Minimum Distance Research Articles

Magnetic resonance imaging verification of anterior capsular impingement in the hip joint: A three-dimensional analysis

BackgroundThis study aimed to clarify whether the anterior hip capsular ligament is impinged between the acetabulum and femur during hip flexion or adduction and to determine the difference in the distance between the femur and capsular ligament in healthy adults and those with hip pain. MethodsMagnetic resonance imaging of the hip joint was conducted at the following hip positions: 0° of flexion, 60° of flexion, maximal flexion, and maximal flexion with adduction. A three-dimensional model of the capsular ligament and femur was constructed. The minimal distance between the femur and capsular ligament, termed the capsule-femur distance, was computed. Because a capsule-femur distance of 0 mm indicates contact between the femur and the capsular ligament, that is, capsular impingement, the distance in each position was compared for each group using a one-sample t-test. The capsule-femur distance in the various groups and for different positions was compared using a split-plot analysis of variance. FindingsFifteen healthy individuals and sixteen individuals experiencing hip pain were enrolled. The capsule-femur distance was significantly greater than 0 mm in all positions in both groups, and none of the groups had a capsule-femur distance of 0 mm. The capsule-femur distance was significantly longer in the other positions than in the 0° flexion position, and significantly longer in the hip pain group than in the healthy group. InterpretationCapsular impingement did not occur in either group, even during hip flexion or adduction. Furthermore, the capsule-femur distance was longer in the hip flexion and hip pain groups.

DNA barcoding and cryptic diversity in fishes from the Ili River Valley in China, Xinjiang.

The Ili River Valley, located in the northwest of China, serves as a vital repository for fish genetic resources. Its extensive water network and diverse climate have given rise to a unique fish composition and endemic species. In this study, we collected the cytochrome c oxidase subunit I (COI) sequences from 660 fish specimens in the Ili River Valley. The effectiveness of DNA barcoding in identifying fish species in the area was assessed by examining genetic distances, constructing phylogenetic trees, and performing ABGD (Automatic Barcode Gap Discovery) analyses, among other methods. In total, 20 species were identified, including one unidentified species (Silurus sp.). Except for Silurus asotus and Hypophthalmichthys molitrix (only one sample), the maximum intraspecific genetic distance among the remaining species was smaller than the minimum interspecific distance, which proves that the species exhibit obvious barcode gaps. In the Neighbor-Joining trees, 20 species formed separate monophyletic branches. According to ABGD analysis, 660 sequences were categorized into 19 Operational Taxonomic Units, with Silurus sp. and S. asotus grouped into a single OTU. The Silurus in this study exhibits shared haplotypes and significant genetic divergence, suggesting the potential presence of cryptic species. Furthermore, the nucleotide diversity across all species fell below the threshold level, indicating that the local fish population is gradually declining. In conclusion, this study has demonstrated the effectiveness of DNA barcoding in identifying fish species in the Ili River Valley, providing valuable data to support the conservation of local fish resources.

Large, but Dispersal-Limited Populations of the Marsh Fritillary Euphydryas aurinia Persist on Abandoned Military Training Areas Three Decades After the End of the Cold War.

