Search Results (5,055)

Dark asphalt surfaces, absorbing about 95% of solar radiation and warming to 60–70 °C during summer, intensify urban heat while providing substantial prospects for energy extraction. This review evaluates four primary technologies—asphalt solar collectors (ASCs, including phase change material (PCM) integration), photovoltaic (PV) systems, vibration-based harvesting, thermoelectric generators (TEGs)—focusing on their principles, efficiencies, and urban applications. ASCs achieve up to 30% efficiency with a 150–300 W/m² output, reducing pavement temperatures by 0.5–3.2 °C, while PV pavements yield 42–49% efficiency, generating 245 kWh/m² and lowering temperatures by an average of 6.4 °C. Piezoelectric transducers produce 50.41 mW under traffic loads, and TEGs deliver 0.3–5.0 W with a 23 °C gradient. Applications include powering sensors, streetlights, and de-icing systems, with ASCs extending pavement life by 3 years. Hybrid systems, like PV/T, achieve 37.31% efficiency, enhancing UHI mitigation and emissions reduction. Economically, ASCs offer a 5-year payback period with a USD 3000 net present value, though PV and piezoelectric systems face cost and durability challenges. Environmental benefits include 30–40% heat retention for winter use and 17% increased PV self-use with EV integration. Despite significant potential, high costs and scalability issues hinder adoption. Future research should optimize designs, develop adaptive materials, and validate systems under real-world conditions to advance sustainable urban infrastructure. Full article

The Carbon Emissions Trading System (ETS) serves as a market-based mechanism to drive renewable energy (RE) investments, yet its heterogeneous impacts on different stakeholders remain underexplored. This paper treats the carbon market as an exogenous shock and develops a multi-agent equilibrium model incorporating carbon pricing, encompassing power generation enterprises, power transmission enterprises, power consumers, and the government, to analyze how carbon prices reshape RE investment layouts under dual-carbon goals. Using panel data from Zhejiang Province (2017–2022), a high-energy-consumption region with 25% net electricity imports, we simulate heterogeneous responses of agents to carbon price fluctuations (CNY 50–250/ton). The results show that RE on-grid electricity increases (+0.55% to +2.89%), while thermal power declines (–4.98% to −15.39%) on the generation side. Transmission-side RE sales rise (+3.25% to +9.74%), though total electricity sales decrease (−0.49% to −2.22%). On the consumption side, RE self-generation grows (+2.12% to +5.93%), yet higher carbon prices reduce overall utility (−0.44% to −2.05%). Furthermore, external electricity integration (peaking at 28.5% of sales in 2020) alleviates provincial entities’ carbon cost pressure under high carbon prices. This study offers systematic insights for renewable energy investment decisions and policy optimization. Full article

This paper builds on place theory and the psycho-social approach to the study of perceived residential environmental quality to examine the relationship between environmental perceptions and residential action in the neighborhood. An exploratory study on (N = 185) Italian respondents assessed the role of perceived residential environmental quality (i.e., perceived quality of green areas and perceived maintenance levels within the neighborhood), awareness of neighborhood environmental problems, neighborhood descriptive norms, and place attachment (attachment to the neighborhood) as predictors of self-reported individual residential engagement (engagement in improving the environmental quality of the neighborhood). Likert-type measures of the corresponding constructs were included in a structured questionnaire and used to carry out an online survey. Findings showed problem awareness and descriptive norms to directly predict residential engagement. Problem awareness mediated the relationship between perceived maintenance levels and residential engagement. Place attachment was directly predicted by perceived residential quality (quality of green areas), but did not show an independent predictive power vis-à-vis residential engagement. Results suggest new possible research avenues for modelling the individual commitment to improve the environmental quality of one’s own residential architectural and green environment. Full article

