Search Results (182)

The six platinum group elements (PGE) are among the rarest elements in the upper continental crust of the earth. Higher values of PGE have been detected in the upper mantle and in chondrite meteorites. The PGE are siderophile and chalcophile elements and are divided into the following: (1) the Ir subgroup (IPGE) = Os, Ir, and Ru and (2) the Pd subgroup (PPGE) = Rh, Pt, and Pd. The IPGE are more refractory and less chalcophile than the PPGE. High concentrations of PGE led, in rare cases, to the formation of mineral deposits. The PGE are carried in discrete phases, the platinum group minerals (PGM), and are included as trace elements into the structure of base metal sulphides (BM), such as pentlandite, chalcopyrite, pyrite, and pyrrhotite. Similarly to PGE, the PGM are also divided into two main groups, i.e., IPGM composed of Os, Ir, and Ru and PPGM containing Rh, Pt, and Pd. The PGM occur both in mafic and ultramafic rocks and are mainly hosted in stratiform reefs, sulphide-rich lenses, and placer deposits. Presently, there are only 169 valid PGM that represent about 2.7% of all 6176 minerals discovered so far. However, 496 PGM are listed among the valid species that have not yet been officially accepted, while a further 641 are considered as invalid or discredited species. The main reason for the incomplete characterization of PGM resides in their mode of occurrence, i.e., as grains in composite aggregates of a few microns in size, which makes it difficult to determine their crystallography. Among the PGM officially accepted by the IMA, only 13 (8%) were discovered before 1958, the year when the IMA was established. The highest number of PGM was discovered between 1970 and 1979, and 99 PGM have been accepted from 1980 until now. Of the 169 PGM accepted by the IMA, 44% are named in honour of a person, typically a scientist or geologist, and 31% are named after their discovery localities. The nomenclature of 25% of the PGM is based on their chemical composition and/or their physical properties. PGM have been discovered in 25 countries throughout the world, with 64 from Russia, 17 from Canada and South Africa (each), 15 from China, 12 from the USA, 8 from Brazil, 6 from Japan, 5 from Congo, 3 from Finland and Germany (each), 2 from the Dominican Republic, Greenland, Malaysia, and Papua New Guinea each, and only 1 from Argentine, Australia, Bulgaria, Colombia, Czech Republic, England, Ethiopia, Guyana, Mexico, Serbia, and Tanzania each. Most PGM phases contain Pd (82 phases, 48% of all accepted PGM), followed, in decreasing order of abundances, by those of Pt 35 phases (21%), Rh 23 phases (14%), Ir 18 phases (11%), Ru 7 phases (4%), and Os 4 phases (2%). The six PGE forming the PGM are bonded to other elements such as Fe, Ni, Cu, S, As, Te, Bi, Sb, Se, Sn, Hg, Ag, Zn, Si, Pb, Ge, In, Mo, and O. Thirty-two percent of the 169 valid PGM crystallize in the cubic system, 17% are orthorhombic, 16% hexagonal, 14% tetragonal, 11% trigonal, 3% monoclinic, and only 1% triclinic. Some PGM are members of a solid-solution series, which may be complete or contain a miscibility gap, providing information concerning the chemical and physical environment in which the mineral was formed. The refractory IPGM precipitate principally in primitive, high-temperature, mantle-hosted rocks such as podiform and layered chromitites. Being more chalcophile, PPGE are preferentially collected and concentrated in an immiscible sulphide liquid, and, under appropriate conditions, the PPGM can precipitate in a thermal range of about 900–300 °C in the presence of fluids and a progressive increase of oxygen fugacity (fO₂). Thus, a great number of Pt and Pd minerals have been described in Ni-Cu sulphide deposits. Two main genetic models have been proposed for the formation of PGM nuggets: (1) Detrital PGM represent magmatic grains that were mechanically liberated from their primary source by weathering and erosion with or without minor alteration processes, and (2) PGM reprecipitated in the supergene environment through a complex process that comprises solubility, the leaching of PGE from the primary PGM, and variation in Eh-pH and microbial activity. These two models do not exclude each other, and alluvial deposits may contain contributions from both processes. Full article

