Search Results (4,277)

Artificial Intelligence (AI), computer simulations, and virtual reality (VR) are increasingly becoming accessible tools that can be leveraged to implement training protocols and educational resources. Typical assessment tools related to sensory and neural processing associated with task performance in virtual environments often rely on self-reported surveys, unlike electroencephalography (EEG), which is often used to compare the effects of different types of sensory feedback (e.g., auditory, visual, and haptic) in simulation environments in an objective manner. However, it can be challenging to know which aspects of the EEG signal represent the impact of different types of sensory feedback on neural processing. Machine learning approaches offer a promising direction for identifying EEG signal features that differentiate the impact of different types of sensory feedback during simulation training. For the current study, machine learning techniques were applied to differentiate neural circuitry associated with haptic and non-haptic feedback in a simulated drilling task. Nine EEG channels were selected and analyzed, extracting different time-domain, frequency-domain, and nonlinear features, where 360 features were tested (40 features per channel). A feature selection stage identified the most relevant features, including the Hurst exponent of 13–21 Hz, kurtosis of 21–30 Hz, power spectral density of 21–30 Hz, variance of 21–30 Hz, and spectral entropy of 13–21 Hz. Using those five features, trials with haptic feedback were correctly identified from those without haptic feedback with an accuracy exceeding 90%, increasing to 99% when using 10 features. These results show promise for the future application of machine learning approaches to predict the impact of haptic feedback on neural processing during VR protocols involving drilling tasks, which can inform future applications of VR and simulation for occupational skill acquisition. Full article

Polymer dielectric materials have recently attracted attention for their versatile applications in emerging electronic devices such as memory, field-effect transistors (FETs), and triboelectric nanogenerators (TENGs). This review highlights the advances in polymer dielectric materials and their integration into these devices, emphasizing their unique electrical, mechanical, and thermal properties that enable high performance and flexibility. By exploring their roles in self-sustaining technologies (e.g., artificial intelligence (AI) and Internet of Everything (IoE)), this review emphasizes the importance of polymer dielectric materials in enabling low-power, flexible, and sustainable electronic devices. The discussion covers design strategies to improve the dielectric constant, charge trapping, and overall device stability. Specific challenges, such as optimizing electrical properties, ensuring process scalability, and enhancing environmental stability, are also addressed. In addition, the review explores the synergistic integration of memory devices, FETs, and TENGs, focusing on their potential in flexible and wearable electronics, self-powered systems, and sustainable technologies. This review provides a comprehensive overview of the current state and prospects of polymer dielectric-based devices in advanced electronic applications by examining recent research breakthroughs and identifying future opportunities. Full article

The continuous worsening of the natural surroundings requires accelerating the exploration of green energy technology. Utilising ambient vibration to power electronic equipment constitutes an important measure to address the power crisis. Vibration power is widely dispersed in the surroundings, such as mechanical vibration, acoustic vibration, wind vibration, and water wave vibration. Collecting vibration energy is one of the research hotspots in the field of energy. Meanwhile, it is also an important way to solve the energy crisis. This paper illustrates the working principles and recent research progress of five known methods of vibrational energy harvesting, namely, electromagnetic, piezoelectric, friction electric, electrostatic, and magnetostrictive vibrational energy harvesters. The strengths and weaknesses of each method are summarised. At the end of the article, the future trends of micro-nano vibrational energy collectors are envisioned. Full article

To investigate the impact of electrode structure on Electrical Stimulation Therapy (EST) for chronic wound healing, this study designed three variants of flexible microelectrodes (FMs) with Ag-Cu coverings (ACCs), each exhibiting distinct geometrical configurations: hexagonal, cross-shaped, and serpentine. These were integrated with PPY/PDA/PANI (3/6) (full name: polypyrrole/polydopamine/polyaniline 3/6). Hydrogel dressing comprehensive animal studies, coupled with detailed electrical and mechanical modeling and simulations, were conducted to assess their performance. Results indicated that the serpentine-shaped FM outperformed its counterparts in terms of flexibility and safety, exhibiting minimal thermal effects and a reduced risk of burns. Notably, FMs with metal coverings under 3% demonstrated promising potential for optoelectronic self-powering capabilities. Additionally, simulation data highlighted the significant influence of hydrogel non-uniformity on the distribution of electrical properties across the skin surface, providing critical insights for optimizing EST protocols when employing hydrogel dressings. Full article