The development of multifunctional conductive hydrogels with rapid self-healing capabilities and powerful sensing functions is crucial for advancing wearable electronics. This study designed and prepared a polyvinyl alcohol (PVA)–borax hydrogel incorporating carbon nanotubes (CNTs) and biomass carbon nanospheres (CNPs) as dual-carbon fillers. This hydrogel exhibits excellent conductivity, mechanical flexibility, and self-recovery properties. Serving as a highly sensitive piezoresistive sensor, it efficiently converts mechanical stimuli into reliable electrical signals. Sensing tests demonstrate that the CNT/CNP/PVA–borax hydrogel sensor possesses an extremely fast response time (88 ms) and rapid recovery time (88 ms), enabling the detection of subtle and rapid human motions. Furthermore, the hydrogel sensor also exhibits outstanding cyclic stability, maintaining stable signal output throughout continuous loading–unloading cycles exceeding 3200 repetitions. The hydrogel sensor’s characteristics, including rapid self-healing, fast-sensing response/recovery, and high fatigue resistance, make the CNT/CNP/PVA–borax conductive hydrogel an ideal choice for multifunctional wearable sensors. It successfully monitored various human motions. This study provides a promising strategy for high-performance self-healing sensing devices, suitable for next-generation wearable health monitoring and human–machine interaction systems. Full article

When scholars view characters in King Lear through a Machiavellian lens, they read Edmund, Goneril, and Regan as stock Machiavels. In contrast, they often perceive Cordelia, Kent, and Edgar as selfless, apolitical characters. This essay argues that the latter characters are more complicated and politically adroit than they are often judged to be. They are Machiavellian as well, but Shakespeare conceives them within a more appreciative view of the concept of realpolitik. This essay explains the characters’ strategies by relating them to Machiavelli’s tenets of achieving and maintaining political power. The central quandary of the play is the lack of a male heir to the throne. Cordelia attempts to solve the problem by marrying the King of France for political reasons. She has an alliance with Kent, who helps her to justify her invasion of her homeland with French forces. Once the plans for a surprise attack go awry, Cordelia does not follow Machiavellian strategies and is consequently killed. Ironically, Edgar is as ambitious as Edmund, whom he lets plot against his father and bring about Gloucester’s slow decline so as to inherit his father’s fortune while Edmund incurs the blame for his father’s demise. Like Kent, he enlists a disguise for self-advancement. The most adroit Machiavellian characters—Edgar, Kent, and the King of France—all survive through chicanery and cunning. Shakespeare illustrates that secular methods of governorship defeat the old world of divine politics. Full article

Integrating variable renewable energy sources such as solar power into existing power grids presents major planning and reliability challenges. This study introduces an approach to optimize the placement of solar plants and allocation of storage in grids with high share of these variable energy sources by using a simulation framework that captures system-wide emergent behaviors. Unlike traditional engineering models focused on detailed component-level dynamics, a modified ORNL-PSERC-Alaska model based on self-organized criticality is used to reproduce the statistical features of blackouts, including cascading failures and long-range correlations. A distinctive feature of this approach is the explicit inclusion of key ingredients that shape these statistics, such as the transmission grid structure, generation and consumer buses, power flow balance, periodic dispatches, system failures, secular demand growth, demand fluctuations, and variability of renewable energy sources. When applied to the Balearic Islands grid, this method identifies generation and storage layouts that minimize storage requirements while maintaining reliability levels comparable to conventional power systems. The results offer a complementary systems-level perspective for planning resilient and efficient renewable energy integration. Full article

