Search Results (23,112)

This study employs a Monte Carlo-based 3D compressive percolation model to systematically analyze the electrical behavior of flexible electronic composites under compressive deformation. By simulating the spatial distribution and connectivity of conductive particles, this study identifies six distinct percolation cases, each describing a unique connectivity evolution under strain. The model reveals that excessive initial connectivity leads to saturation effects, reducing sensitivity, while a high Poisson’s ratio (≥0.3) causes connectivity loss due to shear plane expansion. Notably, asymmetric particle shapes, such as cylinders and rectangles, exhibit superior percolation behavior, forming infinite clusters at lower strain thresholds (~0.4) compared to spherical particles (~0.5). Monte Carlo simulations with 3000 particles validate these findings, showing consistent trends in percolation behavior across different deformation states. By classifying and quantifying these six connectivity scenarios, this research provides a structured framework for optimizing flexible sensor designs, ensuring an optimal balance between conductivity and sensitivity. These findings contribute to advancing flexible electronics, particularly in wearable health monitoring, robotics, and smart textiles. Full article

This study addresses the disruptive impact of incidents on road networks, which often lead to traffic congestion. If not promptly managed, congestion can propagate and intensify over time, significantly delaying the recovery of free-flow conditions. We propose an enhanced model based on an exponential decay of the time required for free flow recovery between incident occurrences. Our approach integrates a shot noise process, assuming that incidents follow a non-homogeneous Poisson process. The increases in recovery time following incidents are modeled using exponential and gamma distributions. We derive key performance metrics, providing insights into congestion risk and the unlocking phenomenon, including the probability of the first passage time for our process to exceed a predefined congestion threshold. This probability is analyzed using two methods: (1) an exact simulation approach and (2) an analytical approximation technique. Utilizing the analytical approximation, we estimate critical extreme quantities, such as the minimum incident clearance rate, the minimum intensity of recovery time increases, and the maximum intensity of incident occurrences required to avoid exceeding a specified congestion threshold with a given probability. These findings offer valuable tools for managing and mitigating congestion risks in road networks. Full article

Understanding the responses of stream ecosystems to environmental disturbances is essential for maintaining and restoring healthy ecosystems. In this study, we analyzed the associations between benthic macroinvertebrate communities and environmental factors using machine learning approaches to identify key stressors potentially influencing stream ecosystem health. Various machine learning models were evaluated, with random forest (RF) and gradient boosting machine (GBM) identified as the optimal models for predicting tolerant species (TS) and Ephemeroptera, Plecoptera, and Trichoptera (EPT) species densities. SHAP analysis revealed that watershed variables, such as elevation, flow velocity, and slope, significantly influenced EPT and TS populations. EPT population density increased with elevation and flow velocity but decreased significantly with higher levels of biochemical oxygen demand (BOD), total nitrogen (TN), and agricultural land-use proportions, with negative effects becoming evident beyond threshold levels. Conversely, TS population density showed a positive response to elevated BOD, TN, and agricultural land-use proportions, stabilizing at the threshold levels of BOD and TN, but continuing to increase with greater agricultural land use. Through machine learning, this study provides critical insights into how environmental variables are associated with the distribution of benthic macroinvertebrate communities. By identifying threshold levels of key stressors, this approach offers actionable guidance for managing agricultural runoff, enhancing riparian buffers, and implementing sustainable land-use practices. These findings contribute to the development of integrated watershed management strategies that promote the long-term sustainability of stream ecosystems. Full article

