Search Results (1,485)

Quality control is mandatory in the food industry and chemical sensors play a crucial role in this field. Coffee is one of the most consumed and commercialized food products globally, and its quality is of the utmost importance. Many scientific papers have analyzed coffee quality using different approaches, such as analytical and sensor analyses, which, despite their good performance, are limited to structured lab implementation. This study aims to evaluate the capability of a smart electrochemical sensor to discriminate among different beverages prepared using coffee beans with different moisture content (0%, 2%, >4%) and ground in three sizes (fine, medium and coarse). These parameters reflect real scenarios where coffee is produced and its quality influenced. The possibility of optimizing coffee quality in real time by tuning these parameters could open the way to intelligent coffee machines. A specific experimental setup has been designed, and the data has been analyzed using machine learning techniques. The results obtained from Principal Component Analysis (PCA) and Partial Least Square Discriminant Analysis (PLS-DA) show the sensor’s capability to distinguish between samples of different quality, with a percentage of correct classification of 86.6%. This performance underscores the potential benefits of this sensor for coffee quality assessment, enabling time and resource savings, while facilitating the development of analytical methods based on smart electrochemical sensors. Full article

The rapid evolution of Li-ion battery technologies and manufacturing processes demands a continual update of environmental impact data. The general objective of this paper is to publish up-to-date primary data on battery manufacturing, which is of great importance to the scientific community and decision-makers. The environmental impacts have been calculated and estimated based on publicly available data disclosed under Hungarian government regulations and official decrees. The gate-to-gate energy use, greenhouse gas (GHG) emissions, water consumption, and N-methyl-2-pyrrolidone (NMP) consumption are estimated for three battery factories in Hungary, with a total annual capacity of approximately 100 GWh. The factories use around 30–35 kWh energy per kWh of battery capacity and the associated GHG emissions are around 10 kgCO₂eq per kWh of cell production. The water consumption varies considerably among factories, with one plant using 28 L per kWh and the other two using 56 and 67 L per kWh. The specific consumption of NMP was calculated for two factories, resulting in close values of 0.51–0.56 kg per kWh of cell production. As a new approach, we distinguish between global and local GHG emissions related to battery production. The main component of the latter is carbon dioxide from the combustion of natural gas, but the local transport related to the battery factories is also a source of emissions. Our estimations include not only the consumptions required directly for the manufacturing technology, but also those for social purposes (e.g., heating offices), giving a more complete picture of the factory’s environmental impact. We believe that up-to-date primary data are crucial for ensuring transparency and holds significant value for both the scientific community and decision-makers. Full article

In this paper, a hydrodynamic model of Jiaozhou Bay was developed using the Regional Ocean Modeling System and validated against observed tidal levels and current data. The model accurately characterizes the tidal and current features of the region. Based on this model, the spatial and temporal distributions of flow fields and tidal energy resources were analyzed. A 100-turbine tidal power plant was simulated utilizing a momentum-based approach that accounts for resource distribution, bathymetry, topography, and turbine parameters. The resulting hydrodynamic changes, including velocity variations peaking at 0.5 m/s within the turbine deployment zone and tidal level shifts confined to the bay (maximum change in ~10 cm), emphasize the importance of localized environmental assessments. However, the findings also highlight broader considerations for the sustainable development of tidal energy in semi-enclosed bays worldwide, where strategic siting and design can mitigate larger ecological disturbances. These findings may provide a scientific foundation for balancing clean energy extraction with minimal environmental impact, thus contributing to global efforts to develop more resilient and sustainable coastal energy systems. Full article

Interdisciplinary research (IDR) is essential for addressing complex global challenges that surpass the capabilities of any single discipline. However, measuring interdisciplinarity remains challenging due to conceptual ambiguities and inconsistent methodologies. To overcome these challenges, we propose a deep learning approach that quantifies interdisciplinarity in scientific articles through semantic analysis of titles and abstracts. Utilizing the Semantic Scholar Open Research Corpus (S2ORC), we leveraged metadata field tags to categorize papers as either interdisciplinary or monodisciplinary, establishing the foundation for supervised learning in our model. Specifically, we preprocessed the textual data and employed a Text Convolutional Neural Network (Text CNN) architecture to identify semantic patterns indicative of interdisciplinarity. Our model achieved an F1 score of 0.82, surpassing baseline machine learning models. By directly analyzing semantic content and incorporating metadata for training, our method addresses the limitations of previous approaches that rely solely on bibliometric features such as citations and co-authorship. Furthermore, our large-scale analysis of 136 million abstracts revealed that approximately 25% of the literature within the specified disciplines is interdisciplinary. Additionally, we outline how our quantification method can be integrated into a TRIZ-based (Theory of Inventive Problem Solving) methodological framework for cross-disciplinary innovation, providing a foundation for systematic knowledge transfer and inventive problem solving across domains. Overall, this approach not only offers a scalable measurement of interdisciplinarity but also contributes to a framework for facilitating innovation through structured cross-domain knowledge integration. Full article

