Small-scale Regions Research Articles

The Potential for Sustainable Tourism Development in Small-Scale Regions: A Case Study of Sulejów Municipality

The Potential for Sustainable Tourism Development in Small-Scale Regions: A Case Study of Sulejów Municipality

ERU-Net: A novel effective 2D residual neural network for brain tumors semantic segmentation from multimodal MRI

Brain tumor segmentation technology is crucial in diagnosing and treating MRI brain tumors. It aids doctors in locating and measuring tumors. It also plays a vital role in the planning and development of treatment and rehabilitation strategies. Recently, challenges have persisted despite the success achieved by deep learning-based methods in improving the segmentation accuracy of MRI brain tumor regions. These challenges are particularly prominent in small-scale tumor regions, where issues arise due to their diminutive size and the substantial variation between regions occupied by different tumor classes. Additionally, limitations in parameters and computational complexity contribute to these challenges. Thus, there remains considerable potential for enhancing these methods and increasing their efficacy. This paper presents an effective 2D residual neural network (2D ERU-Net) for segmenting MRI brain tumors. The proposed architecture consists of residual units in the encoder path to speed up training and convergence, and a deep supervision module (DSM) in the decoder path to address gradient-related issues and obtain high-resolution feature maps. Furthermore, a fusion weighted loss function, incorporating both weighted dice loss and weighted cross-entropy loss, is developed to solve the issues related to network convergence and data imbalance. The results of experiments on the reliable MRI brain tumor BraTS2019 dataset showed that the ERU-Net achieved a mean dice coefficient of 0.881, 0.882, and 0.891 and Hausdorff distances of 20.86 mm, 17.83 mm, and 13.35 mm for the tumor core, enhancing tumor, and whole tumor, respectively. Furthermore, ERU-Net demonstrates exceptional efficiency when compared to state-of-the-art methods.

Spatial resolution improvement method of RDTS assisted by small-scale thermal region length recognition model

The Total Variation Deconvolution (TVD) algorithm plays an important role in signal reconstruction, however, when it is used to improve the spatial resolution of Raman Distributed Temperature Sensor (RDTS), there are certain challenges in parameter settings. This paper proposes to use Fully-Connected Neural Network to identify the length of small-scale thermal regions(SSTR), and based on the recognition results to set the TVD parameters automatically. We constructed training sets based on the periodic changes of SSTR signals in RDTS (which we call Thermal Region Response Modes, TRRM), to verify performance, we conducted comparative experiments between models obtained from a training set containing 100 types of TRRMs and 25 types of TRRMs, the Macro-F1 value of the former one is 0.2749 higher, reaching 0.9087, performed well in SSTR length recognition tasks. the traditional TVD assisted by this model can increase the spatial resolution of RDTS from 1.6 m to 0.4 m without manual intervention, which complements the lack of automation in applications of TVD and has practical value.

An Empirical Spatiotemporal Input Model for Regional Seismic Ground Motions

The development of the economy and technology has attracted greater attention to regional seismic resistance, a key technology for which is the simulation of regional seismic ground motions. This study proposes a simple and highly operable method for the generation of regional seismic ground motions, which includes five steps. First, a seismic ground motion recorded at a seismic station is selected as the original ground motion. The original ground motion is then decomposed into eight sub-signals by wavelet packet transform (WPT). Next, the eight sub-signals are adjusted by the frequency attenuation model. The generated ground motion is then amplitude modulated by the peak ground acceleration (PGA) attenuation model. The frequency attenuation model and the PGA attenuation model are obtained by fitting seismic data from the 1999 Chi-Chi earthquake. Finally, the propagation of the ground motion is considered. The proposed method can provide a time history of seismic ground motions for any point in a region. To investigate the rationality and applicability of the proposed method, the characteristics of the generated seismic ground motions are compared with those of true seismic ground motions. The average error of the PGA of the generated ground motions and the true ground motions is 0.072[Formula: see text][Formula: see text]; thus, the proposed method is suitable for use in small-scale regions. Furthermore, 4-story and 15-story structures are used for structural response analysis. The story drift ratio error between the generated ground and true ground motions is found to be within an acceptable error range. The proposed method provides a new way to generate ground motions for regional seismic investigation.

