows adjacent to woods, confirming the species is hylophilous. The activity of adults from February to July confirms that the bumblebee is an univoltine spring species. Specimens were observed foraging on the flowers of 19 wild and ornamental plants belonging to 12 families (in particular, Lamiaceae), confirming that the species is polylectic. The data collected indicate that B. haematurus are permanently established in the FVG and that a further spread of the species towards the west in the neighbouring Veneto region is likely. Full article

Similarity-based decision support systems have become essential tools for providing tailored and adaptive guidance across various domains. In agriculture, where managing extensive land areas poses significant challenges, the primary objective is often to maximize harvest yields while reducing costs, preserving crop health, and minimizing the use of chemical adjuvants. The application of similarity-based analysis enables the development of personalized farming recommendations, refined through shared data and insights, which contribute to improved plant growth and enhanced annual harvest outcomes. This study employs two algorithms, K-Nearest Neighbour (KNN) and Approximate Nearest Neighbour (ANN) using Locality Sensitive Hashing (LSH) to evaluate their effectiveness in agricultural decision-making. The results demonstrate that, under comparable farming conditions, KNN yields more accurate recommendations due to its reliance on exact matches, whereas ANN provides a more scalable solution well-suited for large datasets. Both approaches support improved agricultural decisions and promote more sustainable farming strategies. While KNN is more effective for smaller datasets, ANN proves advantageous in real-time applications that demand fast response times. The implementation of these algorithms represents a significant advancement toward data-driven and efficient agricultural practices. Full article

The UK net zero strategy aims to fully decarbonize the power system by 2035, anticipating a 40–60% increase in demand due to the growing electrification of the transport and heating sectors over the next thirteen years. This paper provides a detailed technical and economic analysis of the role of energy storage technologies and transmission lines in balancing the power system amidst large shares of intermittent renewable energy generation. The analysis is conducted using the cost-optimizing energy system modelling framework REMix, developed by the German Aerospace Center (DLR). The obtained results of multiple optimization scenarios indicate that achieving the lowest system cost, with a 73% share of electricity generated by renewable energy sources, is feasible only if planning rules in England and Wales are flexible enough to allow the construction of 53 GW of onshore wind capacity. This flexibility would enable the UK to become a net electricity exporter, assuming an electricity trading market with neighbouring countries. Depending on the scenario, 2.4–11.8 TWh of energy storage supplies an average of 11% of the electricity feed-in, with underground hydrogen storage representing more than 80% of that total capacity. In terms of storage converter capacity, the optimal mix ranges from 32 to 34 GW of lithium-ion batteries, 13 to 22 GW of adiabatic compressed air energy storage, 4 to 24 GW of underground hydrogen storage, and 6 GW of pumped hydro. Decarbonizing the UK power system by 2035 is estimated to cost $37–56 billion USD, with energy storage accounting for 38% of the total system cost. Transmission lines supply 10–17% of the total electricity feed-in, demonstrating that, when coupled with energy storage, it is possible to reduce the installed capacity of conventional power plants by increasing the utilization of remote renewable generation assets and avoiding curtailment during peak generation times. Full article

Power transmission lines transfer energy between power plants and substations by means of a linear chain of towers. These towers are often situated over extensive distances, sometimes in regions that are difficult to access. Wireless sensor networks present a viable solution for monitoring these long chains of towers due to their wide coverage, ease of installation and cost-effectiveness. The proposed LoRaBUS approach implements and analyses the benefits of a linear topology using a mixture of LoRa and LoRaWAN protocols. This approach is designed to enable automatic detection of nearby nodes, optimise energy consumption and provide a prioritised transmission mode in emergency situations. On remote, hard-to-reach towers, a prototype fire protection system was implemented and tested. The results demonstrate that LoRaBUS creates a self-configurable linear topology which proves advantageous for installation processes, node maintenance and troubleshooting node failures. The discovery process collects data from a neighbourhood to construct the network and to save energy. The network’s autonomous configuration can be completed within approximately 2 min. In addition, energy consumption is effectively reduced 25% by dynamically adjusting the transmission power based on the detected channel quality and the distance to the nearest neighbour nodes. Full article

Background/Objectives: This study explores the allelopathic effects of volatile organic compounds (VOCs) emitted by the invasive species Ailanthus altissima (Mill.) Swingle on the seed germination of Bidens pilosa. A. altissima is known for releasing allelopathic VOCs that suppress the growth of neighbouring plants, contributing to its invasive potential. Methods: To examine these effects, we exposed B. pilosa seeds to varying concentrations of A. altissima VOCs, assessing germination rates and metabolic changes through untargeted metabolomics. Results: Our findings revealed that VOCs from A. altissima significantly inhibited the germination speed and overall germination rates of B. pilosa in a dose-dependent manner. Metabolomic profiling showed disruptions in energy and amino acid metabolism pathways, specifically involving delayed breakdown of starch and key metabolites, indicating inhibition of critical metabolic processes during early germination stages. This metabolic delay likely impairs B. pilosa’s establishment and competitiveness, enhancing A. altissima’s ecological dominance. Conclusions: The results underscore the potential of VOC-based allelopathy as a mechanism of plant invasion, offering insights into the role of VOCs in interspecies plant competition and ecosystem dynamics. Full article