Epitaxial growth of β-Ga₂O₃ nanowires on silicon substrates was realized by the low-pressure chemical vapor deposition (LPCVD) method. The as-grown Si/Ga₂O₃ heterojunctions were employed in the Underwater DUV detection. It is found that the carrier type as well as the carrier concentration of the silicon substrate significantly affect the performance of the Si/Ga₂O₃ heterojunction. The p-Si/β-Ga₂O₃ (2.68 × 10¹⁵ cm⁻³) devices exhibit a responsivity of up to 205.1 mA/W, which is twice the performance of the devices on the n-type substrate (responsivity of 93.69 mA/W). Moreover, the devices’ performance is enhanced with the increase in the carrier concentration of the p-type silicon substrates; the corresponding device on the high carrier concentration substrate (6.48 × 10¹⁷ cm⁻³) achieves a superior responsivity of 845.3 mA/W. The performance enhancement is mainly attributed to the built-in electric field at the p-Si/n-Ga₂O₃ heterojunction and the reduction in the Schottky barrier under high carrier concentration. These findings would provide a strategy for optimizing carrier transport and interface engineering in solar-blind UV photodetectors, advancing the practical use of high-performance solar-blind photodetectors for underwater application. Full article

Plow loosening machines are essential agricultural machinery in the agricultural production process. Improving the surface strengthening process and extending the working life of the plow teeth of the plow loosening machine are of great significance. In this paper, the preparation of Stellite6/Cu composite coating on the surface of 316L steel substrate intended for strengthening the plow teeth of a plow loosening machine using laser cladding technology was studied. The influence of different laser process parameters on the microstructure and properties of Stellite6/Cu composite coating was investigated. The composite coating powder was composed of Stellite6 powder with a different weight percent of copper. Microstructural analysis, phase composition, elemental distribution, microhardness, wear resistance, and corrosion resistance of the composite coatings on the plow teeth were analyzed using scanning electron microscopy (SEM), X-ray diffraction (XRD), microhardness testing, energy dispersive spectroscopy (EDS), friction and wear testing, and electrochemical workstation measurements. The results showed that (1) When the laser power was 1000 W, the average hardness of the prepared Stellite6/Cu composite layer achieved the highest hardness, approximately 1.36 times higher than the average hardness of the substrate, and the composite coating prepared exhibited the best wear resistance; (2) When the scanning speed was 800 mm/min, the composite coating exhibited the lowest average friction coefficient and the optimal corrosion resistance in a 3.5% wt.% NaCl solution with a self-corrosion current density of −7.55 µA/cm²; (3) When the copper content was 1 wt.%, the composite coating achieved the highest average hardness with 515.2 HV, the lowest average friction coefficient with 0.424, and the best corrosion resistance with a current density of −8.878 µA/cm². Full article

Free space optical communication (FSOC) systems operating in the space–atmosphere channel are susceptible to severe turbulence-induced scintillation, particularly in up-link configurations where the adaptive optics (AO) pre-correction becomes ineffective due to anisoplanatic constraints. This study presents a novel scintillation suppression strategy utilizing self-focusing optical pin-like beams (OPBs) with tailored phase modulation, combining theoretical derivation and numerical simulation. It is found that increasing the shape factor γ and modulation depth C elevates the average received power and reduces the scintillation index at the focal point. Meanwhile, quantitative evaluation of the five OPB configurations shows that the parameter set γ = 1.4 and C = 7 × 10⁻⁵ gives a peak scintillation suppression efficiency. It shows that turbulence induced scintillation is suppressed by 44% with the turbulence intensity D/r₀ = 10, demonstrating exceptional effectiveness in up-link transmission. The findings demonstrate that OPB with optimized γ and C establish an approach for uplink FSOC, which is achieved through suppressed scintillation and stabilized power reception. Full article

