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most exciting work published in the various research areas of the journal.
Current orbit uncertainty propagation (OUP) and orbit determination (OD) methods suffer from drawbacks related to high computational burden, limiting their applications in deep space missions. To this end, this paper proposes a multivariate attention-based method for efficient OUP and OD of Earth–Jupiter transfer.
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Current orbit uncertainty propagation (OUP) and orbit determination (OD) methods suffer from drawbacks related to high computational burden, limiting their applications in deep space missions. To this end, this paper proposes a multivariate attention-based method for efficient OUP and OD of Earth–Jupiter transfer. First, a neural network-based OD framework is utilized, in which the orbit propagation process in a traditional unscented transform (UT) and unscented Kalman filter (UKF) is replaced by the neural network. Then, the sample structure of training the neural network for the Earth–Jupiter transfer is discussed and designed. In addition, a method for efficiently generating a large number of samples for the Earth–Jupiter transfer is presented. Next, a multivariate attention-based neural network (MANN) is designed for orbit propagation, which shows better capacity in terms of accuracy and generalization than the deep neural network. Finally, the proposed method is successfully applied to solve the OD problem in an Earth–Jupiter transfer. Simulations show that the proposed method can obtain a similar estimation to the UKF while saving more than 90% of the computational cost.
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The Internet of Things (IoT) presents great potential in various fields such as home automation, healthcare, and industry, among others, but its infrastructure, the use of open source code, and lack of software updates make it vulnerable to cyberattacks that can compromise access
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The Internet of Things (IoT) presents great potential in various fields such as home automation, healthcare, and industry, among others, but its infrastructure, the use of open source code, and lack of software updates make it vulnerable to cyberattacks that can compromise access to data and services, thus making it an attractive target for hackers. The complexity of cyberattacks has increased, posing a greater threat to public and private organizations. This study evaluated the performance of deep learning models for classifying cybersecurity attacks in IoT networks, using the CICIoT2023 dataset. Three architectures based on DNN, LSTM, and CNN were compared, highlighting their differences in layers and activation functions. The results show that the CNN architecture outperformed the others in accuracy and computational efficiency, with an accuracy rate of 99.10% for multiclass classification and 99.40% for binary classification. The importance of data standardization and proper hyperparameter selection is emphasized. These results demonstrate that the CNN-based model emerges as a promising option for detecting cyber threats in IoT environments, supporting the relevance of deep learning in IoT network security.
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This study presents a novel framework for improving indoor augmented reality (AR) navigation with modern smartphone technology, which is achieved by addressing two major challenges: managing large absolute coordinate spaces and reducing error accumulation in camera-based spatial tracking. Our contribution is significant in
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This study presents a novel framework for improving indoor augmented reality (AR) navigation with modern smartphone technology, which is achieved by addressing two major challenges: managing large absolute coordinate spaces and reducing error accumulation in camera-based spatial tracking. Our contribution is significant in two ways. First, we integrate geofencing with indoor navigation by considering spatial tracking errors, timing for audio guidance, and dynamic 3D arrow visualization for effective local-to-global spatial coordinate transformation. This method achieves precise local positioning and seamlessly integrates with larger spatial contexts, overcoming the limitations of current AR systems. Second, we introduce a periodic image-based calibration approach to minimize the inherent error accumulation in camera-based tracking, enhancing accuracy over longer distances. Unlike prior studies focusing on individual technologies, our work explores the software architecture of indoor AR navigation by providing a comprehensive framework for its design and practical use. The practicality of our approach is validated through the implementation of a smartphone application at the Mineral Industry Museum of Akita University, highlighting the limitations of component technologies and demonstrating our framework’s effectiveness.