Mobility heterogeneity of urban freight areas: Geospatial evidence from shared logistics dynamics

Amid rising mobility services in cities, the logistics sector plays a crucial role in envisioning daily services for local neighborhoods. Previous research has primarily identified key freight areas (KFAs) in small-scale regions using traditional census and travel survey data. However, differing from KFAs, other areas with distinct freight patterns remain largely unexamined, and there is a lack of comprehensive indicators to evaluate the travel behaviors of large-scale freight vehicles. This study introduces a data-driven geospatial framework to characterize and understand the patterns exhibited by different freight areas through freight dynamics data collected in cities. To analyze key aspects of freight mobility within spatial units, a set of areal indicators is developed using GPS trajectories of shared freight vehicles in Hong Kong. Descriptive statistics illustrate the inherent differences across various areas and their spatial distributions. A hierarchical clustering approach groups all areas based on proposed indicators, providing a quantitative evaluation of collective spatial patterns. The analysis also explores the non-linear relationships between different categories of freight activities and the local built environments and socioeconomic conditions. The results suggest a strong spatial heterogeneity in the areal mobility profile of Hong Kong. Loading and unloading behaviors are frequently observed in districts such as Eastern Kowloon, Kwai Chung, and the Airport, highlighting prevalent freight trip origins and destinations. In contrast, areas with high transit times are mostly found in suburban regions, particularly on the periphery. This study provides essential insights for area-based planning and management of urban freight activities, with relevant practical implications.

Real-Time Simulation System for Small Scale Regional Integrated Energy Systems

Regional Integrated Energy Systems (RIESs) integrate wide spectrum of energy sources and storage with optimized energy management and further pollution reduction. This paper presents a real-time simulation system for RIESs powered by multiple digital signal processors (DSPs) with different means of data exchange. The RIES encompasses the DC microgrid (DMG), the district heat network (DHN), and the natural gas network (NGN). To realize multi-energy flow simulation, averaged switch models are investigated for different types of device-level units in the DMG, and the unified energy path method is used to build circuit-dual models of the DHN and NGN. A hierarchical island strategy (HIS) and a multi-energy dispatch strategy (MEDS) are proposed to enhance the energy flow control and operating efficiency. The two-layer HIS can adjust the operating status of device-level units in real time to achieve bus voltage stability in the DMG; MEDS uses energy conversion devices to decouple multi-energy flows and adopts the decomposed flow method to calculate the flow results for each network. The real-time simulation hardware platform is built, and both electricity-led and thermal-led experiments are carried out to verify the accuracy of models and the effectiveness of the proposed strategy. The proposed system with an energy management strategy aims to provide substantial theoretical and practical contributions to the control and simulation of RIESs, thus supporting the advancement of integrated energy systems.

Partition refinement of WorldPop population spatial distribution data method: A case study of Zhuhai, China.

Currently, the core idea of the refined method of population spatial distribution is to establish a correlation between the population and auxiliary data at the administrative-unit level and, then, refine it to the grid unit. However, this method ignores the advantages of public population spatial distribution data. Given these problems, this study proposed a partition strategy using the natural break method at the grid-unit level, which adopts the population density to constrain the land class weight and redistributes the population under the dual constraints of land class and area weights. Accordingly, we used the dasymetric method to refine the population distribution data. The study established a partition model for public population spatial distribution data and auxiliary data at the grid-unit level and, then, refined it to smaller grid units. This method effectively utilizes the public population spatial distribution data and solves the problem of the dataset being not sufficiently accurate to describe small-scale regions and low resolutions. Taking the public WorldPop population spatial distribution dataset as an example, the results indicate that the proposed method has higher accuracy than other public datasets and can also describe the actual spatial distribution characteristics of the population accurately and intuitively. Simultaneously, this provides a new concept for research on population spatial distribution refinement methods.

