Target Density Research Articles

A new method for rapid preparing high-strength saturated clay samples in large-scale model tests

The preparation of high-strength saturated clay samples for large-scale model tests presents a significant challenge in geotechnical engineering. The slurry consolidation method has been conventionally employed to prepare saturated clay, despite its time-consuming and labor-intensive nature. Therefore, this study proposes a rapid preparation technique for clayey soils utilizing the dynamic compaction method, enabling the facile preparation of saturated clay samples by compacting the soils from an unsaturated state. During compaction, the void ratio decreases, thereby increasing the degree of saturation and enhancing the soil strength. Critical to this method are two variables: the moisture water content and the soil density, which are determined through bench-scale compaction tests using the Proctor compaction test apparatus. These tests establish the relationships between moisture content and density, degree of saturation, and soil strength. The moisture content aligning with the target soil strength is selected as the target moisture content for model-scale soil preparation, whereas the moisture content-density relationship sets the target density value. The laboratory tests validate that the soil strength of the saturated model-size clay samples prepared using the proposed method fulfills the requisite criteria, indicating its effectiveness for rapid preparation of high-strength saturated clay samples in large-scale model tests.

YOLOv5s-CEDB: A robust and efficiency Camellia oleifera fruit detection algorithm in complex natural scenes

To solve the problem of poor recognition accuracy caused by various colors, uneven distribution, and occlusion by branches and leaves of Camellia oleifera fruits under natural growth conditions, this study proposes an improved deep learning network, YOLOv5s-CEDB, for Camellia oleifera fruit detection based on YOLOv5s. Coordinate Attention Mechanism (CoordAtt), Deformable Convolution (DConv), and Explicit Visual Center (EVC) are introduced to enhance the network’s local and global feature extraction performance. To improve the detection performance for small and dense targets, the feature fusion module of the network was replaced with the designed light-bidirectional feature pyramid (light-BiFPN) structure. GhostConv was used to reduce the parameters and inference speed of the structure. A dataset with different light conditions, colors, and density levels of Camellia oleifera fruits was established, and performance evaluation experiments were conducted. Experimental results showed that the mean Average Precision (mAP) and F1-score of the designed YOLOv5s-CEDB network reached 91.4 % and 89.6 %, respectively, which were 2.6 % and 3.6 % higher than those of the original YOLOv5s model, respectively, and the influencing frame rate arrived at 37.6 FPS. Under different colors, distribution densities, occlusion scenarios, and light intensities, the detection accuracy of the YOLOv5s-CEDB network model was significantly better than those of the YOLOv5s, YOLOv8s and Faster-RCNN networks. It was verified that the proposed YOLOv5s-CEDB network could significantly improve the accuracy and stability of Camellia oleifera fruit detection, satisfying the requirements of yield estimation and intelligent harvesting.

Effect of Target Layering in Gravity‐Dominated Cratering in Nature, Experiments, and Numerical Simulations

AbstractImpacts into layered targets may generate “concentric craters” where a wider outer crater in the top layer surrounds a smaller, nested crater in the basement, which itself may be complex or simple. The influence of target on cratering depends on the ratio of target strength to lithostatic stress, which, in turn, is affected by gravity, target density, and crater diameter. When this ratio is large, the crater size is primarily determined by target strength, whereas gravitational forces dominate when the ratio is small. In two‐layer targets, strength may dominate in one or both layers, whereby the outer crater develops in the weaker top layer and the nested crater in the stronger substrate. However, large natural craters that should be gravity‐dominated in both cover strata and substrate may be concentric, the reasons for which are not yet fully understood. We performed qualitative impact experiments at 10–502 G and 1.8 km/s with the Boeing Corp. Hypervelocity centrifuge gun, and at 1 G and 0.4 km/s with the CAB CSIC‐INTA gas gun into layered sand targets of different compositions and grain densities but similar granulometry to analyze gravity‐dominated cratering. The results are compared with iSALE‐2D numerical simulations and natural craters on Earth and Mars. We show that target layering also affects the excavation process and concentric crater formation in gravity‐dominated impacts. The most important factors are the density and internal friction of each target layer, respectively. We propose that this is also valid for natural craters of sizes that should make their formation gravity‐dominated.