In recent years, deep learning (DL) has been demonstrated remarkable capabilities in hyperspectral unmixing (HU) due to its powerful feature representation ability. Convolutional neural networks (CNNs) are effective in capturing local spatial information, but limited in modeling long-range dependencies. In contrast, transformer architectures extract global contextual features via multi-head self-attention (MHSA) mechanisms. However, most existing transformer-based HU methods focus only on spatial or spectral modeling at a single scale, lacking a unified mechanism to jointly explore spatial and channel-wise dependencies. This limitation is particularly critical for multiscale contextual representation in complex scenes. To address these issues, this article proposes a novel Spatial-Channel Multiscale Transformer Network (SCMT-Net) for HU. Specifically, a compact feature projection (CFP) module is first used to extract shallow discriminative features. Then, a spatial multiscale transformer (SMT) and a channel multiscale transformer (CMT) are sequentially applied to model contextual relations across spatial dimensions and long-range dependencies among spectral channels. In addition, a multiscale multi-head self-attention (MMSA) module is designed to extract rich multiscale global contextual and channel information, enabling a balance between accuracy and efficiency. An efficient feed-forward network (E-FFN) is further introduced to enhance inter-channel information flow and fusion. Experiments conducted on three real hyperspectral datasets (Samson, Jasper and Apex) and one synthetic dataset showed that SCMT-Net consistently outperformed existing approaches in both abundance estimation and endmember extraction, demonstrating superior accuracy and robustness. Full article

When faced with diverse types of image tampering and image quality degradation in real-world scenarios, traditional image tampering localization methods often struggle to balance boundary accuracy and robustness. To address these issues, this paper proposes a symmetric guided dual-branch image tampering localization network—FENet (Fusion-Enhanced Network)—that integrates adaptive feature fusion and edge attention mechanisms. This method is based on a structurally symmetric dual-branch architecture, which extracts RGB semantic features and SRM noise residual information to comprehensively capture the fine-grained differences in tampered regions at the visual and statistical levels. To effectively fuse different features, this paper designs a self-calibrating fusion module (SCF), which introduces a content-aware dynamic weighting mechanism to adaptively adjust the importance of different feature branches, thereby enhancing the discriminative power and expressiveness of the fused features. Furthermore, considering that image tampering often involves abnormal changes in edge structures, we further propose an edge-aware coordinate attention mechanism (ECAM). By jointly modeling spatial position information and edge-guided information, the model is guided to focus more precisely on potential tampering boundaries, thereby enhancing its boundary detection and localization capabilities. Experiments on public datasets such as Columbia, CASIA, and NIST16 demonstrate that FENet achieves significantly better results than existing methods. We also analyze the model’s performance under various image quality conditions, such as JPEG compression and Gaussian blur, demonstrating its robustness in real-world scenarios. Experiments in Facebook, Weibo, and WeChat scenarios show that our method achieves average F1 scores that are 2.8%, 3%, and 5.6% higher than those of existing state-of-the-art methods, respectively. Full article

This study analyzes the recent scientific literature on advanced biocompatible materials for triboelectric nanogenerators (TENGs) in biomedical applications. Focusing on materials like synthetic polymers, carbon-based derivatives, and advanced hybrids, the study interprets findings regarding their triboelectric properties and performance influenced by surface texture and additive manufacturing techniques. Major findings reveal that precise control over surface morphology, enabled by additive manufacturing (AM) is promising for optimizing transferred charge density and maximizing TENG efficiency. The analysis highlights the relevance of these material systems and fabrication strategies for developing self-powered wearable and implantable biomedical devices through enabling biocompatible energy-harvesting components that can operate autonomously without external power, underscoring the need for stringent biocompatibility and performance stability. This work synthesizes current progress, identifying critical material and process design parameters for advancing the field of biocompatible TENGs. Full article

