Search Results (22,746)

Fourier expansions employing polyharmonic–Neumann eigenfunctions have demonstrated improved convergence over those using the classical trigonometric system, due to the rapid decay of their Fourier coefficients. Building on this insight, we investigate interpolations on a finite interval that are exact for polyharmonic–Neumann eigenfunctions and exhibit similar benefits. Furthermore, we enhance the convergence of these interpolations by incorporating the concept of quasi-periodicity, wherein the basis functions are periodic over a slightly extended interval. We demonstrate that those interpolations achieve significantly better convergence rates away from the endpoints of the approximation interval and offer increased accuracy over the entire interval. We establish these properties for a specific case of polyharmonic–Neumann eigenfunctions known as the modified Fourier system. For other basis functions, we provide supporting evidence through numerical experiments. While the latter methods display superior convergence rates, we demonstrate that interpolations using the modified Fourier basis offer distinct advantages. Firstly, they permit explicit representations via the inverses of certain Vandermonde matrices, whereas other interpolation methods require approximate computations of the eigenvalues and eigenfunctions involved. Secondly, these matrix inverses can be efficiently computed for numerical applications. Thirdly, the introduction of quasi-periodicity improves the convergence rates, making them comparable to those of other interpolation techniques. Full article

The process-induced deformation (PID) during the manufacturing of thermosetting composite materials can significantly compromise manufacturing precision. This paper introduces an innovative method that combines a finite element analysis (FEA), feature classification algorithms, and an Artificial Neural Network (ANN) framework to rapidly predict the PID of a typical L-shaped structure. Initially, a comprehensive range of parameters that influence PID are compiled in this research, followed by the generation of a dataset through FEA considering viscoelastic constitutive models, validated by experimental results. Influential parameters are classified using Random Forest and LASSO regression methods, with each parameter rated according to its impact on PID, delineating their varying degrees of importance. Subsequently, through a hyperparameter analysis, an ANN framework is developed to rapidly predict the PID, while also refining the assessment of the parameters’ significance. This innovative approach achieves a computational time reduction of 98% with less than a 5% loss in accuracy, and highlights that under limited computational conditions, considering only a subset or all of the parameters—the peak temperature, corner angle, coefficient of chemical shrinkage, coefficient of thermal expansion, curing pressure, and —minimizes accuracy loss. The study demonstrates that machine learning algorithms can effectively address the challenge of predicting composite material PID, providing valuable insights for practical manufacturing applications. Full article

To effectively manage the sustainable urban development of cities, it is crucial to quickly understand the geological and geotechnical attributes of the underground. Carrying out such studies entails significant investments and focused reconnaissance efforts, which might not align seamlessly with large-scale territorial planning initiatives within a city accommodating more than 3 million inhabitants, like Casablanca in Morocco. Additionally, various specific investigations have been conducted by municipal authorities in recent times. The primary aim of this study is to furnish city managers and planners with a tool for informed decision-making, enabling them to explore the geological and geotechnical properties of soil foundations using Geographic Information Systems (GISs) and geostatistics. This database, initially intended for utilization by developers and construction engineers, stands to economize a substantial amount of time and resources. During the urban planning of cities and prior to determining land usage (five- or seven-floor structures), comprehending the mechanical traits (bearing capacity, water levels, etc.) of the soil is crucial. To this end, geological and geotechnical maps, along with a collection of 100 surveys, were gathered and incorporated into a GIS system. These diverse data sources converged to reveal that the underlying composition of the surveyed area comprises silts, calcarenites, marls, graywackes, and siltstones. These formations are attributed to the Middle Cambrian and the Holocene epochs. The resultant geotechnical findings were integrated into the GIS and subjected to interpolation using ordinary kriging. This procedure yielded two distinct maps: one illustrating bearing capacity and the other depicting the substratum. The bearing capacity of the soil in the study zone is rated as moderate, fluctuating between two and four bars. The depth of the foundation remains relatively shallow, ranging from 0.8 m to 4.5 m. The outcomes are highly promising, affirming that the soil in Casablanca boasts commendable geotechnical attributes capable of enduring substantial loads and stresses. Consequently, redirecting future urban planning in the region toward vertical expansion seems judicious, safeguarding Casablanca’s remaining green spaces and the small agricultural belt. The results of this work help to better plan the urban development of the city of Casablanca in a smarter way, thus preserving space, agriculture, and the environment while promoting sustainability. In addition, the databases and maps created through this paper aim for a balanced financial management of city expenditures in urban planning. Full article