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This paper focuses on the study of current knowledge regarding the use of hydrogen as a reducing agent in the metallurgical processes of iron and steel production. This focus is driven by the need to introduce environmentally suitable energy sources and reducing agents
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This paper focuses on the study of current knowledge regarding the use of hydrogen as a reducing agent in the metallurgical processes of iron and steel production. This focus is driven by the need to introduce environmentally suitable energy sources and reducing agents in this sector. This theoretical study primarily examines laboratory research on the reduction of Fe-based, metal-bearing materials. The article presents a critical analysis of the reduction in iron oxides using hydrogen, highlighting the advantages and disadvantages of this method. Most experimental facilities worldwide employ their unique original methodologies, with techniques based on Thermogravimetric analysis (TGA) devices, fluidized beds, and reduction retorts being the most common. The analysis indicates that the mineralogical composition of the Fe ores used plays a crucial role in hydrogen reduction. Temperatures during hydrogen reduction typically range from 500 to 900 °C. The reaction rate and degree of reduction increase with higher temperatures, with the transformation of wüstite to iron being the slowest step. Furthermore, the analysis demonstrates that reduction of iron ore with hydrogen occurs more intensively and quickly than with carbon monoxide (CO) or a hydrogen/carbon monoxide (H2/CO) mixture in the temperature range of 500 °C to 900 °C. The study establishes that hydrogen is a superior reducing agent for iron oxides, offering rapid reduction kinetics and a higher degree of reduction compared to traditional carbon-based methods across a broad temperature range. These findings underscore hydrogen’s potential to significantly reduce greenhouse gas emissions in the steel production industry, supporting a shift towards more sustainable manufacturing practices. However, the implementation of hydrogen as a primary reducing agent in industrial settings is constrained by current technological limitations and the need for substantial infrastructural developments to support large-scale hydrogen production and utilization.
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This study documents an investigation of the flow stress properties and microstructural features of Ti6Al4V (ELI) alloy produced using laser powder bed fusion (LPBF). Selected heat treatment strategies were applied to the material to obtain different microstructures. The influence of quasi-static strain rates
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This study documents an investigation of the flow stress properties and microstructural features of Ti6Al4V (ELI) alloy produced using laser powder bed fusion (LPBF). Selected heat treatment strategies were applied to the material to obtain different microstructures. The influence of quasi-static strain rates and temperature on the obtained microstructures of this material and their strain hardening properties are documented in this study. All microstructures of the alloy formed in this study were found to be sensitive to quasi-static strain rates and temperatures, where their flow stresses increased with increasing strain rate and decreased for tests conducted at elevated temperatures. The strain hardening rates of the fine microstructures were found to be high compared to those of coarse microstructures. The strain hardening rates for the various forms of LPBF Ti6Al4V (ELI) examined here were found to diminish with increasing test temperature. Though the deformed surfaces of the built samples were largely dominated by adiabatic shear bands (ASBs), the absence of ASBs was noted for all samples tested at a temperature of 500 °C and an imposed strain of 30%.
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The Program for International Student Assessment highlights the persistent lack of commitment and motivation among students worldwide in their school activities, which are resulting in decreased proficiency levels in reading, mathematics, and science. The magnitude of this phenomenon, with its clear social implications,
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The Program for International Student Assessment highlights the persistent lack of commitment and motivation among students worldwide in their school activities, which are resulting in decreased proficiency levels in reading, mathematics, and science. The magnitude of this phenomenon, with its clear social implications, suggests that we are facing a concerning quest for immediate answers and results. This research focuses on the impact of the relationships between self-regulated learning processes and the planning of time management that is dedicated to school activities on student performance, specifically in the subjects of the Mother Tongue and Mathematics. The instruments used for analysis included the Inventory of Self-Regulated Learning Processes, the Inventory of Time Management Planning, a personal data sheet, and a school data sheet. The sample in this study consisted of 688 students from primary schools in northern Portugal. The results reveal that self-regulated learning has a positive influence on how students plan time management, both in the short and long term. Additionally, a positive and statistically significant relationship is observed between short-term and long-term time management planning and students’ academic performance. This study provides an in-depth perspective on the dynamics between these elements, shedding light on the crucial nuances that shape students’ academic journeys.
