Search Results (122,399)

Betula albosinensis serves as an important constructive and afforestation tree species in mountainous areas. Its suitable habitat and habitat quality are highly vulnerable to the climate. However, few studies have centered on the shrinkage, expansion, and habitat fragmentation of B. albosinensis forests under climate change. In this study, the Random Forest model was employed to predict current and future trends of shrinking and expanding of B. albosinensis, while a composite landscape index was utilized to evaluate the habitat fragmentation in the highly suitable habitats of B. albosinensis. The results indicated that suitable habitats for B. albosinensis were primarily concentrated in the vicinities of the Qinling, Qilian, and Hengduan Mountains, situated in western China. The most influential factor affecting the distribution of B. albosinensis was temperature seasonality (Bio4). In future scenarios, the center of distribution of B. albosinensis was projected to shift towards the west and higher altitudes. The total suitable habitats of B. albosinensis were anticipated to expand under the scenarios of SSP370 and SSP585 in the 2090s, while they were expected to contract under the remaining scenarios. Although these results indicated that the suitable areas of habitat for B. albosinensis were relatively intact on the whole, fragmentation increased with climate change, with the highest degree of fragmentation observed under the SSP585 scenario in the 2090s. The findings of this study provide a foundation for the protection of montane vegetation, the maintenance of montane biodiversity, and the evaluation of species’ habitat fragmentation. Full article

To investigate the molecular structure characteristics and chemical reaction mechanisms of gas coal from the Hong II coal mine of the Ningxia Hongdunzi Coal Industry, this study explores its elemental composition, structural features, and methods for constructing and optimizing molecular models. The basic properties of the coal were determined through proximate and elemental analyses. The carbon structure was characterized using ¹³C-NMR nuclear magnetic resonance, the N and S chemical states were analyzed with XPS, and the distribution of hydroxyl, aliphatic hydrocarbons, aromatic rings, and oxygen-containing functional groups was characterized by FT-IR. Based on the analysis results, a molecular structure model of Hongdunzi gas coal was constructed with the molecular formula C₂₀₄H₁₁₇O₁₇NS, and the calculated results of the model showed high consistency with the experimental spectra of ¹³C-NMR. The macromolecular model of gas coal was constructed using the Materials Studio 2020 software, and its structure was optimized through geometric optimization and dynamic simulations. After optimization, the total energy of the model was significantly reduced from 8525.12 kcal·mol⁻¹ to 3966.16 kcal·mol⁻¹, highlighting the enhanced stability of the coal molecular structure. This optimization indicates that torsional energy plays a dominant role in molecular stability, while van der Waals forces and electrostatic interactions were significantly improved during the optimization process. Full article

Background: Both listening to music during warm-up and consuming caffeine before exercise have been independently shown to enhance athletic performance. However, the potential synergistic effects of combining these strategies remain largely unexplored. To date, only two studies have reported additional benefits to combining music during warm-up with a caffeine dose of 3 mg/kg on taekwondo-specific performance tasks. However, these studies did not evaluate whether this combination produces additive or synergistic effects on other types of sports performance. The present study aimed to assess the effects of listening to music alone or combined with caffeine intake on performance in the Wingate anaerobic test (WAnT) in physically active subjects. Methods: Twenty-four physically active male participants took part in this randomized, double-blind, crossover experiment. Participants underwent WAnT performance evaluations under three conditions: (i) no intervention (control; CON); (ii) music plus placebo (Mus + PLA), involving the intake of a placebo (maltodextrin) 60 min prior and self-selected high-tempo music during warm-up; and (iii) music plus caffeine (Mus + CAF), involving the intake of 3 mg/kg of caffeine 60 min prior and self-selected high-tempo music during warm-up. Under all conditions, participants wore the same Bluetooth headphones (with or without music), performed a 10 min standardized warm-up, and completed the 30 s WAnT with a load of 7.5% of their body weight on a calibrated ergometer. Power output was recorded at a frequency of 1 Hz throughout the exercise. The Feeling Scale was assessed both before and after the exercise test, while heart rate (HR) and the rating of perceived exertion (RPE) were measured immediately following the exercise. Results: Mus + PLA and Mus + CAF significantly improved peak power, mean power, and total work compared with CON (p < 0.05). Furthermore, peak power was higher in Mus + CAF than in Mus + PLA (p = 0.01). Post-exercise HR and RPE showed no significant differences across conditions (p > 0.05). Regarding the Feeling Scale (FS) before exercise, the Mus + PLA and Mus + CAF conditions showed significantly higher scores than CON (p < 0.05), while no differences were found after exercise. The perceived fitness metrics displayed no significant differences among conditions (p > 0.05), except for self-perceived power, which was higher in Mus + CAF than in CON (p = 0.03). Conclusions: Self-selected music during warm-up, either alone or combined with caffeine, significantly enhanced several WAnT performance metrics, including peak power, mean power, and total work. Remarkably, combining music with caffeine further improved peak power and increased self-perceived power compared with music alone. While listening to self-selected music during warm-up provided measurable benefits on anaerobic exercise performance, the combination of music and caffeine demonstrated additive effects, making it the optimal strategy for maximizing anaerobic performance. Full article