As a crucial component of urban economic activities, the layout and optimization of urban commercial spaces directly influence the economic prosperity and quality of life of residents. Therefore, comprehensively and accurately characterizing the distribution characteristics and evolutionary patterns of urban commercial spaces is essential for improving the efficiency of urban spatial allocation and achieving scientific spatial planning and governance. This paper utilizes multisource spatiotemporal data, employing geographic spatial analysis methods and graph neural network models to explore the spatial structure of commercial service facilities in Beijing and their relationships with population density and land use, thereby achieving a detailed classification of the commercial service patterns at the natural neighborhood scale. The research findings indicate a significant association between commercial service facilities and population, as well as land use, with a strong spatial heterogeneity. There exists a dissonance between the layout of commercial service facilities and population distribution, and the differences in commercial service development across various regions pose challenges to balanced urban development. Based on this, this paper provides specific recommendations for optimizing the urban commercial spatial structure, offering reference points for future urban planning and development. Full article

Sepsis is one of the leading causes of mortality in hospital settings, and early diagnosis is a crucial challenge to improve clinical outcomes. Artificial intelligence (AI) is emerging as a valuable resource to address this challenge, with numerous investigations exploring its application to predict and diagnose sepsis early, as well as personalizing its treatment. Machine learning (ML) models are able to use clinical data collected from hospital Electronic Health Records or continuous monitoring to predict patients at risk of sepsis hours before the onset of symptoms. Background/Objectives: Over the past few decades, ML and other AI tools have been explored extensively in sepsis, with models developed for the early detection, diagnosis, prognosis, and even real-time management of treatment strategies. Methods: This review was conducted according to the SPIDER (Sample, Phenomenon of Interest, Design, Evaluation, Research Type) framework to define the study methodology. A critical overview of each paper was conducted by three different reviewers, selecting those that provided original and comprehensive data relevant to the specific topic of the review and contributed significantly to the conceptual or practical framework discussed, without dwelling on technical aspects of the models used. Results: A total of 194 articles were found; 28 were selected. Articles were categorized and analyzed based on their focus—early prediction, diagnosis, mortality or improvement in the treatment of sepsis. The scientific literature presents mixed outcomes; while some studies demonstrate improvements in mortality rates and clinical management, others highlight challenges, such as a high incidence of false positives and the lack of external validation. This review is designed for clinicians and healthcare professionals, and aims to provide an overview of the application of AI in sepsis management, reviewing the main studies and methodologies used to assess its effectiveness, limitations, and future potential. Full article

Currently, subway and underground engineering projects are vital for alleviating urban congestion and enhancing citizens’ quality of life. Among these, excavation engineering for foundation pits involves the most accidents in geotechnical engineering. Although there are various construction methods, most face issues such as a large footprint, high investments, resource waste, and low mechanization. Addressing these, this paper focuses on a subway foundation pit project in Guangzhou using mechanical shaft sinking technology. Using intelligent cloud monitoring, we analyzed the stress–strain patterns of the cutting edge and segments. The results showed significant improvements in construction efficiency, cost reduction, safety, and resource conservation. Based on this work, this paper makes the following conclusions: (1) The mechanical shaft sinking method offers advantages such as small footprint, high mechanization, minimal environmental impact, and cost-effectiveness. The achievements include a 22.22% reduction in construction time, a 20.27% decrease in investment, and lower worker risk. (2) Monitoring confirmed that all cutting edge and segment values remained safe, demonstrating the method’s feasibility and rationality. (3) Analyzing shaft monitoring data and field uncertainties, this study proposes recommendations for future work, including precise segment lowering control and introducing high-precision total stations and GPS technology to mitigate tunneling and assembly inaccuracies. The research validates the mechanical shaft sinking scheme’s scientific and logical nature, ensuring safety and contributing to technological advancements. It offers practical insights, implementable suggestions, and significant economic benefits, reducing project investment by RMB 41,235,600. This sets a benchmark for subway excavation projects in South China and beyond, providing reliable reference values. Furthermore, the findings provide valuable insights and guidance for industry peers, enhancing overall efficiency and sustainable development in subway construction. Full article

