Search Results (131)

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

With the ubiquitous connectivity and exposure of social network service (SNS), the stressors it causes have received extensive attention in the academic community. Unlike previous studies, this research focuses on the cross-cultural dimension and explores the different effects of multiple SNS-generated stressors on user behavior outcomes. Based on the “Stressors-Strain-Outcome” (SSO) theoretical framework, we constructed a “technical stressors—exhaustion—reduced SNS usage intention” pathway to systematically investigate five types of technical stressors. These were perceived information overload, perceived social overload, perceived compulsive use, perceived privacy concern, and perceived role conflict. We introduce “fear of missing out” (FOMO) as a moderating variable to explore its moderating role in SNS exhaustion and reduced SNS usage intention. In this study, we took SNS users from China and the United States as the research subjects (338 samples from China and 346 samples from the United States), and conducted empirical tests using structural equation models and multiple comparative analyses. The results show that there are significant cultural differences between Chinese and American users in terms of the perceived intensity of technostress, the path of stress transmission, and the moderating effect of FOMO. Against the background of collectivist culture in China, perceived information overload, privacy concerns, and role conflicts have a significant positive impact on SNS exhaustion, and SNS exhaustion further positively drives the intention to reduce usage of SNS. However, the direct impacts of perceived social overload and perceived compulsive usage are not significant, and FOMO does not play a significant moderating role. In the context of the individualistic culture found in the United States, only perceived information overload and perceived social overload have a significant positive impact on SNS exhaustion, and FOMO significantly negatively moderates the relationship between exhaustion and reduced SNS usage intention, as high FOMO levels will strengthen the driving effect of exhaustion on reduced usage intention. The innovation this study exhibits lies in verifying the applicability of the SSO model in social media behavior research from a cross-cultural perspective, revealing the cultural boundaries of the FOMO moderating effect, and enriching the cross-cultural research system of reduced usage intention of SNS. The research results not only provide empirical support for a deep understanding of the psychological mechanisms of users’ SNS usage behaviors in different cultural backgrounds, but also offer important references that SNS enterprises can use to formulate differentiated operation strategies and optimize cross-cultural user experiences. Full article

Dynamic edge-server allocation in mobile edge computing (MEC) networks is a challenging multi-objective optimization problem due to highly dynamic user demands, spatiotemporal traffic variations, and the need to simultaneously minimize service latency and workload imbalance. Existing heuristic and metaheuristic-based approaches for this problem often suffer from premature convergence, limited exploration–exploitation balance, and inadequate adaptability to dynamic network conditions, leading to suboptimal edge-server placement and inefficient resource utilization. Moreover, most existing methods lack memory-aware search mechanisms, which restrict their ability to capture long-term system dynamics. To address these limitations, this paper proposes a Fractional-Order Multi-Objective African Vulture Optimization Algorithm (FO-MO-AVOA) for dynamic edge-server allocation. By integrating fractional-order calculus into the standard multi-objective AVOA framework, the proposed method introduces long-memory effects that enhance convergence stability, search diversity, and adaptability to time-varying workloads. The performance of FO-MO-AVOA is evaluated using realistic MEC network scenarios and benchmarked against several well-established metaheuristic algorithms. Simulation outcomes reveal that FO-MO-AVOA achieves 40–46% lower latency, 38–45% reduction in workload imbalance, and up to 28–35% reduction in maximum workload compared to competing methods. Extensive experiments conducted on real-world telecom network data demonstrate that FO-MO-AVOA consistently outperforms state-of-the-art multi-objective optimization algorithms in terms of convergence behaviour, Pareto-front quality, and overall system performance. Full article

