Target Density Research Articles

YOLOv8s-SNC: An Improved Safety-Helmet-Wearing Detection Algorithm Based on YOLOv8

The use of safety helmets in industrial settings is crucial for preventing head injuries. However, traditional helmet detection methods often struggle with complex and dynamic environments. To address this challenge, we propose YOLOv8s-SNC, an improved YOLOv8 algorithm for robust helmet detection in industrial scenarios. The proposed method introduces the SPD-Conv module to preserve feature details, the SEResNeXt detection head to enhance feature representation, and the C2f-CA module to improve the model’s ability to capture key information, particularly for small and dense targets. Additionally, a dedicated small object detection layer is integrated to improve detection accuracy for small targets. Experimental results demonstrate the effectiveness of YOLOv8s-SNC. When compared to the original YOLOv8, the enhanced algorithm shows a 2.6% improvement in precision (P), a 7.6% increase in recall (R), a 6.5% enhancement in mAP_0.5, and a 4.1% improvement in mean average precision (mAP). This study contributes a novel solution for industrial safety helmet detection, enhancing worker safety and efficiency.

Convergence speed and approximation accuracy of numerical MCMC

Abstract When implementing Markov Chain Monte Carlo (MCMC) algorithms, perturbation caused by numerical errors is sometimes inevitable. This paper studies how the perturbation of MCMC affects the convergence speed and approximation accuracy. Our results show that when the original Markov chain converges to stationarity fast enough and the perturbed transition kernel is a good approximation to the original transition kernel, the corresponding perturbed sampler has fast convergence speed and high approximation accuracy as well. Our convergence analysis is conducted under either the Wasserstein metric or the $\chi^2$ metric, both are widely used in the literature. The results can be extended to obtain non-asymptotic error bounds for MCMC estimators. We demonstrate how to apply our convergence and approximation results to the analysis of specific sampling algorithms, including Random walk Metropolis, Metropolis adjusted Langevin algorithm with perturbed target densities, and parallel tempering Monte Carlo with perturbed densities. Finally, we present some simple numerical examples to verify our theoretical claims.

Investigating the use of maleic acid instead of urea formaldehyde resin and its effect on the single-layer particleboard properties

The aim of this research is to replace the urea formaldehyde (UF) resin with maleic acid and evaluate its effect on the physical and mechanical properties of single-layer particleboard. Industrial wood particles and maleic acid were used as the raw material and adhesive, respectively. Maleic acid was utilized at four levels of 0, 10, 20 and 30% by weight. The boards were manufactured under pressing conditions of 180 °C for 10 min. For comparison, a control sample was produced using 10% UF resin without maleic acid. The board size and target density were 450 × 450 × 16 mm and 0.7 g cm−3, respectively. The results showed that the strengths values of boards increased with increasing maleic acid content from 10 to 20 wt% and then slightly decreased. The control sample (10% UF resin) met the requirements of the European Standard for Type P1 boards. While, the boards bonded with 20 wt% maleic acid satisfied the requirements of the European Standard for Type P2 boards .The physical properties of the boards improved with increasing maleic acid content. Infrared (IR) spectral analysis confirmed the formation of ester bonds, suggesting a reaction between the carboxyl groups of maleic acid and the hydroxyl groups of the wood materials. In conclusion, 20% maleic acid content is effective in improving the physical and mechanical properties of particleboard.

A broken-track association method for robust multi-target tracking adopting multi-view Doppler measurement information

Due to reasons such as target maneuvering and track crossing, mistaken track association may be caused. When the target is in Doppler blind zone, it often leads to the loss of measurement information, which is reflected in the situation as track breakage. In response to the demand of track repair in the case of track breakage, we propose a robust multi-target broken-track association method that comprehensively utilizes multi-view Doppler measurement information. Firstly, based on the derivation of measurement coordinate transformation bias, the multi-sensor Doppler measurement after error correction is fused to obtain the heading velocity and heading acceleration measurement of the target. Secondly, the multi-scan association cost function based on the heading velocity and heading acceleration measurement is constructed, and the optimal correlation sequence is obtained by minimizing the complexity of the cost function. Then, for the case of missing measurements, the optimal association sequence is used to extrapolate the missing measurements, thereby accomplishing the correlation among the broken track segment, the extrapolated measurement and the optimal correlation sequence. Thirdly, we design a multi-scan GLMB smoother to perform forward prediction and backward smoothing on the above correlation results to improve the smoothness of the track. Simulation and actual experimental results suggest that our proposed method can effectively deal with the track breakage situation of dense targets, particularly in track integrity, track accuracy and robustness compared with the existing approaches.

