Mechanical Advantage Research Articles

Mechanical advantage makes stomatal opening speed a function of evaporative demand.

Stomatal opening in the light, observed in nearly all vascular land plants, is essential for providing access to atmospheric CO2 for photosynthesis. The speed of stomatal opening in the light is critical for maximizing carbon gain in environments in which light intensity changes, yet we have little understanding of how other environmental signals, particularly evaporative demand driven by vapor pressure deficit (VPD) influences the kinetics of this response. In angiosperms, and some fern species from the family Marsileaceae, a mechanical interaction between the guard cells and the epidermal cells determines the aperture of the pore. Here, we examine whether this mechanical interaction influences the speed of stomatal opening in the light. To test this, we investigated the speed of stomatal opening in response to light across a range of VPDs in seven plant species spanning the evolutionary diversity of guard cell and epidermal cell mechanical interactions. We found that stomatal opening speed is a function of evaporative demand in angiosperm species and Marsilea, which have guard cell and epidermal cell mechanical interactions. Stomatal opening speeds did not change across a range of VPD in species of gymnosperm and fern, which do not have guard cell mechanical interactions with the epidermis. We find that guard cell and epidermal cell mechanical interactions may play a key role in regulating stomatal responsiveness to light. These results provide valuable insight into the adaptive relevance of mechanical advantage.

What makes a successful relay start in swimming?

ABSTRACT Despite the mechanical advantage of preparatory movements on the starting block, current evidence questions the start improvements of competitive swimmers with their relay techniques. Therefore, the aim of the present study was to analyse the kinetic and kinematic parameters of a successful relay start. Twenty national- and international-level swimmers performed several relay starts (n = 145) with their preferred technique (short or long-step start) over an instrumented OBS11 starting platform. Trials were classified as successful or non-successful depending on the 10-m times being faster or slower than their individual start. Linear Mixed Models outlined that successful relay starts were characterised (all p < 0.05) by a later (0.04 s) onset of the leg step, a lower (18%) horizontal force during the leg step, and a later (0.03 s) positioning of the hands at the lowest point of the upper-limb backswing. In addition, greater values for the maximal horizontal (12%) and vertical (9%) forces and faster horizontal (4%) and resultant (3%) velocities were detected when driving off the block. These characteristics were also dependent on the relay technique. Unlike individual track starts, swimmers with fast relay starts employed longer preparatory movements on the block to maximise the time of force application and thus the impulse.

One-Pot Synthesis of a Robust Crosslinker-Free Thermo-Reversible Conducting Hydrogel Electrode for Epidermal Electronics.

Traditional epidermal electrodes, typically made of silver/silver chloride (Ag/AgCl), have been widely used in various applications, including electrophysiological recordings and biosignal monitoring. However, they present limitations due to inherent material mismatches with the skin. This often results in high interface impedance, discomfort, and potential skin irritation, particularly during prolonged use or for individuals with sensitive skin. While various tissue-mimicking materials have been explored, their mechanical advantages often come at the expense of conductivity, resulting in low-quality recordings. We herein report the facile fabrication of conducting and stretchable hydrogels using a "one-pot" method. This approach involves the synthesis of a natural hydrogel, termed Golde, composed of abundant and eco-friendly components, including gelatin, chitosan, and glycerol. To enhance the conductivity of the hydrogel, various conducting materials, such as poly(3,4-ethylenedioxythiophene) polystyrenesulfonate (PEDOT:PSS), thermally reduced graphene (TRG), and MXene, are introduced. The resulting conducting hydrogels exhibit remarkable robustness, do not require crosslinkers, and possess a unique thermo-reversible property, simplifying the fabrication process and ensuring enhanced long-term stability. Moreover, their fabrication is sustainable, as it employs environmentally friendly materials and processes while retaining their skin-friendly characteristics. The resulting hydrogel electrodes were tested for electrocardiogram (ECG) signal acquisition and outperformed commercial electrodes even when implemented in an all-flexible electrode setup simply using copper tape, owing to their inherent adhesiveness.