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Ninth grade on-track is predictive of high school graduation, more than race, socio-economic status, and prior achievement combined. Although initiatives characterized by an intense focus on the ninth-grade year are being increasingly implemented, research has not fully documented and tested mechanisms linked to
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Ninth grade on-track is predictive of high school graduation, more than race, socio-economic status, and prior achievement combined. Although initiatives characterized by an intense focus on the ninth-grade year are being increasingly implemented, research has not fully documented and tested mechanisms linked to improved outcomes. Using survey and transcript-level data and causal mediation analysis, this study tests the effects of students attending high teacher efficacy (self and collective—TSE and CTE) schools on ninth grade on-track in an urban school district in a northeast state in the United States. It further examines the extent to which ambitious instructional practices, defined as culturally relevant and transformative pedagogy, mediate the effects of TSE on ninth grade on-track and how levels of supportive school culture moderate these relationships. The findings indicate that urban ninth-graders attending schools with high TSE and CTE are more likely to be on track at the end of ninth grade. Additionally, the direct effect of students attending a school with high TSE was mediated by the level of ambitious instruction. We discuss implications for teacher education (TE) and professional development.
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In the development of bone graft substitutes, a fundamental step is the use of scaffolds with adequate composition and architecture capable of providing support in regenerative processes both on the tissue scale, where adequate resistance to mechanical stress is required, as well as
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In the development of bone graft substitutes, a fundamental step is the use of scaffolds with adequate composition and architecture capable of providing support in regenerative processes both on the tissue scale, where adequate resistance to mechanical stress is required, as well as at the cellular level where compliant chemical–physical and mechanical properties can promote cellular activity. In this study, based on a previous optimization study of this group, the potential of a three-dimensional construct based on polycaprolactone (PCL) and a novel biocompatible Mg- and Sr-containing glass named BGMS10 was explored. Fourier-transform infrared spectroscopy and scanning electron microscopy showed the inclusion of BGMS10 in the scaffold structure. Mesenchymal stem cells cultured on both PCL and PCL-BGMS10 showed similar tendencies in terms of osteogenic differentiation; however, no significant differences were found between the two scaffold types. This circumstance can be explained via X-ray microtomography and atomic force microscopy analyses, which correlated the spatial distribution of the BGMS10 within the bulk with the elastic properties and topography at the cell scale. In conclusion, our study highlights the importance of multidisciplinary approaches to understand the relationship between design parameters, material properties, and cellular response in polymer composites, which is crucial for the development and design of scaffolds for bone regeneration.
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In the Standard Model, ad hoc hypotheses assume the existence of three generations of point-like leptons and quarks, which possess a point-like structure and follow the Dirac equation involving four anti-commutative matrices. In this work, we consider the sedenion hypercomplex algebra as an
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In the Standard Model, ad hoc hypotheses assume the existence of three generations of point-like leptons and quarks, which possess a point-like structure and follow the Dirac equation involving four anti-commutative matrices. In this work, we consider the sedenion hypercomplex algebra as an extension of the Standard Model and show its close link to SU(5), which is the underlying symmetry group for the grand unification theory (GUT). We first consider the direct-product quaternion model and the eight-element octonion algebra model. We show that neither the associative quaternion model nor the non-associative octonion model could generate three fermion generations. Instead, we show that the sedenion model, which contains three octonion sub-algebras, leads naturally to precisely three fermion generations. Moreover, we demonstrate the use of basis sedenion operators to construct twenty-four 5 × 5 generalized lambda matrices representing SU(5) generators, in analogy to the use of octonion basis operators to generate Gell-Mann’s eight 3 × 3 lambda-matrix generators for SU(3). Thus, we provide a link between the sedenion algebra and Georgi and Glashow’s SU(5) GUT model that unifies the electroweak and strong interactions for the Standard Model’s elementary particles, which obey SU(3)SU(2)U(1) symmetry.