Electric vehicles (EVs) are widely regarded as a crucial tool for carbon reduction due to the gradual increase in their numbers. However, these vehicles are equipped with batteries that have a limited lifespan. It is commonly stated that when the battery capacity falls below 70%, it needs to be replaced, and these discarded batteries are typically sent for recycling. Nevertheless, there is an opportunity to repurpose these worn-out batteries for a second life in electric power systems. This study focuses on the arbitrage situation of a second-life battery (SLB) facility in day-ahead electricity markets. This approach not only contributes to balancing supply and demand in the electric power system but also allows the battery facility to achieve significant gains. We propose an artificial intelligence system that integrates optimized deep learning algorithms for market price predictions with a mixed-integer linear programming (MILP) model for market participation and arbitrage decisions. Our system predicts prices for the next 24 h using Neural Hierarchical Interpolation for Time Series (N-HiTS) and decides when to enter the market using the MILP model and incorporating the predicted data and the statuses of the batteries. We compare the accuracy of our trained deep learning model with other deep learning models such as recurrent neural networks (RNNs), Long Short-Term Memory (LSTM), and Neural Basis Expansion Analysis for Interpretable Time-Series Forecasting (N-BEATS). We test the efficiency of the proposed system using real-world Turkish day-ahead market data. According to the results obtained, this study concludes that substantial gains can be achieved with the predicted prices and the optimal operating model. A facility with a total battery energy capacity of 5.133 MWh can generate a profit of USD 539 in one day, showcasing the potential of our study. Our new system’s approach provides proof of concept of new research opportunities for the participation of SLB facilities in day-ahead markets. Full article

Understanding population structure is a priority for evaluating population dynamics of commercially fished cephalopods under fishing pressure and environmental changes. This study employed a multidisciplinary approach to clarify the population structure of Loliolus (Nipponololigo) uyii, a common squid in inshore fisheries. Sampling was conducted multiple times to cover the distribution range across the East China Sea and South China Sea. High haplotype diversity was revealed by three gene markers (COI, 16S and ODH). Two geographical clades with significant genetic differentiation were divided through phylogenetic trees and haplotype networks. The boundary between the two clades is delineated by the Dongshan population in the southern East China Sea. Furthermore, the neutrality tests and mismatch analysis suggested that L. (N.) uyii populations may have undergone population expansion. Correspondingly, statolith differences in lateral dome and posterior indentation, along with high classification success, further supported the genetic division. The overall difference in statolith shape also efficiently identified seasonal groups in the Beibu Gulf lacking genetic differentiation. This result offers new insights into the influence of genetic and environmental factors on statolith shape. The integrated results provide a comprehensive understanding of the population structure of L. (N.) uyii, laying the foundation for resource development and the conservation of the species. Full article

Drosophila melanogaster usage has provided substantial insights into the pathogenesis of several nucleotide repeat expansion diseases (NREDs), a group of genetic diseases characterized by the abnormal expansion of DNA repeats. Leveraging the genetic simplicity and manipulability of Drosophila, researchers have successfully modeled close to 15 NREDs such as Huntington’s disease (HD), several spinocerebellar ataxias (SCA), and myotonic dystrophies type 1 and 2 (DM1/DM2). These models have been instrumental in characterizing the principal associated molecular mechanisms: protein aggregation, RNA toxicity, and protein function loss, thus recapitulating key features of human disease. Used in chemical and genetic screenings, they also enable us to identify promising small molecules and genetic modifiers that mitigate the toxic effects of expanded repeats. This review summarizes the close to 150 studies performed in this area during the last seven years. The relevant highlights are the achievement of the first fly-based models for some NREDs, the incorporation of new technologies such as CRISPR for developing or evaluating transgenic flies containing repeat expanded motifs, and the evaluation of less understood toxic mechanisms in NREDs such as RAN translation. Overall, Drosophila melanogaster remains a powerful platform for research in NREDs. Full article

The vortex tube, also known as the Ranque–Hilsch vortex tube, is a mechanical device that separates compressed gas into hot and cold streams. It offers a reliable and cost-effective solution to a wide range of cooling applications, as it operates without moving parts, electricity, or refrigerants. Research on vortex tubes has primarily focused on understanding the mechanisms of energy separation and optimizing cooling performance by altering geometric operational parameters. In this study, a Computation Fluid Dynamics (CFD) analysis was conducted to enhance the prediction of energy separation performance and improve the overall energy efficiency of the vortex tube. First, the geometry of the experimental device was modeled to closely match its actual shape, unlike the simplified geometries commonly used in previous CFD studies. Simulations were then carried out with variation in grid systems and turbulence models, and the results demonstrated improved agreement with experimental data compared to those reported in previous studies. Finally, simulations with a modified shape of the inlet nozzle shape were performed, revealing that the energy separation effect of the vortex tube could be enhanced by approximately 15% with an increased inlet expansion ratio ($ϵ$) while maintaining a constant nozzle length. Full article