Porosity and cement controlling the response of artificially cemented tailings under hydrostatic loading to high pressures

Cemented tailings find various applications in mining, such as open-pit and underground backfill, dam decommissioning and filtered tailings stacking. This research investigates the compression behaviour of iron ore tailings (IOT) mixed with distinct amounts of Portland cement and compacted in different conditions through isotropic compression, pulse velocity, and unconfined compression tests. The results show the adequacy of the porosity/cement index (η/Civ) in predicting elastic and plastic characteristics of compacted filtered IOT - Portland cement blends, an original correlation that has not been reported by previous work. This index is useful in selecting the cement content and target density for essential parameters required to design cemented IOT stacks. Besides, both cement addition and compaction have promoted the tailings isotropisation (conversion of an anisotropic system to an isotropic one). The evolution of the Post-Yield Compression Line (PYCL) with cementation is shown. Finally, it is demonstrated that distinct initial (after compaction) porosities of uncemented specimens reach a unique PYCL after isotropic pressures above 100 MPa, and cemented specimens do not reach a unique PYCL even at 120 MPa of isotropic pressures. The results underscore the requirement of rigorous compaction control in the field and offer a methodology for the dosage and technological control of artificially cemented tailings.

The urine formed element instance segmentation based on YOLOv5n

Accurate and efficient detection and segmentation of the urine formed element plays a vital role in the clinical diagnosis and treatment of many diseases, such as urinary system diseases, kidney diseases, and other diseases. However, artificial microscopy is subjective, and time- consuming. The mainstream detection and instance segmentation algorithms lack adequate accuracy and speed for the urine formed element due to small and dense targets. Therefore, this study proposes a quick one-stage urine formed element instance segmentation model based on YOLOv5n. The approach first employs a backbone architecture to extract features named shallow graphical features and semantic features from urine cells. Next, the neck network combines shallow graphical features with different deep semantic features, obtaining multi-scale, and multi-level features. Finally, according to these multi-level features, the head network of YOLOv5n integrates a small FCN network into the YOLOv5 detector. It obtains the location, classification, and segmentation results of the targets. To validate the superiority of this approach in terms of speed and accuracy, a special urine formed element dataset including 500 images was created. Experimental results show that the YOLOv5n method achieves a Mean Average Precision (mAP) at intersection over the union threshold of 0.5 (mAP50) with 91.8%, and Frames Per Second (FPS) of 63.3. Compared to Mask R-CNN and YOLOv8, its FPS increased by 62.6 and 60.9, respectively, resulting in nearly a hundred-fold speedup, and its mAP50 also increased by 3.6 and 1.4% points in accuracy, respectively. Additionally, the YOLOv5n obtains a superior balance of accuracy and speed in comparisons with SOLOv2, BoxInst, and ConvNeXt V2. This study developed a new automated analysis of urinary particles based on deep learning, and this method is expected to be used for the automated analysis and detection of the urine formed element. The experimental results also demonstrate that YOLOv5n can achieve more accurate and faster instance segmentation of urine formed element, providing technical support for clinical disease diagnosis.

Enhanced Fishing Monitoring in the Central-Eastern North Pacific Using Deep Learning with Nightly Remote Sensing

The timely and accurate monitoring of high-seas fisheries is essential for effective management. However, efforts to monitor industry fishing vessels in the central-eastern North Pacific have been hampered by frequent cloud cover and solar illumination interference. In this study, enhanced fishing extraction algorithms based on computer vision were developed and tested. The results showed that YOLO-based computer vision models effectively detected dense small fishing targets, with original YOLOv8 achieving a precision (P) of 89% and a recall (R) of 79%, while refined versions improved these metrics to 93% and 99%, respectively. Compared with traditional threshold methods, the YOLO-based enhanced models showed significantly higher accuracy. While the threshold method could identify similar trend changes, it lacked precision in detecting individual targets, especially in blurry scenarios. Using our trained computer vision model, we established a dataset of dynamic changes in fishing vessels over the past decade. This research provides an accurate and reproducible process for precise monitoring of lit fisheries in the North Pacific, leveraging the operational and near-real-time capabilities of Google Earth Engine and computer vision. The approach can also be applied to dynamic monitoring of industrial lit fishing vessels in other regions.