The classification of lemons by size is a crucial industrial process that ensures specific quality standards. Lemon sorting is typically performed by hand or often using expensive, outdated machines. In this article, we develop Machine Learning and Queuing algorithms, program them on low-cost hardware—specifically, a microcontroller and a single-board computer—and integrate them with an existing fruit-sorting machine, which classifies lemons by size. We acquired a dataset of 3127 lemon images in six industry-standardized sizes. We developed algorithms to extract geometric features, including one based on the peduncle location, which is estimated using a pre-trained Faster Objects, More Objects (FOMO) model. We used these features to train and evaluate five machine learning models, with the best-performing model achieving 87.22% accuracy over a set of lemons acquired under controlled conditions. We tested the proposed system in a real industrial environment, proving its feasibility by sorting 1558 lemons and obtaining an accuracy of 78.00%, despite the industrial-standard sizes having considerable overlap. Full article

Digital well-being refers to the subjective balance between the benefits and drawbacks of technological connectivity. Although it is a relatively recent construct, research has shown that it can be measured reliably. The Digital Well-Being Scale, comprising three dimensions—Digital Satisfaction, Digital Wellness, and Safe and Responsible Behavior—has been validated in other countries, but not yet in Argentina. This study aimed to adapt and validate the scale in the Argentine context and to examine its associations with Fear of Missing Out (FoMO) and identity bubbles, two variables previously linked to digital experiences. A total of 895 participants (55.2% women; aged 18–65) completed an online survey including the Digital Well-Being Scale, the FoMO Scale, and the Identity Bubble Reinforcement Scale (IBRS-9). Exploratory and confirmatory factor analyses supported the original three-factor structure, and all dimensions showed an adequate internal consistency. A significant negative correlation was found between FoMO and the Digital Wellness dimension, suggesting that individuals with higher FoMO experience lower emotional balance in their digital lives. In contrast, associations between identity bubble dimensions and digital well-being were modest and selective. Only Digital Satisfaction and Digital Wellness were weakly related to social identification and homophily; no relationship was observed with safe digital behavior. These findings support the adapted scale’s psychometric soundness in the Argentine context and provide initial insights into how FoMO and digital identity processes may influence digital well-being. Further research is needed to explore these relationships in more diverse populations and cultural contexts. Full article

The Southwestern Fujian Region is one of the important Fe polymetallic metallogenic belts in China. The Makeng-Yangshan Fe skarn sub-belt within it contains several deposits that share a similar geological setting, mineralization age, and genetic type, yet exhibit significant differences in metal endowment. To investigate the poorly constrained factors responsible for these differences, this paper focused on the mineral chemistry of garnets associated with magnetite from the Makeng, Luoyang, and Yangshan Fe deposits within the sub-belt, employing in situ laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) for trace element analysis. Our results reveal that garnet from all three deposits are andradite-dominated and features a chondrite-normalized REE fractionation pattern exhibiting enrichment in LREE relative to HREE, indicating crystallization from unified, mildly acidic fluids under high oxygen fugacity (f_O2) conditions. However, both the Makeng and Luoyang garnets showed a strong positive Eu anomaly, whereas the Yangshan garnets displayed the weakest Eu anomaly among the three deposits, which can likely be attributed to the highest f_O2 environment of the Yangshan deposit. Furthermore, garnet Y/Ho ratios and Y-ΣREE correlations demonstrate that the Makeng and Luoyang garnets crystallized in an open fluid system that were primarily of magmatic-hydrothermal origin with substantial external fluid (e.g., meteoric water) involvement, whereas the Yangshan garnet reflects a relatively closed fluid system that was predominantly of magmatic-hydrothermal origin with limited external fluid input. These geochemical differences have direct implications for exploration: the open-system Makeng deposit holds promise for Mo-W-Sn mineralization, as does the Luoyang deposit for W-Sn, whereas the closed-system Yangshan shows little potential for these metals. In addition, this study reveals that Pb and Zn concentrations in garnet are not reliable exploration indicators. Overall, these findings provide important mineralogical constraints on the factors controlling deposit scale and metal associations, thereby enhancing the understanding of regional metallogeny and guiding future mineral exploration. Full article