This study investigates the development of cognitive, emotional, and social skills in pre-service teachers through extracurricular activities, addressing 21st century challenges in preparing educators for diverse learning environments. It was hypothesized that extracurricular activities would positively influence cognitive skills (self-efficacy, self-regulation), emotional dimensions (professional interest), social competencies (teacher–student relationships), and academic achievement. This study employed predictive correlational methodology based on an integrated theoretical framework combining Social Cognitive Theory, Self-Determination Theory, Self-Regulation Theory, and Interpersonal Relationships Theory within formal–informal learning contexts. A psychometrically robust instrument (“Scale on the Contribution of Extracurricular Activities to Professional Development”) was developed and validated through exploratory and confirmatory factor analyses, yielding a five-factor structure with strong reliability indicators (Cronbach’s α = 0.91–0.93; CR = 0.816–0.912; AVE = 0.521–0.612). Data from 775 pre-service teachers (71.1% female) across multiple disciplines at a Turkish university were analyzed using structural equation modeling (χ²/df = 2.855, RMSEA = 0.049, CFI = 0.93, TLI = 0.92). Results showed that extracurricular participation significantly influenced self-efficacy (β = 0.849), professional interest (β = 0.418), self-regulation (β = 0.191), teacher–student relationships (β = 0.137), and academic achievement (β = 0.167). Notably, an unexpected negative relationship emerged between self-efficacy and academic achievement (β = −0.152). The model demonstrated strong explanatory power for self-efficacy (R² = 72.8%), professional interest (R² = 78.7%), self-regulation (R² = 77.2%), and teacher–student relationships (R² = 63.1%) while explaining only 1.8% of academic achievement variance. This pattern reveals distinct developmental pathways for professional versus academic competencies, leading to a comprehensive practical implications framework supporting multidimensional assessment approaches in teacher education. These findings emphasize the strategic importance of extracurricular activities in teacher education programs and highlight the need for holistic approaches beyond traditional academic metrics, contributing to Sustainable Development Goal 4 by providing empirical evidence for integrating experiential learning opportunities that serve both academic researchers and educational practitioners seeking evidence-based approaches to teacher preparation. Full article

The escalating consumption of fossil fuels and the rapid development of portable electronics have increased interest in alternative energy solutions that can sustainably self-power wearable devices. Triboelectric nanogenerators (TENGs), which convert mechanical energy into electricity through contact electrification and electrostatic induction, have emerged as a promising technology due to their high voltage output, lightweight design, and simple fabrication. However, the performance of TENGs is often limited by a low surface charge density, inadequate dielectric properties, and poor charge retention of triboelectric materials. To address these challenges, recent research has focused on the use of polymer composites that incorporate various functional fillers. The filler materials play roles in improving dielectric performance and enhancing mechanical durability, thereby boosting triboelectric output even in harsh environments, while also diminishing charge loss. This review comprehensively examines the role of polymer composite design in TENG performance, with particular emphasis on materials categorized by their triboelectric polarity. Tribo-negative polymers, such as PDMS and PVDF, benefit from filler incorporation and phase engineering to enhance surface charge density and charge retention. By contrast, tribo-positive materials like nylon and cellulose have demonstrated notable improvements in mechanical robustness and environmental stability through composite strategies. The interplay between material selection, surface engineering, and filler design is highlighted as a critical path toward developing high-performance, self-powered TENG systems. Finally, this review discusses the current challenges and future opportunities for advancing TENG technology toward practical and scalable applications. Full article

With the large-scale global deployment of photovoltaics (PV), traditional monitoring technologies face challenges such as wiring difficulties, high energy consumption, and high maintenance costs in remote or complex terrains, which limit long-term environmental sensing. Therefore, energy-harvesting systems are crucial for the intelligent operation of photovoltaic systems; however, their deployment depends on the accurate mapping of wind energy fields and solar irradiance fields. This study proposes a multi-scale simulation method based on computational fluid dynamics (CFD) to optimize the placement of energy-harvesting systems in photovoltaic power plants. By integrating wind and irradiance distribution analysis, the spatial characteristics of airflow and solar radiation are mapped to identify high-efficiency zones for energy harvesting. The results indicate that the top of the photovoltaic panel exhibits a higher wind speed and reflected irradiance, providing the optimal location for an energy-harvesting system. The proposed layout strategy improves overall energy capture efficiency, enhances sensor deployment effectiveness, and supports intelligent, maintenance-free monitoring systems. This research not only provides theoretical guidance for the design of energy-harvesting systems in PV stations but also offers a scalable method applicable to various geographic scenarios, contributing to the advancement of smart and self-powered energy systems. Full article