The strong development of distributed energy sources has become one of the most important measures for low-carbon development worldwide. With a significant quantity of photovoltaic (PV) power generation being integrated to the grid, accurate and efficient prediction of PV power generation is an essential guarantee for the security and stability of the electricity grid. Due to the shortage of data from PV stations and the influence of weather, it is difficult to obtain satisfactory performance for accurate PV power prediction. In this regard, we present a PV power forecasting model based on a Fourier graph neural network (FourierGNN). Firstly, the hypervariable graph is constructed by considering the electricity and weather data of neighbouring PV plants as nodes, respectively. The hypervariance graph is then transformed in Fourier space to capture the spatio-temporal dependence among the nodes via the discrete Fourier transform. The multilayer Fourier graph operator (FGO) can be further exploited for spatio-temporal dependence information. Experiments carried out at six photovoltaic plants show that the presented approach enables the optimal performance to be obtained by adequately exploiting the spatio-temporal information. Full article

The objective of this work was to establish the gold and mercury losses in an artisanal mining deposit (Uke) in Nigeria to convince miners about their inefficiency and suggest changes in their gold extraction practices. Samples of feeds and tailings from five sluice box concentration processes previously ground in hammer mills below 1 mm (P80 = 0.5 mm) were systematically sampled every 15 min. for 4 h and sent for gold analyses by a fire assay and intensive cyanidation. Dry grain size analyses of primary and amalgamation tailings allowed us to find out in which size fraction gold and mercury are lost. Total mercury losses in sixteen operations were obtained by weighing mercury at the beginning and in all steps of the concentrates’ amalgamation. After analyses, the average gold grade in the feed resulted in 3.80 ± 1.52 ppm (two standard deviations). The gold recovery was 29.24 ± 13.24%, which is low due to a lack of liberation of the fine gold particles from the gangue (silicates). Finer grinding would be necessary. The mercury balance revealed that 42% of the mercury added is lost, in which 26% involves tailings and 16% evaporated. The Hg_Lost-to-Au_Produced ratio was found to be 3.35 ± 9.46, which is exceedingly high for this type of amalgamation process that should have this ratio around 1. One reason is the excessive amount of mercury in the amalgams, 76.5 ± 38.12%, when the normal is around 40%–50%. Mercury lost by evaporation in open bonfires is clearly contaminating amalgamation operators (usually children), neighbours, and the environment. The Hg-contaminated tailings and primary tailings are sold to local cyanidation plants, and this can form toxic soluble Hg(CN)₂ in the process. The results of this research were brought to the attention of the miners and other stakeholders, including the regulatory agencies of the government. The % gold recovery by amalgamation was not established in this study, but if this process recovers 50 to 60% of the liberated gold particles in a concentrate and 30% of gold was recovered in the sluice boxes, then the total gold recovery should be between 15 and 20; i.e., 80 to 85% of gold mined is lost. On average, an operation produces 8.26 g of gold/month, which is split to six miners, representing USD 69/month/miner or USD 2.3/day. It was discussed with miners, authorities, and community members (in particular female miners) how to avoid exposure to mercury, how to improve gold recovery without mercury, and the health and environmental effects of this pollutant. Full article

Canola (Brassica napus L.) is a profitable grain crop for Australian growers. However, weeds remain a major constraint for its production. Chemical herbicides are used for weed control, but this tactic also leads to the evolution of herbicide resistance in different weed species. The suppression of weeds by crop interference (competition and allelopathic) mechanisms has been receiving significant attention. Here, the weed suppressive ability and associated functional traits and stability of four selected canola genotypes (PAK85388-502, AV-OPAL, AV-GARNET, and BAROSSA) were examined at different locations in NSW, Australia. The results showed that there were significant effects of canola genotypes and of genotypes by crop density interaction on weed growth. Among the tested genotypes, PAK85388-502 and AV-OPAL were the most weed suppressive and, at a plant density of 10 plants/m², they reduced the weed biomass of wild radish, shepherd’s purse, and annual ryegrass by more than 80%. No significant differences were found in the primary root lengths among canola varieties; however, plants of the most weed-suppressive genotype PAK8538-502 exhibited a 35% increase in lateral root number relative to plants of the less weed-suppressive genotype BAROSSA. The analysis of variance revealed a significant influence of genotypes with PAK85388-502 and AV-OPAL performing the best across all the research sites. Results showed that canola genotypes PAK85388-502 and AV-OPAL were more weed suppressive than AV-GARNET and BAROSSA and may release specific bioactive compounds in their surroundings to suppress neighboring weeds. This study provides valuable information that could be utilised in breeding programs to select weed-suppressive varieties of canola in Australia. Thus, lateral root number could be a potential target trait for weed-suppressive varieties. Additionally, other root architecture traits may contribute to the underground allelopathic interaction to provide a competitive advantage to the crop. Full article