This study investigates interrelations among social media addiction (SMA), Fear of Missing Out (FoMO), cyberloafing (CL), work engagement (WE), and organizational commitment (OC) using network analysis. An online survey in Germany/Switzerland (n = 452; OC assessed in the employed subsample, n = 173) measured the five constructs. Unregularized and EBICglasso partial-correlation networks were estimated, and centrality and bridge indices were computed. Two robust edges emerged: a strong SMA–FoMO association and a strong positive WE–OC link; the regularized network additionally indicated a triangular SMA–FoMO–CL pattern. FoMO and OC acted as bridge nodes between problematic social media behaviors and work attitudes, whereas direct SMA links to WE/OC were weak or absent. Findings position FoMO as a pivotal mechanism connecting social media use to organizational attitudes and support, distinguishing functional micro-breaks from disruptive CL. Limitations include the cross-sectional design, student-skewed sample, self-report measures, smaller OC subsample, and a German/Swiss context. Full article

Research on Instagram addiction (IA) has examined a range of psychological and socio-relational factors to explain the addiction, including personality traits, self-esteem, mental health, social approval, and fear of missing out (FoMO), among others. However, no study has integrated both social influence processes (subjective norms, group norms, and social identity) and meaningful relationships (attachment, dyadic, and friendship ties) with FoMO in relation to IA. This study examined the interplay among social influence processes, meaningful relationships, and FoMO on IA, as well as the moderating roles of subjective and group norms on the indirect effect of anxious attachment on IA via FoMO. The sample consisted of 180 Italian young adults (aged 18–30) who completed validated questionnaires on IA, social influence, relationships, and FoMO after providing consent. Social media use was also explored through an open-ended question. Results indicate that FoMO, social and group norms, and group identification significantly contribute to IA. Anxious attachment had a significant indirect effect on IA via FoMO, with subjective and group norms moderating this association. Qualitative analysis of open-ended responses enriched the understanding of young adults’ social media use. These findings highlight the importance of social influence, relationships, and FoMO in young adults’ Instagram engagement and suggest directions for addressing problematic use in this group. Full article

Consumer digital hoarding is becoming increasingly common in agricultural live social e-commerce, where the abundance of product information, seasonal promotions, and origin-based narratives make consumers more inclined to accumulate digital content such as product links, coupons, and live-stream screenshots. This phenomenon not only affects consumers’ digital mental health, consumption behavior, and decision-making ability, but also poses challenges to agricultural merchants and platforms in terms of customer conversion, precision marketing, and supply chain management. Drawing on the SOR model and integrating construal level theory, this paper constructs a research framework to analyze the key factors influencing consumers’ willingness to digitally hoard in the context of agricultural live social e-commerce. Based on a questionnaire survey of 322 consumers, and using the Ordered Probit (O-Probit) model, the empirical results show that emotional interaction significantly influences digital hoarding intention through the chain mediating effects of emotional attachment and fear of missing out (FOMO). Furthermore, social distance and immersion serve as boundary conditions in this mechanism. Our findings not only deepen the understanding of consumer digital hoarding behavior in agricultural live e-commerce, but also provide new insights for agricultural merchants and platforms to better design interaction strategies, balance consumers’ digital accumulation with actual purchasing conversion, and enhance the efficiency of agricultural product marketing. Full article

Background/Objectives: Social networks have brought exciting possibilities for interacting with others in real time, anywhere in the world. However, problematic social network use (PSNU) causes distress and dysfunction in daily life. Young people may be vulnerable due to their high degree of digital connectivity and the particularities of psychosocial development. The primary aim of this study was to assess the presence of PSNU and its relationship with anxiety, depression, stress, fear of missing out (FoMO), and experiential avoidance in young people, while also examining gender differences. Methods: An online survey was conducted with a non-probabilistic sample of 219 young people between the ages of 18 and 25 (M = 20.50, SD = 2.42; 74.4% women), which included self-report measures of the aforementioned variables. Results: A total of 27.4% reported PSNU, but there were no differences by gender. PSNU was positively and significantly related to all the variables analyzed, with the highest correlations being with FoMO and experiential avoidance, especially in women. Regression analysis showed that the set of variables explains 17.2% of the variance in PSNU, but only FoMO contributed positively and significantly to PSNU in the overall sample and in women, but not in men. Conclusions: This is the first study to jointly compare the predictive power of key variables (anxiety, depression, stress, experiential avoidance, and FoMO) on PSNU in young adults. Additionally, we examined gender differences and utilized validated instruments. Our results show that only FoMO plays a relevant role in accounting for PSNU variance, although more so in women than in men. Also, the scores in experiential avoidance are significantly higher in women compared with men. These results support the idea that PSNU may serve as a strategy for avoiding distress, specifically FoMO, particularly in women. In terms of clinical implications, it would be highly interesting to analyze the ways and contexts in which social media could be used in a healthier manner and in alignment with personal values. Full article