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Laser bending is a kind of cumulative forming technology and bending efficiency is one of its most important indexes. This study investigates the bending behavior and the microstructure of DP980 steel plates under different laser scanning strategies, using an IPG laser system. Two
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Laser bending is a kind of cumulative forming technology and bending efficiency is one of its most important indexes. This study investigates the bending behavior and the microstructure of DP980 steel plates under different laser scanning strategies, using an IPG laser system. Two sets of experiments varied the accumulated line energy density (AED) by altering the laser scanning velocity and number of scans. The results show that, for the single laser scanning process, the bending angle of the plate increases with AED, due to a larger temperature gradient through the thickness direction; however, this relationship is nonlinear. A higher AED led to a sharper initial increase in bending angle, which then plateaued. Under the same AED conditions, the bending angle of the plate undergoing multiple laser scans increases by at least 26% compared to the single one, due to the microstructure changes. It is revealed that the bending efficiency is affected by both the AED and the resultant microstructure evolution in the DP980 steel. Higher AED values and appropriate peak temperatures facilitate better bending behavior due to the formation of uniform martensite and grain refinement. Conversely, excessive peak temperatures can hinder bending due to grain growth.
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Wood is an abundant and essential renewable resource whose production is threatened in some parts of the world by drought. A better understanding of the molecular mechanisms underlying wood formation during drought is critical to maintaining wood production under increasingly adverse environmental conditions.
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Wood is an abundant and essential renewable resource whose production is threatened in some parts of the world by drought. A better understanding of the molecular mechanisms underlying wood formation during drought is critical to maintaining wood production under increasingly adverse environmental conditions. In this study, we investigated wood formation in black cottonwood (Populus trichocarpa) during drought stress. The morphological changes during drought stress in P. trichocarpa included the wilting and drooping of leaves, stem water loss, and a reduction in whole plant biomass. The water embolism rate indicated that the water transport in stems was blocked under drought conditions. An anatomical analysis of the xylem and cambium revealed that drought stress changed the structure of vessel cells, increased lignin accumulation, and decreased the cambium cell layers. We subsequently identified 12,438 and 9156 differentially expressed genes from stem xylem and cambium tissues under well-watered and drought conditions, respectively. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses revealed that these genes were mainly involved in hormone signal transduction and amino sugar and nucleotide sugar metabolism. To further explore the molecular mechanism of wood formation in response to drought, we analyzed the expression patterns of the genes involved in lignin, cellulose, and hemicellulose biosynthesis in xylem and the genes involved in cambial activity in the cambium. To better understand the regulatory networks governing xylem development and cambium activity in response to drought, we analyzed the MYB (138), AP2 (130), bHLH (89), and NAC (81) transcription factor families to shed light on the interactions between the TFs in these families and the genes they regulate. Identifying the key genes that regulate wood formation in P. trichocarpa during drought provides a genetic foundation for further research on the molecular regulatory networks and physiology underpinning wood formation during drought stress.
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Europe is undergoing rapid social change and is distinguished by its cultural superdiversity. Healthcare is facing an increasing need for professionals to adapt to this environment. Thus, the promotion of cultural competence in healthcare has become a priority. However, the training being developed
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Europe is undergoing rapid social change and is distinguished by its cultural superdiversity. Healthcare is facing an increasing need for professionals to adapt to this environment. Thus, the promotion of cultural competence in healthcare has become a priority. However, the training being developed and their suitability for the European context are not well known. The aim of this qualitative study has been to map the scientific literature in order to comprehend the current state of research on this topic. For this purpose, we conducted a systematic scoping review of the empirical publications focused on cultural competence interventions for healthcare professionals in European countries. The search was conducted in eight thematic (PsycINFO, MedLine, and PubPsych) and multidisciplinary databases (Academic Search Ultimate, E-Journals, Scopus, ProQuest, and Web of Science) to identify relevant publications up to 2023. Results were presented qualitatively. Out of the initial 6506 records screened, a total of 63 publications were included. Although the interventions were implemented in 23 different European countries, cultural competence interventions have not been widely adopted in Europe. Significant heterogeneity was observed in the conception and operacionalización of cultural competence models and in the implementation of the interventions. The interventions have mostly aimed at improving healthcare for minority population groups and have focused on the racial and ethnic dimensions of the individual. Future research is needed to contribute to the conceptual development of cultural competence to design programs tailored to European superdiversity. This scoping review has been registered in OSF and is available for consultation.