The study is focused on the analysis of black crust and soiling on the building materials of the medieval Lipowiec Castle in southern Poland. The castle was constructed using local, partly dolomitic limestones and dolomites, supplemented with other limestones and bricks, during 20th-century renovations of the castle ruins. The crust and soiling components, secondary mineral phases, and particulate matter of anthropogenic origin were analysed using Raman micro-spectroscopy (RS) and scanning electron microscopy coupled with energy-dispersive spectrometry (SEM-EDS). The crust, mostly composed of gypsum and other sulphate phases, was found to contain carbonaceous matter, spherical Si-Al glass particles, and iron oxides, with admixtures of other elements, including heavy metals, as well as irregularly shaped particles containing various metals. These components reflect the air pollution in the region, related to the combustion of solid fuels in both industrial power plants and local domestic furnaces, Zn-Pb ore mining (operational until 2021), and smelting in the neighbouring industrial centre. Despite its location in a rural area, the castle has been exposed to pollution for an extended period due to its proximity to large industrial centres. Therefore, the crust analysed may serve as an environmental indicator of the nature of the air pollution in the region. Full article

The microbial communities of the rhizospheres of vineyards have been subject to a considerable body of research, but it is still unclear how the applied soil cultivation methods are able to change the structure, composition, and level of diversity of their communities. Rhizosphere samples were collected from three neighbouring vineyards with the same time of planting and planting material (rootstock: Teleki 5C; Vitis vinifera: Müller Thurgau). Our objective was to examine the diversity occurring in bacterial community structures in vineyards that differ only in the methods of tillage procedure applied, namely intensive (INT), extensive (EXT), and abandoned (AB). For that we took samples from two depths (10–30 cm (shallow = S) and 30–50 cm (deep = D) of the grape rhizosphere in each vineyard and the laboratory and immediately prepared the slices of the roots for DNA-based analysis of the bacterial communities. Bacterial community structure was assessed by means of PCR-DGGE analysis carried out on the v3 region of 16S rRNA gene. Based on the band composition of the DGGE profiles thus obtained, the diversity of the microbial communities was evaluated and determined by the Shannon–Weaver index (H′). Between the AB and EXT vineyards at the S depth, the similarity of the community structure was 55%; however, the similarity of the D samples was more than 80%, while the difference between the INT samples and the other two was also higher than 80%. Based on our results, we can conclude that intensive cultivation strongly affects the structure and diversity of the bacterial community. Full article

The availability of wild chickpea (Cicer reticulatum L.) accessions has the potential to be used for the improvement of important traits in cultivated chickpeas. The main objectives of this study were to evaluate the phenotypic and genetic variations of chickpea progeny derived from interspecific crosses between C. arietinum and C. reticulatum, and to establish the association between single nucleotide polymorphism (SNP) markers and a series of important agronomic traits in chickpea. A total of 486 lines derived from interspecific crosses between C. arietinum (CDC Leader) and 20 accessions of C. reticulatum were evaluated at different locations in Saskatchewan, Canada in 2017 and 2018. Significant variations were observed for seed weight per plant, number of seeds per plant, thousand seed weight, and plant biomass. Path coefficient analysis showed significant positive direct effects of the number of seeds per plant, thousand seed weight, and biomass on the total seed weight. Cluster analysis based on the agronomic traits generated six groups that allowed the identification of potential heterotic groups within the interspecific lines for yield improvement and resistance to ascochyta blight disease. Genotyping of the 381 interspecific lines using a modified genotyping by sequencing (tGBS) generated a total of 14,591 SNPs. Neighbour-joining cluster analysis using the SNP data grouped the lines into 20 clusters. The genome wide association analysis identified 51 SNPs that had significant associations with different traits. Several candidate genes associated with early flowering and yield components were identified. The candidate genes and the significant SNP markers associated with different traits have a potential to aid the trait introgression in the breeding program. Full article

