Variable Shape Research Articles

Effects of shear stability and residual stresses in computer glasses

Numerical investigations of disordered solids often involve employing a fixed simulation-box geometry and periodic boundary conditions, the latter are intended to minimize surface effects. One consequence of this methodological approach is that the resulting solids typically feature residual macroscopic stresses and could feature negative shear moduli, in which case they are deemed unstable to shear. In this work, we review and validate via numerical simulations the statistical theory that explains how the fraction of shear-unstable solids within an ensemble of solids created via the same protocol depends on the system size. We further demonstrate and rationalize how the fraction of disordered soft-sphere packings that are unstable to shear depends on their proximity to the unjamming point at which solidity is lost. Finally, we show that residual-stress-free structural glasses created in a fixed-box geometry have the same vibrational and elastic properties as glasses that do feature residual stresses. This result seemingly contradicts previous work that shows that residual-stress-free samples created using variable box shape feature different vibrational properties. These observations raise questions about the equivalence of constant-stress vs constant box-geometry ensembles in the thermodynamic limit, which are discussed together with future research directions.

Multi-level feature fusion networks for smoke recognition in remote sensing imagery.

Smoke is a critical indicator of forest fires, often detectable before flames ignite. Accurate smoke identification in remote sensing images is vital for effective forest fire monitoring within Internet of Things (IoT) systems. However, existing detection methods frequently falter in complex real-world scenarios, where variable smoke shapes and sizes, intricate backgrounds, and smoke-like phenomena (e.g., clouds and haze) lead to missed detections and false alarms. To address these challenges, we propose the Multi-level Feature Fusion Network (MFFNet), a novel framework grounded in contrastive learning. MFFNet begins by extracting multi-scale features from remote sensing images using a pre-trained ConvNeXt model, capturing information across different levels of granularity to accommodate variations in smoke appearance. The Attention Feature Enhancement Module further refines these multi-scale features, enhancing fine-grained, discriminative attributes relevant to smoke detection. Subsequently, the Bilinear Feature Fusion Module combines these enriched features, effectively reducing background interference and improving the model's ability to distinguish smoke from visually similar phenomena. Finally, contrastive feature learning is employed to improve robustness against intra-class variations by focusing on unique regions within the smoke patterns. Evaluated on the benchmark dataset USTC_SmokeRS, MFFNet achieves an accuracy of 98.87%. Additionally, our model demonstrates a detection rate of 94.54% on the extended E_SmokeRS dataset, with a low false alarm rate of 3.30%. These results highlight the effectiveness of MFFNet in recognizing smoke in remote sensing images, surpassing existing methodologies. The code is accessible at https://github.com/WangYuPeng1/MFFNet.

A Personalized Comfort Space With Variable Shape Based on Environmental Information for Robot Navigation in Homes

A Personalized Comfort Space With Variable Shape Based on Environmental Information for Robot Navigation in Homes

Rainfall Variability Shapes Lion Movement and Home Range Dynamics in Three Kenyan Parks

Rainfall Variability Shapes Lion Movement and Home Range Dynamics in Three Kenyan Parks

Dinuclear Ru(II) Complexes Containing Tetrapyrido[3,2-a : 2,3-c : 3,2-h : 2''',3'''-j]Phenazine Ligand for Biomedical Applications.

Ruthenium complexes are among the most extensively studied and developed luminescent transition-metal complexes for anticancer applications. Dinuclear Ru(II) complexes have caught significant interest for larger size, higher charge, and variable complex shapes. In this concept, we have explored past and recent works on the possible biological applications of versatile tetrapyrido[3,2-a : 2,3-c : 3,2-h : 2''',3'''-j]phenazine (tppz)-based dinuclear Ru(II) complexes with a focus on their use as quadruplex DNA probes, organelle imaging, and phototherapeutic agents. This concept also points out that a particular type of dinuclear Ru(II) complexes can act as multitargeting and multifunctional anticancer agents -making this an exciting research area in which an array of further applications will likely emerge.