Multimodal question answering (QA) involves integrating information from both visual and textual inputs and requires models that can reason compositionally and accurately across modalities. Existing approaches, including fine-tuned vision–language and prompting, often struggle with generalization, interpretability, and reliance on task-specific data. In this work, we propose a Mixture-of-Reasoning Agents (MiRA) framework for zero-shot multimodal reasoning. MiRA decomposes the reasoning process across three specialized agents—Visual Analyzing, Text Comprehending, and Judge—which consolidate multimodal evidence. Each agent operates independently using pretrained language models, enabling structured, interpretable reasoning without supervised training or task-specific adaptation. Evaluated on the ScienceQA benchmark, MiRA achieves 96.0% accuracy, surpassing all zero-shot methods, outperforming few-shot GPT-4o models by more than 18% on image-based questions, and achieving similar performance to the best fine-tuned systems. The analysis further shows that the Judge agent consistently improves the reliability of individual agent outputs, and that strong linear correlations (r > 0.95) exist between image-specific accuracy and overall performance across models. We identify a previously unreported and robust pattern in which performance on image-specific tasks strongly predicts overall task success. We also conduct detailed error analyses for each agent, highlighting complementary strengths and failure modes. These results demonstrate that modular agent collaboration with zero-shot reasoning provides highly accurate multimodal QA, establishing a new paradigm for zero-shot multimodal AI and offering a principled framework for future research in generalizable AI. Full article

The use of full-battery electric vehicles is an essential strategy for reducing greenhouse gas emissions and mitigating climate change. This study examined the transition to full-battery electric vehicles by conducting a cross-sectional household survey in 2023 that collected information on vehicle preferences, evaluations, purchase intentions, environmental attitudes, and socioeconomic and demographic characteristics. The results show that among households using a vehicle as their primary mode of transportation, approximately 89% relied on fossil fuel vehicles, whereas only 6% used electric vehicles. The study further finds that acceptance of vehicles during inter-vehicle transitions is closely linked to energy type: households currently owning fossil fuel vehicles exhibited a high likelihood of repurchasing a fossil fuel vehicle, while electric vehicle owners were more inclined to choose another electric vehicle across cities and areas of different sizes. Households that own electric vehicles tend to report higher levels of well-being compared with those that own fossil fuel vehicles. In addition, sufficient charging infrastructure, stronger knowledge of environmental issues, participation in altruistic donation activities, and cooperative behavior positively influenced electric vehicle adoption. These findings suggest several policy implications, including the expansion of charging stations for business and public use, setting reasonable vehicle prices, improving charging speed, developing electric vehicles suitable for large families, and encouraging individuals to gain initial driving experience with electric vehicles to promote adoption. Full article

Conventional plant breeding relies on the occurrence of chromosomal crossover and spontaneous or non-targeted mutations in the genome induced by physical or chemical stressors. However, constraints exist concerning the number and variation of genotypes that can be achieved in this way, as the occurrence and combination of mutations are not equally distributed across the genome. The underlying mechanisms and causes of reproductive constraints can be considered the result of evolution to maintain the genomic stability of a species while at the same time allowing necessary adaptations. A continuous horizon scan was carried out to identify plants derived from new genomic techniques (NGTs), which show that CRISPR/Cas is able to circumvent at least some of these mechanisms and constraints. The reason for this is the specific mode of action: While physico-chemical mutagens such as radiation or chemicals merely cause a break in DNA, recombinant enzymatic mutagens (REMs), such as CRISPR/Cas, additionally interfere with cellular repair mechanisms. More recently developed REMs even expand the capabilities of NGTs to introduce new genetic variations within the target sequences. Thus, NGTs introduce genetic changes and combinations that are unknown in the current breeding pool and that are also unlikely to occur as a result of any previously used breeding methods. The resulting genotypes may need to be considered as ‘new to the environment’. The technical potential of NGTs should also be taken into account in regulatory provisions. Previously unknown genotypes and phenotypes may negatively impact plant health, ecosystems, biodiversity, and plant breeding. It must further be acknowledged that the different outcomes of NGTs and conventional breeding are not always evident at first sight. As a starting point, within a process-oriented approval process, molecular characterization can inform the following steps in risk assessment and guide requests for further data. Full article

This paper proposes an integrated ontological framework, Integrated Systems Ontology (ISOnto), for dependable systems engineering by semantically linking design models with real-world operational failure data. Building upon the recently proposed Function–Behaviour–Structure–Failure Modes (FBSFM) framework, ISOnto integrates early-stage design information with field-level evidence to support more informed, traceable, and dependable failure analysis. This extends the semantic scope of the FBSFM ontology to include operational/field feedback from warranty claims and technical inspections, enabling two-way traceability between design-phase assumptions (functions, behaviours, structures, and failure modes) and field-reported failures, causes, and effects. As a theoretical contribution, ISOnto introduces a formal semantic bridge between design and operational phases, strengthening the validation of known failure modes and the discovery of previously undocumented ones. Developed using established ontology engineering practices and formalised in OWL with Protégé, it incorporates domain-specific extensions to represent field data with structured mappings to design entities. A real-world automotive case study conducted with a global manufacturer demonstrates ISOnto’s ability to consolidate multisource lifecycle data into a coherent, machine-readable repository. The framework supports advanced reasoning, structured querying, and system-level traceability, thereby facilitating continuous improvement, data-driven validation, and more reliable decision-making across product development and reliability engineering. Full article