Construction of porous chitosan-based organic/inorganic composite with synergistic antibacterial activity

The increasing resistance of antibiotics and the global spread of pathogenic bacteria highlight the urgent need for novel antibacterial materials. In this study, a terpolymer (PVBA) with carboxyl (-COOH) and benzyl chloride (-C7H6Cl) groups was synthesized using acrylic acid (AA), butyl acrylate (BA), and 4-vinyl benzyl chloride (VBC) as monomers. The -C7H6Cl groups in the PVBA terpolymer was then reacted with N-N-dimethylhexadecane 1-amine (HAM) to prepare a more hydrophilic antibacterial (PVBA-N) terpolymer. Further, a synthetic/natural (PVBA-N/C) composite with quaternary ammonium (QA) groups was prepared by covalent crosslinking of the PVBA-N terpolymer and chitosan (CS). Finally, the AgCl-decorated PVBA-N/C (AgCl@PVBA-N/C) antibacterial composite was were constructed by anchoring silver chloride nanoparticles (AgClNPs) to the surface of the PVBA-N/C composite. Remarkably, the resulting AgCl@PVBA-N/C antibacterial composite had a porous network structure and exhibited favorable antibacterial activity against Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus) based on both contact-typed and release-typed antibacterial modes, and low cytotoxicity against L929 cells. This research provides an effective strategy to combat the threat of bacterial resistance to human health relying on the advantage of the respective antibacterial mechanisms of various antibacterial ingredients.

Active Compliance Smart Control Strategy of Hybrid Mechanism for Bonnet Polishing.

Compliance control strategies have been utilised for the ultraprecision polishing process for many years. Most researchers execute active compliance control strategies by employing impedance control law on a robot development platform. However, these methods are limited by the load capacity, positioning accuracy, and repeatability of polishing mechanisms. Moreover, a sophisticated actuator mounted at the end of the end-effector of robots is difficult to maintain in the polishing scenario. In contrast, a hybrid mechanism for polishing that possesses the advantages of serial and parallel mechanisms can mitigate the above problems, especially when an active compliance control strategy is employed. In this research, a high-frequency-impedance robust force control strategy is proposed. It outputs a position adjustment value directly according to a contact pressure adjustment value. An open architecture control system with customised software is developed to respond to external interrupts during the polishing procedure, implementing the active compliance control strategy on a hybrid mechanism. Through this method, the hybrid mechanism can adapt to the external environment with a given contact pressure automatically instead of relying on estimating the environment stiffness. Experimental results show that the proposed strategy adapts the unknown freeform surface without overshooting and improves the surface quality. The average surface roughness value decreases from 0.057 um to 0.027 um.

Dental Dynamics: A Fast New Tool for Quantifying Tooth and Jaw Biomechanics in 3D Slicer.

Teeth reveal how organisms interact with their environment. Biologists have long looked at the diverse form and function of teeth to study the evolution of feeding, fighting, and development. The exponential rise in the quantity and accessibility of computed tomography (CT) data has enabled morphologists to study teeth at finer resolutions and larger macroevolutionary scales. Measuring tooth function is no easy task, in fact, much of our mechanical understanding is derived from dental shape. Categorical descriptors of tooth shape such as morphological homodonty and heterodonty, overlook nuances in function by reducing tooth diversity for comparative analysis. The functional homodonty method quantitatively assesses the functional diversity of whole dentitions from tooth shape. This method uses tooth surface area and position to calculate the transmission of stress and estimates a threshold for functionally homodont teeth through bootstrapping and clustering techniques. However, some vertebrates have hundreds or thousands of teeth and measuring the shape and function of every individual tooth can be a painstaking task. Here, we present Dental Dynamics, a module for 3D Slicer that allows for the fast and precise quantification of dentitions and jaws. The tool automates the calculation of several tooth traits classically used to describe form and function (i.e., aspect ratio, mechanical advantage, force, etc.). To demonstrate the usefulness of our module we used Dental Dynamics to quantify 780 teeth across 20 salamanders that exhibit diverse ecologies. We coupled these data with the functional homodonty method to investigate the hypothesis that arboreal Aneides salamanders have novel tooth functions. Dental Dynamics provides a new and fast way to measure teeth and increases the accessibility of the functional homodonty method. We hope Dental Dynamics will encourage further theoretical and methodological development for quantifying and studying teeth.