Military training areas can host important biodiversity, due to the preservation of diverse, nutrient-poor historical cultural landscapes and an insect-friendly disturbance regime. In Europe, many training areas were abandoned after the end of the cold war in 1991 and the withdrawal of the Allied and Soviet forces. Many of these are now protected areas, and current management strategies vary from rewilding to active habitat management such as grazing or mowing. In a capture-release-recapture approach, marking 2418 individuals, we assessed the population size and movement patterns of the dry ecotype of the Marsh Fritillary Euphydryas aurinia Rottemburg 1775 on three former military training areas in Germany that varied in size and management (natural succession, mowing, and sheep-/goat grazing). Euphydryas aurinia is a rare and declining butterfly species listed in Annex II of the European Union Habitats Directive. Jolly-Seber models revealed a large population of ca. 19,000 individuals on the largest study site and a smaller population at a second site, whereas recapture rates were too low to predict the population size reliably at a third site. Population densities were 190-194 butterflies ha-1 at the unmanaged, large site and 56-71 butterflies ha-1 at a smaller site grazed with sheep. Thirty-nine percent of the recapture events occurred within the same 1-ha-study plot. The average minimum flight distance between the study plots was 313 m for males and 328 m for females. The maximum lifetime flight distance was 1237 m within 3 days. No dispersal was detected between study sites. Thirty years after cessation of the military use, the large former training site still held what likely is one of the largest populations of the species dry ecotype in Central Europe, including in areas where management ceased already in 1991. This suggests remarkable persistence of the species in areas without regular management, contrary to current opinion. However, regular flight distances seem not to be sufficient to connect the isolated habitat patches. It remains unknown how long the large population at the abandoned military area will persist without active habitat management. Careful, but active habitat management and restoration of habitat connectivity should thus be considered.

Microplastics in the atmospheric of the eastern coast of China: different function areas reflecting various sources and transport.

Suspended atmospheric microplastics (SAMPs) display varying occurrence characteristics on different underlying surfaces in urban areas. This study investigated the occurrence characteristics, source apportionment, and transportation patterns of SAMPs in two typical underlying surfaces: the downtown area (Site T) and the industrial area (Site C) of a coastal city in China. In the spring of 2023, a total of 32 types comprising 1325 SAMPs were detected. The average MP abundances were found to be 3.74 ± 2.86n/m3 in Site T and 2.67 ± 1.68n/m3 in Site C. In Site T, SAMPs attributed to living source constituted 78.05%, while industry was the main source in Site C with a proportion reaching 42.89%, consistent with the functional zoning of the underlying surface. Furthermore, HYSPLIT analysis revealed that there was no significant difference between these two sites in long-distance horizontal transport affected by external airflow regardless of altitude; conversely, PCA indicated a notable correlation between vertical velocity and both abundance and species diversity. According to the hourly average wind speeds, the maximum transmission distance was computed as 350km for updraft and the minimum transmission distances was as low as 32m for downdraft. Subsequently, the coincidence between the source proportion of SAMPs on random day and meteorological parameters confirmed the synergistic impact on SAMPs transport influenced by functional zoning, geographic environment, and vertical velocity.

Classification-based consensus model considering quantum interference in linguistic distribution environment

Multi-attribute group classification (MAGC) is widely used in dealing with a variety of practical problems. However, cognitive biases, resulting from factors such as irrational behaviors, epistemic uncertainty, and interference phenomena, may critically affect the consensus reaching process (CRP) of MAGC. To eliminate such influences, this study proposes an ordinal classification consensus model in a linguistic environment based on quantum probability theory (QPT), with the aim of classifying the alternatives into several predefined ordinal classifications accepted by a majority of experts. First, the linguistic distribution assessments (LDAs), which can depict the quantitative distribution and qualitative vagueness, are used to express the uncertain preference of experts. Second, the LDAs best-worst method (BWM) and LDAs maximizing deviation model (MDM) are developed to identify the subjective and objective weight of attributes, and then these two types of attributes weight are integrated by quantum-like Bayesian networks (QLBN). Third, a maximum consensus model (MCM), maximizing the consensus level of ordinal classification results of experts, is provided to determine the parameters of QLBN. When the consensus level is lower than the predetermined threshold, a minimum adjustment distance model (MADM) is designed to guide the preference-modification of experts to derive the consensus classification of alternatives. Finally, a risk assessment case study is conducted to verify the feasibility and effectiveness of the proposed method. Sensitivity analysis and comparison analysis are performed to verify the feasibility and effectiveness of the proposed method

Analytical prediction model for edge water invasion flow rate in SAGD heavy oil reservoirs

