Search Results (3,224)

This study explored the thermal response of Drosophila suzukii, an injurious insect pest present in many countries worldwide, at different controlled conditions. This species is responsible for several economic losses in soft fruit cultivations, develops on ripening fruits, and has the capability to quickly adapt to new territories and climates, closing multiple generations per year. Given its high invasive potential and the increasing need for low-impact control strategies, an in-depth exploration of the biology of this species and of the stage thermal response is fundamental. Specimens of an Italian strain from Apulia were reared in growth chambers at different constant temperatures (6, 9, 13, 18, 20, 24, 25, 26, 27, 28, 29, 31, 32 and 33 °C). The life cycle of each specimen was individually tracked from the egg to the death of the adults, considering the larval stages distinction as well. Besides development and mortality, egg production over temperature has been recorded. The dataset was first analysed according to life tables studies; then, we also estimated the biological parameters of the most common equations describing development, mortality, and fertility involved in physiologically-based model applications. The results confirmed and extended the information on the thermal response already present in the literature, but with reference to a population adapted to warmer climates. The species successfully developed from egg to adult at 13–29 °C, while between 6–9 and 29–33 °C the development was limited to L2/L3 stages. Optimal temperatures are around 26–28 °C, depending on the life stage. This study provides one of the complete overviews of the thermal response of D. suzukii, which is available in the current literature, and opens the door to more accurate modelling frameworks. Full article

Powder-based fire extinguishing methods are widely used to suppress fires of all kinds efficiently. However, these methods have several drawbacks, the most significant being the large powder residue left behind, which can complicate cleanup and damage sensitive equipment. The present paper investigates reacting flows and develops a methodology for analyzing the interaction of powder particles with fire, addressing both homogeneous and heterogeneous fire inhibition mechanisms. To achieve this, a simplified model was developed using the common principles of the general dynamic equation (GDE) and the population balance equation (PBE) coupled with the reacting flow equations. The model examines the interplay between the initial particles’ diameter and their extinguishing flow rate (concentration), also known as minimal extinguishing concentration (MEC), establishing the relation between the two. Notably, the relation exhibits three different zones, each influenced by different governing mechanisms of combustion inhibition, providing critical insights into optimizing powder-based extinguishing systems. A minimal value of the MEC is found where there is no significant change with the MEC in terms of particle diameter, and the chemical homogeneous mechanism is dominating. The methodology also offers a pathway for finding the maximal extinguishing particle diameter (MED) when the heterogeneous extinguishing mechanism acquires its maximal impact. There is no benefit with a larger particle diameter as it would not practically achieve better extinguishment, but would lead to a potential waste of powder and hence damage equipment. A significant advantage of using extinguishing powders with micro-sized/ultrafine particles is demonstrated where the homogeneous inhibition mechanism becomes predominant. The developed methodology and finding suggest that micro-sized powders are more effective in extinguishing fires, as they offer improved dispersion and reactivity, enhancing the overall efficiency of the fire suppression process. However, considering economic factors such as micron-sized-powder production cost and maintenance may require considering a shift of this set point. Full article

The stability and diversity of the natural landscape is critical to maintaining the ecological functions of a region. However, ecosystems in the Yunnan section of the Tropic of Cancer face increasing pressure from climate change, human activities, and natural disasters, which significantly influence their vulnerability. Ecosystem vulnerability is determined by structural and functional sensitivity, coupled with insufficient adaptability to external stressors. While previous research has emphasized the effects of climate change, the multidimensional impacts of land use and human activities have often been overlooked. This study aims to comprehensively assess the ecological vulnerability of the Yunnan section of the Tropic of Cancer, addressing this research gap by utilizing geographic information system (GIS) technology and the Vulnerability Scoping Diagram (VSD) model. The study constructs a multidimensional evaluation index system based on exposure, sensitivity, and adaptive capacity, with a specific focus on the effects of land use, human activities, and natural disasters. Key indicators include road and population density, soil erosion, and geological hazards, along with innovative considerations of economic adaptive capacity to address gaps in previous assessments. The findings highlight that ecological vulnerability is predominantly concentrated in areas with low vegetation cover and severe soil erosion. Human activities, particularly road and population density, are identified as significant drivers of ecological vulnerability. Sensitivity is heavily influenced by soil erosion and geological disasters, while economic adaptability emerges as a critical factor in mitigating ecological risks. By proposing targeted policy recommendations—such as enhancing ecological protection and restoration, optimizing land use planning, and increasing public environmental awareness—this study provides actionable strategies to reduce ecological vulnerability. The findings offer crucial scientific support for improving the ecological environment in the Tropic of Cancer region and contribute to achieving sustainable development goals. Full article