Properties of ultra lightweight foamed concrete utilizing agro waste ashes as an alkaline activated material

In the present investigation, rice husk ash (RHA), bamboo leaf ash (BLA) and palm oil fuel ash (POFA) known as agricultural-waste materials locally available were used to produce ultra lightweight foamed concretes (ULFCs). BLA, RHA, and POFA were used as replacements for cement in ULFCs at different weight percentages of 0 %, 5 %, 10 %, 15 %, and 20 %. To achieve the target density of 350 kg/m3, cement was substituted with agricultural wastes, and a protein foaming agent was added. The properties of ULFCs were examined including, slump test, setting time, fresh density, splitting tensile, compressive and flexural strengths, thermal insulating, microstructural and transport properties, permeability and pore's structure. The experimental results demonstrated that BLA and POFA ashes significantly outperforms in terms of efficiency and improved the mechanical and transport qualities of ULFCs than RHA ash. By increasing the weight fractions of BLA, RHA, and POFA from 5 % to 20 % in ULFC mixes resulted in an improvement in slump, porosity, capillary sorption, water absorption, bulk density, intrinsic air permeability, and specific heat. The ideal results were seen in terms of compressive strength, bending strength, splitting tensile strength, and UPV when 15 % POFA and BLA, and 10 % RHA were used as substitutes for cement. The increase in weight fraction of BLA, RHA, and POFA resulted in a rise in both thermal conductivity and thermal diffusivity of ULFCs. SEM studies revealed that incorporating different agricultural wastes positively affects the pore size distribution within the microstructure decreasing with increased content of ashes related to the development of ULFCs with stronger matrix. As a result, the formulated ULFCs met the masonry strength criteria while also providing economic and environmental benefits.

Human figure detection in Han portrait stone images via enhanced YOLO-v5

The unearthed Han Dynasty portrait stones are an important part of China’s ancient artistic heritage, and detecting human images in these stones is a critical prerequisite for studying their artistic value. However, high-precision target detection techniques often result in a large number of parameters, making them unsuitable for portable devices. In this work, we propose a new human image target detection model based on an enhanced YOLO-v5. We discovered that the complex backgrounds, dense group targets, and significant scale variations of targets within large scenes in portrait stones present significant challenges for human target image detection. Therefore, we first incorporated the SPD-Conv convolution and Coordinate Attention self-attention mechanism modules into the YOLO-v5 architecture, aiming to enhance the model’s recognition precision for small target images within Han portrait stones and strengthen its resistance to background disturbances. Moreover, we introduce DIoU NMS and Alpha-IoU Loss to improve the detector’s performance in dense target scenarios, reducing the omission of densely packed objects. Finally, the experimental results from our collected dataset of Han Dynasty stone figure images demonstrate that our method achieves fast convergence and high recognition accuracy. This approach can be better applied to the target detection tasks of special character images in complex backgrounds.

Comparison of the scrape‐off layer two‐point model for deuterium and helium plasmas in JET ITER‐like wall low‐confinement plasma conditions

AbstractThe hydrogenic two‐point model (H‐2PM), an analytical model for the scrape‐off layer that predicts a common electron and ion temperature and density along a flux tube from a target temperature and density, is adapted to a single‐species helium (He) model, preserving the 2PM assumptions and analytical nature. Across a range of densities and heating powers, the predicted is within of upstream measurements by high‐resolution Thomson scattering (HRTS) for low‐confinement He plasmas performed in JET ITER‐like wall (JET‐ILW). For high‐recycling conditions, He‐2PM predictions of were within 10% of the Li‐beam diagnostic measurements, excluding the near‐SOL, when assuming , suggesting the divertor SOL was not fully ionised in the JET‐ILW He plasmas.