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Urban regeneration and spatial planning have adopted a new participatory approach in recent decades, highlighting the importance of integrating the community in urban decision-making processes, especially in disadvantaged and socially excluded areas. In this context, the sociogram emerges as an essential tool for
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Urban regeneration and spatial planning have adopted a new participatory approach in recent decades, highlighting the importance of integrating the community in urban decision-making processes, especially in disadvantaged and socially excluded areas. In this context, the sociogram emerges as an essential tool for collaborative governance, allowing the visualization and analysis of the dynamics between the different actors involved. This study employs a comparative case study approach in three disadvantaged neighbourhoods in Córdoba, Spain, to examine how the sociogram can facilitate more effective and democratic participation in urban planning. Using heat maps, scatter plots and average analysis, relationships between actors are identified and characterized, providing a solid basis for more inclusive and equitable planning decisions. This analysis not only reveals the practical utility of the sociogram in participatory research but also underscores its theoretical relevance in building resilient and cohesive communities. Findings confirm the sociogram’s effectiveness in mapping stakeholder dynamics and enhancing participatory governance, ultimately fostering more informed and inclusive urban planning processes.
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In this paper, we investigate static spherically symmetric teleparallel gravity containing a perfect isotropic fluid. We first write the field equations and proceed to find new teleparallel solutions for perfect isotropic and linear fluids. By
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In this paper, we investigate static spherically symmetric teleparallel gravity containing a perfect isotropic fluid. We first write the field equations and proceed to find new teleparallel solutions for perfect isotropic and linear fluids. By using a power-law ansatz for the coframe components, we find several classes of new non-trivial teleparallel solutions. We also find a new class of teleparallel solutions for a matter dust fluid. After, we solve the field equations for a non-linear perfect fluid. Once again, there are several new exact teleparallel solutions and also some approximated teleparallel solutions. All these classes of new solutions may be relevant for future cosmological and astrophysical applications.
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Aroma is an important factor in the measurement of the quality and market value of oolong tea. However, it is hard to develop an oolong tea with good aroma quality using unsuitable tea varieties. To explore the key factors of tea varieties in
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Aroma is an important factor in the measurement of the quality and market value of oolong tea. However, it is hard to develop an oolong tea with good aroma quality using unsuitable tea varieties. To explore the key factors of tea varieties in the formation of oolong tea aromas, the fresh leaves of the Chungui variety (CG, suitable for oolong tea, Camellia sinensis (L.) O. Kuntze) and the Fuyun No. 6 variety (F6, unsuitable for oolong tea, Camellia sinensis (L.) O. Kuntze) were harvested and treated by withering and mechanical stress in order. Then, aroma, transcriptome, and jasmonate (JA) contents, and weighted gene co-expression network analysis (WGCNA), of samples were investigated. The contents of characteristic oolong tea aromas, including indole, (E)-β-ocimene, (E)-nerolidol, α-farnesene, and jasmine lactone, were all accumulated in much higher quantities in the CG variety after withering and mechanical stress. Accordingly, the coding genes of aroma formation synthases TSB2, OCS, NES, AFS, and LOX1, and related genes in MVA, MEP, and ALA pathways, were all much more highly activated. These differential reactions are mainly caused by the higher accumulation of jasmonates, especially methyl jasmonate, a type of important plant signal chemical, in CG after mechanical stress. WGCNA analysis indicated 34 different transcription factors from different families are predicted to be involved in this jasmonate-responsive reaction.