Digital Twin/MARS‐CycleGAN: Enhancing Sim‐to‐Real Crop/Row Detection for MARS Phenotyping Robot Using Synthetic Images

ABSTRACTRobotic crop phenotyping has emerged as a key technology for assessing crops' phenotypic traits at scale, which is essential for developing new crop varieties with the aim of increasing productivity and adapting to the changing climate. However, developing and deploying crop phenotyping robots faces many challenges, such as complex and variable crop shapes that complicate robotic object detection, dynamic and unstructured environments that confound robotic control, and real‐time computing and managing big data that challenge robotic hardware/software. This work specifically addresses the first challenge by proposing a novel Digital Twin(DT)/MARS‐CycleGAN model for image augmentation to improve our Modular Agricultural Robotic System (MARS)'s crop object detection from complex and variable backgrounds. The core idea is that in addition to the cycle consistency losses in the CycleGAN model, we designed and enforced a new DT/MARS loss in the deep learning model to penalize the inconsistency between real crop images captured by MARS and synthesized images generated by DT/MARS‐CycleGAN. Therefore, the synthesized crop images closely mimic real images in terms of realism, and they are employed to fine‐tune object detectors such as YOLOv8. Extensive experiments demonstrate that the new DT/MARS‐CycleGAN framework significantly boosts crop/row detection performance for MARS, contributing to the field of robotic crop phenotyping. We release our code and data to the research community (https://github.com/UGA-BSAIL/DT-MARS-CycleGAN).

Behavioral and molecular effects of micro and nanoplastics across three plastic types in fish: weathered microfibers induce a similar response to nanosized particles.

Micro and nanoplastics (MNPs) are ubiquitous in the environment and have been detected in most ecosystems, including remote regions. The class of contaminants under the MNP umbrella is quite broad and encompasses variable polymer types, shapes, and sizes. Fibers are the most frequently detected in the environment, followed by fragments, but still represent only a small fraction of laboratory studies. Many toxicity studies have been done using polystyrene microbeads which represent neither the polymer nor shape most present in the environment. Additionally, most of these studies are done using virgin particles when the majority of MNP pollution is from secondary microplastics which have weathered and broken down over time. To address these data gaps, we exposed the model fish Inland Silverside, Menidia beryllina, for 21-days to micro and nano cryo-milled tire particles, micro and nano polylactic acid, and polyester microfibers, both weathered and unweathered treatments were tested. We evaluated the impacts of these particles on growth, behavior, and gene expression to compare the relative toxicities of the different particles. We found that overall, the nanoparticles and weathered fibers had the greatest effect on behavior and gene expression. Gene ontology analysis revealed strong evidence suggesting MNP exposure affected pathways involved in muscle contraction and function. Unweathered microfibers decreased growth which may be a result of food dilution. Our results also suggest that under weathering conditions polyester microfibers breakdown into smaller sizes and induce toxicity similar to nanoparticles. This study highlights the variable effects of MNPs in fish and emphasizes the importance of considering particle shape and size in toxicity studies.

Macromolecular Architecture in the Synthesis of Micro- and Mesoporous Polymers.

Polymers with micro- and mesoporous structure are promising as materials for gas storage and separation, encapsulating agents for controlled drug release, carriers for catalysts and sensors, precursors of nanostructured carbon materials, carriers for biomolecular immobilization and cellular scaffolds, as materials with a low dielectric constant, filtering/separating membranes, proton exchange membranes, templates for replicating structures, and as electrode materials for energy storage. Sol-gel technologies, track etching, and template synthesis are used for their production, including in micelles of surfactants and microemulsions and sublimation drying. The listed methods make it possible to obtain pores with variable shapes and sizes of 5-50 nm and achieve a narrow pore size distribution. However, all these methods are technologically multi-stage and require the use of consumables. This paper presents a review of the use of macromolecular architecture in the synthesis of micro- and mesoporous polymers with extremely high surface area and hierarchical porous polymers. The synthesis of porous polymer frameworks with individual functional capabilities, the required chemical structure, and pore surface sizes is based on the unique possibilities of developing the architecture of the polymer matrix.

Rapid acquisition and surface defects recognition based on panoramic image of small-section hydraulic tunnel

Small-section hydraulic tunnels are characterized by small spaces and various section forms, under complex environments, which makes it difficult to carry out an inspection by the mobile acquisition equipment. To resolve these problems, an arbitrarily adjustable camera module deployment method and the corresponding automatic image acquisition equipment with multi-area array cameras are proposed and developed. Such method enables the acquisition of full-length surface images of the hydraulic tunnels with different cross-section forms and diameters by a one-way travel, and the overlap rate and accuracy of the acquired image sets meet the requirements of three-dimensional reconstruction and panoramic image generation. In addition, to improve the speed and accuracy of traditional algorithms for tunnel surface defects detection, this paper proposes an improved YOLOv5s-DECA model. The algorithm introduces DenseNet to optimize the backbone feature extraction network and incorporates an efficient channel attention ECA module to make a better extraction of features of defects. The experimental results show that mAP, and F1-Score of YOLOv5-DECA are 73.4% and 74.6%, respectively, which are better than the common model in terms of accuracy and robustness. The proposed YOLOv5-DECA has great detection performance for targets with variable shapes and can solve the problem of classification imbalance in surface defects. Then, by combining YOLOv5-DECA with the direction search algorithm, a “point-ring-section” method is established to allow rapid identification of common surface defects by detecting them layer by layer with the bottom image of the stitched panorama as the seed. The presented method in this paper effectively solves the problem that a single image fails to show the overall distribution of the defects and their accurate positioning in a whole large tunnel section and the effective features of defects in an excessively large panoramic image size are difficult to be captured by the neural network. Field applications demonstrated that the presented method is adequate for high-precision and intelligent surface defect detection and positioning for different small-section hydraulic tunnels such as circular, arch-wall, and box-shaped hydraulic tunnels.