The addition of even minute amounts of solid polymers, measured in parts per million (ppm), into a simple Newtonian fluid like water significantly alters the flow behavior of the resulting polymer solutions due to the introduction of fluid viscoelasticity. This viscoelastic behavior, which arises due to the stretching and relaxation phenomena of polymer molecules, leads to complex flow dynamics that are starkly different from those seen in simple Newtonian fluids under the same conditions. In addition to polymer solutions, many other fluids, routinely used in various industries and our daily lives, exhibit viscoelastic properties, including emulsions; foams; suspensions; biological fluids such as blood, saliva, and cerebrospinal fluid; and suspensions of biomolecules like DNA and proteins. In various microfluidic platforms, these viscoelastic fluids are often transported using electro-osmotic flows (EOFs), where an electric field is applied to control fluid movement. This method provides more precise and accurate flow control compared to pressure-driven techniques. However, several experimental and numerical studies have shown that when either the applied electric field strength or the fluid elasticity exceeds a critical threshold, the flow in these viscoelastic fluids becomes unstable and asymmetric due to the development of electro-elastic instability (EEI). These instabilities are driven by the normal elastic stresses in viscoelastic fluids and are not observed in Newtonian fluids under the same conditions, where the flow remains steady and symmetric. As the electric field strength or fluid elasticity is further increased, these instabilities can transition into a more chaotic and turbulent-like flow state, referred to as electro-elastic turbulence (EET). This article comprehensively reviews the existing literature on these EEI and EET phenomena, summarizing key findings from both experimental and numerical studies. Additionally, this article presents a detailed discussion of future research directions, emphasizing the need for further investigations to fully understand and harness the potential of EEI and EET in various practical applications, particularly in microscale flow systems where better flow control and increased transport rates are essential. Full article

This study examines the quality and availability of daylight in childcare facilities, focusing on compliance with Polish Technical Conditions (TCs) and comparing them with international certification systems such as BREEAM, LEED, and WELL. Polish regulations regarding sunlight exposure require revisions to support the sustainable development of buildings, impacting children’s well-being, their health, and the building’s energy efficiency. Daylight’s significance for children’s health and development underpins the investigation, highlighting its impact on the circadian rhythm, cognitive abilities, and well-being. The research utilized computational simulations with Rhinoceros 7 and Ladybug and Honeybee plugins to model a preschool room’s daylight performance under various window configurations and orientations. Quantitative and qualitative analyses were conducted, focusing on parameters such as Daylight Factor (DF), Daylight Autonomy (DA), and Useful Daylight Illuminance (UDI). The findings revealed that while the TCs’ requirements ensure minimum daylight access, they result in nonoptimal lighting quality as defined by international standards. Almost half of the surveyed rooms in the case with a WFR of one-eighth did not meet the condition for having acceptable daylight levels, as defined in the study. In the same study, only about one-third of the analyzed variants achieved the threshold for good daylight quality. At a WFR of one-eighth, no room variant reached the level considered to indicate excellent daylight quality. The results show the need for revised regulations incorporating comprehensive metrics like Climate-Based Daylight Modeling (CBDM). This study suggests that integrating advanced methods of assessing daylight quality could significantly improve daylight conditions in childcare environments. This research is a starting point for discussing the need to modernize the Polish Technical Conditions (TC) to support the sustainable development of childcare facilities in Poland. Full article

Despite significant advancements in flood forecasting using machine learning (ML) algorithms, recent events have revealed hydrological behaviors deviating from historical model development trends. The record-breaking 2019 flood in the Ottawa River basin, which exceeded the 100-year flood threshold, underscores the escalating impact of climate change on hydrological extremes. These unprecedented events highlight the limitations of traditional ML models, which rely heavily on historical data and often struggle to predict extreme floods that lack representation in past records. This calls for integrating more comprehensive datasets and innovative approaches to enhance model robustness and adaptability to changing climatic conditions. This study introduces the Next-Gen Group Method of Data Handling (Next-Gen GMDH), an innovative ML model leveraging second- and third-order polynomials to address the limitations of traditional ML models in predicting extreme flood events. Using HEC-RAS simulations, a synthetic dataset of river flow discharges was created, covering a wide range of potential future floods with return periods of up to 10,000 years, to enhance the accuracy and generalization of flood predictions under evolving climatic conditions. The Next-Gen GMDH addresses the complexity and limitations of standard GMDH by incorporating non-adjacent connections and optimizing intermediate layers, significantly reducing computational overhead while enhancing performance. The Gen GMDH demonstrated improved stability and tighter clustering of predictions, particularly for extreme flood scenarios. Testing results revealed exceptional predictive accuracy, with Mean Absolute Percentage Error (MAPE) values of 4.72% for channel width, 1.80% for channel depth, and 0.06% for water surface elevation. These results vastly outperformed the standard GMDH, which yielded MAPE values of 25.00%, 8.30%, and 0.11%, respectively. Additionally, computational complexity was reduced by approximately 40%, with a 33.88% decrease in the Akaike Information Criterion (AIC) for channel width and an impressive 581.82% improvement for channel depth. This methodology integrates hydrodynamic modeling with advanced ML, providing a robust framework for accurate flood prediction and adaptive floodplain management in a changing climate. Full article