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Intraoperative neurophysiological monitoring (IONM) is a crucial advancement in neurosurgery, enhancing procedural safety and precision. This technique involves continuous real-time assessment of neurophysiological signals, aiding surgeons in timely interventions to protect neural structures. In addition to inherent limitations, IONM necessitates a detailed anesthetic
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Intraoperative neurophysiological monitoring (IONM) is a crucial advancement in neurosurgery, enhancing procedural safety and precision. This technique involves continuous real-time assessment of neurophysiological signals, aiding surgeons in timely interventions to protect neural structures. In addition to inherent limitations, IONM necessitates a detailed anesthetic plan for accurate signal recording. Given the growing importance of IONM in neurosurgery, we conducted a narrative review including the most relevant studies about the modalities and their application in different fields of neurosurgery. In particular, this review provides insights for all physicians and healthcare professionals unfamiliar with IONM, elucidating commonly used techniques in neurosurgery. In particular, it discusses the roles of IONM in various neurosurgical settings such as tumoral brain resection, neurovascular surgery, epilepsy surgery, spinal surgery, and peripheral nerve surgery. Furthermore, it offers an overview of the anesthesiologic strategies and limitations of techniques essential for the effective implementation of IONM.
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In ascomycetous fungi, sexual mate recognition requires interaction of the Ste2 receptor protein produced by one partner with the α-factor peptide pheromone produced by the other partner. In some fungi, Ste2 is further needed for chemotropism towards plant roots to allow for subsequent
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In ascomycetous fungi, sexual mate recognition requires interaction of the Ste2 receptor protein produced by one partner with the α-factor peptide pheromone produced by the other partner. In some fungi, Ste2 is further needed for chemotropism towards plant roots to allow for subsequent infection and colonization. Here, we investigated whether this is also true for the pine pitch canker fungus, Fusarium circinatum, which is a devastating pathogen of pine globally. Ste2 knockout mutants were generated for two opposite mating-type isolates, after which all strains were subjected to chemotropism assays involving exudates from pine seedling roots and synthetic α-factor pheromone, as well as a range of other compounds for comparison. Our data show that Ste2 is not required for chemotropism towards any of these other compounds, but, in both wild-type strains, Ste2 deletion resulted in the loss of chemotropism towards pine root exudate. Also, irrespective of mating type, both wild-type strains displayed positive chemotropism towards α-factor pheromone, which was substantially reduced in the deletion mutants and not the complementation mutants. Taken together, these findings suggest that Ste2 likely has a key role during the infection of pine roots in production nurseries. Our study also provides a strong foundation for exploring the role of self-produced and mate-produced α-factor pheromone in the growth and overall biology of the pitch canker pathogen.
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We introduce a novel microscopic image dataset augmented with segmentation and detection labels specifically designed for microplastic analysis in sewage environments. Recognizing the increasing concern over microplastics—particles of synthetic polymers smaller than 5 mm—and their detrimental effects on marine ecosystems and human health,
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We introduce a novel microscopic image dataset augmented with segmentation and detection labels specifically designed for microplastic analysis in sewage environments. Recognizing the increasing concern over microplastics—particles of synthetic polymers smaller than 5 mm—and their detrimental effects on marine ecosystems and human health, our research focuses on enhancing detection and analytical methodologies through advanced computer vision and deep learning techniques. The dataset comprises high-resolution microscopic images of microplastics collected from sewage, meticulously labeled for both segmentation and detection tasks, aiming to facilitate accurate and efficient identification and quantification of microplastic pollution. In addition to dataset development, we present example deep learning models optimized for segmentation and detection of microplastics within complex sewage samples. The models demonstrate significant potential in automating the analysis of microplastic contamination, offering a scalable solution to environmental monitoring challenges. Furthermore, we ensure the accessibility and reproducibility 12 of our research by making the dataset and model codes publicly available, accompanied by detailed 13 documentation on GitHub and LabelBox.