Design and analysis of a passive adaptive wall-climbing robot on variable curvature ship facades

To improve the adaptive motion performance of traditional wall-climbing robots on variable-curvature facades, a crawler-type wall-climbing robot suitable for ship wall features is proposed by utilizing the advantages of passive mechanisms in realizing autonomous robots. The robot consists of two passive adaptive crawler mechanisms and a connecting module. Each track structure contains multiple permanent magnets that can passively adapt to concave and convex facades of different curvatures. A static failure model is established according to the characteristics of the triangular distributed load, and the minimum adsorption force required for the robot to achieve safe motion is determined. The Halbach Array magnetic circuit design method was used to construct a gap-type permanent magnet adsorption model for lightweight design. The influence of wall thickness and air gap distance on the adsorption force is analyzed by parametric simulation. The prototype platform test shows that the robot can realize adaptive variable curvature motion through passive adjustment of the mobile mechanism attitude and has a certain load capacity.

CVTStego-Net: A convolutional vision transformer architecture for spatial image steganalysis

The principal investigations in image steganalysis in the spatial domain have concentrated on convolutional neural network (CNN) designs. However, existing CNNs increase the local receptive field of steganographic noise without considering global steganographic noise. This study introduces CVTStego-Net, a convolutional vision transformer for spatial domain image steganalysis that merges the strengths of convolutions and the advantages of attention mechanisms to capture both local and global dependencies. CVTStego-Net is composed of three stages: preprocessing stage, noise extraction, and analysis stage, and classification stage. The preprocessing stage involves a bifurcation with trainable and untrainable 30 SRM (Spatial Rich Models) filters to enhance steganographic noise. The noise extraction and analysis stage combines the SE-Block (Squeeze-and-Excitation) with residual operations to increase the sensitivity to steganographic noise and suppressing the influence of redundant information, and the classification stage combines SE-Block with a convolutional vision transformer to connect the local and global spatial relationships of the steganographic noise. This work enhanced the classification accuracies for steganographic algorithms compared to YEDROUDJ-Net, SR-Net, ZHU-Net, GBRAS-Net, and SNMC-Net. Specifically, the accuracy of CVTStego-Net for WOW at 0.2 bpp was 86.58%, and 0.4 bpp was 93.80%. Moreover, for S-UNIWARD at 0.2 and 0.4 bpp, the accuracies were 80.70% and 90.45%, respectively. For MiPOD at 0.2 and 0.4 bpp, the accuracies were 74.70% and 81.48%, respectively. For HILL at 0.2 and 0.4 bpp, the accuracies were 76.70% and 85.80%, respectively, and for HUGO at 0.2 and 0.4 bpp, the accuracies were 78.20% and 86.98%, respectively, using test data from the BOSSbase 1.01. The results demonstrate that convolutional vision transformers can classify steganographic images in the spatial domain.

Advancing clinical cancer care: Unveiling the power of surface‐enhanced Raman spectroscopy

AbstractTumors are characterized by abnormal cell growth, which leads to uncontrolled cell proliferation, invasion of surrounding tissues, and potential metastasis to other parts of the body. Current treatments for tumors include surgery, chemotherapy, radiotherapy, targeted therapy, and biological therapy. However, many tumors are detected at advanced stages, limiting the effectiveness of these treatments. Therefore, there is a need to develop more sensitive and efficient techniques to improve patient survival rates. Surface‐enhanced Raman scattering (SERS) technology is a highly sensitive molecular detection and characterization technique. It can be combined with histomorphology, immunolabeling assays, and molecular hybridization to achieve quantitative diagnosis at morphological, molecular, and genetic levels. SERS offers advantages such as objectivity, reproducibility, and comparability. However, there are challenges to address, including complex substrate or probe preparation, cumbersome data analysis, and signal reproducibility and reliability. To fully utilize the potential of SERS in the biomedical field and for clinical translation, it is important to optimize the technology and develop more sensitive, accurate, and reliable substrate or probe preparation and analysis methods. This review aims to comprehensively explain the mechanism and unique advantages of SERS technology and discuss its current status, challenges, and prospects in tumor‐related research.

