Search Results (20,536)

Tutton salts have received considerable attention due to their potential applications in thermochemical energy storage (TCHS) systems. This technology requires high-purity materials that exhibit reversible dehydration reactions, significant variations in dehydration enthalpy, and high-temperature melting points. In this study, K₂Cu(SO₄)₂(H₂O)₆ Tutton salt in the form of single crystals was grown using the slow solvent evaporation method. Their structural, morphological, and thermal characteristics are presented and discussed, as well as temperature-induced phase transformations. At room temperature, the salt crystallizes in a monoclinic structure belonging to the P2₁/a space group, which is typical for Tutton salts. The lack of precise control over the solvent evaporation rate during crystal growth introduced structural disorder, resulting in defects on the crystal surface, including layer discontinuities, occlusions, and pores. Thermoanalytical analyses revealed two stages of mass loss, corresponding to the release of 4 + 2 coordinated H₂O molecules — four weakly coordinated and two strongly coordinated to the copper. The estimated dehydration enthalpy was ≈ 80.8 kJ/mol per mole of H₂O. Powder X-ray diffraction measurements as a function of temperature showed two phase transformations associated with the complete dehydration of the starting salt occurring between 28 and 160 °C, further corroborating the thermal results. The total dehydration up to ≈ 160 °C, high enthalpy associated with this process, and high melting point temperature make K₂Cu(SO₄)₂(H₂O)₆ a promising candidate for TCHS applications. Full article

Automated water body (WB) extraction is one of the hot research topics in the field of remote sensing image processing. To address the challenges of over-extraction and incomplete extraction in complex water scenes, we propose an encoder–decoder architecture semantic segmentation network for high-precision extraction of WBs called EDWNet. We integrate the Cross-layer Feature Fusion (CFF) module to solve difficulties in segmentation of WB edges, utilizing the Global Attention Mechanism (GAM) module to reduce information diffusion, and combining with the Deep Attention Module (DAM) module to enhance the model’s global perception ability and refine WB features. Additionally, an auxiliary head is incorporated to optimize the model’s learning process. In addition, we analyze the feature importance of bands 2 to 7 in Landsat 8 OLI images, constructing a band combination (RGB 763) suitable for algorithm’s WB extraction. When we compare EDWNet with various other semantic segmentation networks, the results on the test dataset show that EDWNet has the highest accuracy. EDWNet is applied to accurately extract WBs in the Weihe River basin from 2013 to 2021, and we quantitatively analyzed the area changes of the WBs during this period and their causes. The results show that EDWNet is suitable for WB extraction in complex scenes and demonstrates great potential in long time-series and large-scale WB extraction. Full article

Individual physiotherapy is crucial in treating patients with various pain and health issues, and significantly impacts abdominal surgical outcomes and further medical problems. Recent technological and artificial intelligent advancements have equipped healthcare professionals with innovative tools, such as sensor systems and telemedicine equipment, offering groundbreaking opportunities to monitor and analyze patients’ physical activity. This paper investigates the potential applications of mobile accelerometers in evaluating the symmetry of specific rehabilitation exercises using a dataset of 1280 tests on 16 individuals in the age range between 8 and 75 years. A comprehensive computational methodology is introduced, incorporating traditional digital signal processing, feature extraction in both time and transform domains, and advanced classification techniques. The study employs a range of machine learning methods, including support vector machines, Bayesian analysis, and neural networks, to evaluate the balance of various physical activities. The proposed approach achieved a high classification accuracy of 90.6% in distinguishing between left- and right-side motion patterns by employing features from both the time and frequency domains using a two-layer neural network. These findings demonstrate promising applications of precise monitoring of rehabilitation exercises to increase the probability of successful surgical recovery, highlighting the potential to significantly enhance patient care and treatment outcomes. Full article

As older adults face increasing mobility challenges due to diminished driving ability, they encounter heightened risks of social exclusion, impacting their mental, social, and physical well-being. This study investigates spatial gaps in the availability of sustainable transportation options—including public transit, paratransit, and specialized senior services—and examines their alignment with the distribution of older adults segmented by age group (65–74, 75–84, and 85+). Using Florida as a case study, Geographic Information System (GIS) was employed to conduct a geospatial analysis, identifying statistically significant clusters of low transportation availability alongside high concentrations of older adults. The primary contribution of this research lies in its innovative methodology, which integrates precise transportation service boundaries with age-segmented demographic data, offering a nuanced assessment of transportation equity as a crucial aspect of sustainability. Findings provide a comprehensive framework for policymakers, enabling targeted resource allocation and planning that enhance mobility, accessibility, and quality of life for older adults. This study contributes to advancing sustainable development goals by addressing transportation disparities, supporting equitable, age-sensitive transportation solutions, and informing broader discussions on sustainable urban planning. Full article

