Search Results (13,903)

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

Offshore wind turbines are rapidly scaling in size, which amplifies the need for credible integrated load analyses that consistently resolve the coupled dynamics among rotor–nacelle–tower systems and their support substructures. This study presents a comprehensive ultimate limit state (ULS) load assessment for a fixed jack-up-type substructure (hereafter referred to as K-wind) coupled with the IEA 15 MW reference wind turbine. Unlike conventional monopile or jacket configurations, the K-wind concept adopts a self-installable triangular jack-up foundation with spudcan anchorage, enabling efficient transport, rapid deployment, and structural reusability. Yet such a configuration has never been systematically analyzed through full aero-hydro-servo-elastic coupling before. Hence, this work represents the first integrated load analysis ever reported for a jack-up-type offshore wind substructure, addressing both its unique load-transfer behavior and its viability for multi-MW-class turbines. To ensure numerical robustness and cross-code reproducibility, steady-state verifications were performed under constant-wind benchmarks, followed by time-domain simulations of standard prescribed Design Load Case (DLC), encompassing power-producing extreme turbulence, coherent gusts with directional change, and parked/idling directional sweeps. The analyses were independently executed using two industry-validated solvers (Deeplines Wind v5.8.5 and OrcaFlex v11.5e), allowing direct solver-to-solver comparison and establishing confidence in the obtained dynamic responses. Loads were extracted at the transition-piece reference point in a global coordinate frame, and six key components (Fx, Fy, Fz, Mx, My, and Mz) were processed into seed-averaged signed envelopes for systematic ULS evaluation. Beyond its methodological completeness, the present study introduces a validated framework for analyzing next-generation jack-up-type foundations for offshore wind turbines, establishing a new reference point for integrated load assessments that can accelerate the industrial adoption of modular and re-deployable support structures such as K-wind. Full article

Complex distortion cancellation methods are often used at the radio frequency (RF) front end of multiantenna full-duplex transceivers to mitigate signal distortion; however, these methods have high computational complexity and limited practicality. To address these problems, the present study explored the complexities associated with such transceivers to develop a practical multistep approach for suppressing distortions arising from in-phase and quadrature (I/Q) imbalance, nonlinear power amplifier (PA) responses, and multipath self-interference caused by simultaneous transmissions on the same frequency. In this approach, the I/Q imbalance is estimated and then compensated for, following which nonlinear PA distortion is estimated and pre-compensated for. Subsequently, an auxiliary RF transmitter is combined with linearly regenerating self-interference signals to achieve full-duplex self-interference cancellation. The proposed method was implemented on a software-defined radio platform, with the distortion factor calibration specifically optimized for multiantenna full-duplex transceivers. The experimental results indicate that the image signal caused by I/Q imbalance can be suppressed by up to 60 dB through iterative computation. By combining IQI and DPD preprocessing, the nonlinear distortion spectrum can be reduced by 25 dB. Furthermore, integrating IQI, DPD, and self-interference preprocessing achieves up to 180 dB suppression of self-interference signals. Experimental results also demonstrate that the proposed method achieves approximately 20 dB suppression of self-interference. Thus, the method has high potential for enhancing the performance of multiantenna RF full-duplex systems. Full article

The escalating demand for non-renewable resources is anticipated to intensify extractive activities, which are invariably associated with significant environmental externalities. The rehabilitation of mined landscapes, undertaken to mitigate ecological degradation and reinstate ecosystem functions and biodiversity, is frequently constrained by substantial financial requirements as well as intricate technical, logistical, and environmental challenges. As a consequence, a considerable proportion of extractive sites worldwide remain unreclaimed. There is a critical need for sustainable, cost-effective, and versatile restoration practices. This article presents a bibliographic review focusing on problems encountered in mine remediation and the role of technosols in addressing these issues. Mine restoration initiatives are confronted with a suite of interrelated challenges, including suboptimal soil physicochemical characteristics, hydrological instability, geomorphological hazards, and the exacerbating effects of extreme climatic events. Technosols, formulated from various waste materials, prove to be a versatile and cost-effective biotechnology that can significantly improve soil fertility, reduce erosion, enhance water retention, and restore biological activity. Their application, which can include mining waste and organic residues, substantially lowers costs estimated globally at EUR 829.711 billion for soil formation and contributes to a circular economy. Technosols represent a promising and efficient biotechnology for mine restoration. Their use facilitates the creation of stable, functional, and self-sustaining landscapes, enabling not only environmental recovery but also social and economic benefits through post-restoration land uses. Further research and knowledge transfer are vital for their broader and optimised implementation. Full article