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Recent decades in the Lancang River Basin have witnessed extensive construction of hydropower dams, profoundly impacting the local environment. Utilizing high-precision satellite data, we conducted a comprehensive analysis of vegetation cover and carbon emissions, integrating data-driven time series and spatial analysis models to
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Recent decades in the Lancang River Basin have witnessed extensive construction of hydropower dams, profoundly impacting the local environment. Utilizing high-precision satellite data, we conducted a comprehensive analysis of vegetation cover and carbon emissions, integrating data-driven time series and spatial analysis models to capture both temporal and spatial dynamics. Our findings reveal that hydropower dam construction in the Lancang River Basin has significantly promoted vegetation restoration and growth, concurrently facilitating a reduction in regional carbon emissions. Employing deep learning models for time-series prediction, we observed a substantial increase in the sum of the local normalized difference vegetation index (NDVI) post-construction, with an average rise of from 16.15% to a maximum of 20.12% during the pivotal hydropower dams’ operational phase. Between 2001 and 2020, the construction of hydropower dams in the basin corresponded to notable changes in ecological and carbon metrics. Specifically, vegetation cover expansion intensity (VCEI) reversed from a negative mean of −0.009 to a positive mean of 0.008. Additionally, the carbon emission intensity (CEI) around these dams drastically reduced, shifting from an average of 0.877 to 0.052. Importantly, the Global Moran’s I for VCEI significantly increased from 0.288 pre-2016 to 0.679 post-2015, reflecting a stronger spatial autocorrelation in vegetation patterns. Accordingly, these findings illustrate the complex interplay between hydropower dams and environmental outcomes, underscoring the critical role of pivotal hydropower dam construction in ecological improvement. The research results have improved and complemented those of previous studies on the environmental impact of hydraulic engineering, providing valuable insights for the construction management and policy formulation of hydropower dams in other similar river basins around the world.
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Precision point positioning (PPP) utilizing the Global Navigation Satellite System (GNSS) is a traditional and widely employed technology. Its performance is susceptible to observation discontinuities and unfavorable geometric configurations. Consequently, the integration of the Inertial Navigation System (INS) and GNSS makes full use
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Precision point positioning (PPP) utilizing the Global Navigation Satellite System (GNSS) is a traditional and widely employed technology. Its performance is susceptible to observation discontinuities and unfavorable geometric configurations. Consequently, the integration of the Inertial Navigation System (INS) and GNSS makes full use of their respective advantages and effectively mitigates the limitations of GNSS positioning. However, the GNSS/INS integration faces significant challenges in complex and harsh urban environments. In recent years, the geometry between the user and the satellite has been effectively improved with the advent of lower-orbits and faster-speed Low Earth Orbit (LEO) satellites. This enhancement provides more observation data, opening up new possibilities and opportunities for high-precision positioning. Meanwhile, in contrast to the traditional extended Kalman filter (EKF) approach, the performance of the LEO-enhanced GNSS/INS tightly coupled integration (TCI) can be significantly improved by employing the factor graph optimization (FGO) method with multiple iterations to achieve stable estimation. In this study, LEO data and the FGO method were employed to enhance the GNSS/INS TCI. To validate the effectiveness of the method, vehicle data and simulated LEO observations were subjected to thorough analysis. The results suggest that the integration of LEO data significantly enhances the positioning accuracy and convergence speed of the GNSS/INS TCI. In contrast to the FGO GNSS/INS TCI without LEO enhancement, the average enhancement effect of the LEO is 22.16%, 7.58%, and 10.13% in the north, east, and vertical directions, respectively. Furthermore, the average root mean square error (RMSE) of the LEO-enhanced FGO GNSS/INS TCI is 0.63 m, 1.21 m, and 0.85 m in the north, east, and vertical directions, respectively, representing an average improvement of 41.91%, 13.66%, and 2.52% over the traditional EKF method. Meanwhile, the simulation results demonstrate that LEO data and the FGO method effectively enhance the positioning and convergence performance of GNSS/INS TCI in GNSS-challenged environments (tall buildings, viaducts, underground tunnels, and wooded areas).