SAGD (Steam Assisted Gravity Drainage) technology is a highly developed method for the extracting heavy oil. In the Liaohe Oilfield in China, there are 72 SAGD well groups within a massive heavy oil reservoir containing edge water and the risk associated with edge water invasion is substantial. Currently, the injected steam is less than 50 m away from the edge water, which increases the urgency to address the potential invasion of edge water. If the edge water flows into the reservoir, it will impact nearly 8.0 × 105 tons of crude oil production. Recognizing the importance of mitigating the edge water invasion, this paper proposes a prediction model for the edge water invasion flow rate in SAGD reservoirs. The model integrates mass and heat transfer dynamics between the steam chamber and edge water, the relative permeabilities of oil and water phases, and the viscosity-temperature relationship of water. The model is validated through numerical simulations and field data. The findings indicate that for SAGD reservoirs with a permeability greater than 1 D, maintaining a minimum distance of 25 m between the edge water and the steam chamber is advisable, especially when the pressure differential is 0.5 MPa. Furthermore, the prediction model is used to forecast the edge water breakthrough time and evaluate water invasion risk. A prediction chart for the edge water invasion flow rate is developed, providing a practical tool for estimating invasion flow rate curves. Also, a method of edge water invasion warning is established, with a corresponding chart that offers a quick assessment of water invasion risks in heavy oil reservoirs. Overall, this research lays a foundational framework for understanding and managing the edge water invasion in SAGD operations, offering critical insights for the future study aimed at addressing the challenge posed by edge water in heavy oil reservoirs.

Integrating small data and shape prior knowledge with gradient-enhanced Kriging through adaptive knowledge sampling

Integrating prior knowledge into surrogate models constitutes an advanced approach to tackling the challenges posed by small data. The existing gradient-enhanced Kriging (GEK) method can enhance accuracy by incorporating gradient points of shape prior knowledge. Nonetheless, our findings indicate that the strategy employed for setting gradient points significantly impacts accuracy, even when the number of gradient points remains constant. To enhance the efficient utilization of gradient information, we propose a novel Adaptive Knowledge Sampling (AKS) strategy with an innovative acquisition function that balances local exploitation and global exploration, effectively extracting gradient points from shape prior knowledge. The acquisition function consists of three parts: (1) The Local Information Filling Criterion (LIFC), designed to mitigate model error; (2) The Global Information Filling Criterion (GIFC), aimed at reducing model uncertainty in data-sparse areas; and (3) The Minimum Distance Constraint (MDC), implemented to prevent excessive clustering of gradient points. To strike a balance between model accuracy and computational cost, an adaptive stopping condition is introduced to regulate the number of gradient points sampled. The impact of hyperparameters in the AKS strategy was analyzed in detail, and the method’s performance was compared to existing approaches using six benchmark functions and a missile aerodynamic prediction case. The results demonstrate that AKS-GEK offers higher prediction accuracy and robustness when integrating small data with shape prior knowledge.

Sinkhorn Distance Minimization for Adaptive Semi-Supervised Social Network Alignment.

Social network alignment, aiming at linking identical identities across different social platforms, is a fundamental task in social graph mining. Most existing approaches are supervised models and require a large number of manually labeled data, which are infeasible in practice considering the yawning gap between social platforms. Recently, isomorphism across social networks is incorporated as complementary to link identities from the distribution level, which contributes to alleviating the dependency on sample-level annotations. Adversarial learning is adopted to learn a shared projection function by minimizing the distance between two social distributions. However, the hypothesis of isomorphism might not always hold true as social user behaviors are generally unpredictable, and thus a shared projection function is insufficient to handle the sophisticated cross-platform correlations. In addition, adversarial learning suffers from training instability and uncertainty, which may hinder model performance. In this article, we propose a novel meta-learning-based social network alignment model Meta-SNA to effectively capture the isomorphism and the unique characteristics of each identity. Our motivation lies in learning a shared meta-model to preserve the global cross-platform knowledge and an adaptor to learn a specific projection function for each identity. Sinkhorn distance is further introduced as the distribution closeness measurement to tackle the limitations of adversarial learning, which owns an explicitly optimal solution and can be efficiently computed by the matrix scaling algorithm. Empirically, we evaluate the proposed model over multiple datasets, and the experimental results demonstrate the superiority of Meta-SNA.