Graph theory is a branch of mathematics that is crucial to modelling applicable systems and networks using matrix representations. In this article, a novel graph-theoretic model was used to assess an urban water distribution system (WDS) in Saudi Arabia. This graph model is based on representing its elements through nodes and links using a weighted adjacency matrix. The nodes represent the points where there can be a water input or output (sources, treatment plants, tanks, reservoirs, consumers, connections), and links represent the edges of the graph that carry water from one node to another (pipes, pumps, valves). Four WDS benchmarks, pumps, tanks, reservoirs, and external sources were used to validate the framework at first. This validation showed that the worst-case scenarios for vulnerability were provided by the fault sequence iterating the calculation of the centrality measurements. The vulnerability framework’s application to the Saudi Arabian WDS enabled the identification of the system’s most vulnerable junctions and zones. As anticipated, the regions with the fewest reservoirs were most at risk from unmet demand, indicating that this system is vulnerable to the removal of junctions and pipes that are intricately associated with their neighbours. Different centrality metrics were computed, from which the betweenness centrality offered the worst vulnerability prediction measures. The aspects and zones of the WDS that can more significantly impact the water supply in the event of a failure were identified by the vulnerability framework utilising attack tactics. Full article

Presently, as human activity and climate warming gradually increase, straw burning leads to more accidental burning in neighbouring wetlands, which threatens wetland carbon stores. Plants are important carbon fixers in wetlands, converting carbon dioxide to biomass through photosynthesis and releasing carbon into the soil as plants die off. Nitrogen and phosphorus limitation in wetlands is a key factor affecting plant growth, and different burning seasons have different effects on mitigating this limitation. To further elucidate the effects of nitrogen and phosphorus distribution on wetland inter-month nutrient dynamics after different burning seasons, we selected a Calamagrostis angustifolia wetland in the Sanjiang Plain that was burned in spring and autumn, respectively, and conducted a monthly survey from May to September. We found that the leaf nitrogen content in September at spring burning sites was 3.59 ± 2.69 g/kg, which was significantly lower than that in July, while the difference at the unburned sites was only 0.60 ± 3.72 g/kg, and after the autumn burning, soil nitrogen and phosphorus contents remained higher than at the unburned sites in August, being 0.55 ± 1.74 g/kg and 0.06 ± 0.12 g/kg, respectively. Our results indicate that spring burning immediately increased the nitrogen and phosphorus contents in soil and plants but that these effects only lasted for a short time, until June. In comparison, autumn burning had a long-term effect on soil nitrogen and phosphorus levels and significantly increased the aboveground biomass. Thus, we recommend that conducting autumn burning before the commencement of agricultural burning not only reduces combustible accumulation to prevent fires but also promotes nitrogen and phosphorus cycling in wetlands, and the increase in plant biomass after autumn burning also enhances the carbon fixation capacity of the wetland. Full article

Smart agricultural systems have received a great deal of interest in recent years because of their potential for improving the efficiency and productivity of farming practices. These systems gather and analyze environmental data such as temperature, soil moisture, humidity, etc., using sensor networks and Internet of Things (IoT) devices. This information can then be utilized to improve crop growth, identify plant illnesses, and minimize water usage. However, dealing with data complexity and dynamism can be difficult when using traditional processing methods. As a solution to this, we offer a novel framework that combines Machine Learning (ML) with a Reinforcement Learning (RL) algorithm to optimize traffic routing inside Software-Defined Networks (SDN) through traffic classifications. ML models such as Logistic Regression (LR), Random Forest (RF), k-nearest Neighbours (KNN), Support Vector Machines (SVM), Naive Bayes (NB), and Decision Trees (DT) are used to categorize data traffic into emergency, normal, and on-demand. The basic version of RL, i.e., the Q-learning (QL) algorithm, is utilized alongside the SDN paradigm to optimize routing based on traffic classes. It is worth mentioning that RF and DT outperform the other ML models in terms of accuracy. Our results illustrate the importance of the suggested technique in optimizing traffic routing in SDN environments. Integrating ML-based data classification with the QL method improves resource allocation, reduces latency, and improves the delivery of emergency traffic. The versatility of SDN facilitates the adaption of routing algorithms depending on real-time changes in network circumstances and traffic characteristics. Full article

After the Fukushima nuclear power plant disaster in 2011, the Japanese government implemented a return policy, lifting most evacuation orders in former evacuation areas. Consequently, the return of residents is currently underway. However, it has become common for a large number of residents to carry out multisite living, a lifestyle involving returning to their hometown while maintaining their house at the evacuation site, or living at more than two sites. This report focuses on one aspect of the secondary effects of the nuclear incident, which forced affected residents to adopt a multisite lifestyle. Disasters always have a strong impact, via displacement, on those who are socially vulnerable, such as older people in an ageing society. They need intense support to resume their daily life as it was before the incident. For this report, we interviewed an elderly lady in her 90s, who is executing “multisite living” at evacuation sites, in order to obtain reassurance from neighbours and the local community. Our findings may provide valuable suggestions on how older people can restart their lives with the local community in an ageing society after disasters, which could apply to any kind of disaster preparedness. Full article