A fuzzy control based strategy for adjusting band blade feed rate in constant power sawing

Abstract The variable cross-sectional shape of the blank reduces the efficiency of the sawing machine. Additionally, the wear of the band saw blade causes a mismatch between the feed rate and the load, further exacerbating the wear. This study studied a feed rate control strategy considering sawtooth wear to balance the relationship between cutting efficiency and band life. Initially, speed parameters for the cut-in and cut-out areas, along with acceleration and deceleration control algorithms, were established to ensure the smooth transition of the saw blade during the machining process. Additionally, a constant power sawing feed rate model that considers the influence of wear was developed, enabling dynamic adjustments to the feed rate based on the saw blade’s wear state. This study also proposes a fuzzy control-based target current correction method, which dynamically adjusts the target current according to the current wear state of the saw blade, ensuring that sawing requirements were met under varying levels of wear. The accuracy of the sawing model and control strategy was verified through experiments. The results demonstrated that this strategy improved efficiency by 44% compared to the traditional uniform feed rate method. This study introduced a novel feed rate control strategy for sawing and offers an effective solution to the problem of saw tooth wear.

Urine Sediment Detection Algorithm Based on Channel Enhancement and Deformable Convolution.

Urine sediment detection is a vital method in clinical urine analysis for evaluating an individual's kidney and urinary system health, as well as identifying potential diseases. Nevertheless, urine sediment images exhibit the characteristic of diverse shapes for the same category of targets. These characteristics pose a considerable challenge to the accurate identification of the visible components within the images. We approach urine sediment detection as an object detection task and have introduced the specialized YOLOv7-CSD algorithm for this purpose. In particular, we have integrated channel enhancement feature pyramid network (CE-FPN) and selective kernel (SK) into the YOLOv7 model to address the issue of model confusion in classifying and identifying tasks caused by the feature aliasing effects of feature pyramid network (FPN). Furthermore, we enhance the efficient layer aggregation networks (ELAN) network by adding a second channel, enabling the model to acquire a more extensive set of feature information. On top of this, we introduce the deformable convolutional v3 (DCNv3) operator, allowing the model to dynamically adjust its receptive field, addressing the issue of variable shapes. Tested on the USE dataset and a dataset for urine crystals, YOLOv7-CSD achieves accuracies of 92.8 and 89.6 , respectively.

Correction of Microtia with Constricted Ear Features Using Helix Costal Cartilage Scaffold and Postauricular Flap Advancement.

Microtia with constricted ear features represents a challenging congenital deformity characterized by the variable shape and large volume of the remnant cartilage and skin involved. This study aims to evaluate the efficacy and safety of a method using helix costal cartilage scaffold and postauricular flap advancement for the correction. This retrospective study analyzed the clinical and postoperative data of 121 patients diagnosed with microtia and constricted ear features, treated at our institution between January 2020 and January 2023. All patients underwent helix reconstruction using the eighth or ninth costal cartilage combined with postauricular flap advancement. Outcomes were assessed based on the incidence of postoperative complications and auricular aesthetic scores. The aesthetic evaluation of the reshaped auricle was based on a four-point Likert scale (i.e. 1 = poor, 2 = fair, 3 = good, 4 = excellent). The postoperative follow-up period ranged from 12 months to 3 years (mean 18.5 months). All patients achieved successful helix expansion, with reconstructed ears showing improved symmetry and shape comparable to the contralateral normal ears. The average aesthetic score was 3.4 out of 4. Complications included suture exposure in 8.3% of cases and cartilage protrusion in 4.1%, both managed without significant issues. There were no instances of skin necrosis, costal cartilage exposure, or infection. The combination of helix costal cartilage scaffold and postauricular advancing flap offers a reliable and effective method for correcting microtia with constricted ear features, providing satisfactory aesthetic outcomes with minimal complications.