Determination of mercury in soil and water from a gold mining area in Karang Jaya Sub-district, Musi Rawas Utara, Indonesia

The present study focuses on the assessment of Hg distribution in both soil and water samples from the small-scale gold mining region of Karang Jaya Sub-district, Musi Rawas Utara Regency, South Sumatra Province. For over 30 years, this region has witnessed persistent utilization of mercury as an amalgam for gold extraction. The investigation encompasses an analysis of total mercury levels in soil samples obtained from Sukamenang, Muara Batang Empu, and Terusan Villages. As a comparative reference point devoid of gold mining activities, the neighboring Muara Lakitan Village was selected. The obtained results indicate a moderate to strong contamination level across the 30 sampled locations based on the Geoaccumulation Index (Igeo), with an average Igeo value of 2.6. Furthermore, Pollution Load Index (PLI) and Contamination Factor (CF) values demonstrate high contamination. Conversely, analysis of river water samples from 30 stations along the Rupit River reveals that most of the samples comply with quality standards (<0.0005 mg/L), with an average value at 0.000322 mg/L; only four stations exhibit total Hg concentrations surpassing regulatory thresholds. All soil and river water samples from the control area (10 stations) exhibited no signs of pollution. Appropriate investments in technology, finance, and technical skills are needed to reduce mercury pollution due to small-scale gold mining activities.

Agent-based modeling methodology and temporal simulation for Natech events in chemical clusters

In chemical clusters, technological accidents triggered by natural events (Natech events) can lead to the failure of large number of units and cause catastrophic consequences. Previous studies on Natech events give primary concerns on the static analysis at small-scale region with few thoughts the impact of various safety barriers. From the perspective of the large-scale region and temporal characteristics, an agent-based modeling methodology is proposed to present the dynamic evolution process. All the units associated with the evolution are divided into natural hazard agent, hazard-affected facility agent, safety barrier agent, and escalation factor agent to reduce the computational complexity. The time-dependent state transition mechanism of each agent is analyzed to represent the evolution process, simultaneously, the temporal effect of natural hazards, safety barrier and multi escalation factors is considered by utilization of time steps. Moreover, Monte Carlo simulation is adopted to quantify the uncertainties accompanying an accident evolution. The proposed methodology is demonstrated on a complex chemical system in the large-scale region. The results of simulation show that the number of failed hazard-affected facilities in the Natech events scenario will cyclically increase and keep constant, with strong temporal characteristic. The intensity of natural hazards and the probability of failure of safety barriers make a big impact on the temporal characteristic. The results are valuable to support natural hazard prevention in chemical cluster.

Quantifying the variation in water use efficiency across climates and biomes on the Qinghai-Tibetan Plateau

Water use efficiency (WUE) is an important indicator in linking the carbon and water cycles of ecosystems. Previous studies performed in alpine and cold regions have contributed greatly to understanding the WUE response of single ecosystem or specific species in small-scale regions to climatic factors; however, studies focusing on differences in the WUE response among ecosystems or species are insufficient, which has limited the understanding of the climatic adaptation of WUE in alpine and cold regions. Here, based on the 134 leaf δ13C records measured for the 46 dominant species in two main ecosystems on the Qinghai-Tibetan Plateau, variation in the response of WUE among biomes was analyzed by grouped linear regression, redundancy analysis (RDA), and structural equation models (SEMs). The results showed that (1) photosynthetically active radiation (PAR0), moisture index (MI) and vapor pressure deficit (VPD) were significantly correlated with the variation in WUE; (2) compared with the WUE of alpine grasslands, alpine desert grasslands was more sensitive to climatic factors, which presenting a steeper slope that varied with modified growing degree days (MGDD0), PAR0, MI, VPD and CO2; (3) composition of genera distributions explained 32.22% of the variation in WUE, while the WUE of Stipa was more sensitive compared to other species to most climatic factors; (4) climate (composition of genera) and their joint effects explained 58.98% (63.53%) of the variation in WUE of alpine desert grasslands (alpine grasslands), while altitude indirectly controlled the WUE variation in alpine grasslands and MI directly controlled the WUE variation in alpine desert grasslands. This study quantified the driving effects of climates and biomes on the variation in WUE and contributed to the understanding of vegetation adaptation in alpine and cold regions.