A dense false target jamming suppression method based on inter-pulse phase agility waveform

A dense false target jamming suppression method based on inter-pulse phase agility waveform

Sintering densification behavior of nano-ITO powder with high oxygen vacancy induced via plasma stimulation

With the growing electronics display industry, indium tin oxide (ITO) powder plays a crucial role in target preparation. To enhance the sintering performance of ITO powder, a high-activity nano ITO powder was prepared using plasma spraying, and compared to a commercial powder. This study aimed to investigate the effects of different preparation methods on powder morphology, dispersibility, oxygen vacancy concentration, and the physical properties of powders on target density, surface morphology, grain size, element distribution, and solid solubility. The results indicate that the plasma spraying high-activity nano ITO powder exhibits better dispersibility, higher oxygen vacancy concentration, and a specific surface area of up to 88.39 m2/g compared to the commercial powder. Under normal pressure sintering conditions, plasma spraying nanoscale ITO powder exhibits higher sintering activity, leading to easier densification of ceramic targets with reduced porosity, more uniform surface grains, and significant stepped growth between surface grains. High-resolution transmission electron microscopy (HRTEM) analysis suggests that targets prepared with high-activity powder have greater tin solubility in indium oxide compared to traditional commercial ceramic targets. This article further explores the relationship between powder specific surface area, oxygen vacancy, and sintering activity, along with the formation mechanism of high oxygen vacancy.

Image convolution techniques integrated with YOLOv3 algorithm in motion object data filtering and detection.

In order to address the challenges of identifying, detecting, and tracking moving objects in video surveillance, this paper emphasizes image-based dynamic entity detection. It delves into the complexities of numerous moving objects, dense targets, and intricate backgrounds. Leveraging the You Only Look Once (YOLOv3) algorithm framework, this paper proposes improvements in image segmentation and data filtering to address these challenges. These enhancements form a novel multi-object detection algorithm based on an improved YOLOv3 framework, specifically designed for video applications. Experimental validation demonstrates the feasibility of this algorithm, with success rates exceeding 60% for videos such as "jogging", "subway", "video 1", and "video 2". Notably, the detection success rates for "jogging" and "video 1" consistently surpass 80%, indicating outstanding detection performance. Although the accuracy slightly decreases for "Bolt" and "Walking2", success rates still hover around 70%. Comparative analysis with other algorithms reveals that this method's tracking accuracy surpasses that of particle filters, Discriminative Scale Space Tracker (DSST), and Scale Adaptive Multiple Features (SAMF) algorithms, with an accuracy of 0.822. This indicates superior overall performance in target tracking. Therefore, the improved YOLOv3-based multi-object detection and tracking algorithm demonstrates robust filtering and detection capabilities in noise-resistant experiments, making it highly suitable for various detection tasks in practical applications. It can address inherent limitations such as missed detections, false positives, and imprecise localization. These improvements significantly enhance the efficiency and accuracy of target detection, providing valuable insights for researchers in the field of object detection, tracking, and recognition in video surveillance.

Dense Contrastive-based Federated Learning for Dense Prediction Tasks on Medical Images.

Deep learning (DL) models have achieved remarkable success in various domains. But training an accurate DL model requires large amounts of data, which can be challenging to obtain in medical settings due to privacy concerns. Recently, federated learning (FL) has emerged as a promising solution that shares local models instead of raw data. However, FL in medical settings faces challenges of client drift due to the data heterogeneity across dispersed institutions. Although there exist studies to address this challenge, they mainly focus on the classification tasks that learn global representation of an entire image. Few have been studied on the dense prediction tasks, such as object detection. In this study, we propose dense contrastive-based federated learning (DCFL) tailored for dense prediction tasks in FL settings. DCFL introduces dense contrastive learning to FL, which aligns the local optimization objectives towards the global objective by maximizing the agreement of representations between the global and local models. Moreover, to improve the performance of dense target prediction at each level, DCFL applies multi-scale contrastive representation by utilizing multi-scale representations with dense features in contrastive learning. We evaluated DCFL on a set of realistic datasets for pulmonary nodule detection. DCFL demonstrates an overall performance improvement compared with the other federated learning methods in heterogeneous settings-improving the mean average precision by 4.13% and testing recall by 6.07% in highly heterogeneous settings.