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Yeast, crucial in beer production, holds great potential owing - to its ability to transform into a valuable by-product resource, known as brewer’s spent yeast (BSY), with potentially beneficial physiological effects. This study aimed to compare the composition and soluble polysaccharide content of
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Yeast, crucial in beer production, holds great potential owing - to its ability to transform into a valuable by-product resource, known as brewer’s spent yeast (BSY), with potentially beneficial physiological effects. This study aimed to compare the composition and soluble polysaccharide content of Brewer’s spent yeast with those of cultured yeast strains, namely Saccharomyces cerevisiae (SC) and S. boulardii (SB), to facilitate the utilization of BSY as an alternative source of functional polysaccharides. BSY exhibited significantly higher carbohydrate content and lower crude protein content than SC and SB cells. The residues recovered through autolysis were 53.11%, 43.83%, and 44.99% for BSY, SC, and SB, respectively. Notably, the polysaccharide content of the BSY residue (641.90 μg/mg) was higher than that of SC (553.52 μg/mg) and SB (591.56 μg/mg). The yields of alkali-extracted water-soluble polysaccharides were 33.62%, 40.76%, and 42.97% for BSY, SC, and SB, respectively, with BSY comprising a comparable proportion of water-soluble saccharides made with SC and SB, including 49.31% mannan and 20.18% β-glucan. Furthermore, BSY demonstrated antioxidant activities, including superoxide dismutase (SOD), ABTS, and DPPH scavenging potential, suggesting its ability to mitigate oxidative stress. BSY also exhibited a significantly higher total phenolic compound content, indicating its potential to act as an effective functional food material.
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Reverberation in real environments is an important factor affecting the high resolution of target sound source localization (SSL) methods. Broadband low-frequency signals are common in real environments. This study focuses on the localization of this type of signal in reverberant environments. Because the
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Reverberation in real environments is an important factor affecting the high resolution of target sound source localization (SSL) methods. Broadband low-frequency signals are common in real environments. This study focuses on the localization of this type of signal in reverberant environments. Because the time reversal (TR) method can overcome multipath effects and realize adaptive focusing, it is particularly suitable for SSL in a reverberant environment. On the basis of the significant advantages of the sparse Bayesian learning algorithm in the estimation of wave direction, a novel SSL is proposed in reverberant environments. First, the sound propagation model in a reverberant environment is studied and the TR focusing signal is obtained. We then use the sparse Bayesian framework to locate the broadband low-frequency sound source. To validate the effectiveness of the proposed method for broadband low-frequency targeting in a reverberant environment, simulations and real data experiments were performed. The localization performance under different bandwidths, different numbers of microphones, signal-to-noise ratios, reverberation times, and off-grid conditions was studied in the simulation experiments. The practical experiment was conducted in a reverberation chamber. Simulation and experimental results indicate that the proposed method can achieve satisfactory spatial resolution in reverberant environments and is robust.
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The use of express packaging and its recycling produces large amounts of carbon dioxide. In order to achieve China’s “dual carbon” goal, this study adopted a literature research method to explore the idea of intelligent express packaging recycling cabinets. Based on the current
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The use of express packaging and its recycling produces large amounts of carbon dioxide. In order to achieve China’s “dual carbon” goal, this study adopted a literature research method to explore the idea of intelligent express packaging recycling cabinets. Based on the current design and use of intelligent express packaging recycling cabinets, new ideas for their improvement are proposed. This study also explored methods for improving people’s willingness to use intelligent express packaging recycling cabinets through experimental research and quantitative analysis. The results show that a reward mechanism has a significant effect on people’s willingness to use intelligent express packaging recycling cabinets. Of the two types of rewards, immediate rewards, compared to delayed rewards, can further increase people’s use of intelligent express packaging recycling cabinets. Gain and loss trade-offs play a mediating role between a reward mechanism and people’s willingness to use it, and consumers make that choice after weighing up the advantages and disadvantages. If consumers feel that it is worthwhile to protect the environment, in terms of the rewards they obtain compared to the time and effort they have to spend using intelligent express packaging recycling cabinets, and that the gain outweighs the loss, they will be inclined to use this system. Environmental responsibility plays a moderating role in mediating the trade-off between gains and losses. In the context of low environmental responsibility, cash rewards lead to greater gain and loss trade-offs compared to point rewards, while in the context of high environmental responsibility, there is no difference between cash rewards and point rewards. This study provides ideas for the design and promotion of the use of intelligent express packaging recycling cabinets, with the goal of effectively improving the recycling rate of express packaging waste.
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