As a crucial component of urban economic activities, the layout and optimization of urban commercial spaces directly influence the economic prosperity and quality of life of residents. Therefore, comprehensively and accurately characterizing the distribution characteristics and evolutionary patterns of urban commercial spaces is essential for improving the efficiency of urban spatial allocation and achieving scientific spatial planning and governance. This paper utilizes multisource spatiotemporal data, employing geographic spatial analysis methods and graph neural network models to explore the spatial structure of commercial service facilities in Beijing and their relationships with population density and land use, thereby achieving a detailed classification of the commercial service patterns at the natural neighborhood scale. The research findings indicate a significant association between commercial service facilities and population, as well as land use, with a strong spatial heterogeneity. There exists a dissonance between the layout of commercial service facilities and population distribution, and the differences in commercial service development across various regions pose challenges to balanced urban development. Based on this, this paper provides specific recommendations for optimizing the urban commercial spatial structure, offering reference points for future urban planning and development. Full article

Urban Agglomerations (UAs), as the primary form of China’s new urbanization and an essential spatial unit for promoting coordinated regional development, play a crucial role in measuring the sustainable and healthy development of urban clusters through the assessment of spatial network connections among cities within the UAs. Taking the 16 prefecture-level cities of the Chengdu-Chongqing Urban Agglomeration (CCUA) as the research subject, this study constructs six types of element flow networks, including population flow, logistics, and information flow. Employing network visualization analysis, the Self-Organizing Maps (SOM) neural network machine learning models, and Quadratic Assignment Procedure (QAP) relational regression models, the research analyzes the spatial network characteristics of the CCUA from the perspective of multi-dimensional element flows and explores the influencing factors of the UA’s connectivity pattern. The results indicate that: The various element flows within the CCUA exhibit a bipolar spatial network characteristic with Chengdu and Chongqing as the poles. In the element network grouping features, a multi-centered group differentiation structure is presented, and the intensity of internal element flow varies. Based on the results of the SOM neural network machine learning model, the connectivity capabilities of cities within the CCUA are divided into five levels. Among them, Chengdu and Chongqing have the strongest comprehensive connectivity capabilities, showing a significant difference compared to other cities, and there is an imbalance in the connectivity capabilities between cities. In terms of the influencing factors of the urban connectivity pattern within the CCUA, the differences in permanent population size and urbanization rates have a significant negative impact on the information flow network, technology flow network, and capital flow network. The differences in the secondary industrial structure and public budget expenditures have a significant positive impact on the intensity of inter-city element flows, and the differences in per capita consumption expenditures have a significant negative impact, collectively influencing the formation of the spatial connectivity pattern of the CCUA. The findings of this study can provide a scientific basis for the construction and optimization of the spatial connectivity pattern of the CCUA. Full article

Evaluating the state of health (SOH) of lithium-ion batteries (LIBs) is essential for their safe deployment and the advancement of electric vehicles (EVs). Existing machine learning methods face challenges in the automation and effectiveness of feature extraction, necessitating improved computational efficiency. To address this issue, we propose a collaborative approach integrating an enhanced whale optimization algorithm (EWOA) for feature selection and a lightweight support vector regression (SVR) model for SOH estimation. Key features are extracted from charging voltage, current, temperature, and incremental capacity (IC) curves. The EWOA selects features by initially assigning weights based on importance scores from a random forest model. Gaussian noise increases population diversity, while a dynamic threshold method optimizes the selection process, preventing local optima. The selected features construct the SVR model for SOH estimation. This method is validated using four aging datasets from the NASA database, conducting 50 prediction experiments per battery. The results indicate optimal average absolute error (MAE) and root mean square error (RMSE) within 0.41% and 0.71%, respectively, with average errors below 1% and 1.3%. This method enhances automation and accuracy in feature selection while ensuring efficient SOH estimation, providing valuable insights for practical LIB applications. Full article