Acousto-optic holography for pseudo-two-dimensional dynamic light patterning.

Optical systems use acousto-optic deflectors (AODs) mostly for fast angular scanning and spectral filtering of laser beams. However, AODs may transform laser light in much broader ways. When time-locked to the pulsing of low repetition rate laser amplifiers, AODs permit the holographic reconstruction of 1D and pseudo-two-dimensional (ps2D) intensity objects of rectangular shape by controlling the amplitude and phase of the light field at high (20-200kHz) rates for microscopic light patterning. Using iterative Fourier transformations (IFTs), we searched for AOD-compatible holograms to reconstruct the given ps2D target patterns through either phase-only or complex light field modulation. We previously showed that phase-only holograms can adequately render grid-like patterns of diffraction-limited points with non-overlapping diffraction orders, while side lobes to the target pattern can be cured with an apodization mask. Dense target patterns, in contrast, are typically encumbered by apodization-resistant speckle noise. Here, we show the denoised rendering of dense ps2D objects by complex acousto-optic holograms deriving from simultaneous optimization of the amplitude and phase of the light field. Target patterns lacking ps2D symmetry, although not translatable into single holograms, were accessed by serial holography based on a segregation into ps2D-compatible components. The holograms retrieved under different regularizations were experimentally validated in an AOD random-access microscope. IFT regularizations characterized in this work extend the versatility of acousto-optic holography for fast dynamic light patterning.

A new method of ship detection in complex background of SAR images based on YOLO

Synthetic Aperture Radar (SAR) is a remote sensing technology capable of high-resolution imaging of the ground surface under any weather and light conditions. SAR image ship detection is a technique that uses the ship target features in SAR images to identify and locate ships, which is of great value for applications in the fields of maritime traffic management, marine environment monitoring, and maritime security and defence. However, SAR image ship detection also faces some challenges, such as complex sea surface background, low signal-to-noise ratio, and the diversity and density of ship targets, which lead to the shortcomings of traditional feature extraction and classifier-based detection methods in terms of accuracy and efficiency. To address these challenges, this paper proposes a new method for ship detection in SAR images based on YOLOv7. When dealing with SAR images containing clutter interference and complex backgrounds, many models face the dual challenge of insufficient real-time and accuracy. To address this problem, this study proposes a new PCAN aggregation network, which firstly introduces the SimAM attention mechanism, which significantly enhances the model's ability to identify the differences between ships and background clutter, further improves the ability of focusing on target features, and effectively It further improves the ability to focus on target features, effectively reduces the interference of background noise, and improves the detection accuracy in complex scenes. At the same time, the PConv strategy is added to further lighten the model structure, thus accelerating the computation process and improving the inference efficiency. We trained and tested the model on the SAR-Ship-Dataset dataset, and achieved an accuracy of 91.1%, an F1 score of 92.9, and an FPS of 46, demonstrating its excellent detection capability. By comparing and analysing the model with several classical and cutting-edge methods in the current field, the proposed model in this study demonstrates obvious performance advantages, and in addition, through ablation experiments, we further confirm the effectiveness and contribution of PCAN.

Improving the maize crop row navigation line recognition method of YOLOX.

The accurate identification of maize crop row navigation lines is crucial for the navigation of intelligent weeding machinery, yet it faces significant challenges due to lighting variations and complex environments. This study proposes an optimized version of the YOLOX-Tiny single-stage detection network model for accurately identifying maize crop row navigation lines. It incorporates adaptive illumination adjustment and multi-scale prediction to enhance dense target detection. Visual attention mechanisms, including Efficient Channel Attention and Cooperative Attention modules, are introduced to better extract maize features. A Fast Spatial Pyramid Pooling module is incorporated to improve target localization accuracy. The Coordinate Intersection over Union loss function is used to further enhance detection accuracy. Experimental results demonstrate that the improved YOLOX-Tiny model achieves an average precision of 92.2 %, with a detection time of 15.6 milliseconds. This represents a 16.4 % improvement over the original model while maintaining high accuracy. The proposed model has a reduced size of 18.6 MB, representing a 7.1 % reduction. It also incorporates the least squares method for accurately fitting crop rows. The model showcases efficiency in processing large amounts of data, achieving a comprehensive fitting time of 42 milliseconds and an average angular error of 0.59°. The improved YOLOX-Tiny model offers substantial support for the navigation of intelligent weeding machinery in practical applications, contributing to increased agricultural productivity and reduced usage of chemical herbicides.