The increasing use of cone beam computed tomography (CBCT) has led to a growing demand for DICOM software that enables the assessment and measurement of craniofacial structures. This study aimed to compare the airway volume and the minimum axial area in patients with cleft lip and palate using five different imaging software programs: Dolphin3D, InVivo Dental, ITK Snap, InVesalius, and NemoFAB. Initially, 100 CBCT scans were selected by an examiner, and their corresponding DICOM files were collected. The oropharyngeal segments were delineated following the manufacturer’s guidelines, using two different segmentation techniques: interactive and fixed threshold. The results were analyzed using the Friedman test and Wilcoxon post hoc test, with a 5% significance level for all statistical tests. The findings for both the minimum axial area and total volume revealed that the median values across the software groups were higher than expected, and significant differences were observed when comparing the groups (p < 0.001). All five software programs showed notable differences in their outputs. Specifically, a statistically significant difference in volume was found across all groups, except between InVivo and ITK-Snap. It is recommended that pre- and post-treatment comparisons be performed using the same software for consistency. Full article

Algae are vital to aquatic ecosystems, with their structure and abundance influencing ecological health. However, automated detection in real water samples is hindered by complex backgrounds, species diversity, and size variations. Traditional methods are deemed costly and species-specific, leading to deep learning adoption. Current studies rely on CNN-based models and limited datasets. To improve the detection accuracy of multiple algal species in real, complex backgrounds, this study collected multi-species algae samples from actual water environments and implemented an integrated Transformer-based framework for automated localization and recognition of small, medium, and large algae species. Specifically, algae samples from five different regions were collected to construct a comprehensive dataset containing 25 algal species with diverse backgrounds and rich category diversity. To address dataset imbalances in minority species, a segmentation-fusion data augmentation method was proposed, which enhanced performance across YOLO, Faster R-CNN, and Deformable DETR models, with YOLO achieving a 7.1% precision increase and a 1.5% mAP improvement. Model optimization focused on an improved Deformable DETR, incorporating multi-scale feature extraction, deformable attention mechanisms, and the normalized Wasserstein distance loss function. This improvement enhanced small target and overlapping object detection, achieving a 10.4% mAP increase at an intersection over union (IoU) threshold of 0.5 and outperforming unmodified Deformable DETR. Full article

La Graciosa, a Natura 2000 site in the Canary Islands, faces substantial conservation challenges, including a large free-roaming cat population that threatens the island’s native biodiversity. In July 2024, a Trap–Neuter–Return (TNR) campaign achieved an 81.4% sterilization rate within urban areas, highlighting TNR’s short-term effectiveness in reducing reproductive potential and, consequently, mitigating predation pressures primarily through the prevention of new litters and reduced reproductive activity in cats. The campaign’s success relied heavily on the active involvement of the local community, who assisted with identifying, trapping, and monitoring free-roaming cats, thereby facilitating a high sterilization rate. However, administrative restrictions hindered access to peri-urban zones, leaving essential population clusters unsterilized and limiting the campaign’s overall scope. Additionally, strong opposition from conservation groups, amplified by extensive media coverage, halted the project prematurely, reducing the effective sterilization rate to 69.3% within three months. Population Viability Analysis (PVA) suggests that achieving high sterilization rates could lead to population reduction over time; however, the inability to access all population segments and to reach the ideal 93–95% sterilization threshold limits TNR’s potential as a long-term standalone solution. Our findings underscore the need for adaptive, context-specific management frameworks in ecologically sensitive areas that integrate TNR with complementary measures, consider regulatory barriers, and value community involvement. This case study provides crucial insights for policymakers and conservationists seeking to balance biodiversity conservation with humane management practices in protected areas. Full article