Τhe styloid process length and the stylohyoid chain ossification affect its relationship with the carotid arteries

PurposeThe elongated styloid process (SP) is associated with internal carotid artery (ICA) dissection, due to its close relationship with the vessel. However, the relationship with the variable ossified stylohyoid chain (SHC) has been scarcely studied. The current study investigated the relationship between the temporal bone's SP and the external and internal carotid arteries (ECA and ICA). The hypothesis was that the morphological variants of the SP influence this relationship. MethodsSixty multidetector computed tomography angiographies (120 heminecks) were randomly selected and retrospectively analyzed. SP elongation and the degree of SHC ossification were assessed. The distances (mm) between the SP and the carotid arteries (CAs) were measured. ResultsElongated SPs were present in 21 patients (35%), while 18 patients (30%) exhibited variable degrees of SHC ossification. The mean distance from the SP tip and ECA was 5.45±3.01mm from the ECA and 6.16±3.11mm to the ICA. The minimum distance between SP and ECA was 3.96±2.74mm, and between SP and ICA was 4.72±2.73mm. A statistically significant reduction in SP-ICA distance was observed in cases with elongated SP and SHC ossification, while the SP-ECA distance was statistically significantly shorter in cases of SP elongation. Additionally, three novel topographical patterns between the SP and surrounding vascular structures were identified. ConclusionsSP elongation and variable SHC ossification may impact the spatial relationship between the SP and the ICA/ECA. The proximity of the SP to the carotid arteries, particularly the ICA, holds clinical significance, as a reduced distance may predispose patients to carotid artery dissection, increasing the risk of stroke.

Comparative assessment of global warming potential of gasoline, battery, and hybrid vehicles in India

This study reports a comparative analysis of life cycle greenhouse gas (GHG, CO2-eq.) emissions of three fuel-type vehicles, viz. internal combustion engine (ICEVs), battery electric (BEVs), and hybrid electric vehicles (HEVs), from an Indian perspective. Vehicles in hatchback and compact sports utility vehicles (SUVs) categories were analyzed. GHG emissions in fuel life cycle (well-to-tank and tank-to-wheel) and vehicle life cycle were estimated using data from websites of OEMs and ministries of Government of India, and the GREET software. The vehicle life cycle constitutes a smaller fraction (15–20 %) of total life cycle GHG emissions for ICEVs and HEVs compared to BEVs (∼35 %). The vehicle cycle emissions of BEVs are higher than ICEV and HEV in both categories (52–63 % for hatchback and approx. 45 % for compact SUV). The fuel cycle emissions of BEVs are smaller than ICEV and HEV in both categories (29–34 % for hatchback and 32–40 % for compact SUV). The total life cycle GHG emissions of BEVs were lower than ICEV and HEV (15–17 % in hatchback and 17–25 % in compact SUV category) for the 2023 energy mix in India with a significant share of thermal route. With anticipated doubling of renewable energy sources in India by 2030, the total life cycle GHG emission of BEV will decrease by approximately 20 %. However, sensitivity analysis revealed that BEVs produce higher GHG emissions than ICEVs and HEVs until a minimum lifetime travel distance. Thermal power plant efficiency and battery lifespan also influence BEVs lifetime GHG emissions.