The Effect of Sputtering Target Density on the Crystal and Electronic Structure of Epitaxial BaTiO3 Thin Films

Barium titanate (BaTiO3) is a promising material for silicon-integrated photonics due to its large electro-optical coefficients, low loss, high refractive index, and fast response speed. Several deposition methods have been employed to synthesize BaTiO3 films. Magnetron sputtering is one of these methods, which offers specific advantages for growing large-scale films. However, there is a scarcity of studies investigating the effect of sputtering target density on the quality of BaTiO3 films. Therefore, this study aims to uncover the effect of sputtering targets on the crystal and electronic structures of epitaxial BaTiO3 thin films. Two BaTiO3 ceramic targets were sintered at different densities by altering the sintering temperatures. The crystal structure and chemical composition of the targets were then characterized using X-ray diffraction, Raman spectroscopy, and scanning electron microscopy with energy-dispersive X-ray spectroscopy. Subsequently, BaTiO3 epitaxial films were grown by magnetron sputtering using these two targets. The crystal and electronic structures of the BaTiO3 films were analyzed using high-resolution X-ray diffraction, X-ray photoemission spectroscopy, atomic force microscopy, and spectroscopic ellipsometry. Notably, the BaTiO3 films grown with high-density targets show superior quality but contain oxygen vacancies, whereas those films synthesized with low-density targets display high surface roughness. These findings provide insights into the effect of sputtering target density on the crystal and electronic structures of epitaxial BaTiO3 thin films.

Proton-boron fusion scheme taking into account the effects of target degeneracy

The proton-boron (p-B11) reaction is regarded as the holy grail of advanced fusion fuels, since the primary reaction produces three α particles with few neutrons and induced radioactivities from second order reactions. Compared to the deuterium-tritium reaction a much higher reaction temperature is required. Moreover, bremsstrahlung energy losses due to the high nuclear charge of boron deem it seemingly apparent than a fusion reactor based on deuterium-tritium plasma in equilibrium is to say the least very difficult. It is becoming more appealing to collide intense laser beams or accelerated proton beams with a boron target to produce p-B11 reactions. The fusion yield of p-B11 reactions is closely related to proton beam parameters and boron target conditions such as density, temperature, and ingredients. Degeneracy will increase fusion yields by reducing the stopping power of injected protons. In this work, we suggest a scheme for beam-target p-B11 fusions via injecting a MeV proton beam into a highly compressed quantum degenerated boron target. Such a boron target can be achieved via quasi-isentropic compression of solid boron by using precisely shaped laser pulses. Our results indicate that for densities ranging from 103 to 104ρs, where ρs is the density of solid boron, contributions of bound and free electrons to the stopping of protons can be completely disregarded and dramatically reduced, respectively. The result is an increase in fusion yield by orders of magnitude. Furthermore, in order to achieve multiplication factor F greater than one, with F defined as the ratio of output fusion energy to the energy of injected protons, it is found there exists a minimum possible density of boron target, which is 1.8×105ρs when the kinetic energy of injected protons is 880 keV. Published by the American Physical Society 2024

Aero-YOLO: An Efficient Vehicle and Pedestrian Detection Algorithm Based on Unmanned Aerial Imagery