Social Networking Sites (SNSs) play a central role in university students’ social and academic lives by facilitating relationship maintenance, emotional support, and the exchange of information, especially for those studying away from home. However, it remains unclear how different patterns of SNS use influence academic outcomes and psychosocial well-being. Grounded in social capital and self-determination theory, the present study adopted a person-centered approach using Latent Profile Analysis (LPA) to identify distinct profiles of SNS engagement, academic outcomes and well-being. A sample of 275 Greek undergraduate students completed anonymous self-report questionnaires [SNSs use intensity, bonding and bridging social capital, perceived social support via SNSs, fear of missing out (FoMO), phubbing, nomophobia (NoMo), academic outcomes and well-being]. LPA revealed four user profiles: (1) Low Use-Low Support (poorest well-being, moderate academic outcomes); (2) Active and Supported (high well-being and academic outcomes); (3) At-Risk Heavy Users (intermediate academic outcomes and moderate well-being, comparable to Profile 2) and (4) Low Use-High Support (highest well-being, poorest academic outcomes). These findings indicate that SNS engagement may be associated with both benefits and risks for students, depending on how and why they are used. Adopting a person-centered perspective allowed the identification of meaningful usage patterns, providing critical insights for developing targeted interventions to support student adjustment. Full article

The Zhunuo porphyry Cu deposit (2.9 Mt Cu @ 0.48%) in the Gangdese belt, southern Xizang, represents a key Miocene post-collisional system. This study integrates textural, major-, and trace-element analyses of pyrite from distinct alteration zones to unravel its hydrothermal evolution and metal precipitation mechanisms. Our study identifies four distinct pyrite types (Py1-Py4) that record sequential hydrothermal stages: main-stage Py2-Py3 formed at 354 ± 48 to 372 ± 43 °C (based on Se thermometry), corresponding to A and B vein formation, respectively, and late-stage Py4 crystallized at 231 ± 30 °C, coinciding with D-vein development. LA-ICP-MS data revealed pyrite contains diverse trace elements with concentrations mostly below 1000 ppm, showing distinct distribution patterns among different pyrite types (Py1-Py4). Elemental correlations revealed coupled behaviors (e.g., Au-As, Zn-Cd positive correlations; Mo-Sc negative correlation). Tellurium variability (7–82 ppm) records dynamic fO₂ fluctuations during system cooling. A comparative analysis of pyrite from the regional deposits (Xiongcun, Tiegelongnan, Bada, and Xiquheqiao) highlighted discriminative geochemical signatures: Zhunuo pyrite was enriched in Co-Bi-Ag-Pb (galena inclusions); Tiegelongnan exhibited the highest Cu but low Au-As; Xiquheqiao had the highest Au-As coupling; and Bada showed epithermal-type As enrichment. Partial Least Squares Discriminant Analysis (PLS-DA) identified Cu, As, and Bi as key discriminators for deposit types (VIP > 0.8), with post-collisional systems (Zhunuo and Xiquheqiao) showing intermediate Cu-Bi and elevated As versus arc-related deposits. This study establishes pyrite trace-element proxies (e.g., Se/Te, Co/Ni, and As-Bi-Pb) for reconstructing hydrothermal fluid evolution and proposes mineral-chemical indicators (Cu-As-Bi) to distinguish porphyry-epithermal systems in the Qinghai-Tibet Plateau. The results underscore pyrite’s utility in decoding metallogenic processes and exploration targeting in collisional settings. Full article