Huntington’s disease (HD) is a hereditary neurodegenerative disorder caused by a CAG tract expansion in the huntingtin gene (HTT). HD is characterized by involuntary movements, cognitive decline, and behavioral changes. Pathologically, patients with HD show selective striatal neuronal vulnerability at the early disease stage, although the mutant protein is ubiquitously expressed. Activation of the immune system and glial cell-mediated neuroinflammatory responses are early pathological features and have been found in all neurodegenerative diseases (NDDs), including HD. However, the role of inflammation in HD, as well as its therapeutic significance, has been less extensively studied compared to other NDDs. This review highlights the significantly elevated levels of inflammatory proteins and cellular markers observed in various HD animal models and HD patient tissues, emphasizing the critical roles of microglia, astrocytes, and oligodendrocytes in mediating neuroinflammation in HD. Moreover, it expands on recent discoveries related to the peripheral immune system’s involvement in HD. Although current immunomodulatory treatments and inflammatory biomarkers for adjunctive diagnosis in HD are limited, targeting inflammation in combination with other therapies, along with comprehensive personalized treatment approaches, shows promising therapeutic potential. Full article

Over recent decades, intense urbanization, city expansion, and unregulated construction have led to a scarcity of green spaces and environmental degradation. Green spaces significantly enhance residents’ quality of life by supporting mental and physical health, improving environmental conditions, and benefiting the local microclimate. However, adding green spaces alone is insufficient for modern cities. Increasing population mobility and demand for sustainable transportation modes highlight the role of bicycles and safe bike lane networks in urban development. This study focuses on the perspectives of Trikala’s citizens regarding the contributions of green spaces to their quality of life and cycling habits, and to the effectiveness of current cycling infrastructure. Using a structured questionnaire, data were analyzed with SPSS through descriptive and multivariate analysis. The results demonstrate a strong public acknowledgment of green spaces and bicycles as essential components for sustainable urban planning. Nevertheless, challenges with bike lane safety and network continuity were evident. This study concludes that enhancing both green spaces and cycling infrastructure is crucial for fostering a more environmentally friendly and healthy urban environment. Policy recommendations include improving bike lane safety and expanding green space access, creating a foundation for sustainable, resilient urban living. Full article

This paper deals with the non-contact measurement of heartbeat and respiration using a millimeter-wave multiple-input–multiple-output (MIMO) frequency-modulated continuous-wave (FM-CW) radar. Monitoring heartbeat and respiration is useful for detecting cardiac diseases and understanding stress levels. Contact sensors are not suitable for these sorts of long-term measurements due to the discomfort and skin irritation they cause. Therefore, the use of non-contact sensors, such as radars, is desirable. In this study, we obtained heartbeat and respiration information from phase data measured using a millimeter-wave MIMO FM-CW radar. We propose a two-wave model based on a Fourier series expansion and extract respiration and heartbeat information as a minimization problem. This model makes it possible to produce respiration and heartbeat waveforms. The produced heartbeat waveform can be used for estimating the interbeat interval (IBI). Experiments were conducted to confirm the usefulness of the proposed method. Moreover, the estimated results were compared with the contact sensor’s results. The results for both types of sensors were in good agreement. Full article

Previous studies indicate that increased healthcare access through Medicaid expansion and alleviation of socioeconomic stressors via higher minimum wages improved health outcomes. This study investigates the spatial relationships between the Medicaid expansion, minimum wage policy, and Alzheimer’s Disease (AD) dementia prevalence across the US. We used county-level AD dementia prevalence adjusted for age, sex, race/ethnicity, and education. Social Vulnerability Index (SVI) data, Medicaid expansion status, and state minimum wage law status were incorporated from CDC, Kaiser Family Foundation, and US Department of Labor sources, respectively. We employed the Getis-Ord Gi* statistic to identify hotspots and cold spots of AD dementia prevalence at the county level. We compared these locations with the overall SVI scores using univariate analyses. We also assessed the proportion of hot and cold spots at the state level based on Medicaid expansion and minimum wage status using the logistic regression model. The most vulnerable SVI quartile (Q4) had the highest number of hotspots (n = 311, 64.8%), while the least vulnerable quartile (Q1) had the fewest hotspots (n = 22, 4.6%) ($\chi$² = 307.41, p < 0.01). States that adopted Medicaid expansion had a significantly lower proportion of hotspots compared to non-adopting states (p < 0.05), and the non-adopting states had significantly higher odds of having hotspots than adopting states (OR = 2.58, 95% CI: 2.04–3.26, p < 0.001). Conversely, the non-adopting states had significantly lower odds of having cold spots compared to the adopting states (OR = 0.24, 95% CI: 0.19–0.32, p < 0.01). States with minimum wage levels at or below the federal level showed significantly higher odds of having hotspots than states with a minimum wage above the federal level (OR = 1.94, 95% CI: 1.51–2.49, p < 0.01). Our findings suggest significant disparities in AD dementia prevalence related to socioeconomic and policy factors and lay the groundwork for future causal analyses. Full article