The cost-effectiveness, compact size, and inherent flexibility of UAV technology have garnered significant attention. Utilizing sensors, UAVs capture ground-based targets, offering a novel perspective for aerial target detection and data collection. However, traditional UAV aerial image recognition techniques suffer from various drawbacks, including limited payload capacity, resulting in insufficient computing power, low recognition accuracy due to small target sizes in images, and missed detections caused by dense target arrangements. To address these challenges, this study proposes a lightweight UAV image target detection method based on YOLOv8, named Aero-YOLO. The specific approach involves replacing the original Conv module with GSConv and substituting the C2f module with C3 to reduce model parameters, extend the receptive field, and enhance computational efficiency. Furthermore, the introduction of the CoordAtt and shuffle attention mechanisms enhances feature extraction, which is particularly beneficial for detecting small vehicles from a UAV perspective. Lastly, three new parameter specifications for YOLOv8 are proposed to meet the requirements of different application scenarios. Experimental evaluations were conducted on the UAV-ROD and VisDrone2019 datasets. The results demonstrate that the algorithm proposed in this study improves the accuracy and speed of vehicle and pedestrian detection, exhibiting robust performance across various angles, heights, and imaging conditions.

Abstract 1868: Target-dependent considerations for the design of bispecific T-cell engagers

Abstract In this study, we present data from four T-cell engager (TCE) programs in which we leveraged our highly diverse CD3-binding antibodies to generate functional bispecifics for each tumor target. By aggregating tumor-cell killing and cytokine data across four tumor targets with different properties and target expression levels, we have gained novel insights into target-specific considerations for the development of CD3 TCEs. TCEs recapitulate a synapse between T cells and target cells. T cell function downstream of this synapse is determined by the complex interplay between multiple independent factors. These include properties of the CD3- and tumor-binding arms, such as binding kinetics and epitope, as well as target-dependent factors such as cell type and target density. We used our diverse CD3 antibodies to engineer bispecific TCEs against four solid tumor targets: PSMA, B7-H4, 5T4, and the peptide-MHC target MAGE-A4. For each target, we engineered and characterized hundreds of bispecific molecules using high-throughput assays, including T-cell dependent tumor-cell killing, cytokine release, developability assessments, and structural studies. Here, we compare data from across these programs to elucidate the impact of target-dependent parameters on TCE function. These data provide important insights for the selection of appropriate TCE targets and efficient design of bispecific antibodies with high therapeutic potential. Citation Format: Matt Mai, Raffi Tonikian, Peter Bergqvist, Alaa Amash, Nathalie Blamey, Gabrielle Conaghan, Valentine de Puyraimond, Patrick Farber, Allison Goodman, Ahn Lee, Jessica Fernandes Scortecci, Cindy-Lee Crichlow, Akram Khodabandehloo, Tova Pinsky, Kate Caldwell, Jessica Patterson, Philippe Pouliot, Davide Tortora, Oscar Urtatiz, Ping Xiang, Irene Yu, Kirstin Brown, Kelly Bullock, Andrea Chee, Stephanie K. Masterman, Neil Aubuchon, Lindsay Devorkin, Bryan C. Barnhart, Tim Jacobs. Target-dependent considerations for the design of bispecific T-cell engagers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1868.

Abstract 1871: Discovery and characterization of NXV01c, an EGFR × cMET bispecific nanobody drug conjugate with potent anti-tumor activity