Design for remanufacturing (DfRem) is a green design mode that ensures good remanufacturability at the end-of-life (EOL) of the product. However, the diversity of service environments and operating modes makes it difficult to generate accurate DfRem solutions for the smooth implementation of remanufacturing. Moreover, the historical remanufacturing process contains a great deal of information conducive to DfRem. It will greatly enhance the efficiency and accuracy of remanufacturing design by feeding effective remanufacturing information back into the product design process. Unfortunately, there is a lack of direct correlation between them, which prevents remanufacturing information from effectively guiding DfRem. To improve the accuracy of DfRem solutions and the utilization rate of remanufacturing information, an intelligent design method for product remanufacturing based on remanufacturing information reuse is proposed. Firstly, rough set theory (RST) is used to identify key remanufacturability demand, and the quality function development (QFD) is used to establish a relationship between remanufacturability demand and engineering characteristics, which can accurately obtain the design objectives. Then, the correlation between remanufacturability demand, remanufacturing information, and DfRem parameters is analyzed, and the ontology technology is applied to construct the DfRem knowledge by ingratiating remanufacturing information. In addition, case-based reasoning (CBR) is applied to search for design cases from DfRem knowledge that best match the design objectives, and gray relational analysis (GRA) is used to calculate the similarity between design knowledge. Finally, the feasibility of the method is verified by taking an ordinary lathe as an example. This method has been implemented as a DfRem interface application using Visual Studio 2022 and Microsoft SQL Server 2022, and the research results indicate that this design method can accurately generate a reasonable DfRem scheme. Full article

Recent advances in vision–language models (VLMs) have transformed the field of robotics. Researchers are combining the reasoning capabilities of large language models (LLMs) with the visual information processing capabilities of VLMs in various domains. However, most efforts have focused on terrestrial robots and are limited in their applicability to volatile environments such as ocean surfaces and underwater environments, where real-time judgment is required. We propose a system integrating the cognition, decision making, path planning, and control of autonomous marine surface vehicles in the ROS2–Gazebo simulation environment using a multimodal vision–LLM system with zero-shot prompting for real-time adaptability. In 30 experiments, adding the path plan mode feature increased the success rate from 23% to 73%. The average distance increased from 39 m to 45 m, and the time required to complete the task increased from 483 s to 672 s. These results demonstrate the trade-off between improved reliability and reduced efficiency. Experiments were conducted to verify the effectiveness of the proposed system and evaluate its performance with and without adding a path-planning step. The final algorithm with the path-planning sub-process yields a higher success rate, and better average path length and time. We achieve real-time environmental adaptability and performance improvement through prompt engineering and the addition of a path-planning sub-process in a limited structure, where the LLM state is initialized with every application programming interface call (zero-shot prompting). Additionally, the developed system is independent of the vision–LLM archetype, making it scalable and adaptable to future models. Full article

The accurate prediction of remaining useful life (RUL) is crucial in order to reasonably and efficiently utilize lithium-ion batteries (LiBs). In this paper, a construction method of periodic time series is applied to the degradation data of LiBs to address the issues of insufficient training data and smooth degradation in the RUL interval prediction method based on trend filtering segmentation and fuzzy information granulation. The construction method for periodic time series is used to form a new dataset from the original data, based on which the fusion model, by combining the variational mode decomposition (VMD) and gated recurrent unit (GRU), is used as the RUL interval prediction model of LiBs. Moreover, the effectiveness and advantage of the RUL interval prediction method proposed in this paper was verified and analyzed by utilizing the CALCE battery dataset and NCA battery dataset. Full article