The green economy in the Italian Alps: A geospatial indicator-based territorial assessment

This paper, through a geospatial multicriteria analysis, evaluates the green economy's (GE) development in over 2700 Local Administrative Units (LAUs) of Italian Alps, incorporating geographical, environmental, and socio-economic variables). LAUs have been classified across altitude and urbanization to examine how socio-demographic and territorial peculiarities impact GE indexes performance. Results highlight heterogeneous GE domain performance that depend on socio-demographic and territorial conditions. Low-density municipalities score lower in local economic competitiveness, accessibility, GHG reduction, and air pollution. High-density municipalities have lower equality and human well-being scores. Findings suggest regional and province-level regulations' relevance in driving performance variation, aiding local and national governments in shaping ad hoc policies for territorial advancement.

Isolator shape transition impact on hypersonic internal waverider intake flow distortion

This paper examines the impact of scramjet isolator shape transition on hypersonic internal waverider (IWR) intake. The IWR intake is designed using the osculating axisymmetric flows and streamline-tracing methods. The new Internal Conical Flow “M” basic flowfield is utilized to provide the flow information for the design method. The intake is equipped with three isolators: one with a constant cross section and two with variable cross sections with circular and rectangular exits. The entrance shape and area of the three isolators are fixed to the intake throat shape and area. The exit area of the three isolators is maintained as the entrance one. Numerical computations of three-dimensional configurations reveal that the isolators with variable cross section shapes demonstrate a higher uniformity index than those with constant cross section shape. Thus, the isolator shape transition has decreased the flow distortion of the hypersonic IWR intake system. The three isolators exhibit varied wall pressure distribution depending on the isolator cross section shape, and the total pressure recovery ratios at the three isolators' exit planes are similar. The wall pressure distributions and key performance parameters at the intake throat section, including total pressure recovery, compression ratio, and Mach number, remained consistent across the first part of the intakes. Therefore, changing the cross section shape of the isolator while keeping the area constant could enhance the flow uniformity of compressed air without negatively impacting the intake system's performance. This allows a separate shape selection of the IWR intake throat and the scramjet combustor entrance to fulfill their special requirements.

Nuclear instance segmentation and tracking for preimplantation mouse embryos.

For investigations into fate specification and morphogenesis in time-lapse images of preimplantation embryos, automated 3D instance segmentation and tracking of nuclei are invaluable. Low signal-to-noise ratio, high voxel anisotropy, high nuclear density, and variable nuclear shapes can limit the performance of segmentation methods, while tracking is complicated by cell divisions, low frame rates, and sample movements. Supervised machine learning approaches can radically improve segmentation accuracy and enable easier tracking, but they often require large amounts of annotated 3D data. Here, we first report a previously unreported mouse line expressing near-infrared nuclear reporter H2B-miRFP720. We then generate a dataset (termed BlastoSPIM) of 3D images of H2B-miRFP720-expressing embryos with ground truth for nuclear instances. Using BlastoSPIM, we benchmark seven convolutional neural networks and identify Stardist-3D as the most accurate instance segmentation method. With our BlastoSPIM-trained Stardist-3D models, we construct a complete pipeline for nuclear instance segmentation and lineage tracking from the eight-cell stage to the end of preimplantation development (>100 nuclei). Finally, we demonstrate the usefulness of BlastoSPIM as pre-train data for related problems, both for a different imaging modality and for different model systems.

Pre-Surgical Planning Using Virtual And 3D Printing Model as Guidance for SECONDARY Mandible Reconstruction with Bone Graft: A Case Report

Reconstruction of mandibular defects due to trauma or tumor resection is a significant challenge for surgeons, given the unique and variable shape of the mandible in terms of curvature, length, and height. Numerous techniques have been developed to achieve both aesthetic and functional outcomes. This case report presents a 28-year-old patient with mandibular irregularity following ameloblastoma dissection, which was reconstructed with non-vascularized bone grafts (NBGs) from the left iliac crest approximately 10 years ago. Prior to our procedure, virtual planning and 3D modeling were employed to accurately assess the bone defect. After debridement, a 5 cm defect was identified, and NBGs were harvested from the right iliac crest for reconstruction. NBGs are commonly used for small mandibular defects (<6 cm), non-continuity defects, and benign pathologies, and they can be performed in centers without microsurgical expertise. The use of virtual planning and 3D printing enables the surgical team to thoroughly study the case, anticipate potential issues, and improve precision, resulting in better outcomes and reduced operative time. NBGs, guided by virtual planning and 3D modeling, prove to be an effective technique in reducing surgical time and minimizing the risk of failure during the postoperative recovery period.