The influence of steep faults at various avoidance distances on the stability of hard rock caverns: Physical model experiments and DEM simulations

The influence of avoidance distances from steep faults on the stability of hard rock caverns is pivotal in deep engineering, yet research in this area remains scant. This study constructed 10 physical models of granite caverns, incorporating artificial cement-filled faults at various avoidance distances ranging from 0 to 2.4 cavern diameters. Biaxial compression experiments were conducted, utilizing acoustic emission and digital image correlation techniques for monitoring. Coupled with discrete element method simulations, the analysis explored the influence mechanisms of fault-cavern distance on the failure modes of hard rock caverns. The results reveal a positive correlation between the peak strength of specimens and fault-cavern distance. As the distance approaches 0.6 cavern diameters, the fault significantly influences the displacement field, crack number, distribution, and crack initiation location within the specimens. Furthermore, even at 1.2 cavern diameters, the fault still exhibits a notable influence on the stress field, external load transfer paths, cavern failure modes, and the size distribution of spalling debris. These findings can guide the selection of sites for deep hard rock caverns, ensuring a minimum fault-cavern distance of 1.2 cavern diameters to mitigate the risks posed by steep faults to such caverns.

Python-based numerical study on bolted joints between Fe-SMA and steel plates for structural reinforcements

In order to rehabilitate the performance of deteriorated steel structures, the active prestressed reinforcement employing new intelligent material, Iron-based shape memory alloy (Fe-SMA), has received extensive attentions. To reveal the mechanical properties of bolted joints between Fe-SMA and steel plates, a parametric method to establish finite element model via secondary development of ABAQUS based on Python scripts was proposed. It greatly reduces modelling costs and improves modeling efficiency. Besides, the proposed numerical models were verified against the experimental results by failure mode, final deformation, load-displacement characteristics, ultimate load-bearing capacity and initial stiffness. It is demonstrated that the numerical simulation results are in good agreement with the experimental results. What is more, a parametric study on thickness and geometry of steel plates, as well as the end distance, edge distance and thickness of Fe-SMA plate was conducted to verify the applicability of current leading specifications for steel structures. It is evident from the results that the thickness of Fe-SMA should be controlled to prevent failure of parent steel structures. Furthermore, similar to steel plates, the thickness of Fe-SMA plate can be considered linear and the failure of bolt can be avoided, as described in Chinese code GB 50017–2017 and Eurocode 3 Part 1–8. However, the minimum allowable edge distance in Chinese code GB 50017–2017 and the ultimate load-bearing capacity calculated by Eurocode 3 Part 1–8 is conservative for bolted joints between Fe-SMA and steel plates. Hence, it is urgent to formulate design specifications for this kind of bolted joints for the scientific and economical application of Fe-SMA in structural active reinforcements.

On the largest minimum distances of [n,6] LCD codes

Linear complementary dual (LCD) codes can be used to resist side-channel attacks and fault noninvasive attacks. Let da(n,6) and dl(n,6) be the minimum weights of all binary optimal linear codes and LCD codes with length n and dimension 6, respectively. In this article, we aim to obtain the values of dl(n,6) for n≥51 by investigating the nonexistence and constructions of LCD codes with given parameters. Suppose that s≥0 and 0≤t≤62 are two integers and n=63s+t. Using the theories of defining vectors, generalized anti-codes, reduced codes and nested codes, we exactly determine dl(n,6) for t∉{21,22,25,26,33,34,37,38,45,46}, while we show that dl(n,6)∈{da(n,6)−1,da(n,6)} for t∈{21,22,26,34,37,38,46} and dl(n,6)∈{da(n,6)−2, da(n,6)−1} for t∈{25,33,45}.

Linking landscape structure, floral resource distribution, pollen use and movement distances of a generalist predator