This study explores the development and implementation of Shadow, an advanced mobile social robot designed to meet specific functional requirements. Shadow is intended to serve both as a versatile tool and a human companion, assisting in various tasks across different environments. The construction emphasizes cost efficiency and high agility, utilizing 3D printing technology exclusively. The robot features omnidirectional kinematics and a flexible power electronics system, accommodating diverse energy needs with lithium batteries that ensure at least seven hours of autonomous operation. An integrated sensor array continuously monitors the power system, tracks tilt and acceleration, and facilitates self-diagnostic functions. Rapid prototyping allows for swift iteration, testing, and refinement to align with project goals. This paper provides a comprehensive blueprint for designing cost-effective, highly agile robots using advanced manufacturing techniques. Extensive testing, including stability and sensory skills evaluations, demonstrates Shadow’s adherence to its design objectives. Shadow has advanced from technology readiness level (TRL) 2 to TRL 7 within a year and is currently undergoing trials with advanced functionalities, offering significant insights into overcoming practical design challenges and optimizing robot functionality. Full article

Our recent analysis of empirical limit order flow data in financial markets reveals a power-law distribution in limit order cancellation times. These times are modeled using a discrete probability mass function derived from the Tsallis q-exponential distribution, closely aligned with the second form of the Pareto distribution. We elucidate this distinctive power-law statistical property through the lens of agent heterogeneity in trading activity and asset possession. Our study introduces a novel modeling approach that combines fractional Lévy stable motion for limit order inflow with this power-law distribution for cancellation times, significantly enhancing the prediction of order imbalances. This model not only addresses gaps in current financial market modeling but also extends to broader contexts such as opinion dynamics in social systems, capturing the finite lifespan of opinions. Characterized by stationary increments and a departure from self-similarity, our model provides a unique framework for exploring long-range dependencies in time series. This work paves the way for more precise financial market analyses and offers new insights into the dynamic nature of opinion formation in social systems. Full article

The research on MEMS wireless sensing technology adapted to strong power frequency electromagnetic field environments is of great significance to our energy security, economic society, and even national security. Here, we propose a subwavelength cross-meandering resonator (0.49λ₀ × 0.49λ₀) to simultaneously achieve power frequency electromagnetic field shielding and wireless communication signal transmission. The element size of the resonator is only λ₀/11, which is much smaller than that of previous works. In the resonator, a resonance mode with the significant near-field enhancement effect (about 180 times that at f = 1 GHz) is supported. Based on the self-made shielding box experimental setup, the measured shielding effectiveness of the resonator sample can reach more than 33 dB. Moreover, by integrating the cross-meandering resonator with the MEMS sensor, a wireless communication signal can be successfully transmitted. A dual-function cross-meandering resonator integrated with sensors may find potential applications in many military and civilian industries associated with strong power frequency electromagnetic fields. Full article

This work explores the potential of self-powered MEMS devices for application in the preventive conservation of cultural heritage. The main objective is to evaluate the effectiveness of piezoelectric aluminum nitride MEMS (AlN-MEMS) for monitoring vibrations and to investigate its potential for harvesting energy from vibrations, including those induced by visitors. A preliminary laboratory comparison was conducted between AlN-MEMS and the commercial device Tromino^®. The study was then extended to the Picture Gallery of Ursino Castle, where joint measurements with the two devices were carried out. The analysis focused on identifying natural frequencies and vibrational energy levels by key metrics, including spectral peaks and the Power Spectral Density (PSD). The results indicated that the response of the AlN-MEMS aligned well with the data collected by the commercial device, especially observing high vibrational energy around 100 Hz. Such results validate the potential of AlN-MEMS for effective vibration measurement and for converting kinetic energy into electrical power, thereby eliminating the need for external power sources. Additionally, the vibrational analysis highlighted specific locations, such as the measurement point Cu4, as exhibiting the highest vibrational energy levels. These points could be used for placing MEMS sensors to ensure efficient vibration monitoring and energy harvesting. Full article