In recent years, land subsidence has become a crucial factor affecting urban safety and sustainable development, especially in Wan’an Town. To accurately monitor and analyze the land subsidence in Wan’an Town, this study uses the PS-InSAR technique combined with an improved DEM for detailed research on land subsidence in Wan’an Town. PS-InSAR, or Permanent Scatterer Interferometric SAR, is suitable for high-precision monitoring of surface deformation. The natural neighbor interpolation method optimizes DEM data, improving its spatial resolution and accuracy. In this study, multiple periods of SAR imagery data of Wan’an Town were collected and preprocessed through radiometric calibration, phase unwrapping, and other steps. Using the PS-InSAR technique, the phase information of permanent scatterers (PS points) on the surface was extracted to establish a deformation model and preliminarily analyze the land subsidence in Wan’an Town. Concurrently, the DEM data were optimized using the natural neighbor interpolation method to enhance its accuracy. Finally, the optimized DEM data were combined with the surface deformation information extracted through the PS-InSAR technique for a detailed analysis of the land subsidence in Wan’an Town. The research results indicate that the DEM data optimized by the natural neighbor interpolation method have higher accuracy and spatial resolution, providing a more accurate reflection of the topographical features of Wan’an Town. The research found that the optimized DEM provided a more accurate reflection of Wan’an Town’s topographical features. By combining PS-InSAR data, subsidence information from 2016 to 2024 was calculated. The study area showed varying degrees of subsidence, with rates ranging from 6 mm/year to 10 mm/year. Four characteristic deformation areas were analyzed for causes and influencing factors. The findings contribute to understanding urban land subsidence, guiding urban planning, and providing data support for geological disaster warning and prevention. Full article

Over the last several years, there has been a considerable improvement in the possible methods for solving fractional-order chaotic systems; however, achieving high accuracy remains a challenge. This work proposes a new precise numerical technique for fractional-order chaotic systems. Through simulations, we obtain new types of complex and previously undiscussed dynamic behaviors.These phenomena, not recognized in prior numerical results or theoretical estimations, underscore the unique dynamics present in fractional systems. We also study the effects of the fractional parameters ${\beta }_1$, ${\beta }_2$, and ${\beta }_3$ on the system’s behavior, comparing them to integer-order derivatives. It has been demonstrated via the findings that the suggested technique is consistent with conventional numerical methods for integer-order systems while simultaneously providing an even higher level of precision. It is possible to demonstrate the efficacy and precision of this technique through simulations, which demonstrates that this method is useful for the investigation of complicated chaotic models. Full article

Weed canopy cover assessment, particularly using drone-acquired data, plays a vital role in precision agriculture by providing accurate, timely, and spatially detailed information, enhancing weed management decision-making in response to environmental and management variables. Despite the significance of this approach, few studies have investigated weed canopy cover through drone-based imagery. This study aimed to fill this gap by evaluating the effects of conventional tillage (CT) and no-till (NT) practices on weed canopy cover in a winter wheat field over two growing seasons. Results indicated that in the 2022–2023 season, weed populations were similar between tillage systems, with a high mean weed cover of 1.448 cm² ± 0.241 in CT plots. In contrast, during the 2023–2024 season, NT plots exhibited a substantially higher mean weed cover (1.784 cm² ± 0.167), with a significant overall variation (p < 0.05) in weed distribution between CT and NT plots. These differences suggest that, while CT practices initially mask weed emergence by burying seeds and disrupting root systems, NT practices encourage greater weed establishment over time by leaving seeds near the soil surface. These findings provide valuable insights for optimizing weed management practices, emphasizing the importance of comprehensive approaches to improve weed control and overall crop productivity. Full article

The exploitation of the spatial and spectral characteristics of hyperspectral remote sensing images (HRSIs) for the high-precision classification of earth observation targets is crucial. Convolutional neural networks (CNNs) have good classification performance and are widely used neural networks. Herein, a morphological processing (MP)-based HRSI classification method and a 3D–2D CNN are proposed to improve HRSI classification accuracy. Principal component analysis is performed to reduce the dimensionality of the HRSI cube, and MP is implemented to extract the spectral–spatial features of the low-dimensional HRSI cube. The extracted features are concatenated with the low-dimensional HRSI cube, and the designed 3D–2D CNN framework completes the classification task. Residual connections and an attention mechanism are added to the CNN structure to prevent gradient vanishing, and the scale of the control parameters of the model structure is optimized to guarantee the model’s feature extraction ability. The CNN structure uses multiscale convolution, involving depthwise separable convolution, which can effectively reduce the amount of parameter calculation. Two classic datasets (Indian Pines and Pavia University) and a self-made dataset (My Dataset) are used to compare the performance of this method with existing classification techniques. The proposed method effectively improved classification accuracy despite its short classification time. Full article