Artificial neural networks are parametric machine learning models that have been applied successfully to an extended series of classification and regression problems found in the recent literature. For the effective identification of the parameters of the artificial neural networks, a series of optimization techniques have been proposed in the relevant literature, which, although they present good results in many cases, either the optimization method used is not efficient and the training error of the network is trapped in sub-optimal values, or the neural network exhibits the phenomenon of overfitting which means that it has poor results when applied to data that was not present during the training. This paper proposes an innovative technique for constructing the weights of artificial neural networks based on appropriate BNF grammars, used in the evolutionary process of Grammatical Evolution. The new procedure locates an interval of values for the parameters of the artificial neural network, and the optimization method effectively locates the network parameters within this interval. The new technique was applied to a wide range of data classification and adaptation problems covering a number of scientific areas and the experimental results were more than promising. Full article

Climate change effects have a significant global negative impact, prompting global leaders to promote clean energy use to reduce carbon emissions. Electric vehicles powered by lithium-ion batteries are crucial to achieving this goal. Lithium is an essential material for the efficient operation of electric batteries, so in recent years, its demand has increased, and it is considered a strategic mineral. This paper aims to describe and analyze the scientific development of lithium-based clean energy technologies and reveal future areas of scientific production priority. This research is conducted through a bibliometric analysis in the Scopus database from 1929 to April 2024. Using the software Bibliometrix 4.1 and Biblioshiny the exported literature data are analyzed. The number of papers on lithium topics has significantly increased since 2018, with China leading in publications and collaborating with many countries. The trending topics are geological prospection, lithium ore characterization, chemical engineering, and lithium energy technologies. Lithium research is a growing field, but its development is uneven. Only a few countries lead in scientific production and lithium energy technologies, and sustainability lithium topics related to Life-Cycle Analysis (LCA) require further attention. Lithium research development is influenced by global economic trends. Full article

This study examines cyber sextortion research using a comprehensive bibliometric analysis. In the field of cybersecurity, cyber sextortion is a form of cybercrime that leverages privacy violations to exploit a victim. This study reviewed research developments on cyber sextortion progressively over time by looking at scientific productions, thematic developments, scholars’ contributions, and the future thematic trajectory. A bibliometric approach to analyzing the data was applied, which covered 548 peer-reviewed articles, conference papers, and book chapters retrieved from the Scopus database. Results showed a growth trajectory on various thematic concerns in the cyber sextortion field, which has continued to gain traction since the year 2023. Notably, online child sexual abuse is a growing theme in cyber sextortion research. In addition, among other themes, adolescents, mental health, and dating violence are receiving interest among scholars in this field. Additionally, institutions and prolific scholars from countries such as the United States of America, Australia, and the United Kingdom have established research collaborations to improve understanding in this field. The results also showed that research is observed to be emerging from South Africa and Ghana in the African region. Overall, there is potential for more scientific publications and researchers from Africa to contribute to this growing field. The value this study holds is moving beyond deficit-based approaches to how adolescent youth can be resilient and protected from cyber sextortion. A call for a multidisciplinary approach that moves beyond deficit-based approaches toward resilient and autonomy-based approaches is encouraged so that adolescent youth are protected from exploitation. This approach should focus on investigating proactive and resilience-based interventions informed by individuals’ traits and contexts to aid in building digital resilience in adolescents. Full article

In the current context of economic globalisation and intensifying market competition, refined management of construction projects is crucial, particularly regarding economic performance management, which directly impacts the stable development of construction companies. Most existing studies evaluate performance using a single research method. This paper combines qualitative and quantitative research methods and integrates a comprehensive, scientific, rigorous economic performance analysis system with multi-criteria decision-making. First, utilising numerous journal articles, interview data, book materials, and 407 questionnaire responses, a preliminary economic performance analysis system for engineering project management was developed through three-level coding using grounded theory. Subsequently, a factor analysis was employed to select indicators for the analysis system, resulting in an evaluation framework for the economic performance of engineering project management with eight core assessment indicators. Finally, based on the evaluation system, the relationships between the indicators and the calculation formulas for the three-level indicators were summarised, culminating in a closed-loop, systematic, and multi-dimensional analysis framework. This study enhances the evaluation framework for the economic performance dimension in engineering project management within construction enterprises. It provides robust methodological and data support for assessing economic performance in engineering project management. Full article