Evaluating eggshells as sustainable weighting agents in water-based drilling muds: A novel approach for enhanced efficiency and environmental consciousness

This study investigates the utilization of eggshells, a renewable material, as a weighting additive in water-based drilling muds with different exploring concentrations. The primary objectives were to assess the impact of eggshells on the rheological properties of drilling muds and to determine the optimal concentration of eggshells for achieving desired density and stability, drawing comparisons with calcium carbonate (CaCO3). Both eggshell powder (ESP) and CaCO3 effectively increase mud weight to the target density of 8.75 ppg at 30 g. Notably, ESP exhibits favorable rheological properties at 20 g, maintaining low plastic viscosity 2.7, consistent yield points 1.1, and gel strength comparable to CaCO3. Conversely, CaCO3 shows signs of potential deterioration at 30 g indicated by increased viscosity to 3.5 and decreased yield point to 0.5. ESP demonstrates superior filtration performance, displaying a progressive increase in cake thickness with increasing weight 1.32 mm to 3.12 mm compared to the slower cake build-up of CaCO3 0.92 mm to 2.9 mm. Both additives slightly elevate mud pH, potentially enhancing overall stability.

Logic-gated and contextual control of immunotherapy for solid tumors: contrasting multi-specific T cell engagers and CAR-T cell therapies.

CAR-T cell and T cell engager therapies have demonstrated transformational efficacy against hematological malignancies, but achieving efficacy in solid tumors has been more challenging, in large part because of on-target/off-tumor toxicities and sub-optimal T cell anti-tumor cytotoxic functions. Here, we discuss engineering solutions that exploit biological properties of solid tumors to overcome these challenges. Using logic gates as a framework, we categorize the numerous approaches that leverage two inputs instead of one to achieve better cancer selectivity or efficacy in solid tumors with dual-input CAR-Ts or multi-specific TCEs. In addition to the "OR gate" and "AND gate" approaches that leverage dual tumor antigen targeting, we also review "contextual AND gate" technologies whereby continuous cancer-selective inputs such a pH, hypoxia, target density, tumor proteases, and immune-suppressive cytokine gradients can be creatively incorporated in therapy designs. We also introduce the notion of "output directionality" to distinguish dual-input strategies that mechanistically impact cancer cell killing or T cell fitness. Finally, we contrast the feasibility and potential benefits of the various approaches using CAR-T and TCE therapeutics and discuss why the promising "IF/THEN" and "NOT" gate types pertain more specifically to CAR-T therapies, but can also succeed by integrating both technologies.

Annealed adaptive importance sampling method in PINNs for solving high dimensional partial differential equations

Physics-informed neural networks (PINNs) have emerged as powerful tools for solving a wide range of partial differential equations (PDEs). However, despite their user-friendly interface and broad applicability, PINNs encounter challenges in accurately resolving PDEs, especially when dealing with singular cases that may lead to unsatisfactory local minima. To address these challenges and improve solution accuracy, we propose an innovative approach called Annealed Adaptive Importance Sampling (AAIS) for computing the discretized PDE residuals of the cost functions, inspired by the Expectation Maximization (EM) algorithm used in finite mixtures to mimic target density. Our objective is to approximate discretized PDE residuals by strategically sampling additional points in regions with elevated residuals, thus enhancing the effectiveness and accuracy of PINNs. Implemented together with a straightforward resampling strategy within PINNs, our AAIS algorithm demonstrates significant improvements in efficiency across a range of tested PDEs, even with limited training datasets. Moreover, our proposed AAIS-PINNs method shows promising capabilities in solving high-dimensional singular PDEs. The adaptive sampling framework introduced here can be integrated into various PINN frameworks.

Gamma Secretase Inhibition May Mitigate the Impact of Low BCMA Target Density in Relapsed/Refractory Multiple Myeloma: Results from a Comparative Analysis of Two Phase 1 Clinical Trials

Gamma Secretase Inhibition May Mitigate the Impact of Low BCMA Target Density in Relapsed/Refractory Multiple Myeloma: Results from a Comparative Analysis of Two Phase 1 Clinical Trials

Enhanced laser-target energy coupling through counter-propagating lasers: Insights from electron recirculation

In the interaction between lasers of relativistic intensity and targets, a portion of the laser energy is carried by relativistic fast electrons, which usually cannot be deposited inside but escapes from the target. Here, we explored a method to reduce this energy waste and enhance laser-target energy coupling through counter-propagating lasers. Particle-in-cell simulation results show that high-energy fast electrons generated by the laser on one side can be re-accelerated by the laser on the other side through the synergistic effect of the reflected laser and longitudinal electric field after passing through the target, and then reflected back into the target by a potential barrier, forming an electron recirculation. Through this electron recirculation, the energy conversion efficiency of each laser is significantly improved, and the temperature of electrons and ions inside the target is effectively increased by 118 % and 29 %, respectively. We also explored the effects of laser intensity and target density through multiple sets of simulations with controlled variables, and summarized the electron recirculation model in such counter-propagating lasers configuration.