COMMERCIALLY PURE (CP-TI) TITANIUM MEDICAL IMPLANT PRODUCTION USING LASER POWDER BED FUSION (L-PBF) TECHNOLOGY

Decreasing the bulk weight without losing the biomechanical properties of commercial pure titanium (Cp-Ti) medical implants is now possible by using Laser Powder Bed Fusion (L-PBF) technology. Gyroid lattice structures that have precious mechanical and biological advantages because of similarity to trabecular bone. The aim of the study was to design and develop L-PBF process parameter optimization for manufacturing gyroid lattice Cp-Ti structures. The cleaning process was then optimized to remove the non-melted powder from the gyroid surface without mechanical loss.Gyroid cubic designs were created with various relative densities by nTopology. L-PBF process parameter optimization was progressed using with Cp-Ti (EOS TiCP Grade2) powder in the EOS M290 machine to achieve parts that have almost full dense and dimensional accuracy. The metallography method was made for density. Dimensional accuracy at gyroid wall thicknesses was investigated between designed and manufactured via stereomicroscope, also mechanical tests were applied with real time experiment and numerical analysis (ANSYS). Mass loss and strut thickness loss were investigated for chemical etching cleaning process.Gyroid parts had 99,5% density. High dimensional accuracy was achieved during L-PBF process parameters optimization. Final L-PBF parameters gave the highest 19% elongation and 427 MPa yield strength values at tensile test. Mechanical properties of gyroid were controlled with changing relative density. A minute chemical etching provided to remove non-melted powders.Compression test results of gyroids at numerical and real-time analysis gave unrelated while deformation behaviors were compatible with each other. Gyroid Cp-Ti osteosynthesis mini plates will be produced with final L-PBF process parameters. MTT cytotoxicity test will be characterized for cell viability.Acknowledgements This project is granted by TUBITAK (120N943). Feza Korkusuz MD is a member of the Turkish Academy of Sciences (TÜBA).

Influence of Mango Seed Oil on Surface Roughness and Cutting Temperature During Sustainable Turning of AISI 1525 Steel Under Minimum Quantity Lubrication Environment

Mineral oil-based cutting fluids have several mechanical advantages. The use of mineral oil has been questioned due to its adverse effect on machinists and the environment. There is need for a sustainable and biodegradable cutting fluid that can perform the task of problematic mineral oil. This study considered a non-edible vegetable oil, mango oil, as a lubricant in the turning operation of AISI 1525 steel using an MQL mode of fluid application. The performance of mango oil was compared with commercial mineral oil using SR and CT as performance metrics. Experiments were conducted under three levels of SS (355, 500, and 710 rev/min), FR (0.10, 0.15, and 0.20 mm/rev), and DOC (0.75, 1.00, and 1.25 mm). Taguchi L9 orthogonal array was adopted for the experimental settings. Afterward, TOPSIS, a multi-optimization tool was employed to determine the best cutting parameters for machining the workpiece with the tungsten carbide tool. The finding showed that mineral oil outperformed mango oil lubricant in terms of both SR and CT. The optimum CT and SR can be achieved using an SS of 355 rev/min, FR of 0.15 mm/rev, and DOC of 1.00 mm for both mango and mineral oil lubricants.