Abstract Understanding the habitat use and movement patterns of natural enemies in agricultural landscapes is essential for enhancing biological pest control. Since many natural enemies rely on floral resources, the distribution of these resources in combination with movement behaviour likely influence biological control in field crops. Nevertheless, few studies have assessed natural enemy movement at the landscape scale. Here we estimated minimal movement distances of the green lacewing Chrysoperla carnea based on consumed pollen and the spatial distribution of the respective plant species in 24 agricultural landscapes (500 m radius). Lacewings were sampled using sticky traps in the centre of each landscape, and the consumed pollen were identified. The location of the most important pollen‐providing plants was mapped in each landscape. Distances to potential sources of pollen consumed by 346 lacewings were used to derive minimal movement distances. Lacewings consumed mostly pollen from insect‐pollinated plants that were present within 500 m from the sampling location. The distance to the nearest source of consumed pollen exceeded 200 m in 31% of lacewing individuals, demonstrating the relevance of the landscape scale to understand their population dynamics. Distances were shorter to insect‐pollinated than to wind‐pollinated plants, and shorter early than late in the season. Mean and median distances to pollen sources were negatively associated to flower availability and edge density in the landscape, but this was not the case for minimal distances. Synthesis and applications. Our findings suggest that the spatial distribution of flowering wild plants can inform movement patterns of lacewings and other flower‐visiting insects in agricultural landscapes. In addition, the location of floral resources in the landscape is important for its capacity to sustain natural enemies. Given the strong reliance of lacewings on pollen from nearby insect pollinated woody plants, the promotion of native shrubs and trees, such as Prunus, Salix and Castanea, should be prioritized for natural enemy enhancement in agricultural landscapes. Doing this in the form of hedgerows or agroforestry systems would lead to only minimal reduction in production areas and provide additional benefits such as biodiversity conservation.

Advancements in Blind Source Separation for EEG Artifact Removal: A comparative analysis of Variational Mode Decomposition and Discrete Wavelet Transform approaches

Electroencephalography (EEG) is a vital tool for elucidating cerebral processes; however, it is inherently vulnerable to physiological interference, including cardiac rhythm, ocular movement, and muscular activity. To guarantee the reliability of essential neuronal data, it is imperative to implement efficacious denoising methodologies. This study compares the efficacy of two advanced blind source separation (BSS) techniques applied to EEG signals: variational mode decomposition-based BSS (VMD-BSS) and discrete wavelet transform-based BSS (DWT-BSS). The efficacy of these methods is rigorously assessed using performance metrics such as the Euclidean Distance (ED) and the Spearman Correlation Coefficient (SCC), which evaluate the precision of signal reconstruction and the correlation between the original and denoised signals, respectively. The findings indicate that both methods yield robust results, with minimal Euclidean distances of 704.04 for VMD-BSS and 703.64 for DWT-BSS, and a strong correlation coefficient of 0.82. The results demonstrate the effectiveness of the proposed techniques in removing artifacts while preserving essential neural information in EEG recordings. Furthermore, the proposed techniques are benchmarked against previous studies, considering factors such as signal properties, computational complexity, frequency localization, and flexibility. These findings highlight the importance of customized parameter selection tailored to the specific characteristics of EEG datasets and research objectives.

Shortest Path Problem under Pythagorean Fuzzy Network Using Dijkstra’s Algorithm

In this article investigates the Shortest Path Problem (SPP) in uncertain settings using Pythagorean fuzzy numbers (PFNs). A heuristic methodology using modified Dijkstra's algorithm is developed to exploit error, uncertainty, and partial truth for a low-cost solution. The method calculates minimum distance between starting and destination nodes and evaluates its effectiveness. Pythagorean fuzzy graphs can handle ambiguous situations.

Shift Pruning-Based SCL Decoding for Polar Codes

In the context of the high-speed development of 5G communications, high-performance decoding schemes for polar codes are a hot spot in channel coding research. Shift pruning successive cancellation list (SP-SCL) decoding aims to recover the correct path by shift pruning in the extra SCL decoding. However, the current SP-SCL decoding is inflexible in determining the shift positions. In this paper, a flexible shift pruning SCL (FSP-SCL) decoding is proposed. Firstly, the reasons for movement and the eliminated states of the correct path are analyzed in detail using the path metric range (PMR), and on this basis, the validity of the method adopted in this paper for determining the shift priority of the information bits is verified. Secondly, the FSP-SCL decoding proposes two methods for determining the shift positions. One is the log-likelihood ratio (LLR) threshold method, which compares the LLR values of the eliminated paths on the shift bit with the corresponding LLR threshold to determine the shift positions. The other is the path distance method. It combines the minimum distance between the eliminated paths and the received vector with the path metrics to determine the shift positions. Both methods are more flexible and practical, as they can calculate the corresponding shift positions online based on a specific shift bit, avoiding the high complexity caused by the simulation method. Finally, this paper designs various experimental schemes to verify the decoding performance of the FSP-SCL. The experimental results show that in terms of error-correction performance, the LLR threshold-based FSP-SCL (FSPL (LLR threshold)) decoding, the path distance-based FSP-SCL (FSPL (path distance)) decoding and the existing SP-SCL decoding are roughly equal overall. In terms of decoding complexity, FSPL (LLR threshold) decoding is slightly better than FSPL (path distance) decoding, and the decoding complexity of both is lower than that of SP-SCL decoding, with the difference being more pronounced in the medium to high SNR regions.