Background: Pain is a complex condition influenced by peripheral, central, immune, and psychological factors. Multitarget approaches offer a more effective and safer alternative to single-target analgesics by enhancing efficacy, reducing side effects, and minimizing tolerance. This study aimed to identify a novel multitarget analgesic with improved pharmacological properties. Methods: An in vivo-guided screening approach was used to discover a new analgesic compound. Compound 29, derived from a novel scaffold inspired by opiranserin and vilazodone pharmacophores, was identified through analog screening in the formalin test. Its efficacy was further evaluated in the spinal nerve ligation (SNL) model of neuropathic pain. Mechanistic studies explored its interaction with neurotransmitter transporters and receptors, while pharmacokinetic and safety assessments were conducted to determine its stability, brain penetration, and potential toxicity. Results: Compound 29 demonstrated high potency in the formalin test, with an ED50 of 0.78 mg/kg in the second phase and a concentration-dependent effect in the first phase. In the SNL model, it produced dose-dependent analgesic effects, increasing withdrawal thresholds by 24% and 45% maximum possible effect (MPE) at 50 and 100 mg/kg, respectively. Mechanistic studies revealed strong triple uptake inhibition, particularly at dopamine (DAT) and serotonin (SERT) transporters, alongside high-affinity 5-HT2A receptor antagonism. Pharmacokinetic analysis indicated enhanced stability and blood–brain barrier permeability. In vitro studies confirmed its nontoxicity to HT-22 cells but revealed potential hERG inhibition and strong CYP3A4 inhibition. Conclusions: Compound 29 is a promising multitarget analgesic with potent efficacy and favorable pharmacokinetics. Ongoing optimization efforts aim to mitigate side effects and enhance its therapeutic profile for clinical application. Full article

As a vital component of urban planning, urban vitality profoundly affects the sustainable development and well-being of cities. Existing evaluation methods struggle to effectively explain the spatial distribution between nonlinear indicators while simultaneously considering geographical location and spatial attributes. How do we propose a research framework to address this nonlinear spatial distribution? This question is crucial for the study of urban vitality. To bridge this research gap, this paper proposes an SOFM neural network utilizing multisource geospatial big data to explore the spatial distribution of urban vitality. Our results showed the following: (1) Urban vitality in the five dimensions of concentration, functional diversity, contact opportunity, accessibility, and distance from border vacuums decreased from the core area to the periphery, except for building diversity, which exhibited an opposite trend. (2) The urban vitality of Beijing’s central areas primarily showed a circled spatial structure and extended along the Beijing Central Axis and Chang’an Avenue. Additionally, a 15 km radius serves as a significant threshold, encompassing clusters 0, 1, and 2, which align with an important circle delineated by the Master Plan of Beijing (2016–2035). The findings of our research serve as valuable insights for enhancing urban vitality and urban planning. Full article

Vegetation anomalies are frequently occurring and may greatly affect ecological functions. Many near-real-time (NRT) detection methods have been developed to detect these anomalies in a timely manner whenever a new satellite observation is available. However, the undisturbed vegetation conditions captured by these methods are only applicable to a particular pixel or vegetation type, resulting in a lack of universality. Also, most methods that use single characteristic parameter may ignore the multi-spectral expression of vegetation anomalies. In this study, we developed a universal framework to simultaneously detect various vegetation anomalies in NRT from Landsat observations. Firstly, Landsat surface reflectance data from the Benchmark Land Multisite Analysis and Intercomparison of Products (BELMANIP) sites were selected as a reference vegetation dataset to calculate the normalized difference vegetation index (NDVI) and the normalized burn ratio (NBR), which describe vegetation conditions from the perspectives of greenness and moisture, respectively. After the elimination of cloud-contaminated pixels, the high-quality NDVI and NBR data over the BELMANIP sites were further normalized in order to remove the differences in the growth of the varying vegetation. Based on the normalized NDVI and NBR, kernel density estimation (KDE) was used to create a universal measure of undisturbed vegetation, which described the uniform spectral frequency distribution of different undisturbed vegetation with a series of accumulated probabilities on a monthly basis. Whenever a new Landsat observation is collected, the vegetation anomalies are determined according to the universal measure in NRT. To demonstrate the potential of this framework, three study areas with different anomaly types (deforestation, fire event, and insect outbreak) in distinct ecozones (rainforest, coniferous forest, and deciduous broad-leaf forest) were used. The quantitative analyses showed generally high overall accuracies (>90% with the kappa >0.82). The user accuracy for the fire event and the producer accuracy for the earlier insect infestation were relatively lower. The accuracies may be affected by the complexity of the land surface, the quality of the Landsat image, and the accumulated probability threshold. Full article