Radio astronomy requires precise target localization and tracking to ensure accurate observations. Conventional regulation methodologies, encompassing PID controllers, frequently encounter difficulties due to orientation inaccuracies precipitated by mechanical limitations, environmental fluctuations, and electromagnetic interferences. To tackle these obstacles, this investigation presents a reinforcement learning (RL)-oriented framework for high-accuracy monitoring in radio telescopes. The suggested system amalgamates a localization control module, a receiver, and an RL tracking agent that functions in scanning and tracking stages. The agent optimizes its policy by maximizing the signal-to-noise ratio (SNR), a critical factor in astronomical measurements. The framework employs a reconditioned 12-m radio telescope at King Mongkut’s Institute of Technology Ladkrabang (KMITL), originally constructed as a satellite earth station antenna for telecommunications and was subsequently refurbished and adapted for radio astronomy research. It incorporates dual-axis servo regulation and high-definition encoders. Real-time SNR data and streaming are supported by a HamGeek ZedBoard with an AD9361 software-defined radio (SDR). The RL agent leverages the Proximal Policy Optimization (PPO) algorithm with a self-attention actor–critic model, while hyperparameters are tuned via Optuna. Experimental results indicate strong performance, successfully maintaining stable tracking of randomly moving, non-patterned targets for over 4 continuous hours without any external tracking assistance, while achieving an SNR improvement of up to 23.5% compared with programmed TLE-based tracking during live satellite experiments with Thaicom-4. The simplicity of the framework, combined with its adaptability and ability to learn directly from environmental feedback, highlights its suitability for next-generation astronomical techniques in radio telescope surveys, radio line observations, and time-domain astronomy. These findings underscore RL’s potential to enhance telescope tracking accuracy and scalability while reducing control system complexity for dynamic astronomical applications. Full article

The study presents a scalable decision-support framework to assess energy-sharing strategies within mixed-use urban districts, with a focus on planning, sustainability, and policy relevance. Two renewable energy-sharing mechanisms—energy sharing (ES) and net metering (NM)—are compared through a techno-economic analysis applied to a real neighborhood in Montréal, Canada. The workflow integrates irradiance-aware PV simulation, archetype-based urban building modeling, and financial sensitivity analysis adaptable to local regulatory conditions. Key performance indicators (KPIs)—including Self-Consumption Ratio (SCR), Self-Sufficiency Ratio (SSR), and peak load reduction—are used to evaluate technical performance. Results show that ES outperforms NM, achieving higher SCR (77% vs. 66%) and SSR (40% vs. 35%), and seasonal analysis reveals that peak shaving reaches 30.3% during summer afternoons, while PV impact is limited to 15.6% in winter mornings and negligible during winter evenings. Although both mechanisms are currently unprofitable under existing Québec tariffs, scenario analysis reveals that a 50% CAPEX subsidy or a 0.12 CAD/kWh feed-in tariff could make the system viable. The novelty of this study lies in the development of a replicable, archetype-driven, and policy-oriented simulation framework that enables the evaluation of renewable energy communities in mixed-use and data-scarce urban environments, contributing new insights into the Canadian energy transition context. Full article