Energy consumption demands are rapidly increasing every year, with an 8% annual growth rate projected for the next five years. As buildings represent over 35% of this demand, a metering system is required for monitoring to accurately calculate costs. This paper explores the evolution and impact of energy management through smart meters, emphasizing their superiority over traditional electromechanical devices, in applications such as minimizing power losses and enhancing grid reliability. This study compares the performance of five distinct metering systems, including electromechanical and advanced smart meters. Real-time testing across various scenarios is incorporated, examining parameters such as real and reactive power measurement, accuracy and adaptability to smart grids. Key findings revealed that smart meters, notably the EDMI Mk10A, outperform legacy systems in precision, data transmission and energy optimization. In addition, the potential of smart meters to enable dynamic cost calculation and prevent electricity theft is evident. Despite their advantages, challenges such as data privacy, installation costs and electromagnetic radiation concerns, persist. Future investigations to address the identified limitations are required. Full article

Background: Neurogenic orthostatic hypotension (NOH) is a significant non-motor manifestation of Parkinson’s disease (PD), that substantially affects patient disability and has a powerful impact on the quality of life of PD patients, while also contributing to increased healthcare costs. This narrative review aims to summarize key insights into the diagnosis and management of NOH in individuals with PD. Methods: For diagnosing NOH, a recently introduced and valuable metric is the ΔHr/ΔSBP index. Additional tools, such as autonomic reflex testing and various blood tests, also can be used to help distinguish orthostatic hypotension (OH) from NOH. Results: Treatment strategies for NOH involve both non-pharmacological and pharmacological approaches. As NOH frequently coexists with other abnormal blood pressure patterns (supine hypertension, nocturnal hypertension, and non-dipping hypertension), its treatment can be a challenge for the clinician. Droxidopa and midodrine are the primary pharmacological agents for NOH, though emerging therapies, such as norepinephrine transporter inhibitors, are being investigated. Conclusions: Despite these advancements, further research is needed to better understand the underlying pathophysiology of NOH, enabling more tailored and effective treatment options for individuals with PD. Full article

Industrial process heat typically requires large amounts of fossil fuels. Solar energy, while abundant and free, has low energy density, and so large collector areas are needed to meet thermal needs. Land costs in developed areas are often prohibitively high, making rooftop-based concentrating solar power (CSP) attractive. However, limited rooftop space and the low energy density of solar power are usually insufficient to meet a facility’s demands. Maximizing annual CSP energy generation within a bounded rooftop space is necessary to mitigate fossil fuel consumption. This is a different optimization objective than minimizing the Levelized Cost of Energy (LCOE) in typical open-land, utility-scale heliostat layout optimization. Innovative designs are necessary, such as compact, energy-dense central receiver systems with non-flat heliostat field topographies that use spatially efficient Tilt–Roll heliostats or multi-rooftop and multi-height distributed urban systems. A novel ray-tracing simulation tool was developed to evaluate these unique scenarios. For compact systems, optimized annual energy production occurred with maximum heliostat spatial density, and the best non-flat heliostat field topography found is a shallow section of a parabolic cylinder with an East–West focal axis, yielding a 10% optical energy improvement. Tightly packed Tilt–Roll heliostats showed a double improvement in optical energy at the receiver compared to Azimuth–Elevation heliostats. Full article

