Search Results (1,263)

Mycotoxin research is facing unprecedented challenges, starting from the urgent need to cope with the consequences of climate change, the global shortage of grain due to unstable political scenarios, and the major transformation of the supply chains after the COVID-19 pandemic. In this scenario, the mycotoxin contamination of human and animal foods is still unavoidable, thus representing a major challenge to global food security. Next to this, the shift to sustainable and circular food production might be accompanied by an increase in food safety issues involving mycotoxins, e.g., when new technologies are applied to reuse side streams from the food industry, it is not known if and how mycotoxins accumulate in these by-products. MycoTWIN is an EU-funded Horizon 2020 project which fosters knowledge transfer and scientific cooperation within the Mediterranean area, involving worldwide experts, decision makers, and stakeholders in the field of mycotoxigenic fungi and mycotoxins. The MycoTWIN project hosted working group meetings, whose aim was to propose operational plans and/or scientific strategic plans to shape the future research directions to better cope with these challenges. In the working group cycle “Future proof approaches for the management of toxigenic fungi and associated mycotoxins along the food chain”, a multi-actor group was guided in co-creation exercises to elaborate on future research directions and propose relevant actions to be implemented for the present to long-term time periods. The discussion focused on three main topics relevant to the assessment and management of risks associated with mycotoxins and toxigenic fungi: (i) needs for the harmonization of molecular and chemical methods and data analysis, (ii) from lab research to marketable solutions: how to fill the gap, and (iii) gaps in data quality for risk assessment. Full article

The transport sector heavily relies on the use of fossil fuels, which are causing major environmental concerns. Solutions relying on the direct or indirect use of electricity through e-fuel production are emerging to power the transport sector. To ensure environmental benefits are achieved over this transition, an accurate estimation of the impact of the use of electricity is needed. This requires a high temporal resolution to capture the high variability of electricity. This paper presents a previously unseen temporal analysis of the carbon intensity of e-fuels using grid electricity in countries that are members of the European Network of Transmission System Operators (ENTSO-E). It also provides an estimation of the potential load factor for producing low-carbon e-fuels according to the European Union legislative framework. This was achieved by building on top of the existing EcoDynElec tool to develop EcoDynElec_xr, a python tool enabling—with an hourly time resolution—the calculation, visualisation, and analysis of the historical time-series of electricity mixing from the ENTSO-E. The results highlight that, in 2023, very few European countries were reaching low carbon intensity for electricity that enables the use of grid electricity for the production of green electrolytic hydrogen. The methodological assumptions consider the consumption of the electricity mix instead of the production mix, and the considered time step is of paramount importance and drastically impacts the potential load factor of green hydrogen production. The developed tools are released under an open-source license to ensure transparency, result reproducibility, and reuse regarding newer data for other territories or for other purposes. Full article

As the fashion industry faces increasing scrutiny over its environmental impact, collaborative consumption models such as online fashion renting offer potential solutions for fostering sustainability. This study examines the role of environmental, social, and governance (ESG) factors alongside behavioral drivers in shaping consumer intentions toward online fashion renting in China, a model of collaborative consumption that contributes to sustainability by reducing new product demand and promoting the reuse of fashion items. The data was gathered from 403 Chinese customers using a standardized questionnaire. Structural equation modeling (SEM) was used to examine the given study hypotheses. The current study empirically demonstrates that customers’ attitudes, past sustainable behavior, and subjective norms are significant indicators of consumers’ intentions toward online fashion renting. The results further indicate that relative advantage, compatibility, perceived ownership, psychological risk, green self-identity, and experience value are the key drivers of consumers’ attitudes toward online fashion renting. Additionally, the ESG factors were found to have a significant positive impact on consumer attitudes toward online fashion renting, underscoring their importance in driving sustainable consumption patterns. By integrating behavioral and ESG perspectives, the study contributes to the growing discourse on how sustainable consumption patterns can be encouraged within the fashion industry, offering theoretical and managerial implications for fostering sustainable behavior. Directions for future research are also suggested. Full article