We present a comprehensive quantum field theoretical analysis of graviton self-energy and mass generation in 3+1 dimensional BTZ black hole spacetime, incorporating axion interactions within the framework of dark matter theory. Using a novel mathematical approach based on ultrahyperfunctions, generalizations of Schwartz tempered distributions to the complex plane, we derive exact quantum relativistic expressions for graviton and axion self-energies without requiring ad hoc regularization procedures. Our approach extends the Gupta–Feynman quantization framework to BTZ gravity while introducing a new constraint that eliminates unitarity violations inherent in previous formulations, thereby avoiding the need for ghost fields. Through systematic application of generalized Feynman parameters, we evaluate both bradyonic and tachyonic graviton modes, revealing distinct quantum correction patterns that depend critically on momentum, energy, and mass parameters. Key findings include (1) natural graviton mass generation through cosmological constant interactions, yielding $m^2=2\left|\Lambda \right|/\kappa (1-\kappa )$; (2) qualitatively different quantum behaviors between bradyonic and tachyonic modes, with bradyonic corrections reaching amplitudes 6 times larger than their tachyonic counterparts; (3) the discovery of momentum-dependent quantum dissipation effects that provide natural ultraviolet regulation; and (4) the first explicit analytical expressions and graphical representations for 17 distinct graviton self-energy contributions. The ultrahyperfunction formalism proves essential for handling the non-renormalizable nature of the theory, providing mathematically rigorous treatment of highly singular integrals while maintaining Lorentz invariance. Our results suggest observable consequences in gravitational wave propagation through frequency-dependent dispersive effects and modifications to black hole thermodynamics, potentially bridging theoretical quantum gravity with experimental constraints. Full article

Multidimensional approaches to understanding the daily lived experiences and well-being among spousal dyads, where one partner has diagnosed mild cognitive impairment (MCI) and the other serves as an informal caregiver, is a relatively unexplored area of research. This study examined contextual day-to-day patterns of spousal dyads’ caregiver burden, depressive affect, stress, relationship mutuality, sleep, and cognition from the perspective of both dyad members. For 14 consecutive nights, 27 dyads (n = 54 individuals) completed online daily diary forms. The forms included self and informant reports about daily caregiver burden, depressive affect, stress, dyadic interactions, memory, and sleep quality. Exploratory multilevel modeling was performed to understand how daily fluctuations among these aspects of everyday living for both dyad members were associated. Mutuality emerged as an important moderator for caregiver burden and depressive affect outcomes, underscoring the significance of the relationship between care recipients with MCI and their caregivers. Sleep debt was also associated with contagion effects among partners’ depressive affect, stress, mutuality, and cognition. The present study demonstrates the value of multifaceted investigations that account for contextually relevant factors using daily repeated measures with both dyad members to better understand the MCI caregiver experience. Larger, more diverse samples are needed for generalizability of findings. Full article

The method of pollination significantly affects the growth and development of blueberry fruit. However, there remains a deficiency in systematic research regarding the impact of various pollination treatments on the cellular structure of blueberries. In the present study, a cross-pollination experiment was conducted on the blueberry varieties ‘Reka’, with the objective of comparing the characteristics of blueberry fruit cells under different pollination treatments. The results showed that different pollen sources had certain effects on the pedicel marks, calyx width and seed number of blueberry fruit. Simultaneously, various pollination treatments significantly affected both the transverse and longitudinal diameters as well as the individual fruit weight of the fruits. However, no significant differences were noted in the fruit shape index across the various pollination treatments. Within the internal cellular tissue of each treated fruit, certain indicators exhibited distinct variances. In the cell height of the inner region of the mesocarp, all cross-pollination treatments were significantly higher than self-pollination. The fruit of ‘Northland’ and ‘Blomidon’ were significantly lower than self-pollination in the number of cells in the outer area of the mesocarp. These effects lead to differences in cell arrangement and spatial distribution, resulting in differences in the correlation between cell indexes and fruit morphological indexes among different treatments. In the ‘Reka’ self-pollination treatment, the endocarp width was significantly negatively correlated with the fruit weight (p < 0.05), while the correlation in the other four treatments showed an upward trend, showing a positive correlation. This study comprehensively examined the formation of fruit cells under cross-pollination at the cellular level, thereby providing a cytological basis for understanding the development of blueberry fruit. Full article