Abstract EGFR and cMET are proven cancer targets co-expressed in diverse tumor types. EGFR × cMET bispecific antibody has been approved for the treatment of NSCLC, supporting a simultaneous targeting strategy. In addition to this dual targeting benefit, bispecific antibody drug conjugates (ADCs) targeting EGFR and cMET have also been developed to further improve anti-tumor activity and tissue selectivity. Sufficient target affinity, good cellular internalization and high tumor infiltration are critical for an ADC to mediate therapeutic activity. To this end, we developed the first EGFR × cMET bispecific nanobody conjugated with monomethyl auristatin E (MMAE) as payload (NXV01c). Lead nanobodies against EGFR and cMET were respectively selected from immune libraries by phage display, followed by highly efficient humanization and optimization by neoX’s computation platform. The bispecific nanobody with Fc (NXV01) was constructed by “knobs-into-holes” heterodimerization and then homogeneously conjugated with MMAE via a lysosomal cleavable valine-citrulline dipeptide linker. The resulting bispecific nanobody drug conjugate (NDC), NXV01c, was evaluated in multiple tumor cell lines and tumor xenograft models. NXV01, the pre-conjugate bispecific nanobody, bound EGFR and cMET with nanomolar potency (BLI) and inhibited the phosphorylation of cellular EGFR and cMET with nanomolar IC50 (ELISA). Moreover, it did not activate cellular cMET. Importantly, the NDC NXV01c is highly homogeneous: it has an average DAR of 3.87 and >95% of NXV01c has a DAR of 4. To examine stability of conjugation, NXV01C was incubated in human plasma (37°C) for 14 days, the maximum free drug release rate (by LC/MS/MS) is 0.68%, which is much lower than that of DS8201. In vitro, NXV01c was rapidly internalized into H1975 cells which co-expressed EGFR and cMET. It inhibited the growth of H1975 (lung) and SNU5 (gastric) cancer cells with picomolar activity but spared normal keratinocytes. NXV01c also inhibited the proliferation of additional cell lines derived from lung, gastric, esophageal, and liver cancer, and the level of inhibition positively associated with the expression density of both targets. In an H1975 cell line-derived xenograft model, NXV01C exhibited potent and dose-dependent anti-tumor activity. Treatments at 3 and 10 mg/kg once per week for 2 weeks led to shrinkage and complete regression of tumor, respectively. In patient-derived xenograft models of NSCLC and esophageal cancer, NXV01C led to tumor regression and elimination without noticeable toxic effects. EGFR × cMET bispecific nanobody drug conjugate NXV01c has favorable drug-like properties and demonstrated superior anti-tumor effect in vitro and in vivo. The results suggest NXV01C can be an effective solution for EGFR/cMET bearing tumors commonly found in diverse malignancies. Citation Format: Ran Wu, Puwei Yuan, Yang Xie, Jianxiu Guo, Fei Zhang, Fan Liu, Taylor B. Guo. Discovery and characterization of NXV01c, an EGFR × cMET bispecific nanobody drug conjugate with potent anti-tumor activity [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1871.

A lightweight vehicle detection network fusing feature pyramid and channel attention

Vehicle detection is an important supporting technology for the realization of intelligent transportation, autonomous driving, etc. Poor accuracy or low inference vehicle detectors are limited in application, this paper proposes a fast and accurate vehicle detector termed as MCE-SSD. First, the front-end feature extraction network VGG16 is replaced by MobileNetV3_Large, which reduces the number of parameters and computation, and increases the ability to extract high-dimensional features. Next, the BiFPN idea is used to construct a weighted bi-directional fusion network CBiFPN to obtain multi-dimensional vehicle features, while introducing ECA-Net in the feature extraction layer to re-calibrate the importance of different feature channels and further improve the model performance. In the end, CIoU is introduced to better regress the bounding box of the target vehicle, and DIoU-NMS is used to solve the problem of error suppression for dense targets. Compared with SSD, our proposed MCE-SSD improves mAP by 8.30% and 3.50% on KITTI dataset and BDD100K dataset, and with real-time inference (more than 40 FPS), it reports a better trade-off in terms of detection accuracy and speed, illustrating the effectiveness of our method.

Microscopic optical potentials for medium-mass isotopes derived at the first order of Watson multiple-scattering theory

We perform a first-principles calculation of optical potentials for nucleon elastic scattering off medium-mass isotopes. Fully based on a saturating chiral Hamiltonian, the optical potentials are derived by folding nuclear density distributions computed with self-consistent Green's function theory with a nucleon-nucleon t matrix computed with a consistent chiral interaction. The dependence on the folding interaction as well as the convergence of the target densities are investigated. Numerical results are presented and discussed for differential cross sections and analyzing powers, with focus on elastic proton scattering off calcium and nickel isotopes. Our optical potentials generally show a remarkable agreement with the available experimental data for laboratory energies in the range 65–200 MeV. We study the evolution of the scattering observables with increasing proton-neutron asymmetry by computing theoretical predictions of the cross section and analyzing power over the calcium and nickel isotopic chains. Published by the American Physical Society 2024