This study focused on developing a functional bazlama with a lower glycemic index (GI) that is high in β-glucan and rich in plant-based protein. Functional bazlama samples were produced by supplementing bread wheat flour with high β-glucan content hull-less barley flour and high protein content lentil flour (15%, 30%, and 45%). Additionally, mixed bazlama samples (Mix1, Mix2, Mix3, and Mix4) were produced by supplementing them with both barley and lentil flours. The results showed that 3 g of β-glucan could be provided from the bazlama sample and supplemented with 45% barley flour, which meets the threshold to carry health claims. Supplementing with 30% and 45% lentil flour increased the protein content of the bazlama samples to a level qualifying them as a “high protein”. The control bazlama had a high GI, while samples supplemented with 30% and 45% barley or lentil flour and all mixed bazlama samples had medium GI values, and Mix2 had the lowest GI value among all bazlama samples. Also, as the supplementation levels of barley and lentil flour increased, the phenolic contents and antioxidant capacities of the bazlama samples increased. The results of the present study indicate that barley and lentils can be used as an ingredient in traditional flatbreads to obtain products with better functional and nutritional properties. Full article

Plastic products are used in agriculture to increase crop yield and improve crop quality to face a double challenge: a growing world population and a depletion and scarcity of natural resources. In this framework, the European Commission is working on establishing biodegradation criteria under natural conditions for certain plastic products. Such criteria are particularly important for products where biodegradation is key once reaching the end of their shelf life, considering an end-of-life scenario where their waste management is either unfeasible or highly complex. Under this scope, this work aims to provide a comprehensive assessment of the current status of European regulations in terms of plasticulture product biodegradability, highlighting the specific tests and standards regarding the biodegradability assessment. Biodegradation of plasticulture products in soil and water has been considered for biodegradability criteria, establishing a threshold of at least 90% of the organic carbon converted into CO₂. These regulations have followed a tool-based study of a mathematical prediction model for the main existing families of biodegradable polymers in soil. These regulations will help the fertilizer industry to develop new formulations that are more sustainable and effective in the agriculture field. Full article

Spoofing detection is critical for GNSS security. To address the issues of low detection rates and insufficient coverage in traditional methods, this study proposes an eye diagram detection method based on the multiscale Canny algorithm with minimum misjudgment probability (EDDM-MSC-MMP). Unlike conventional correlation peak distortion detection techniques, the proposed method uses the MSC-MMP algorithm to perform multiscale edge extraction from the eye diagram generated from the receiver's correlation values. It then calculates the image threshold using minimum misjudgment probability to ensure the accuracy of the eye diagram's edges. This enables the accurate detection of subtle changes in the eye diagram, leading to the better identification of spoofing signals. The results show that the MSC-MMP outperforms traditional edge extraction algorithms by over 0.072 in terms of the optimal dataset scale F score (ODS-F). Compared to signal quality monitoring (SQM) and Carrier-to-Noise Ratio methods, the EDDM-MSC-MMP method increases spoofing detection coverage by over 60%, achieving the highest detection rate in the TEXBAT dataset. Overall, the EDDM-MSC-MMP method improves the reliability and coverage of spoofing detection, providing an effective solution for GNSS spoofing detection. Full article