Background: Effective gentamicin dosing is crucial to the survival of neonates with suspected sepsis but requires a careful balance between attaining both effective peak and safe trough concentrations. We aimed to systematically compare existing gentamicin dosing guidelines for neonates in Australia to determine the extent to which they reach therapeutic targets. Methods: Simulations of a single gentamicin dose to a virtual representative neonatal population according to each Australian guideline were performed using population pharmacokinetic modelling. We determined the proportion of neonates who would achieve peak gentamicin concentrations of ≥5 or ≥10 mg/L and trough concentrations of ≤1 or ≤2 mg/L. We calculated the probability of target attainment (PTA) according to gestation at birth (22 to 40 weeks) and postnatal age (1–7, 8–14, 15–21, 22–28 days). Results: Five unique dosing guidelines were identified. Guidelines varied considerably with respect to dose (4.5 to 7 mg/kg), dosing interval (24 to 48 h), and characteristics used to individualise dosing regimens (e.g., gestation at birth and postnatal age). Guidelines were satisfactory in routinely achieving effective peak concentrations ≥ 5 mg/L, but PTAs for effective peak concentrations ≥ 10 mg/L varied considerably from 5% to 100% based on dose, gestation, and postnatal age. PTAs for trough concentrations ≤ 1 mg/L ranged from 0% to 100%, being lowest among extremely preterm infants. Conclusions: Current Australian gentamicin guidelines demonstrate significant variability in their ability to achieve defined therapeutic targets, necessitating efforts to improve standardisation of dosing recommendations. Further research to define optimal pharmacodynamic targets in neonates with respect to clinical outcomes are also urgently warranted. Full article

Traditional villages are crucial for the sustainable development of both urban and rural areas, and identifying their spatial patterns is key to guiding village construction and promoting urban–rural integration. This research selected 274 traditional Huizhou villages located in the upper basin of the Xin’an River. It examined how the four main factors—construction period, geography, ecology, and social and economic development—shape and influence each other. By incorporating an optimal parameters-based geographical detector model, this study further explored the driving mechanisms behind spatial differentiation. The villages exhibit a “one belt, two cores, and multiple dispersion” pattern, with Shexian and Yixian counties as hot gathering areas of traditional villages. Population migration, internal growth, and external cultural and commercial exchanges drove village formation in three stages. Spatial distribution favors locations with gentle slopes, sunny aspects, proximity to water, suitable climates, convenient transportation, and distance from crowded areas. Topography, water systems, and external communication are key drivers, while the synergy between water systems and transportation is particularly significant. This study concludes that water systems have the greatest influence on village spatial patterns, recommending watersheds as regional boundaries and advocating a clustering development model for planning and protection efforts. Full article

In recent years, unmanned aerial vehicle (UAV) technology has advanced significantly, enabling its widespread use in critical applications such as surveillance, search and rescue, and environmental monitoring. However, planning reliable, safe, and economical paths for UAVs in real-world environments remains a significant challenge. In this paper, we propose a multi-strategy improved red-tailed hawk (IRTH) algorithm for UAV path planning in real environments. First, we enhance the quality of the initial population in the algorithm by using a stochastic reverse learning strategy based on Bernoulli mapping. Then, the quality of the initial population is further improved through a dynamic position update optimization strategy based on stochastic mean fusion, which enhances the exploration capabilities of the algorithm and helps it explore promising solution spaces more effectively. Additionally, we proposed an optimization method for frontier position updates based on a trust domain, which better balances exploration and exploitation. To evaluate the effectiveness of the proposed algorithm, we compare it with 11 other algorithms using the IEEE CEC2017 test set and perform statistical analysis to assess differences. The experimental results demonstrate that the IRTH algorithm yields competitive performance. Finally, to validate its applicability in real-world scenarios, we apply the IRTH algorithm to the UAV path-planning problem in practical environments, achieving improved results and successfully performing path planning for UAVs. Full article