The present study pursued three objectives: (i) to examine whether self-access web-based instruction could significantly improve EFL students’ ability to formulate pragmatically appropriate email requests; (ii) to determine whether L2 proficiency influenced students’ pragmatic performance and their gains following instruction; and (iii) to explore changes in learners’ confidence when evaluating the appropriateness of their own email requests. Sixty-eight first-year English Studies students at a Spanish university completed a five-week intervention integrated into their curriculum. Their L2 proficiency was assessed using the Oxford Placement Test, which categorized them into B1 (n = 22), B2 (n = 23), and C1 (n = 23) levels. Using a pre–post-test design, learners’ performance was assessed through email tasks varying in imposition, and their confidence was measured via Likert-scale ratings. Results showed statistically significant improvements across all dimensions of an analytic rubric—particularly in request appropriateness and organization—indicating that self-access instruction effectively enhanced learners’ pragmatic competence. Gains were similar across the three proficiency groups, with B2 students showing slightly higher though not statistically significant improvements. Participants also reported significantly increased confidence in evaluating their own email appropriateness post-intervention. Findings support the integration of self-access pragmatic resources into EFL curricula to develop academic communication skills in higher education contexts. Full article

Resin-based binders are one of the main materials used in foundry molding and core sands. Self-curing sand with furfuryl resin is one of the most popular technologies in the production of molds and cores for complex, critical castings made of iron and non-ferrous alloys. It has dominated small-batch production and the production of large-sized castings. This work is part of the research on new molding sands for mold additive manufacturing (3D printing). Three-dimensional printing technology in the production of sand-casting molds and cores is finding increasing industrial application in the production of castings from non-ferrous metal alloys. The aim of the research presented in this paper was to determine the influence of furfuryl resin type (classical and designed for 3D printing of sand molds) on cast iron casting properties. The pouring parameters were elaborated on the basis of the MAGMA software. Microscopic observations of castings, produced in classical and 3D-printed molds, were conducted, as well as an assessment of the roughness of the samples. The gas emissions from molding sands with both types of furfuryl resin were tested and analyzed in the context of the roughness of the castings obtained. It was proven that molding sand with furfuryl resin designed for 3D printing was characterized by lower gas emissions, which, in the case of molding sands with organic binders, is beneficial from an environmental point of view. Full article

Recent advancements in deep learning have significantly improved fault diagnosis methods. However, challenges such as insufficient feature extraction, limited long-range dependency modeling, and environmental noise continue to hinder their effectiveness. This paper presents a novel mixture of multi-view convolutional (MOM-Conv) layers integrating the Mixture of Experts (MOE) mechanism. This design effectively captures and fuses both local and contextual information, thereby enhancing feature extraction and representation. This proposed approach aims to improve prediction accuracy under varying noise conditions, particularly in rolling ball bearing systems characterized by noisy signals. Additionally, we propose the Mamba-enhanced adaptive self-attention long short-term memory (MASA-LSTM) model, which effectively captures both global and local dependencies in ultra-long time series data. This model addresses the limitations of traditional models in extracting long-range dependencies from such signals. The architecture also integrates a multi-step temporal state fusion mechanism to optimize information flow and incorporates adaptive parameter tuning, thereby improving dynamic adaptability within the LSTM framework. To further mitigate the impact of noise, we transform vibration signals into denoised multi-channel representations, enhancing model stability in noisy environments. Experimental results show that our proposed model outperforms existing state-of-the-art approaches on both the Paderborn and Case Western Reserve University bearing datasets, demonstrating remarkable robustness and effectiveness across various noise levels. Full article

Accurate and timely precipitation forecasting is critical for climate risk management, agriculture, and hydrological regulation. However, this task remains challenging due to the dynamic evolution of atmospheric systems, heterogeneous environmental factors, and frequent missing data in multi-source observations. To address these issues, we propose DAHG, a novel long-term precipitation forecasting framework based on dynamic augmented heterogeneous graphs with reinforced graph generation, contrastive representation learning, and long short-term memory (LSTM) networks. Specifically, DAHG constructs a temporal heterogeneous graph to model the complex interactions among multiple meteorological variables (e.g., precipitation, humidity, wind) and remote sensing indicators (e.g., NDVI). The forecasting task is formulated as a dynamic spatiotemporal regression problem, where predicting future precipitation values corresponds to inferring attributes of target nodes in the evolving graph sequence. To handle missing data, we present a reinforced dynamic graph generation module that leverages reinforcement learning to complete incomplete graph sequences, enhancing the consistency of long-range forecasting. Additionally, a self-supervised contrastive learning strategy is employed to extract robust representations of multi-view graph snapshots (i.e., temporally adjacent frames and stochastically augmented graph views). Finally, DAHG integrates temporal dependency through long short-term memory (LSTM) networks to capture the evolving precipitation patterns and outputs future precipitation estimations. Experimental evaluations on multiple real-world meteorological datasets show that DAHG reduces MAE by $\stackrel{\sim }{3}$% and improves R² by $\stackrel{\sim }{0}$.02 over state-of-the-art baselines (p < 0.01), confirming significant gains in accuracy and robustness, particularly in scenarios with partially missing observations (e.g., due to sensor outages or cloud-covered satellite readings). Full article