To address the issue of efficiently reusing the massive amount of unstructured knowledge generated during the handling of track circuit equipment faults and to automate the construction of knowledge graphs in the railway maintenance domain, it is crucial to leverage knowledge extraction techniques to efficiently extract relational triplets from fault maintenance text data. Given the current lag in joint extraction technology within the railway domain and the inefficiency in resource utilization, this paper proposes a joint extraction model for track circuit entities and relations, integrating Global Pointer and tensor learning. Taking into account the associative characteristics of semantic relations, the nesting of domain-specific terms in the railway sector, and semantic diversity, this research views the relation extraction task as a tensor learning process and the entity recognition task as a span-based Global Pointer search process. First, a multi-layer dilate gated convolutional neural network with residual connections is used to extract key features and fuse the weighted information from the 12 different semantic layers of the RoBERTa-wwm-ext model, fully exploiting the performance of each encoding layer. Next, the Tucker decomposition method is utilized to capture the semantic correlations between relations, and an Efficient Global Pointer is employed to globally predict the start and end positions of subject and object entities, incorporating relative position information through rotary position embedding (RoPE). Finally, comparative experiments with existing mainstream joint extraction models were conducted, and the proposed model’s excellent performance was validated on the English public datasets NYT and WebNLG, the Chinese public dataset DuIE, and a private track circuit dataset. The F1 scores on the NYT, WebNLG, and DuIE public datasets reached 92.1%, 92.7%, and 78.2%, respectively. Full article

Irrigation plays a vital role in many agricultural crop production regions. Drainage water recycling (DWR) is a popular irrigation water management system that collects excess water drained from cropland fields and stores it in on-site reservoirs for reuse. The efficacy of these systems varies by location, climate, irrigation frequency, and crop demands. Simulating this system would be beneficial for assessing the impact of water and land management practices on agriculture and natural resources. This study presents the development of computational algorithms for DWR simulation with the Agricultural Policy Environmental eXtender (APEX) model, along with the results for 39 testing sites where both reservoir and drainage systems are adopted. Simulating a DWR system with the revised reservoir module, the APEX model simulates irrigation water reuse ranging between 29% and 93%; sediment reduction of around 66%; nitrogen loss reduction of 23% and 73% for the mineral and organic forms, respectively; and phosphorus loss reduction of 22% and 79% for the soluble and sediment-transported forms, respectively. In conclusion, the results provided by the APEX model for sediment loss reduction align with field data, but discrepancies for nitrogen and phosphorus losses emerged from this test. Full article

In CPU microarchitecture, caches store frequently accessed instructions and data by exploiting their locality, reducing memory access latency and improving application performance. However, contemporary applications with large code footprints often experience frequent Icache misses, which significantly degrade performance. Although Fetch-Directed Instruction Prefetching (FDIP) has been widely adopted in commercial processors to reduce Icache misses, our analysis reveals that FDIP still suffers from Icache misses caused by branch mispredictions and late prefetch, leaving considerable opportunity for performance optimization. Priority-Directed Instruction Prefetching (PDIP) has been proposed to reduce Icache misses caused by branch mispredictions in FDIP. However, it neglects Icache misses due to late prefetch and suffers from high storage overhead. In this paper, we proposed a branch-triggered instruction prefetcher (BTIP), which aims to prefetch Icache lines that FDIP cannot efficiently handle, including the Icache misses due to branch misprediction and late prefetch. We also introduce a novel Branch Target Buffer (BTB) organization, BTIP BTB, which stores prefetch metadata and reuses information from existing BTB entries, effectively reducing storage overhead. We implemented BTIP on the Champsim simulator and evaluated BTIP in detail using traces from the 1st Instruction Prefetching Championship (IPC-1). Our evaluation shows that BTIP outperforms both FDIP and PDIP. Specifically, BTIP reduces Icache misses by 38.0% and improves performance by 5.1% compared to FDIP. Additionally, BTIP outperforms PDIP by 1.6% while using only 41.9% of the storage space required by PDIP. Full article

The annual increase in the world’s population significantly contributes to recent climate change and variability. Therefore, researchers, engineers, and professionals in all fields must integrate sustainability criteria into their decision-making. These criteria aim to minimize the environmental, social, economic, and energy impacts of human activities and industrial processes, helping mitigate climate change. This research focuses on developing scalable technology for the comprehensive use of avocados, adhering to sustainability principles. This work presents the modeling, simulation, and the WEP (Water-Energy-Product) technical evaluation of the process for obtaining bio-oil, chlorophyll, and biopesticide from the Creole-Antillean avocado. For this, the extractive-based biorefinery data related to water, energy, and products are taken from the material balance based on experimental results and process simulation. Then, eight process parameters are calculated, and eleven technical indicators are determined. Later, the extreme technical limitations for every indicator are demarcated, and an evaluation of the performance of the indicators is carried out. Results showed that the process has a high execution in aspects such as fractional water cost (TCF) and energy cost (TCE), as well as solvent reuse during extraction processes (SRI) and production yield, noting that the mentioned indicators are above 80%. In contrast, the metrics related to water management (FWC) and specific energy (ESI) showed the lowest performance. These discoveries support the use of optimization techniques like mass process integration. The energy-related indicators reveal that the process presents both benefits and drawbacks. One of the drawbacks is the energy source due to the high demand for electrical energy in the process, compared to natural gas. The specific energy intensity indicator (ESI) showed an intermediate performance (74%), indicating that the process consumes high energy. This indicator enables us to highlight that we can find energy aspects that require further study; for this reason, it is suitable to say that there is potential to enhance the energy efficiency of the process by applying energy integration methods. Full article