Objective: Pars fractures are a common cause of lower back pain, especially among young individuals. Although computed tomography (CT) and magnetic resonance imaging (MRI) scanning are commonly used in developed regions, traditional radiography remains the main diagnostic method in many developing countries. This study assessed whether the standard radiographic angles suggested in textbooks are optimal for an Asian population since Asian groups have lower lumbar lordosis. This study found a 35° angle to be the most effective angle for lumbar oblique X-ray imaging. Additionally, the potential for a customized positioning auxiliary device was examined to improve image quality and reduce patient discomfort in cost-sensitive healthcare settings like Taiwan’s single-payer system. Methods: A total of 100 participants underwent lumbar oblique radiography using a specially designed footboard with angle markings. Radiologists evaluated 600 images based on waist-to-hip ratio (WHR) and body mass index to identify the optimal angulation for various body types. Results: For individuals with a WHR of 0.85, a 35° angle provided superior image quality, while 45° was more effective for slimmer patients. This optimized approach indicates the cost-effectiveness and diagnostic value of traditional X-ray imaging. Conclusions: The 35° angulation standardizes lumbar X-ray imaging for an Asian cohort, reducing repeat scans and improving accuracy. Using a positioning device further enhances image quality and patient comfort, supporting the clinical utility of traditional radiography in resource-limited environments. Full article

Shrimp farming is a growing industry, and automating certain processes within aquaculture tanks is becoming increasingly important to improve efficiency. This paper proposes an image-based system designed to address four key tasks in an aquaculture tank with Penaeus vannamei: estimating shrimp length and weight, counting shrimps, and evaluating feed pellet food attractiveness. A setup was designed, including a camera connected to a Raspberry Pi computer, to capture high-quality images around a feeding plate during feeding moments. A dataset composed of 1140 images was captured over multiple days and different times of the day, under varying lightning conditions. This dataset has been used to train a segmentation model, which was employed to detect and filter shrimps in optimal positions for dimensions estimation. Promising results were achieved. For length estimation, the proposed method achieved a mean absolute percentage error (MAPE) of 1.56%, and width estimation resulted in a MAPE of 0.15%. These dimensions were then used to estimate the shrimp’s weight. Shrimp counting also yielded results with an average MAPE of 7.17%, ensuring a satisfactory estimation of the population in the field of view of the image sensor. The paper also proposes two approaches to evaluate pellet attractiveness, relying on a qualitative analysis due to the challenges of defining suitable quantitative metrics. The results were influenced by environmental conditions, highlighting the need for further investigation. The image capture and analysis prototype proposed in this paper provides a foundation for an adaptable system that can be scaled across multiple tanks, enabling efficient, automated monitoring. Additionally, it could also be adapted to monitor other species raised in similar aquaculture environments. Full article

We propose the adaptive t-distribution spiral search Dung Beetle Optimization (TSDBO) Algorithm to address the limitations of the vanilla Dung Beetle Optimization Algorithm (DBO), such as vulnerability to local optima, weak convergence speed, and poor convergence accuracy. Specifically, we introduced an improved Tent chaotic mapping-based population initialization method to enhance the distribution quality of the initial population in the search space. Additionally, we employed a dynamic spiral search strategy during the reproduction phase and an adaptive t-distribution perturbation strategy during the foraging phase to enhance global search efficiency and the capability of escaping local optima. Experimental results demonstrate that TSDBO exhibits significant improvements in all aspects compared to other modified algorithms across 12 benchmark tests. Furthermore, we validated the practicality and reliability of TSDBO in robotic path planning applications, where it shortened the shortest path by 5.5–7.2% on a 10 × 10 grid and by 11.9–14.6% on a 20 × 20 grid. Full article