As the global imperative to decarbonize infrastructure intensifies, wastewater treatment plants (WWTPs) are emerging as critical nodes for implementing circular and energy-positive solutions. Among these, thermal energy recovery from sewage sludge presents a transformative opportunity to reduce greenhouse gas (GHG) emissions, enhance energy self-sufficiency, and valorize waste streams. While anaerobic digestion remains the dominant stabilization method in large-scale WWTPs, it often underutilizes the full energy potential of sludge. Recent advancements in thermal processing, including pyrolysis, gasification, hydrothermal carbonization, and incineration with energy recovery, offer innovative pathways for extracting energy in the form of biogas, bio-oil, syngas, and thermal heat, with minimal carbon footprint. This review explores the physicochemical variability of sewage sludge in relation to treatment processes, highlighting how these characteristics influence thermal conversion efficiency and emissions. It also compares conventional and emerging thermal technologies, emphasizing energy yield, scalability, environmental trade-offs, and integration with combined heat and power (CHP) systems. Furthermore, the paper identifies current research gaps and outlines future directions for optimizing sludge-to-energy systems as part of net-zero strategies in the water–energy nexus. This paper contributes to a paradigm shift toward sustainable, decarbonized wastewater management systems by reframing sewage sludge from a disposal challenge to a strategic energy resource. Full article

Background/Objectives: Anxiety and depression are prevalent global health concerns, especially prominent in vulnerable groups such as older adults, individuals with chronic health conditions (e.g., neurodegeneration and cancer), and those from low socioeconomic backgrounds. Digital interventions, including computerized cognitive training (CCT), show promise in addressing emotional dysfunctions in a more accessible and cost-effective manner. The CREATE platform aims to enhance Emotion Regulation (ER) through targeted Working Memory (WM) training, aesthetic engagement, and creativity, while accounting for dopamine activity via spontaneous Eye Blink Rate (sEBR). The purpose of the present study is to evaluate the platform’s feasibility and validity through a single pilot trial. Methods: The study enrolled twenty-seven healthy adults (aged 21–44) who completed standardized self-report questionnaires on sleep quality and ER. They were also enrolled in sEBR recordings and performed a CCT-adapted Corsi block-tapping task and an aesthetic art evaluation. Affective textual narratives and valence/arousal ratings were also collected. Participants were divided into “Good Sleepers” and “Poor Sleepers”. The platform evaluation enrolled a multi-modal pipeline including correlations and regression analysis of intervention metrics, sentiment analysis, and group comparisons. Results: WM task performance correlated positively with global ER and Cognitive Reappraisal scores. Post-training sEBR was significantly associated with ER, and lower sleep efficiency negatively impacted changes in dopamine activity (sEBR Diff). Dopamine activity of “Good Sleepers”, as indicated by sEBR, reached the high levels of the “Poor Sleepers” group after the training, suggesting a dopamine boost caused by the CREATE platform for those with quality sleep. Creativity and emotional expression, as indicated by sentiment analysis, were related to sleep quality. Conclusions: The CREATE platform shows promise in enhancing ER through multi-modal digital engagement, integrating cognitive training, art, and creativity. The findings support the inclusion of sleep and dopamine markers in intervention evaluation. Further studies with larger samples and clinical cohorts are warranted to establish efficacy and generalizability, as the present one was not powered to test the effectiveness of our training platform but was designed to assess its feasibility and validity instead. Full article