Optimizing the allocation of basic educational facilities in mountainous rural areas is important for narrowing the education gap between urban and rural areas, constructing high-quality regional education systems, and achieving sustainable education development. This paper considered preschool, primary, and secondary schools in Qixingguan District, which is located in a mountainous area of China, using vector data of rural residential areas and educational facility points as a source of information on supply and demand. The study combined travel modes and acceptable time of rural school-age population, and applied the Gaussian two-step mobile search method to calculate the level of accessibility of basic educational facilities at the scale of residential areas. Location optimization and scale optimization models were used to determine the optimal location and service qualities for basic educational facilities. Our results yielded three main conclusions. First, the spatial pattern for the distribution density and accessibility of basic educational facilities in Qixingguan differed at all stages, but all of them showed a strong orientation toward the central urban area. Service capacity in each stage tended to extend toward the northeast and southwest, except for a certain orientation toward the central urban area. Second, the main reason for the low spatial accessibility of schools was that the density and service capacity of the available schools did not align with the distribution of the school-age population. Third, after optimizing for location and service capacity, schools at all stages shifted to the northeast of Qixingguan, which reduced the difference in service capacity between schools and improved the accessibility and balance of schools in the northeast and southwest. Full article

This study investigated how undergraduate students collaborated when working with ChatGPT and what teamwork approaches they used, focusing on students’ preferences, conflict resolution, reliance on AI-generated content, and perceived learning outcomes. In a course on the Applications of Information Systems, 153 undergraduate students were organized into teams of 3. Team members worked together to create a report and a presentation on a specific data mining technique, exploiting ChatGPT, internet resources, and class materials. The findings revealed no strong preference for a single collaborative mode, though Modes #2, #4, and #5 were marginally favored due to clearer structures, role clarity, or increased individual autonomy. Students reasonably encountered initial disagreements (averaging 30.44%), which were eventually resolved—indicating constructive debates that improve critical thinking. Data also showed that students moderately modified ChatGPT’s responses (50% on average) and based nearly half (44%) of their overall output on AI-generated content, suggesting a balanced yet varied level of reliance on AI. Notably, a statistically significant relationship emerged between students’ perceived learning and actual performance, implying that self-assessment can complement objective academic measures. Students also employed a diverse mix of communication tools, from synchronous (phone calls) to asynchronous (Instagram) and collaborative platforms (Google Drive), valuing their ease of use but facing scheduling, technical, and engagement issues. Overall, these results reveal the need for flexible collaborative patterns, more supportive AI use policies, and versatile communication methods so that educators can apply collaborative learning effectively and maintain academic integrity. Full article

Accurately and timely estimating three-dimensional ocean states is crucial for improving operational ocean forecasting capabilities. Although satellite observations provide valuable evolutionary information, they are confined to surface-level variables. While in situ observations can offer subsurface information, their spatiotemporal distribution is highly uneven, making it difficult to obtain complete three-dimensional ocean structures. This study developed an operational-oriented lightweight framework for three-dimensional ocean state reconstruction by integrating multi-source observations through a computationally efficient multivariate empirical orthogonal function (MEOF) method. The MEOF method can extract physically consistent multivariate ocean evolution modes from high-resolution reanalysis data. We utilized these modes to further integrate satellite remote sensing and buoy observation data, thereby establishing physical connections between the sea surface and subsurface. The framework was tested in the South China Sea, with optimal data integration schemes determined for different reconstruction variables. The experimental results demonstrate that the sea surface height (SSH) and sea surface temperature (SST) are the key factors determining the subsurface temperature reconstruction, while the sea surface salinity (SSS) plays a primary role in enhancing salinity estimation. Meanwhile, current fields are most effectively reconstructed using SSH alone. The evaluations show that the reconstruction results exhibited high consistency with independent Argo observations, outperforming traditional baseline methods and effectively capturing the vertical structure of ocean eddies. Additionally, the framework can easily integrate sparse in situ observations to further improve the reconstruction performance. The high computational efficiency and reasonable reconstruction results confirm the feasibility and reliability of this framework for operational applications. Full article

Decision-making is often influenced by contextual information on the human–computer interface (HCI), with the attraction effect being a common situational effect in digital nudging. To address the role of visual cognition and coding in the HCI based on the attraction effect, this research takes online websites as experimental scenarios and demonstrates how the coding modes and attributes influence the attraction effect. The results show that similarity-based attributes enhance the attraction effect, whereas difference-based attributes do not modulate its intensity, suggesting that the influence of the relationship driven by coding modes is weaker than that of coding attributes. Additionally, variations in the strength of the attraction effect are observed across different coding modes under the coding attribute of similarity, with color coding having the strongest effect, followed by size, and labels showing the weakest effect. This research analyzes the stimulating conditions of the attraction effect and provides new insights for exploring the relationship between cognition and visual characterization through the attraction effect at the HCI. Furthermore, our findings can help apply the attraction effect more effectively and assist users in making more reasonable decisions. Full article