To address the challenges of slow convergence speed, poor convergence precision, and getting stuck in local optima for unmanned aerial vehicle (UAV) three-dimensional path planning, this paper proposes a path planning method based on an Improved Human Evolution Optimization Algorithm (IHEOA). First, a mathematical model is used to construct a three-dimensional terrain environment, and a multi-constraint path cost model is established, framing path planning as a multidimensional function optimization problem. Second, recognizing the sensitivity of population diversity to Logistic Chaotic Mapping in a traditional Human Evolution Optimization Algorithm (HEOA), an opposition-based learning strategy is employed to uniformly initialize the population distribution, thereby enhancing the algorithm’s global optimization capability. Additionally, a guidance factor strategy is introduced into the leader role during the development stage, providing clear directionality for the search process, which increases the probability of selecting optimal paths and accelerates the convergence speed. Furthermore, in the loser update strategy, an adaptive t-distribution perturbation strategy is utilized for its small mutation amplitude, which enhances the local search capability and robustness of the algorithm. Evaluations using 12 standard test functions demonstrate that these improvement strategies effectively enhance convergence precision and algorithm stability, with the IHEOA, which integrates multiple strategies, performing particularly well. Experimental comparative research on three different terrain environments and five traditional algorithms shows that the IHEOA not only exhibits excellent performance in terms of convergence speed and precision but also generates superior paths while demonstrating exceptional global optimization capability and robustness in complex environments. These results validate the significant advantages of the proposed improved algorithm in effectively addressing UAV path planning challenges. Full article

Background: Over the past decade, several trials and observational studies have validated the use of minimally invasive cardiac interventions as viable treatment options for various cardiac diseases. Transcatheter techniques for severe aortic valve stenosis have rapidly emerged as alternatives to surgical aortic valve replacement in certain patient populations. Additionally, non-surgical treatment options have expanded for conditions affecting other cardiac valves, such as the mitral valve. These emerging minimally invasive interventions complement already well-established endovascular techniques for, among others, atrial septal defect closure, left atrial appendage occlusion and pulmonary vein isolation in patients with atrial fibrillation. Given their non-surgical nature and lack of direct visualisation of the targeted anatomy, these procedures heavily rely on precise pre-procedural radiological imaging for optimal patient selection and procedural success. Method: This paper is based on the expert opinion of the authors and an exhaustive literature research. Results: This manuscript reviews the most commonly employed minimally invasive cardiac interventions, highlighting the essential pre-procedural imaging information and key aspects that must be included in radiological reports to mitigate potential complications. Conclusion: Accurate pre-procedural imaging is crucial for ensuring safe and effective minimally invasive cardiac interventions, underscoring the importance of the radiologist in the pre-procedural work-up of these patients. Full article

As a habitat for waterbirds, wetlands are key to their survival, reproduction and development. Waterbirds usually prefer breeding, wintering and resting in fixed locations. Siberian cranes (Grus leucogeranus), which are highly dependent on wetlands, have long fed on farmland at migratory stopover sites. To explore the reason for this phenomenon, the time budgets of Siberian crane populations stopping over on farmland or in wetland habitats were studied and compared in this study. The results showed that the farmlands visited by the Siberian cranes are rich in food resources and have experienced low levels of disturbance. The temporal distribution of feeding behavior on farmland (53.50%) was greater than that in wetland habitats (31.96%). The variations in warning, flying and walking behavior on farmland were less than those in wetlands. The feeding efficiency on farmland was significantly greater than that in wetlands. Therefore, Siberian cranes transiting the Songnen Plain leave wetland habitats and stop over on farmland, representing a behavior that occurs more than just occasionally. Instead, they change their foraging habitat choices based on the optimal foraging theory. As a transit feeding area for Siberian cranes, farmland poses a significant risk, and the restoration of wetland habitats and food resources is still needed. This study can provide theoretical support for the conservation of rare and endangered species (the Siberian crane) and the management of stopover sites. Full article