This study investigates the enhancement of power transfer efficiency (PTE) in wireless power transfer (WPT) systems for electric vehicles (EVs) through simulations and experimental evaluations using metamaterial (MTM) configurations. The MTM model, validated against existing research, was designed for operation at 85 kHz. The influence of MTM on the magnetic field alignment and flux density at the receiver coil significantly improved PTE compared to systems without an MTM configuration. We tested various arrangements of three, six, and nine MTM cells positioned at left, right, top, bottom, and combined locations across coil distances of 0–5.0 cm. The results showed that a nine-cell MTM arrangement yielded greater PTE than a three-cell arrangement because of improved electromagnetic flux distribution. However, the T-shaped arrangement of six MTM cells achieved the maximum PTE at a 2.0 cm coil distance. This performance exceeded that of the configuration with 3 × 3 MTM cells, indicating that the T-shaped design optimizes electromagnetic flux distribution. The six-cell T-shaped arrangement boosted the PTE by 7.7% compared to the nine-cell version, demonstrating its potential as an innovative and efficient WPT system for future EV applications. Full article

The growing demand for electric energy requires smarter and quicker distribution systems. In urban contexts, a smarter distribution of electric power to various classes of consumers, according to their demands, is possible through compact sorters. Since this type of device must be protected from unwanted access by people, wildlife and inclement weather, protection must be placed around the main components. When deployed in urban areas, housing can be built using solid panels. However, there is a risk of overheating the vital and costly parts of the system with the possibility to cause malfunction and, in extreme cases, damage. Moreover, leaving the system open exposes nearby residents to the impulsive noise produced during the operation of the switchgears being part of the system. Hence, there is a need to reach a suitable trade-off between optimal heat transfer and noise propagation. This article attempts to explain the thermal design backed by experimental validation and the noise-spreading modelling necessary to assess the respect of environmental legislation. Full article

In the present study, for the first time, aluminum-doped zinc oxide (AZO) thin films with nanoinclusions of amorphous carbon have been synthesized via spin coating, and the thermoelectric performances were investigated varying the aging period of the solution, the procedure of carbon nanoparticles’ addition, and the annealing atmosphere. The addition of nanoparticles has been pursued to introduce phonon scattering centers to reduce thermal conductivity. All the samples showed a strong orientation along the [002] crystallographic direction, even though the substrate is amorphous silica, with an intensity of the diffraction peaks reaching its maximum in samples annealed in the presence of hydrogen, and generally decreasing by the addition of carbon nanoparticles. Absolute values of the Seebeck coefficient improve when nanoparticles are added. At the same time, electric conductivity is higher for the sample with 1 wt.% of carbon and annealed in Ar with 1% of H₂, both increasing in absolute value with the temperature rise. Among all the samples, the lowest thermal conductivity value of 1.25 W/(m∙K) was found at room temperature, and the highest power factor was 111 μW/(m∙K²) at 325 °C. Thus, the introduction of carbon effectively reduced thermal conductivity, while also increasing the power factor, giving promising results for the further development of AZO-based materials for thermoelectric applications. Full article

This study presents the optimization of the friction stir welding (FSW) process using polynomial regression to predict the maximum tensile load (MTL) of welded joints. The experimental design included varying spindle speeds from 600 to 2200 rpm and welding speeds from 100 to 350 mm/min over 28 experimental points. The resulting MTL values ranged from 1912 to 15,336 N. A fifth-degree polynomial regression model was developed to fit the experimental data. Diagnostic tests, including the Shapiro–Wilk test and kurtosis analysis, indicated a non-normal distribution of the MTL data. Model validation showed that fifth-degree polynomial regression provided a robust fit with high fitted and predicted R² values, indicating strong predictive power. Hill-climbing optimization was used to fine-tune the welding parameters, identifying an optimal spindle speed of 1100 rpm and a welding speed of 332 mm/min, which was predicted to achieve an MTL of 16,852 N. Response surface analysis confirmed the effectiveness of the identified parameters and demonstrated their significant influence on the MTL. These results suggest that the applied polynomial regression model and optimization approach are effective tools for improving the performance and reliability of the FSW process. Full article