Photogrammetry is a common tool for evaluating ecosystem-scale questions on coral reefs due to the ability to measure complex structures in situ. This technique is also increasingly being used at smaller scales to collect growth and morphometric data about individual coral fragments in manipulative experiments. However, there are substantial uncertainties in data quality and interpretation and limited reporting of the parameters useful for standardization across studies. There is a need to characterize the capabilities of photogrammetry as applied to coral fragments, to establish validation metrics for reporting, and to determine sources of variation in measurements to refine and improve methods. Here, we used fragments of two common reef-building corals (Montipora capitata and Porites compressa) and known size standards to evaluate accuracy and precision and present suggested validation metrics. We also used a tiered experimental design to evaluate sources of error in a photogrammetry workflow in a manipulative experiment using a purpose-built multi-camera workstation. We demonstrate extremely high accuracy (R² > 0.999) in determining the surface area and volume of known objects, as well as very high precision (average CV < 0.01) in coral measurements during tests of reproducibility. These outcomes show the utility of fragment photogrammetry for experimental coral reef science and present suggested validation metrics and approaches that can help standardize data evaluation and interpretation in any application of photogrammetry to coral fragments. Full article

Cold pilgering is widely utilized in high-end applications for the precise shaping of seamless tubes due to its capacity for large deformation, which reduces the number of deformation processes and shortens production cycles. However, there is a gap in the research on the cold pilgering of small-diameter, thick-walled seamless tubes, specifically those with an outer diameter–wall thickness ratio of ≤3. In this study, cold pilgering tests were performed on Cr-Mo-V hot-working die steel small-diameter thick-walled tubes. It was discovered that increasing the feed rate results in greater deviations in both inner diameter and wall thickness, although it has little effect on inner wall roughness. In contrast, increasing wall thickness reduction leads to higher wall thickness deviation but reduces inner surface roughness without significantly affecting inner diameter deviation. The study also found that a decrease in the initial inner wall roughness before pilgering results in improved final roughness. Under optimal conditions, the average inner surface roughness S_a can reach 0.177 μm, and small-diameter thick-walled seamless tubes with deviations in the inner diameter and wall thickness of 0.05 mm and 0.03 mm, respectively, are obtained. After tempering at 600 °C, the tensile strength (R_m) and yield strength (R_p0.₂) of the cold-pilgered tube reach 1092 MPa and 947 MPa, respectively, and the elongation (δ₅%) and impact energy (A_kU) increase to 20.4% and 61.5 J, respectively. Full article

One of the consequences of the globalisation of food markets is the effort enabling the control of food security and its origin. This might be traced by using different chemical composition analyses. However, for Central Europe, there is a lack of knowledge about the original reference values as well as their heterogeneity among the lands and countries. This study focused on characterizing the mineral profiles of apple tree fruits and comparing these profiles among different districts in Central Europe. The fruits of the apple cultivars ‘Gala’ and ‘Golden Delicious’ originated in the Czech Republic and Poland. The mineral and isotopic content of the apple fruit flesh was analysed using ICP-MS. The data were processed using the ANOVA test and compositely analysed using the PCA and LDA models. The results show relatively high variation in element distribution, particularly ⁸⁷Sr/⁸⁶Sr, Mn, Zn, Cu, Ca, P, and B, ranging between 20.6 and67.9% for both cultivars on average. However, their high variability within particular districts complicates the resolution of the LDA model. The reasons are linked to the geomorphological and pedological heterogeneity of the analysed districts as well as the particular sensitivity of the set of chosen primers to agronomic practices and tree performance. For this region, only partial separation among districts could be obtained by P, Ca, and Cu content, as well as the isotopic ratio of ¹⁰B/¹¹B. However, the resolution of the geographical discrimination needs to be improved by an enhanced set of primers, the use of more precise analytical techniques for the Sr isotopic ratio, or by multiple chemical analyses. Furthermore, the heterogeneity of the analysed districts could be tackled by more detailed analyses at the level of micro-regions. Full article