Effects of grain size, dislocation density, and texture type on the etching behavior of Cu target in magnetron sputtering process

Abstract Copper thin film is commonly applied in the microelectronics and battery industries, and environmentally friendly magnetron sputtering process is one of the main coating processes. The effects of grain size, dislocation density, and texture type of Cu target on sputtering efficiency were researched in this work. High-purity copper cast ingots were rolled into plates. Pieces were then cut from different surfaces of the plates and assembled into the targets named as Target 1, Target 2, and Target 3 for magnetron sputtering. To investigate the etching behavior of the three targets, EBSD technology was utilized for analysis, and the micro-morphologies of the etching grooves were observed using SEM. After the sputtering process, the weight loss rate and etching depth of the various targets were measured to compare the respective sputtering efficiency. Target 1 exhibited the highest sputtering weight loss rate at any time period compared to Target 2 and Target 3, and reached its maximum depth after every 15 minutes of sputtering. In the first 45 minutes, the etching depth of Target 2 was slightly greater than that of Target 3. While the etching depth of Target 3 increased significantly in the last 15 minutes compared to that of Target 2. The most significant factor influencing the sputtering efficiency of the target material was grain size, with finer grains leading to higher sputtering efficiency. The dislocation density had a greater impact in the first 30 minutes, but the texture type became more important as the sputtering process continued.

Lightweight and efficient deep learning models for fruit detection in orchards

The accurate recognition of apples in complex orchard environments is a fundamental aspect of the operation of automated picking equipment. This paper aims to investigate the influence of dense targets, occlusion, and the natural environment in practical application scenarios. To this end, it constructs a fruit dataset containing different scenarios and proposes a real-time lightweight detection network, ELD(Efficient Lightweight object Detector). The EGSS(Efficient Ghost-shuffle Slim module) module and MCAttention(Mix channel Attention) are proposed as innovative solutions to the problems of feature extraction and classification. The attention mechanism is employed to construct a novel feature extraction network, which effectively utilizes the low-latitude feature information, significantly enhances the fine-grained feature information and gradient flow of the model, and improves the model’s anti-interference ability. Eliminate redundant channels with SlimPAN to further compress the network and optimise functionality. The network as a whole employs the Shape-IOU loss function, which considers the influence of the bounding box itself, thereby enhancing the robustness of the model. Finally, the target detection accuracy is enhanced through the transfer of knowledge from the teacher’s network through knowledge distillation, while ensuring that the overall network is sufficiently lightweight. The experimental results demonstrate that the ELD network, designed for fruit detection, achieves an accuracy of 87.4%. It has a relatively low number of parameters (4.3×105\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$4.3 \ imes 10^5$$\\end{document}), a GLOPs of only 1.7, and a high FPS of 156. This network can achieve high accuracy while consuming fewer computational resources and performing better than other networks.

Mission Planning Method for Dense Area Target Observation Based on Clustering Agile Satellites

To address the mission planning challenge for agile satellites in dense point target observation, a clustering strategy based on an ant colony algorithm and a heuristic simulated genetic annealing optimization algorithm are proposed. First, the imaging observation process of agile satellites is analyzed, and an improved ant colony algorithm is employed to optimize the clustering of observation tasks, enabling the satellites to complete more observation tasks efficiently with a more stable attitude. Second, to solve for the optimal group target observation sequence and achieve higher total observation benefits, a task planning model based on multi-target observation benefits and attitude maneuver energy consumption is established, considering the visible time windows of targets and the time constraints between adjacent targets. To overcome the drawbacks of traditional simulated annealing and genetic algorithms, which are prone to local optimal solution and a slow convergence speed, a novel Simulated Genetic Annealing Algorithm is designed while optimizing the sum of target observation weights and yaw angles while also accounting for factors such as target visibility windows and satellite attitude transition times between targets. Ultimately, the feasibility and efficiency of the proposed algorithm are substantiated by comparing its performance against traditional heuristic optimization algorithms using a dataset comprising large-scale dense ground targets.

EXPERIMENTAL DATA GENERALIZATION OF DELTA ELECTRON YIELD INDUCED BY HIGH ENERGY ELECTRON INTERACTION WITH AMORPHOUS AND SINGLE-CRYSTAL TARGETS

The generalized experimental results of delta electron yield obtained at the NSC KIPT electron accelerators in the energy range from 0.5 MeV to 1.6 GeV are presented. Thin foils of Be, Al, Si, Ni, and Nb were used as experimental targets. It was established in general that delta electron yield is roportional to the product √T∙Z/A, where T is the thickness of the sample in g/cm2 and it has a linear dependence. It is shown that the main factor of delta elec- tron yield increasing is not primary beam energy, but the electron density of the experimental target

Leveraging Spruce Bark Particle Morphology for Enhanced Internal Bonding in Particleboard Production.