Selective laser sintering (SLS) is one of the prominent methods of polymer additive manufacturing (AM). A low-power laser source is used to directly melt and sinter polymer material into the desired shape. This study focuses on the utilization of the low-power laser SLS system to successfully manufacture metallic components through the development of a metal–polymer composite material. In this study, 17-4 PH stainless powders are used and mixed with polyoxymethylene (POM) and high-density polyethylene (HDPE) to prepare the composite powder material. The polymeric mixture is removed during the thermal degreasing process and subsequent sintering results in a solid metallic component. Sinterit Lisa with a 5 W, 808 nm laser source is used to fabricate the green part. For the printing parameters of 140 °C, laser power of 35.87 mJ/mm², and layer thickness of 100 μm, the printed samples achieved a maximum density of 3.61 g/cm³ and a complete shape. After sintering at 1310 °C for 180 min, the tensile strength of the shrunk sample is 605.64 MPa, the hardness is HRC 14.8, the average shrinkage rate is 22%, and the density is 7.57 g/cm³, which can reach 97% of the theoretical density. This process allows the use of a wide range of particle sizes that the usual AM technologies have, making it a low-cost, low-energy-consumption, high-speed AM technology. Full article

Pre-trained neural networks like GPT-4 and Llama2 have revolutionized intelligent information processing, but their deployment in industrial applications faces challenges, particularly in harsh environments. To address these related issues, model offloading, which involves distributing the computational load of pre-trained models across edge devices, has emerged as a promising solution. While this approach enables the utilization of more powerful models, it faces significant challenges in harsh environments, where reliability, connectivity, and resilience are critical. This paper introduces failure-resilient inference in mobile networks (FRIM), a framework that ensures robust offloading and inference without the need for model retraining or reconstruction. FRIM leverages graph theory to optimize partition redundancy and incorporates an adaptive failure detection mechanism for mobile inference with efficient fault tolerance. Experimental results on DNN models (AlexNet, ResNet, VGG-16) show that FRIM improves inference performance and resilience, enabling more reliable mobile applications in harsh operating environments. Full article

Nowadays, with increasing concerns about the environment and energy security, efforts have intensified to develop effective energy generation technologies based on renewable sources that align with the principles of sustainable growth. In response to these demands, biomass-fueled furnaces have become essential components of modern combined heat and power generation systems. This work aims to predict the thermal performance of a helically coiled multi-tube heat exchanger designed to recover heat from waste biomass incineration flue gases. The working fluid used is thermal oil. The work focuses on determining the thermal output of a heat exchanger for prescribed design parameters, including the thermal parameters of cooling oil and the temperature difference of flue gas, and the geometrical details. A novel in-house stationary lumped multi-section model, utilizing the iterative calculation method, was developed, allowing fast predictions of the operation parameters of helically coiled multi-tube type heat exchangers. Two different configurations of the exchanger, three-pipe (case I) and four-pipe (case II), were considered. The thermal output obtained from calculations for case I showed a satisfactory convergence with the value based on the measurement data, at about 6%. Once validated, the model was used to determine the required heat exchange surface area of a four-pipe heat exchanger of larger design heat output (2.2 MW) and assumed tube dimensions and configurations. The accuracy of the heat exchanger capacity prediction was below 12%, proving the developed calculation tool to be reliable for design and optimization purposes. Full article

With the escalating global demand for clean energy, the proton exchange membrane fuel cell (PEMFC), as an efficient and environmentally friendly energy conversion device, has emerged as a pivotal component of new power systems, playing a crucial role in achieving global carbon emission reduction targets. At present, the application of PEMFC technology is gradually expanding to the shipping industry and other fields, indicating its potential role in the future transformation of the energy structure. This article focuses on the marine salt spray environment; summarizes the impact of salt ionic contamination on PEMFC performance in recent years; and mainly explores the influence mechanism of the internal components of PEMFC, including the bipolar plate, the gas diffusion layer, catalyst layer, and proton exchange membrane. In addition, this study analyzes and summarizes the polarization curve variations in the marine salt spray environment, as well as the recovery methods after contamination, in order to provide certain references of PEMFC research for marine application. Full article

This research focuses on developing and testing a method for classifying disturbances in power systems using machine learning algorithms and phasor measurement unit (PMU) data. To enhance the speed and accuracy of disturbance classification, we employ a range of ensemble machine learning techniques, including Random forest, AdaBoost, Extreme gradient boosting (XGBoost), and LightGBM. The classification method was evaluated using both synthetic data, generated from transient simulations of the IEEE24 test system, and real-world data from actual transient events in power systems. Among the algorithms tested, XGBoost achieved the highest classification accuracy, with 96.8% for synthetic data and 85.2% for physical data. Additionally, this study investigates the impact of data sampling frequency and calculation window size on classification performance. Through numerical experiments, we found that increasing the signal sampling rate beyond 5 kHz and extending the calculation window beyond 5 ms did not significantly improve classification accuracy. Full article