Assessment of ecosystem quality changes based on optimizing key indicators in Nanwenghe National Nature Reserve, Heilongjiang, China.

Net primary productivity (NPP) is an indicator to reflect the production capacity of terrestrial ecosystems, as well as a key indicator for ecological quality. NPP at large scale is difficult to be measured. At present, most of the assessment of ecosystem quality uses NPP products with low resolution, which cannot capture the detailed characteristics of the ecosystem and is not conducive to the assessment of ecosystem quality at small-scale. The establishment of a rapid and efficient assessment method for small-scale ecosystem quality will greatly promote the protection and restoration of ecosystems in China. We focused on the calculation method of ecosystem quality assessment and NPP, and optimized the calculation process of the NPP, and obtained NPP by remote sensing without ground observation data. We established a regression model for remote sensing inversion of leaf area index, and estimated the vegetation coverage by using dimidiate pixel model, developed a set of methods for rapid assessment of ecosystem quality by remote sensing. Taking Nanwenghe National Nature Reserve as an example, we evaluated the change of ecosystem quality from 2001 to 2022. The results showed that from 2001 to 2022, the ecosystem qua-lity of the reserve was good as a whole, and that the area with good and excellent quality accounted for more than 85% in 2022. High vegetation coverage was the backbone of the sustainable good ecosystem quality of the reserve. From 2001 to 2022, ecosystem quality of the reserve showed a trend of first decreasing and then increasing, with the lowest point of ecosystem quality in 2013. This method had achieved good results in the evaluation of ecosystem quality in Nanwenghe National Nature Reserve. The index optimization method proposed in this study could facilitate the rapid and accurate assessment of ecosystem quality in small-scale regions, and thus provides technical reference for the precise improvement of ecosystem quality.

Research on the treatment of black and odorous water bodies in rural areas under the background of rural revitalization

This research addresses the excessive presence of pollutants, such as total nitrogen (TN), total phosphorus (TP), ammonia nitrogen (NH3-N), and chemical oxygen demand (COD), among others, in rural water bodies that are heavily contaminated and emit a foul odor. The base substrate used in this research consists of nano-particles of ferric chloride. Subsequently, these particles undergo chemical modifications using various agents, such as hydroxybenzoic acid, sodium hydroxide, chitosan, acrylamide, sodium polyacrylate, and zinc sulfate. To alleviate the contamination and foul odor in rural water bodies, the resulting composite material was combined with microbial effective microorganisms (EM) agents and subjected to an aeration process. The findings of this study indicate a significant reduction in the chemical oxygen demand (COD) value over a 70-day experimental period. The COD concentration in the water decreased significantly from 80 mg/L to a mere 15 mg/L, resulting in a reduction percentage of approximately 68.56%. Additionally, the water quality improved from a fifth-class rating, indicating inferior quality, to a third-class rating, denoting a higher quality. The oxidation-reduction potential (ORP) value remained stable at levels below 50 mV, while the redox potential increased fivefold, effectively eliminating black and smelly water. Moreover, the levels of TN and TP also improved from inferior fifth-class to third-class water quality. This research highlights the capacity of using a combination of flocculants and microbial treatments, in aerated environments, to significantly enhance the water quality in heavily contaminated and foul-smelling rural water bodies. The proposed method exhibits the potential to efficiently remediate heavily contaminated and foul-smelling water bodies in small-scale rural regions, indicating its robust scope for future application.

Silicosis and silicotuberculosis among respiratory hospital admissions: A cross-sectional survey in northern Tanzania.