Forecasting high-volume, univariate, and multivariate longitudinal data streams is a critical challenge in Big Data systems, especially with constrained computational resources and pronounced data variability. However, existing approaches often neglect multivariate statistical complexity (e.g., covariance, skewness, kurtosis) of multivariate time series or rely on recency-only windowing that discards informative historical fluctuation patterns, limiting robustness under strict resource budgets. This work makes two core contributions to big data forecasting. First, we establish a formal, multi-dimensional framework for quantifying “data bigness” across statistical, computational, and algorithmic complexities, providing a rigorous foundation for analyzing resource-constrained problems. Second, guided by this framework, we extend and validate the Adaptive High-Fluctuation Recursive Segmentation (AHFRS) algorithm for multivariate time series. By incorporating higher-order statistics such as covariance, skewness, and kurtosis, AHFRS improves predictive accuracy under strict computational budgets. We validate the approach in two stages. First, a real-world case study on a univariate Bitcoin time series provides a practical stress test using a Long Short-Term Memory (LSTM) network as a robust baseline. This validation reveals a significant increase in forecasting robustness, with our method reducing the Root Mean Squared Error (RMSE) by more than 76% in a challenging scenario. Second, its generalizability is established on synthetic multivariate data sets in Finance, Retail, and Healthcare using standard statistical models. Across domains, AHFRS consistently outperforms baselines; in our multivariate Finance simulation, RMSE decreases by up to 62.5% in Finance and Mean Absolute Percentage Error (MAPE) drops by more than 10 percentage points in Healthcare. These results demonstrate that the proposed framework and AHFRS advances the theoretical modeling of data complexity and the design of adaptive, resource-efficient forecasting pipelines for real-world, high-volume data ecosystems. Full article

Previous cross-sectional studies have explored associations between basic psychological need satisfaction, fear of missing out (FoMO), and depression. However, the longitudinal nature of these relationships and their underlying mechanisms remain unclear. This study aimed to utilize longitudinal tracking methods to investigate the relationships among basic psychological need satisfaction, fear of missing out, and depression in university students. Longitudinal data collection was conducted among 750 university students (mean age = 18.12 ± 0.73) in China over two years at three time points. Participants were investigated using paper–pencil survey versions of the Basic Psychological Needs Scale, the Fear of Missing Out scale, and The Center for Epidemiological Studies Depression Scale. The results revealed that, over the two-year study period, basic psychological need satisfaction (β = −6.239, p < 0.001) among university students demonstrated a declining trend, while FoMO (β = 1.360, p < 0.001) and depression (β = 3.602, p < 0.001) demonstrated an upward trend. The initial levels and development rates of basic psychological need satisfaction directly predicted the initial levels (β = −0.236, p = 0.031) and development rates of depression (β = −0.144, p < 0.001; β = −0.181, p = 0.005). The initial level of FoMO mediated the relationship between basic psychological need satisfaction and depression (β = −0.132, p = 0.007; β = −0.104, p = 0.036), and this mediating effect did not exhibit significant gender differences. These findings help to reveal the temporal relationships among the three variables from a dynamic perspective, providing important practical guidance for mental health education in universities. Full article

This study investigates the influence of FOMO, loss aversion, and herd behavior on gold investment decisions in the Vietnamese market. Employing data collected from 727 investors and the Partial Least Squares Structural Equation Modeling (PLS-SEM) method, the analysis results confirm the pivotal role of FOMO, with both direct and indirect impacts on gold investment decisions. Notably, both loss aversion and herd behavior positively influence FOMO, thereby indirectly encouraging relatively hasty and inadequately considered investment decisions. The study also finds that FOMO has a negative relationship with anticipated regret but is positively correlated with subjective expected pleasure. Furthermore, as determined through Multi-Group Analysis (MGA), psychological messages featuring “self-decision” or “risk warning” demonstrate a significant moderating role, potentially reducing or enhancing the influence of FOMO on investment decisions. These findings contribute to enriching behavioral finance theory and provide an empirical basis for developing effective risk management policies and gold market regulation aimed at mitigating the negative impacts of FOMO. Full article