DAFNet: A dual attention-guided fuzzy network for cardiac MRI segmentation

&lt;abstract&gt; &lt;sec&gt;&lt;title&gt;Background&lt;/title&gt;&lt;p&gt;In clinical diagnostics, magnetic resonance imaging (MRI) technology plays a crucial role in the recognition of cardiac regions, serving as a pivotal tool to assist physicians in diagnosing cardiac diseases. Despite the notable success of convolutional neural networks (CNNs) in cardiac MRI segmentation, it remains a challenge to use existing CNNs-based methods to deal with fuzzy information in cardiac MRI. Therefore, we proposed a novel network architecture named DAFNet to comprehensively address these challenges.&lt;/p&gt; &lt;/sec&gt; &lt;sec&gt;&lt;title&gt;Methods&lt;/title&gt;&lt;p&gt;The proposed method was used to design a fuzzy convolutional module, which could improve the feature extraction performance of the network by utilizing fuzzy information that was easily ignored in medical images while retaining the advantage of attention mechanism. Then, a multi-scale feature refinement structure was designed in the decoder portion to solve the problem that the decoder structure of the existing network had poor results in obtaining the final segmentation mask. This structure further improved the performance of the network by aggregating segmentation results from multi-scale feature maps. Additionally, we introduced the dynamic convolution theory, which could further increase the pixel segmentation accuracy of the network.&lt;/p&gt; &lt;/sec&gt; &lt;sec&gt;&lt;title&gt;Result&lt;/title&gt;&lt;p&gt;The effectiveness of DAFNet was extensively validated for three datasets. The results demonstrated that the proposed method achieved DSC metrics of 0.942 and 0.885, and HD metricd of 2.50mm and 3.79mm on the first and second dataset, respectively. The recognition accuracy of left ventricular end-diastolic diameter recognition on the third dataset was 98.42%.&lt;/p&gt; &lt;/sec&gt; &lt;sec&gt;&lt;title&gt;Conclusion&lt;/title&gt;&lt;p&gt;Compared with the existing CNNs-based methods, the DAFNet achieved state-of-the-art segmentation performance and verified its effectiveness in clinical diagnosis.&lt;/p&gt; &lt;/sec&gt; &lt;/abstract&gt;

An innovative network intrusion detection system (NIDS): Hierarchical deep learning model based on Unsw-Nb15 dataset

With the increasing prevalence of network intrusions, the development of effective network intrusion detection systems (NIDS) has become crucial. In this study, we propose a novel NIDS approach that combines the power of long short-term memory (LSTM) and attention mechanisms to analyze the spatial and temporal features of network traffic data. We utilize the benchmark UNSW-NB15 dataset, which exhibits a diverse distribution of patterns, including a significant disparity in the size of the training and testing sets. Unlike traditional machine learning techniques like support vector machines (SVM) and k-nearest neighbors (KNN) that often struggle with limited feature sets and lower accuracy, our proposed model overcomes these limitations. Notably, existing models applied to this dataset typically require manual feature selection and extraction, which can be time-consuming and less precise. In contrast, our model achieves superior results in binary classification by leveraging the advantages of LSTM and attention mechanisms. Through extensive experiments and evaluations with state-of-the-art ML/DL models, we demonstrate the effectiveness and superiority of our proposed approach. Our findings highlight the potential of combining LSTM and attention mechanisms for enhanced network intrusion detection.

LCGNet: Local Sequential Feature Coupling Global Representation Learning for Functional Connectivity Network Analysis with fMRI.

Analysis of functional connectivity networks (FCNs) derived from resting-state functional magnetic resonance imaging (rs-fMRI) has greatly advanced our understanding of brain diseases, including Alzheimer's disease (AD) and attention deficit hyperactivity disorder (ADHD). Advanced machine learning techniques, such as convolutional neural networks (CNNs), have been used to learn high-level feature representations of FCNs for automated brain disease classification. Even though convolution operations in CNNs are good at extracting local properties of FCNs, they generally cannot well capture global temporal representations of FCNs. Recently, the transformer technique has demonstrated remarkable performance in various tasks, which is attributed to its effective self-attention mechanism in capturing the global temporal feature representations. However, it cannot effectively model the local network characteristics of FCNs. To this end, in this paper, we propose a novel network structure for Local sequential feature Coupling Global representation learning (LCGNet) to take advantage of convolutional operations and self-attention mechanisms for enhanced FCN representation learning. Specifically, we first build a dynamic FCN for each subject using an overlapped sliding window approach. We then construct three sequential components (i.e., edge-to-vertex layer, vertex-to-network layer, and network-to-temporality layer) with a dual backbone branch of CNN and transformer to extract and couple from local to global topological information of brain networks. Experimental results on two real datasets (i.e., ADNI and ADHD-200) with rs-fMRI data show the superiority of our LCGNet.

Tactile Weight Rendering: A Review for Researchers and Developers.