When using point-by-point data input with former series models for wind power prediction, the prediction accuracy decreases due to data distribution shifts and the inability to extract local information. To address these issues, this paper proposes an ultra-short-term wind power prediction model based on the Z-score (ZS), Dish-TS (DT), and Patch time series Transformer (PatchTST). Firstly, to reduce the impact of data distribution shift on prediction accuracy, ZS standardization is applied to both training and testing datasets. Additionally, the DT algorithm, which can self-learn the mean and variance, is introduced for window data standardization. Secondly, the PatchTST model is employed to convert point input data into local-level input data. Feature extraction is then performed using the multi-head attention mechanism in the Encoder layer and a feed-forward network composed of one-dimensional convolution to obtain the prediction results. These results are subsequently de-standardized using DT and ZS to restore the original data amplitude. Finally, experimental analysis is conducted, comparing the proposed ZS-DT-PatchTST model with various prediction models. The proposed model achieves the highest prediction accuracy, with a mean absolute error of 5.95 MW, a mean squared error of 10.89 MW, and a coefficient of determination of 97.38%. Full article

Persistent homology is a powerful tool for quantifying various structures, but it is equally crucial to maintain its interpretability. In this study, we extracted interpretable geometric features from the persistent diagrams (PDs) of scanning transmission electron microscopy (STEM) images of self-assembled Pt-CeO₂ nanostructures synthesized under different annealing conditions. We focused on PD quadrants and extracted five interpretable features from the zeroth and first PDs of nanostructures ranging from maze-like to striped patterns. A combination of hierarchical clustering and inverse analysis of PDs reconstructed by principal component analysis through vectorization of the PDs highlighted the importance of the number of arc-like structures of the CeO₂ phase in the first PDs, particularly those that were smaller than a characteristic size. This descriptor enabled us to quantify the degree of disorder, namely the density of bends, in nanostructures formed under different conditions. By using this descriptor along with the width of the CeO₂ phase, we classified 12 Pt-CeO₂ nanostructures in an interpretable way. Full article

Flexible DC power grids are widely recognized as an important component of building smart grids. Compared with traditional AC power grids, flexible DC power grids have strong technical advantages in islanding power supplies, distributed power supplies, regional power supplies, and AC system interconnection. In multi-terminal flexible DC power grids containing renewable energy sources such as solar and wind power, due to the instability and intermittency of renewable energy, it is usually necessary to add energy storage units to pre-regulate the power of the multi-terminal flexible DC power grid in islanded operation. Aiming at the important problem of large current impact and serious consequences when the flexible DC distribution network fails, a combined location method combining an improved impedance method (series current-limiting reactors at both ends of the line to obtain a more accurate current differential value) and a particle swarm optimization algorithm is proposed. Initially, by establishing the enhanced impedance model, the differential variables under the conditions of inter-electrode short-circuit and single-pole grounding fault can be obtained. Then tailor-made fitness functions are designed for these two models to optimize parameter identification. Subsequently, the iterative parameters of the particle swarm optimization algorithm are fine-tuned, giving it dynamic sociality and self-learning ability in the iterative process, which significantly improves the convergence speed and successfully avoids local optimization. Finally, various fault types in a six-terminal DC distribution network are simulated and analyzed by MATLAB, and the results show that this method has good accuracy and robustness. This research provides strong theoretical and methodological support for improving the safety and reliability of DC distribution systems. Full article