There is little evidence describing respiratory disease among 40.5 million small-scale miners worldwide. To describe the prevalence and clinical characteristics of adult respiratory inpatients with silicosis and silicotuberculosis in a tertiary hospital in Tanzania that serves a small-scale mining region. In this retrospective, cross-sectional survey, patient files from admissions between 2010 and 2020 were opportunistically selected and included if a respiratory diagnosis had been made. Of 223 patients with respiratory conditions, 32 (14.3%; 95% confidence interval (CI) 10.0 - 19.6) were diagnosed with silicosis and 17 (7.6%; 95% CI 4.5 - 11.9) with silicotuberculosis. Mining was the most frequent occupation in those with silicosis (n=15/32; 46.9%) and silicotuberculosis (n=15/17; 88.2%). Of those with silicosis or silicotuberculosis, 26/49 (53.1%) were aged <45 years. Our study suggests that silicosis and silicotuberculosis are common among male and female respiratory inpatients with occupational exposure. The study highlights the role of occupational exposures in respiratory disease in developing economies. What the study adds. This retrospective, cross-sectional survey describes the prevalence of silicosis and silicotuberculosis among adult respiratory inpatients admitted to a tertiary hospital in northern Tanzania. It is the first study to describe the prevalence and characteristics of respiratory inpatients with silicosis and silicotuberculosis in a small-scale mining region of Africa. A high prevalence of silicosis (14.3%) and silicotuberculosis (7.6%) was found. Patients were often aged <45 years, and the majority required oxygen therapy.Implications of the findings. The high prevalence of advanced silicosis and silicotuberculosis in miners presenting at a young age raises concerns about high occupational silica exposures and, importantly, suggests a need for community-based research, which our team is planning to undertake.

Use of molecular composition and compound-specific isotope analysis for source appointment of PAHs in sediments of a highly industrialized area

This study utilized both conventional molecular analysis and compound-specific isotopic techniques to identify the sources of polycyclic aromatic hydrocarbons (PAHs) in sediments of Ulsan Bay, South Korea. The concentrations of 15 traditional and 11 emerging PAHs were determined in sediments from 21 source sites and 26 bay sites. The concentrations and compositions of traditional and emerging PAHs varied significantly, even at sites close to the source areas. The results obtained from diagnostic ratios and the positive matrix factorization model for source identification were inconsistent in adjacent source areas. The δ13C profiles of PAHs, such as phenanthrene (Phe), fluoranthene (Fl), pyrene (Py), and benz[a]anthracene (BaA) in the sediments showed distinct features depending on the surrounding sources. In urban sediments, lighter δ13CPhe values were observed (mean: −25.1‰), whereas relatively heavier values of δ13CPy were found in petroleum industry areas (mean: −23.4‰). The Bayesian isotope mixing model indicates that the predominant source of PAHs in Ulsan Bay sediments was the petroleum industry (45%), followed by the non-ferrous metals industry (30%), automobile industry (18%), and urban areas (6.3%). These results demonstrated the utility of stable isotopes in assessing the sources and contributions of PAHs in small-scale regions. However, there are still limitations in compound-specific isotope analysis of PAHs, including insufficient end-members for each source, difficulty in analysis, and the influence of non-point sources; thus, further study is needed to expand its application.

DME-DeepLabV3+: a lightweight model for diabetic macular edema extraction based on DeepLabV3+ architecture.