The continuous rise in global demand for wood products has led to an increase in prices and a surge in research into alternative resources. As a byproduct of the timber industry, bark has emerged as a promising supplement in particleboard (PB) production. However, its anatomical structure, the presence of extractives, and its inferior mechanical properties complicate the production process, which have not yet been fully overcome at a commercial scale. This study proposes a paradigm shift, advocating for separate and specialized bark constituent processing in a wet state. Three bark-based raw materials-namely, outer bark particles, bark fiber clumps, and bark fibers-were investigated under varying wood content scenarios. PBs with a target density of 0.7 g/cm3 and a thickness of 16 mm were produced using mixtures of these bark-based materials and wood particles in different ratios bonded with a urea-formaldehyde adhesive. The results demonstrated that these bark constituents exhibit distinct properties that can be optimized through tailored processing techniques. Compared to bark fibers, outer bark particles displayed about 40% lower water absorption and thickness swelling. However, bark fibers improved the internal bond by about 50% due to their favorable morphology compared to outer bark. These findings highlight the potential of bark as a valuable resource for particleboard production and pave the way for its efficient utilization through specialized processing strategies.

Influence of Granular Medium Parameters on Poncelet Drag Coefficient

The Poncelet equation can effectively characterize the resistance of a granular medium to a projectile. However, the determination of the drag coefficient remains challenging. This study aims to explore the potential effects of granular medium parameters on the drag coefficient and to identify key granular medium parameters. Horizontal ballistic tests and quasi-static penetrometer tests were conducted on dry silica sand of four different grain sizes (0.3–0.6 mm, 0.6–2.0 mm, 2.0–4.0 mm and 4.0–8.0 mm). During the ballistic tests, the bulk density of the sand targets was kept consistent, and the entering velocities and the penetration depth of the projectiles were recorded. The bearing strength of the sand targets with different relative densities (30%, 50% and 70%) was measured in the quasi-static tests. Furthermore, two methods were employed to determine the drag coefficients, namely, (1) single-parameter fitting (SF), where the static resistance term was defined as the average bearing strength and the drag coefficients were fitted using the ballistic test data, and (2) two-parameter fitting (TF), where the optimal drag coefficients and bearing strengths were derived from ballistic data. The results showed that the TF method was susceptible to the size effect, differing significantly from the SF method when the projectile diameter was less than 21 times the grain size. The relative density was identified by the SF method as a critical factor affecting the drag coefficient of the granular medium.

Potential for suprathermal chain reactions in degenerate deuterium plasmas at high densities

Abstract It is argued that fusion chain reactions in the D-D system are feasible with
supra-thermal deuterons in the MeV regime, with new generations of deuterons being
generated either via neutron-deuteron and proton-deuteron collisions, provided that fuel
densities in excess of 1000 gcm −3 are reached. The propagation of supra-thermal deuterons
in an infinite, dense deuterium target was studied using a Monte Carlo method that includes
multiple nuclear reactions, electron and ion stopping, along with neutron and proton
knock-ons. Prior studies into this system are extended and it is shown that : (a)dense
pure deuterium may be more supportive of chain reactions via refinements to the ion-ion
stopping number, (b) such chain reactions can be supported even when the target is electron
degenerate, and (c) the D-T reaction plays a critical role in sustaining these chain reactions.

Decentralized Multi-Agent Search for Moving Targets Using Road Network Gaussian Process Regressions

Unmanned aerial vehicles (UAVs) can collaborate as teams to accomplish diverse mission objectives, such as target search and tracking. This paper introduces a method that leverages accumulated target-density information over the course of a UAV mission to adapt path-planning rewards, guiding UAVs toward areas with a higher likelihood of target presence. The target density is modeled using a Gaussian process, which is iteratively updated as the UAVs search the environment. Unlike conventional search algorithms that prioritize unexplored regions, this approach incentivizes revisiting target-rich areas. The target-density information is shared across UAVs using decentralized consensus filters, enabling cooperative path selection that balances the exploration of uncertain regions with the exploitation of known high-density areas. The framework presented in this paper provides an adaptive cooperative search method that can quickly develop an understanding of the region’s target-dense areas, helping UAVs refine their search. Through Monte Carlo simulations, we demonstrate this method in both a 2D grid region and road networks, showing up to a 26% improvement in target density estimates.