The urban heat island (UHI) effect is one of the most prominent surface climate changes driven by human activities. This study examines the UHI characteristics and influencing factors in the Southern Xinjiang urban agglomeration using MODIS satellite data combined with observational datasets. Our results reveal a significant increase in impervious surfaces in the region between 1995 and 2015, with the most rapid expansion occurring from 2010 to 2015. This urban expansion is the primary driver of changes in UHI intensity. The analysis from 2000 to 2015 shows substantial spatial variation in UHI effects across cities. Hotan recorded the highest annual average daytime UHI intensity of 3.7 °C, while Aksu exhibited the lowest at approximately 1.6 °C. Daytime UHI intensity generally increased during the study period, with the highest intensities observed in the summer. However, nighttime UHI trends varied across cities, with most showing an increase in intensity. Temperature, precipitation, and aerosol optical depth (AOD) were identified as the main factors influencing annual average daytime UHI intensity, while PM10 concentration showed a weak and inconsistent correlation with UHI intensity, varying by city and season. Full article

With the gradual improvement and implementation of unconventional wells drilling and environmental regulations, there is an urgent need for high-performance and more environmentally friendly lubricants for water-based drilling fluids (WD). Developing green oilfield chemicals from natural products is a shortcut. In this work, Abelmoschus esculentus (L.) Moench/okra has been studied as the lubricant in WD. The green drilling fluid lubricant developed demonstrates excellent lubrication performance, as well as good filtration loss reduction and inhibition of bentonite hydration expansion. The results show that with the addition of 2.5% okra slurry to water-based drilling fluid, the coefficient of friction decreased by 51.68%, the apparent viscosity (AV) increased by 51.32%, the plastic viscosity (PV) increased by 42.99%, and the fluid loss decreased by 39.88%. Moreover, through TGA, SEM, FT-IR, particle distribution tests, and contact angle tests, the lubrication mechanism of okra slurry was discussed. Finally, the economic feasibility of using okra as an environmentally friendly lubricant for drilling fluids was analyzed. This work combines agricultural products with industrial production, which not only solves industrial problems but also enhances the added value of agricultural products, providing a reference for the coordinated development of industry and agriculture. Full article

Acute care provided in the hospital’s emergency department (ED) is a key component of the healthcare system that serves as an essential bridge between outpatient and inpatient care. However, due to the emergency-driven nature of presenting problems and the urgency of care required, the ED is more prone to unintended medication regimen changes than other departments. Ensuring quality use of medicines (QUM), defined as “choosing suitable medicines and using them safely and effectively”, remains a challenge in the ED and hence requires special attention. The role of pharmacists in the ED has evolved considerably, transitioning from traditional inventory management to delivering comprehensive clinical pharmacy services, such as medication reconciliation and review. Emerging roles for ED pharmacists now include medication charting and prescribing and active participation in resuscitation efforts. Additionally, ED pharmacists are involved in research and educational initiatives. However, the ED setting is still facing heightened service demands in terms of the number of patients presenting to EDs and longer ED stays. Addressing these challenges necessitates innovation and reform in ED care to effectively manage the complex, rising demand for ED care and to meet government-imposed service quality indicators. An example is redesigning the medication use process, which could necessitate a shift in skill mix or an expansion of the roles of ED pharmacists, particularly in areas such as medication charting and prescribing. Collaborative efforts between pharmacists and physicians have demonstrated positive outcomes and should thus be adopted as the standard practice in improving the quality use of medicines in the ED. Full article

Large quantities of water are often used to extinguish fires when accidental fuel leakage occurs during storage and transportation. This may lead to spill fires on water. Boilover and splash of heavy oil spill fires in particular can pose a serious thermal threat to surrounding facilities and personnel. In this work, a series of diesel spill fire experiments were conducted on the surface of water. The results showed that, for the non-ignition cases, the fuel spread velocity was fast at first, then maintained a long period of steady spread, which can be successfully predicted by a developed spread model. During the ignition process, the burning of diesel fuel is divided into four phases. Following a brief quasi-steady burning phase, we observed an expansion of the burning area during the intermittent boilover phase, which was primarily driven by boilover. During the quasi-steady burning phase, the burning rate was lower than that of pool fires, which is attributed to the heat loss between the diesel and water layers. This heat loss also results in a lower flame height than the pool fire, and a dimensionless equation was proposed to eliminate discrepancies. During the intermittent boilover phase, the increase in the burning area was used to characterize the boilover intensity, which was found to be negatively correlated with the number of boilovers. Furthermore, the emergence of the boilover also caused flame radiation to rise rapidly; it was about 19% to 30% higher than that in the quasi-steady burning phase. Full article