This article introduces a methodology for a novel data-driven computational model aimed at aiding public administrations in managing and evaluating the adaptative reuse of buildings while tackling ecological and digital challenges. Drawing from the 2030 Agenda for Sustainable Development, the study underscores the significance of innovative approaches in harnessing the economic potential of data. Focusing on Barcelona’s Ciutat Vella district, the research selects five historic public buildings for analysis, strategically positioned to spur local entrepreneurship and counteract tourism dominance. Through an extensive literature review, the article identifies a gap in computational models for building adaptative reuse and proposes a methodological framework that integrates data collection, processing, and computational modelling, underscored by GIS technology and open data sources. The proposed methodology for a computational algorithm aims to systematise spatial characteristics, assess programmatic needs, and optimise building usage, while addressing challenges such as data integration and quality assurance. Ultimately, the research presents a pioneering approach to building adaptative reuse, aimed at fostering sustainable urban development and offering replicable insights applicable to similar challenges in other cities. Full article

The architecture, engineering, construction, and operations industry faces an urgent need to enhance construction and demolition waste management in urban areas, driven by increasing demolition and construction activities and a desire to align with sustainable practices and the circular economy principles. To address this need, a systematic literature review on the building information modelling methodology was conducted, employing a structured protocol and specific tools for the analysis of academic studies, based on PRISMA guidelines and StArt software (version 3.4 BETA). Ninety relevant studies published between 1998 and 2024, were analysed and selected from the Web of Science, Scopus, and Engineering Village databases. Findings indicate that China leads in publications with 34%, followed by Brazil (8%) and the United Kingdom (7%). The analysis emphasises the use of drones and LiDAR scanners for precise spatial data, processed by 3D reconstruction tools like Pix4D and FARO As-Built. Revit excels in 3D modelling, providing a robust platform for visualisation and analysis. Visual programming tools such as Dynamo automate processes and optimise material reuse. The study presents a conceptual framework that integrates these technologies with the principles of the circular economy, clarifying the interactions and practical applications that promote the sustainable management of demolition waste from urban buildings and process efficiency. Although the approach promotes material reuse and sustainability, it still faces barriers such as the need for waste segregation at the source, the adaptation of innovative technologies, like the iPhone 15 Pro LiDAR and thermal cameras, as well as associated costs. These factors may limit its adoption in larger-scale projects, particularly due to the increased complexity of buildings. Full article

In this study, a new composite adsorbent consisting of aluminum-modified activated carbon (abbreviated hereafter AC@Al) was synthesized for the removal of the Ibuprofen compound (IBU), a non-steroidal anti-inflammatory drug (NSAID). Coconut shells were used as a source material for activated carbon, which was then modified with AlCl₃ to improve its properties. Adsorbent dosage, pH and initial IBU concentration, as well as contact time and temperature, are some of the factors affecting adsorption that were investigated in this work. Specifically, at pH 2.0 ± 0.1 with the application of 0.5 g/L of AC@Al in 100 mg/L of IBU, more than 90% was removed, reaching 100% with the addition of 1.0 g/L of the adsorbent. The IBU kinetic data followed the pseudo-second-order kinetic model. Non-linear Langmuir, Freundlich, Sips and Redlich–Peterson isotherm models were used to interpret the adsorption. According to the correlation coefficient (R²), the Langmuir model was found to best match the experimental data. The maximum adsorption capacity (Q_max) according to the Langmuir model was found to be as high as 2053 mg/g. The positive values of ΔH⁰ (42.92 kJ/mol) confirmed the endothermic nature of the adsorption. Due to the increasing values of ΔG⁰ with temperature, the adsorption of IBU onto AC@Al proved to be spontaneous. Also, the adsorbent was regenerated and reused for five cycles. This study shows that AC@Al could be used as a cost-effective adsorbent. Full article