This study addresses the issue of poor air quality and thermal comfort in rural outdoor toilets by proposing a ventilation system powered by a building-applied photovoltaic (BAPV) roof. A numerical model is established and validated through comparison with the literature and experimental data. Based on a consensus, four influential variables, namely, inlet position, outlet height, supply air temperature, and ventilation rate, are selected for optimization to achieve multiple objectives: reduction in ammonia concentration, a predicted mean vote (PMV) value of 0, minimization of age of air, and energy consumption. The present study represents a pioneering effort in integrating the Taguchi method, computational fluid dynamics (CFD), and grey relational analysis to concurrently optimize the influential variables for outdoor toilet ventilation systems through design and simulation. The results indicate that all four variables exhibit nearly equal importance. Ventilation rate demonstrates a dominant effect on ammonia concentration and significantly impacts the age of air and energy consumption, while supply air temperature noticeably influences PMV. The optimal scheme features an inlet at center top position, an outlet height of 0.2 m, a supply air temperature of 12 °C and a ventilation rate of 20 times/h. This scheme improves ammonia concentration by 18.9%, PMV by 6.8%, and age of air by 30.0% at a height of 0.5 m, while achieving respective improvements by 18.9%, 5.5%, and 22.2% at a height of 1.5 m. The BAPV roof system generates an annual electricity output of 582.02 kWh, which covers the energy consumption of 358.1 kWh for toilet ventilation, achieving self-sufficiency. This study aims to develop a zero-carbon solution for outdoor toilets that provides a safe, comfortable, and sanitary environment. Full article

Conventional grid-forming control often destabilizes voltage source converters (VSCs) in stiff grids, and transient synchronization instability will occur in the grid fault condition. Therefore, power self-synchronization control (PSSC) is first introduced for enhancing the small-signal stability of grid-forming control in the case of a short circuit ratio ranging from 1 to infinity. Meanwhile, the transient synchronization instability for the grid-forming converter with PSSC in the grid fault condition is analyzed by the phase portrait, and a transient stability enhancement control (TSEC) scheme is combined with a PSSC-based VSC, which can efficiently eliminate the risk of losing synchronization in the arbitrary SCR. Finally, experimental results are provided to confirm the theoretical analysis. Full article

This essay reflects on the concept of the death of God as part and parcel of modern philosophical theology: a genre of thinking that came into existence with Hegel’s announcement of the “speculative Good Friday” as the most natural expression of die Religion der neuen Zeiten, “the religion of modern times”. In my interpretation, the death of God not only does not spell the end of the era of atheism but, on the contrary, inaugurates a new era of characteristically modern theism that steers away from theological absolutism. The new theos is no longer conceived as the eternal omnipotent Absolute but as the Derridean diminished Infinite: contracted and self-negated—even “unto death”. Such God, however, although coming to the foremost visibly in modernity, is not completely new to the monotheistic religions, which from the beginning are engaged in the heated debate concerning the status of the divine power: is it absolute and unlimited or rather self-restricted and conditioned? I will enter this debate by conducting a comparison between the two traditional models of divine self-restriction—Christian kenosis and Jewish-kabbalistic tsimtsum—and then present their modernised philosophical variants, most of all in the thought of Hegel. Full article

In recent years, global climate change has profoundly influenced natural ecosystems and human societies, making climate mitigation and carbon emission reduction a point of consensus among the international community. The issue of carbon emissions in agriculture, particularly in the livestock sector, is garnering increasing attention. This study focuses on large-scale dairy farms in the central and western regions of Inner Mongolia, exploring their low-carbon production behavioral intentions and influencing factors. By constructing a structural equation model (PLS-SEM), we systematically analyze the relationships between variables such as climate perception, value judgment, attitude, subjective norms, and perceived control and their combined effects on low-carbon production behavioral intentions. The findings suggest that the influence of climate perception and low-carbon awareness is mediated. Thus, the stronger the farm owners’ perception of climate change, the more they recognize the value of low-carbon production and the greater the social pressure they experience and their sense of self-efficacy. The farm owners’ attitudes, perceptions of social norms, and evaluations of their own capabilities collectively determine their intentions regarding low-carbon production. Furthermore, multi-group analysis showed significant heterogeneity in behavioral intentions between different scales of dairy farms. Small-scale farms, due to their weaker economic capacity, tend to harbor negative attitudes towards low-carbon production, while large-scale farms, with greater economic power and sensitivity to policy and market demands, are more likely to take low-carbon actions. This study provides theoretical support for formulating effective low-carbon policies, contributing to the sustainable development of the livestock sector and agriculture as a whole. Full article