In a country where almost the totality of the native population is baptized and raised in the Catholic Church, recent surveys have shown several inconsistencies, especially among the young who claim that they do not believe. This study is a follow-up of another one that showed marked differences between the younger generation and older ones regarding the importance of religion in their life. Other surveys gave a similar picture. This study seeks to acquire a deeper understanding of the religiosity of these adolescents and young adults, this time with the use of two validated instruments. The first, the Meaning and Purpose Scales (MAPS), was meant to capture the essence of religion as a meaning-making mode. For the second, since the majority of the participants came from an organized religion, it was worth investigating the reasons why these adolescents were abandoning their religion and where they were going. This was attempted through the administration of the Adolescent Deconversion Scale (ADS). In addition, to detect deconversion-related changes, the participants were asked to undertake the Retrospective Analysis of Religiosity, a graphical method representing their religious development over the years by the plotting of a “religiosity line”. Following a number of contrasts between the test variables and others from the demographic information, a more defined and detailed picture of the religiosity of this segment of the population emerged. The absolute majority of the participants continue to profess their religion, and faith continues to be a major source of meaning in their life. In addition, there is a strong correlation between their personal sense of security and religion and the family, particularly for two-parent families. This study exposed a particular critical point in their religious journey, marking the beginning of a decline in their religion. This also coincides with the major developmental changes that take place during puberty. For the rest, perseverance in the faith journey was very strongly related to having participated for a number of years in a faith group. The family of origin and, later, belonging to a faith group seem to be decisive factors in the transmission and preservation of religiosity. As for those who left religion, the main reasons differed, including existential quests, peer influence, or simply indifference. Most, however, do not seem to have migrated to another religion or sect, and there are signs that many of them might have retained their own personal spirituality privately. Finally, it could be argued that, for some, their religious journey might not be over yet. Full article

A novel total joint replacement (TJR) that treats lumbar spine degeneration was previously assessed under Mode I and Mode IV conditions. In this study, we relied on these previous wear tests to establish a relationship between finite element model (FEM)-based bearing stresses and in vitro wear penetration maps. Our modeling effort addressed the following question of interest: Under reasonably worst-case misaligned conditions, do the lumbar total joint replacement (L-TJR) polyethylene stresses and strains remain below values associated with Mode IV impingement wear tests? The FEM was first formally verified and validated using the risk-informed credibility assessment framework established by ASME V&V 40 and FDA guidance. Then, based on criteria for unreasonable misuse outlined in the surgical technique guide, a parametric analysis of reasonably worst-case misalignment using the validated L-TJR FEM was performed. Reasonable misalignment was created by altering device positioning from the baseline condition in three scenarios: Axial Plane Convergence (20–40°), Axial Plane A-P Offset (0–4 mm), and Coronal Plane Tilt (±20°). We found that, for the scenarios considered, the contact pressures, von Mises stresses, and effective strains of the L-TJR-bearing surfaces remained consistent with Mode I (clean) conditions. Specifically, the mechanical response values fell below those determined under Mode IV (worst-case) boundary conditions, which provided the upper-bound benchmarks for the study (peak contact pressure 83.3 MPa, peak von Mises stress 32.2 MPa, and peak effective strain 42%). The L-TJR was judged to be insensitive to axial and coronal misalignment under the in vitro boundary conditions imposed by the study. Full article

The steering system plays a critical role in the vehicle’s handling and directly influences its lateral dynamics. Faults or abnormal behavior in this system can affect performance, cause vehicle instability, and even lead to accidents. Therefore, considering these potential events is essential for designing robust controllers for autonomous vehicles. For this reason, in this work, a fault-tolerant path-tracking Static Output-Feedback controller is designed to handle steering actuator faults in autonomous vehicle steering systems. The controller adopts a Linear Parameter Varying approach to effectively handle nonlinearities associated with varying vehicle speeds and tire behavior. Furthermore, it only uses information from sensors, avoiding estimation stages. This controller can operate in two modes: a no-fault mode where only the steering is controlled to follow the reference path and a fault mode where the controller manages both the steering and torque vectoring. In fault mode, torque vectoring compensates for faults in the steering actuator. The design of the controller is completed considering gain faults in the steering system. The simulation results show that the proposed controller successfully maintains vehicle stability and significantly reduces tracking errors during high-risk maneuvers, achieving reductions of up to 50.65% in lateral error and 47.26% in heading error under worst-case fault scenarios. Full article