In the field of target detection, a prominent area is represented by ship detection in SAR imagery based on deep learning, particularly for fine-grained ship detection, with dataset quality as a crucial factor influencing detection accuracy. Datasets constructed with commonly used slice-based annotation methods suffer from a lack of scalability and low efficiency in repeated editing and reuse. Existing SAR ship datasets mostly consist of medium to low resolution imagery, leading to coarse ship categories and limited background scenarios. We developed the “annotate entire image, then slice” workflow (AEISW) and constructed a sub-meter SAR fine-grained ship detection dataset (SDFSD) by using 846 sub-meter SAR images that include 96,921 ship instances of 15 ship types across 35,787 slices. The data cover major ports and shipping routes globally, with varied and complex backgrounds, offering diverse annotation information. Several State-of-the-Art rotational detection models were used to evaluate the dataset, providing a baseline for ship detection and fine-grained ship detection. The SDFSD is a high spatial resolution ship detection dataset that could drive advancements in research on ship detection and fine-grained detection in SAR imagery. Full article

The recovery of potato waste for circular-economy purposes is a growing area of industrial research. This waste, rich in nutrients and potential for reuse, can be a valuable source of starch for packaging applications. Rheology plays a crucial role in characterizing film-forming solutions before casting. In this work, packaging film was prepared from potato waste using rheological information to formulate the film-forming solution. To this aim, rheological measurements were carried out on starch/glycerol-only samples, and the data obtained were used to optimize the formulation from the waste. The polyphenol content of the peels was analyzed, and the resulting films were comprehensively characterized. This included assessments of color, extensibility, Fourier-transform infrared (FT-IR) spectroscopy, surface microscopy, and contact angle. Polyphenol-loaded films, suitable for packaging applications, were developed from potato waste. These films exhibited distinct properties compared to those made with pure starch, including an improved wettability of about 75° for the best sample and a high elastic modulus of about 36 MPa, which reduces the deformability but enhances the resistance against the stress. Through rheological studies, we were able to design films from potato peel waste. These films demonstrated promising mechanical performance. Full article

The concealment of assets is a critical challenge in financial fraud and asset recovery investigations, posing significant obstacles for creditors and regulatory authorities. National governments commonly possess the necessary data for detecting and combating this type of fraud, typically related to personal data and asset ownership. However, this information is often dispersed across different departments within the same government and sometimes in databases shared by other countries. This leads to difficulty semantically integrating this large amount of data in various formats and correlating entities through identifying hidden relationships, which are essential in this type of analysis. In this regard, this work proposes an ontology to support the data integration process in the domain of asset concealment and recovery and fill the gap in the existence of a public ontology for this domain. The applicability of this ontology in the context of integration between data from different departments and countries was validated. The use of the ontology in a pilot project in the context of a tool for investigating this type of fraud was conducted with a Brazilian government agency, and the users validated its applicability. Finally, a new method for constructing ontologies is proposed. The proposed process was evaluated during the asset concealment ontology building and proved to be more suitable than the similar processes analyzed concerning the partial reuse of existing ontologies and the construction of ontologies for data with a transnational scope. Full article

Pd EnCat™ 30 is a palladium catalyst broadly used in several hydrogenation and cross-coupling reactions. It is known for its numerous beneficial features, which include high-yielding performance, easy recovery, and reusability. However, the available data regarding its recyclability in Suzuki coupling reactions are limited to a few reaction cycles and, therefore, fail to explore its full potential. Our work focuses on investigating the extent of Pd EnCat™ 30 reusability in Suzuki cross-coupling reactions by measuring its performance according to isolated yields of product. Our findings demonstrate that Pd EnCat™ 30 can be reused over a minimum of 30 reaction cycles, which is advantageous in terms of cost reduction and more sustainable chemical production. Full article

The large amount of available data, generated every second via sensors, social networks, organizations, and so on, has generated new lines of research that involve novel methods, techniques, resources, and/or technologies. The development of big data systems (BDSs) can be approached from different perspectives, all of them useful, depending on the objectives pursued. In particular, in this work, we address BDSs in the area of software engineering, contributing to the generation of novel methodologies and techniques for software reuse. In this article, we propose a methodology to develop reusable BDSs by mirroring activities from software product line engineering. This means that the process of building BDSs is approached by analyzing the variety of domain features and modeling them as a family of related assets. The contextual perspective of the proposal, along with its supporting tool, is introduced through a case study in the agrometeorology domain. The characterization of variables for frost analysis exemplifies the importance of identifying variety, as well as the possibility of reusing previous analyses adjusted to the profile of each case. In addition to showing interesting findings from the case, we also exemplify our concept of context variety, which is a core element in modeling reusable BDSs. Full article