A ranging communication system is a key technology for achieving precise ranging and scientific data exchange in space gravitational wave detection, with the aim of realizing the symmetry of interferometer arms. This system is integrated into the phase measurement payload, the ’phasemeter’. Achieving high-ranging accuracy and low-bit error rate communication while mitigating the impact of phase noise has become a focus of current research. This paper starts with the coding methods for ranging communication and analyzes phase modulation noise based on Binary Phase Shift Keying (BPSK). The study found that the main lobe phase noise caused by BPSK modulation is approximately 158 μrad/Hz, which is two orders of magnitude higher than the phase-tracking criteria for gravitational wave detection. To address this, this paper proposes a Bit-Balanced Code (BBC) sequence design and optimization method aimed at eliminating main lobe noise. The experimental results show that the optimized BBC sequence improves the metrics of even autocorrelation, odd autocorrelation, maximum spectral amplitude, and even cross-correlation by 7.17, 2.83, 1.22, and 7.16, respectively, compared to the original sequence. Furthermore, experiments have demonstrated that the BBC sequence is insensitive to random data and can achieve dynamic bit balancing to eliminate the DC component. The proposed BBC sequence design method can serve as a reference for technologies related to space gravitational wave detection. Full article

This study applied high-resolution satellite data to address the siting and evaluation challenges for potential cage aquaculture areas of large yellow croaker in Zhejiang Province. A typical template of water environmental factors for large yellow croaker cage aquaculture was developed, incorporating various environmental parameters and considerations that were not fully addressed in previous studies. This paper established the Site Selection Method for Large Yellow Croaker potential aquaculture (SSM-LYC) based on the template. Site selection and grading evaluation of potential cage aquaculture areas were performed using SSM-LYC. The findings include the following: (1) Potential aquaculture sites for large yellow croaker include 11 areas with water depths of 15–60 m along the coast of Zhejiang Province from 27° to 31° north latitude, of which 7 are in water depths of less than 40 m, and 4 are in water depths of 40–60 m. (2) Assessment and scoring for potential aquaculture sites were performed, pinpointing 4 central locations of first-level aquaculture areas offering scientific evidence for the feasibility of deep-sea aquaculture of large yellow croaker along the Zhejiang coast. (3) The conclusions drawn from this research provide significant guidance for future aquaculture strategies and regional planning. Moreover, SSM-LYC can be applied to other coastal waters in the world. Full article

This study highlights the Digital Transformation issues in recent scientific topics, as well as the trends in the European Union’s Digital Economy dynamics. The aim is to identify key and promising research topics in the digital field and define priorities for adopting digital innovations in the EU in terms of bibliographical and statistical aspects. The study includes a bibliographic analysis using publication metrics statistics, word cloud diagrams, and network clustering. There is also a quantitative analysis of the leading Digital Economy trends in the EU using correlation and cluster analyses and visualizations of the selected economic and Digital Transformation metrics. The results identify critical keywords in digitalization publications related to other key research and multidisciplinary areas. A grouping is proposed of research paper topics and research issues related to Digital Transformation in the EU and worldwide based on the identified trends in recent research proposals. The study examines the correlation of some digital indices and trends in EU countries’ GDP dynamics, R&D investment, and digital inclusion. From the clustering based on the data of a single digital market that promotes e-commerce for individuals and businesses, groups of EU countries have been identified as having the potential to increase digital inclusion and convergence growth rate. The results provide a basis for future research on Digital Transformation and determine the need for further intensification of EU digitalization. Full article

The introduction of the holobiont concept has triggered scientific interest in depicting the structural and functional diversity of animal microbial symbionts, which has resulted in an unprecedented wealth of such cross-domain biological associations. The steadfast technological progress in nucleic acid-based approaches would cause one to expect that scientific works on the microbial symbionts of animals would be balanced at least for the farmed animals of human interest. For some animals, such as ruminants and a few farmed fish species of financial significance, the scientific wealth of the microbial worlds they host is immense and ever growing. The opposite happens for other animals, such as snails, in both the wild and farmed species. Snails are evolutionary old animals, with complex ecophysiological roles, living in rich microbial habitats such as soil and sediments or water. In order to create a stepping stone for future snail microbiome studies, in this literature review, we combined all the available knowledge to date, as documented in scientific papers, on any microbes associated with healthy and diseased terrestrial and aquatic snail species from natural and farmed populations. We conducted a Boolean search in Scopus, Web of Science, and ScienceDirect until June 2024, identifying 137 papers, of which 60 were used for original data on snail bacterial communities in the gastrointestinal tract, hepatopancreas, and feces. We provide a synthesis on how representative this knowledge is towards depicting the possible snail core microbiota, as well as the steps that need to be taken in the immediate future to increase the in-depth and targeted knowledge of the bacterial component in snail holobionts. Full article