Diabetic macular edema (DME) is a major cause of vision impairment in the patients with diabetes. Optical Coherence Tomography (OCT) is an important ophthalmic imaging method, which can enable early detection of DME. However, it is difficult to achieve high-efficiency and high-precision extraction of DME in OCT images because the sources of OCT images are diverse and the quality of OCT images is not stable. Thus, it is still required to design a model to improve the accuracy of DME extraction in OCT images. A lightweight model (DME-DeepLabV3+) was proposed for DME extraction using a DeepLabV3+ architecture. In this model, MobileNetV2 model was used as the backbone for extracting low-level features of DME. The improved ASPP with sawtooth wave-like dilation rate was used for extracting high-level features of DME. Then, the decoder was used to fuse and refine low-level and high-level features of DME. Finally, 1711 OCT images were collected from the Kermany dataset and the Affiliated Eye Hospital. 1369, 171, and 171 OCT images were randomly selected for training, validation, and testing, respectively. In ablation experiment, the proposed DME-DeepLabV3+ model was compared against DeepLabV3+ model with different setting to evaluate the effects of MobileNetV2 and improved ASPP on DME extraction. DME-DeepLabV3+ had better extraction performance, especially in small-scale macular edema regions. The extraction results of DME-DeepLabV3+ were close to ground truth. In comparative experiment, the proposed DME-DeepLabV3+ model was compared against other models, including FCN, UNet, PSPNet, ICNet, and DANet, to evaluate DME extraction performance. DME-DeepLabV3+ model had better DME extraction performance than other models as shown by greater pixel accuracy (PA), mean pixel accuracy (MPA), precision (Pre), recall (Re), F1-score (F1), and mean Intersection over Union (MIoU), which were 98.71%, 95.23%, 91.19%, 91.12%, 91.15%, and 91.18%, respectively. DME-DeepLabV3+ model is suitable for DME extraction in OCT images and can assist the ophthalmologists in the management of ocular diseases.

Numerical modelling of wave and tidal current interactions and their impact on wave parameters

In regions where both waves and tidal currents coexist, tidal flow can significantly alter wave parameters and hence affects the estimation of wave energy resources. This study uses a coupled wave-current numerical model to evaluate the influence of wave-current interactions on wave parameters. To achieve this, the simulation was performed in 3 stages. At first, a large-scale North Atlantic wave model was constructed using the spectral wave model TOMAWAC to generate wave conditions and boundary inputs. This wave model was calibrated and validated at four sites around the UK using field measurements. Secondly, a small-scale numerical model covering Pentland Firth and Orkney Waters, Scotland, UK, was chosen, and tidal flow and current speeds were simulated by the three-dimensional flow model TELEMAC 3D. As with the wave model, the flow model was also calibrated and validated with site measurements from an ADCP; thus, both models were validated. In the third stage, a coupled TOMAWAC-TELEMAC 3D model was employed for the small-scale region, and the wave parameters generated by the large-scale model were input as boundary conditions. The TOMAWAC-TELEMAC 3D coupled model was validated with field measurements at two locations in Orkney Waters, where waves and currents coexist. Various wave and tidal currents parameters produced from the coupled model are presented in the paper. To evaluate the wave-current impact on wave parameters, a qualitative and quantitative analysis of these parameters is carried out, and the results are presented and discussed in the paper. The large-scale and small-scale numerical models developed in this study are useful tools for generating wave boundary conditions and wave energy resource assessment, helping researchers and engineers better understand the characteristics of wave-current interactions.

Small Region, Big Impact: Highly Anisotropic Lyman-continuum Escape from a Compact Starburst Region with Extreme Physical Properties

Extreme, young stellar populations are considered to be the primary contributor to cosmic reionization. How the Lyman continuum (LyC) escapes these galaxies remains highly elusive, and it is challenging to observe this process in actual LyC emitters without resolving the relevant physical scales. We investigate the Sunburst Arc, a strongly lensed LyC emitter at z = 2.37 that reveals an exceptionally small-scale (tens of parsecs) region of high LyC escape. The small (<100 pc) LyC-leaking region has extreme properties: a very blue UV slope (β = −2.9 ± 0.1), a high ionization state ([O iii] λ5007/[O ii] λ3727 = 11 ± 3 and [O iii] λ5007/Hβ = 6.8 ± 0.4), strong oxygen emission (EW([O iii]) = 1095 ± 40 Å), and a high Lyα escape fraction (0.3 ± 0.03), none of which are found in nonleaking regions of the galaxy. The leaking region’s UV slope is consistent with approximately “pure” stellar light that is minimally contaminated by the surrounding nebular continuum emission or extinguished by dust. These results suggest a highly anisotropic LyC escape process such that LyC is produced and escapes from a small, extreme starburst region where the stellar feedback from an ionizing star cluster creates one or more “pencil-beam” channels in the surrounding gas through which LyC can directly escape. Such anisotropic escape processes imply that random sight-line effects drive the significant scatters between measurements of galaxy properties and LyC escape fraction, and that strong lensing is a critical tool for resolving the processes that regulate the ionizing budget of galaxies for reionization.