Enhancing the Minimum Awareness Failure Distance in V2X Communications: A Deep Reinforcement Learning Approach.

Vehicle-to-everything (V2X) communication is pivotal in enhancing cooperative awareness in vehicular networks. Typically, awareness is viewed as a vehicle's ability to perceive and share real-time kinematic information. We present a novel definition of awareness in V2X communications, conceptualizing it as a multi-faceted concept involving vehicle detection, tracking, and maintaining their safety distances. To enhance this awareness, we propose a deep reinforcement learning framework for the joint control of beacon rate and transmit power (DRL-JCBRTP). Our DRL-JCBRTP framework integrates LSTM-based actor networks and MLP-based critic networks within the Soft Actor-Critic (SAC) algorithm to effectively learn optimal policies. Leveraging local state information, the DRL-JCBRTP scheme uses an innovative reward function to increase the minimum awareness failure distance. Our SLMLab-Gym-VEINS simulations show that the DRL-JCBRTP scheme outperforms existing beaconing schemes in minimizing awareness failure probability and maximizing awareness distance, ultimately improving driving safety.

Minimum profile Hellinger distance estimation for single-index models

Single-index models are the most commonly used covariate models and are widely used in statistical applications. Such models include linear models and generalised linear models as special cases. This article investigates efficient and robust estimates for single-index models. For this purpose, we employ the minimum distance approach which in general is automatically robust with respect to the stability of the quantity being estimated. In particular, the minimum Hellinger distance approach introduced by Beran [(1977), ‘Minimum Hellinger Distance Estimators for Parametric Models’, Annals of Statistics, 5, 445–463] produces estimators that are asymptotically efficient at the model density and simultaneously possess excellent robustness properties. In this paper, we construct a minimum profile Hellinger distance estimator (MPHDE) for single-index models. We prove the consistency of the proposed MPHDE and examine its finite-sample performance and robustness properties via Monte Carlo simulation studies and real data analysis.

An algorithm for cattle counting in rangeland based on multi‐scale perception and image association

AbstractTo effectively address common issues such as cattle being obscured by fences and images prone to colour shifts and high brightness in a ranch setting, this paper proposes an algorithm for counting cows based on multi‐scale perception and image correlation. The algorithm first adjusts the model output scale to enhance cattle detection under current conditions. It incor‐porates efficient Partial Convolution (PConv) to replace 3 × 3 convolutions in the Neck segment of the YOLOv7 network, boost‐ing computational speed and reducing complexity. To streamline feature fusion, Dynamic Head (DyHead) unifies multiple at‐tentional operations in the Neck segment, enhancing efficiency. Additionally, it introduces a novel bounding box similarity metric Minimum Point DioU (MPDIoU) based on minimum point distance, encompassing factors from existing loss functions, while simplifying computations. Experimental results demonstrate the algorithm significantly improves detection, achieving 98.8% accuracy, 99.0% recall, and a 92.1% mAP value. Compared with mainstream SOTA models, Precision increases by 0.4%, Recall by 2.0%, and mAP value by 2.2%. Model size decreases by 23.9%, parameter count by 23.0%, and computational load by 6.1%. the algorithm shows improvements across all indices, meeting the challenge of real‐time cattle counting in ranches under complex conditions.