RPDNet: A reconstruction-regularized parallel decoders network for rectal tumor and rectum co-segmentation

Accurate segmentation of rectal cancer tumor and rectum in magnetic resonance imaging (MRI) is significant for tumor precise diagnosis and treatment plans determination. Variable shapes and unclear boundaries of rectal tumors make this task particularly challenging. Only a few studies have explored deep learning networks in rectal tumor segmentation, which mainly adopt the classical encoder-decoder structure. The frequent downsampling operations during feature extraction result in the loss of detailed information, limiting the network's ability to precisely capture the shape and boundary of rectal tumors. This paper proposes a Reconstruction-regularized Parallel Decoder network (RPDNet) to address the problem of information loss and obtain accurate co-segmentation results of both rectal tumor and rectum. RPDNet initially establishes a shared encoder and parallel decoders framework to fully utilize the common knowledge between two segmentation labels while reducing the number of network parameters. An auxiliary reconstruction branch is subsequently introduced by calculating the consistency loss between the reconstructed and input images to preserve sufficient anatomical structure information. Moreover, a non-parameter target-adaptive attention module is proposed to distinguish the unclear boundary by enhancing the feature-level contrast between rectal tumors and normal tissues. The experimental results indicate that the proposed method outperforms state-of-the-art approaches in rectal tumor and rectum segmentation tasks, with Dice coefficients of 84.91 % and 90.36 %, respectively, demonstrating its potential application value in clinical practice.

Population age structure shapes selection on social behaviour in a long-lived insect.

Social traits are expected to experience highly context-dependent selection, but we know little about the contextual factors that shape selection on social behaviours. We hypothesized that the fitness consequences of social interactions will depend on the age of social partners, and therefore that population age structure will shape evolutionary pressures on sociality. Here, we investigate the consequences of age variation at multiple levels of social organization for both individual fitness and sexual selection on social network traits. We experimentally manipulated the age composition of populations of the forked fungus beetle Bolitotherus cornutus, creating 12 replicate populations with either young or old age structures. We found that fitness is associated with variance in age at three different levels of organization: the individual, interacting social partners, and the population. Older individuals have higher reproductive success, males pay a fitness cost when they interact with old males and females achieve lower fitness in older populations. In addition to influencing fitness, population age structure also altered the selection acting on social network position in females. Female sociality is under positive selection only in old populations. Our results highlight age structure as an understudied demographic variable shaping the landscape of selection on social behaviour.This article is part of the discussion meeting issue 'Understanding age and society using natural populations'.

A new virtual water qualimeter with a changeable broken linear pointer based on a quadrant circular constellation graph

ABSTRACT As water is a very important natural resource for humans and its quality is closely related to human health, water quality evaluation (WQE) is very important for the protection of human health, livelihoods, environment, and management of water. Although various WQE methods have been developed and used, a common drawback is that the methods lack visualization of the WQE results and require additional complicated graphical techniques. The Pointer meter is an analogue measuring device with a pointer indicating the measured value with its location. A constellation graph (CG) enables to map multi-dimensional data on a two-dimensional plane. By introducing the pointer meter and CG for WQE, this paper proposes a new visualized pointer meter-type virtual water qualimeter (VWQ-meter) based on a quadrant circular constellation graph (QCCG). It has a changeable broken linear pointer with variable lengths and shapes, while the traditional pointer meters have straight-line pointers with a fixed length and a straight-line shape. It evaluates the water quality not only quantitatively but also intuitively as in the pointer meters. To demonstrate its effectiveness, it is applied to one example of WQE. It could be widely applied to not only WQE but also various multi-criteria decision-making (MCDM) problems arising in water pollution evaluation, water resource management, water supply, and wastewater treatment.

Multi-stage cascade GAN for synthesis of contrast enhancement CT aorta images from non-contrast CT

Recently in diagnosis of Aortic dissection (AD), the synthesis of contrast enhanced CT (CE-CT) images from non-contrast CT (NC-CT) images is an important topic. Existing methods have achieved some results but are unable to synthesize a continuous and clear intimal flap on NC-CT images. In this paper, we propose a multi-stage cascade generative adversarial network (MCGAN) to explicitly capture the features of the intimal flap for a better synthesis of aortic dissection images. For the intimal flap with variable shapes and more detailed features, we extract features in two ways: dense residual attention blocks (DRAB) are integrated to extract shallow features and UNet is employed to extract deep features; then deep features and shallow features are cascaded and fused. For incomplete flaps or lack of details, we use spatial attention and channel attention to extract key features and locations. At the same time, multi-scale fusion is used to ensure the continuity of the intimal flap. We perform the experiment on a set of 124 patients (62 with AD and 62 without AD). The evaluation results show that the synthesized images have the same characteristics as the real images and achieves better results than the popular methods.