Invariant Attribute-Driven Binary Bi-Branch Classification of Hyperspectral and LiDAR Images

The fusion of hyperspectral and LiDAR images plays a crucial role in remote sensing by capturing spatial relationships and modeling semantic information for accurate classification and recognition. However, existing methods, such as Graph Convolutional Networks (GCNs), face challenges in constructing effective graph structures due to variations in local semantic information and limited receptiveness to large-scale contextual structures. To overcome these limitations, we propose an Invariant Attribute-driven Binary Bi-branch Classification (IABC) method, which is a unified network that combines a binary Convolutional Neural Network (CNN) and a GCN with invariant attributes. Our approach utilizes a joint detection framework that can simultaneously learn features from small-scale regular regions and large-scale irregular regions, resulting in an enhanced structural representation of HSI and LiDAR images in the spectral–spatial domain. This approach not only improves the accuracy of classification and recognition but also reduces storage requirements and enables real-time decision making, which is crucial for effectively processing large-scale remote sensing data. Extensive experiments demonstrate the superior performance of our proposed method in hyperspectral image analysis tasks. The combination of CNNs and GCNs allows for the accurate modeling of spatial relationships and effective construction of graph structures. Furthermore, the integration of binary quantization enhances computational efficiency, enabling the real-time processing of large-scale data. Therefore, our approach presents a promising opportunity for advancing remote sensing applications using deep learning techniques.

Comparison of age and growth of grass and silver carp above and below Changzhou Dam in the Pearl River, China

The life cycles of potamodromous fishes are generally interrupted by the presence of a dam, and populations often display different life history traits between upstream and downstream. This study compared the age and growth rates of grass carp and silver carp above and below Changzhou Dam in the Pearl River. Standard length (SL) composition and back-calculated SL at ages one and two years were greater for grass carp upstream than for downstream individuals; and the length–weight relationship differed between upstream and downstream populations, while the sex ratio, age structure and growth were not significantly different. For silver carp, only SL compositions differed between upstream and downstream populations; sex ratio, age structure, SL-at-age and growth rates were similar. Mean SL-at-age and growth rates of both species were lower at high latitudes than at middle and low latitudes, and populations in the Yangtze River had greater mean SL-at-ages and faster growth relative to silver carp at low latitudes. There were no differences in mean SL or growth among populations at middle and low latitudes. For grass carp, age and growth varied with small-scale ecological processes and regions or latitude, while for silver carp, such variations were proportionate to latitude and the large-scale geographical range. This study provides not only guidelines for fisheries management and conservation of exploited potamodromous fishes, but also insights into the relationship between life history trait variation and adaptive capacity.

A new path planning method for AUV based on the Navier–Stokes equations for ocean currents

The autonomous underwater vehicle (AUV), due to its flexibility, safety, and other characteristics, has been widely used in various marine applications such as underwater mapping, marine ecological monitoring and marine scientific. When navigating in complex and ever-changing ocean currents, one of the key challenges in AUV control is how to effectively avoid unknown obstacles such as sunken ships and reefs, and plan the route to reach the target point. In this paper, based on partially measured ocean current velocities, the Navier–Stokes equations (N–S equations) are used to characterize the ocean current velocities within a small-scale region. Furthermore, the improved artificial potential field method is used for path planning after integrating the velocity information. Simulation and experiments indicate that the proposed algorithm is better suited to complex marine environments.