The inner ear organs responsible for hearing (cochlea) and balance (vestibular system) are susceptible to oxidative stress due to the high metabolic demands of their sensorineural cells. Oxidative stress-induced damage to these cells can cause hearing loss or vestibular dysfunction, yet the precise mechanisms remain unclear due to the limitations of animal models and challenges of obtaining living human inner ear tissue. Therefore, we developed an in vitro oxidative stress model of the pre-natal human inner ear using otic progenitor cells (OPCs) derived from human-induced pluripotent stem cells (hiPSCs). OPCs, hiPSCs, and HeLa cells were exposed to hydrogen peroxide or ototoxic drugs (gentamicin and cisplatin) that induce oxidative stress to evaluate subsequent cell viability, cell death, reactive oxygen species (ROS) production, mitochondrial activity, and apoptosis (caspase 3/7 activity). Dose-dependent reductions in OPC cell viability were observed post-exposure, demonstrating their vulnerability to oxidative stress. Notably, gentamicin exposure induced ROS production and cell death in OPCs, but not hiPSCs or HeLa cells. This OPC-based human model effectively simulates oxidative stress conditions in the human inner ear and may be useful for modeling the impact of ototoxicity during early pregnancy or evaluating therapies to prevent cytotoxicity. Full article

Nano-transfer printing (nTP) has emerged as an effective method for fabricating three-dimensional (3D) nanopatterns on both flat and non-planar substrates. However, most transfer-printed 3D patterns tend to exhibit non-discrete and/or non-porous structures, limiting their application in high-precision nanofabrication. In this study, we introduce a simple and versatile approach to produce highly ordered, porous 3D cross-bar arrays through precise control of the nTP process parameters. By selectively adjusting the polymer solution concentration and spin-coating conditions, we successfully generated discrete, periodic line patterns, which were then stacked at a 90-degree angle to form a porous 3D cross-bar structure. This technique enabled the direct transfer printing of PMMA line patterns with well-defined, square-arrayed holes, without requiring additional deposition of functional materials. This method was applied across diverse substrates, including planar Si wafers, flexible PET, metallic copper foil, and transparent glass, demonstrating its adaptability. These well-defined 3D cross-bar patterns enhance the versatility of nTP and are anticipated to find broad applicability in various nano-to-microscale electronic devices, offering high surface area and structural precision to support enhanced functionality and performance. Full article

The GF−7 satellite, China’s inaugural sub-meter-level stereoscopic mapping satellite, has been deployed for a wide range of applications, including natural resource investigation, environmental monitoring, fundamental surveying, and the development of global geospatial information resources. The satellite’s stable platform and reliable imaging systems are crucial for achieving high-quality imaging and precise attitude measurements. However, the satellite’s operation is affected by both internal and external factors, which induce vibrations in the satellite platform, thereby affecting image quality and mapping accuracy. To address this challenge, this paper proposes a novel method for constructing a satellite platform vibration model based on geographic location information. The model is developed by integrating composite data from star sensors and gyroscopes (gyro) with subsatellite point location data. The experimental methodology involves the composite processing of gyro data and star sensor optical axis angles, integration of the processed data through time-matching and normalization, and denoising of the integrated data, followed by trigonometric fitting to capture the periodic characteristics of platform vibrations. The positions of the satellite substellar points are determined from the satellite orbit data. A rigorous geometric imaging model is then used to construct a vibration model with geographic location correlation in combination with the satellite subsatellite point positions. The experimental results demonstrate the following: (1) Over the same temporal range, there is a significant convergence in the waveform similarities between the gyro data and the star sensor optical axis angles, indicating a strong correlation in the jitter information; (2) The platform vibration exhibits a robust correlation with the satellite’s geographic location along its orbit. Specifically, the model reveals that the GF−7 satellite experiences the maximum vibration amplitude between 5° S and 20° S latitude during its ascending phase, and the minimum vibration amplitude between 5° N and 20° N latitude during the descending phase. The model established in this study offers theoretical support for optimizing satellite attitude and mitigating platform vibrations. Full article

The sea surface skin temperature (SST_skin) is of critical importance with regard to air–sea interactions and marine carbon circulation. At present, no single remote sensor is capable of providing a gap-free SST_skin. The use of data fusion techniques is therefore essential for the purpose of filling these gaps. The extant fusion methodologies frequently fail to account for the influence of depth disparities and the diurnal variability of sea surface temperatures (SSTs) retrieved from multi-sensors. We have developed a novel approach that integrates depth and diurnal corrections and employs advanced data fusion techniques to generate hourly gap-free SST datasets. The General Ocean Turbulence Model (GOTM) is employed to model the diurnal variability of the SST profile, incorporating depth and diurnal corrections. Subsequently, the corrected SSTs at the same observed time and depth are blended using the Markov method and the remaining data gaps are filled with optimal interpolation. The overall precision of the hourly gap-free SST_skin generated demonstrates a mean bias of −0.14 °C and a root mean square error of 0.57 °C, which is comparable to the precision of satellite observations. The hourly gap-free SST_skin is vital for improving our comprehension of air–sea interactions and monitoring critical oceanographic processes with high-frequency variability. Full article