Haptic rendering of weight plays an essential role in naturalistic object interaction in virtual environments. While kinesthetic devices have traditionally been used for this aim by applying forces on the limbs, tactile interfaces acting on the skin have recently offered potential solutions to enhance or substitute kinesthetic ones. Here, we aim to provide an in-depth overview and comparison of existing tactile weight rendering approaches. We categorized these approaches based on their type of stimulation into asymmetric vibration and skin stretch, further divided according to the working mechanism of the devices. Then, we compared these approaches using various criteria, including physical, mechanical, and perceptual characteristics of the reported devices. We found that asymmetric vibration devices have the smallest form factor, while skin stretch devices relying on the motion of flat surfaces, belts, or tactors present numerous mechanical and perceptual advantages for scenarios requiring more accurate weight rendering. Finally, we discussed the selection of the proposed categorization of devices together with the limitations and opportunities for future research. We hope this study guides the development and use of tactile interfaces to achieve a more naturalistic object interaction and manipulation in virtual environments.

In situ polymerization of EDOT onto sulfonated onion-like carbon for efficient pseudocapacitor electrodes

A facile and scalable approach to combine the mechanical and capacitive advantages of onion-like carbon (OLC) with the pseudocapacitance of PEDOT in a templated architecture without dead volume and binder was developed.

Experimental investigations on lime mortars reinforced with hemp braids

The need of experimenting sustainable components for building products is underlined by the prominent energy and raw material consumptions and carbon dioxide emissions for which the construction industry is responsible. Natural fibres, particularly hemp fibres, are worth of mention thanks to their acceptable mechanical properties and environmental advantages. The research aims to experiment two types of lime-based plasters reinforced with hemp braids, having different diameters, added in the mixture with different percentages. Two pre-mixed Natural Hydraulic Lime (NHL) mortars of M5 and M15 types, and three diameters of hemp braids (0.4 mm, 1 mm and 2.2 mm) in percentage by lime weight from 0.25% to 3% were considered. A first phase of the research consisted in physical tests to assess the amount of water absorbed by the hemp fibres to evaluate the plaster workability. Afterwards, only the mixtures with acceptable workability values were subjected to mechanical tests. The results of the laboratory tests showed that hemp braids produce a decrease in the workability of the plasters, that remains acceptable for low percentages of fibres. Compared to the unreinforced plaster, the braids allowed an increase in the flexural strength and a decrease of the compressive strength. In both mechanical tests it was evident the sewing and confinement effect of the fibres that led to a more ductile behavior of the mortar.

Quantum Computing: Principles of Breaking Encryption

The basis of modern security relies on encryption methods that are practically impossible, but theoretically possible, to break. Quantum computing is a process by which a computer takes advantage of quantum mechanics to solve complex problems. IBM has been working on and has made quantum computers available for almost a decade now. These machines can be used for cryptography related problems like decrypting complex encryptions much faster than the classical computers we have today. So much faster in fact, that a quantum computer can factor a 300-digit number in the same amount of time that a normal computer could multiply the two numbers together, making our current encryption methods obsolete.

Protein-based materials for sustainable, multifunctional air filtration

Diverse air pollutants pose a significant threat to human health, making the development of multifunctional air filtration necessary. To mitigate the risk of secondary pollution associated with conventional materials, there has been a growing focus on biomaterials. Compared with other bio-polymer air filters, natural proteins include a huge amount of various functional groups (e.g. amino acids) enabling multiple filtration capabilities. In recent years, abundant natural proteins as raw materials, such as zein (corn protein), soy protein, gelatin silk, etc., for constructing multifunctional air filters were reported, and many important results caused attentions. Thus, a comprehensive overview of the progress and achievements of protein-based air filters is necessary. In this review, the advantages of protein materials, filtration mechanisms, and structural designs for optimizing filtration performance, as well as other features such as moisture resistance and antimicrobial properties for protein-based air filters are comprehensively summarized and/or analyzed. Finally, future research perspectives and developments of multifunctional protein materials for air filtration are proposed.

Isopod mouthpart traits respond to a tropical forest recovery gradient.

Functional trait ecology has the potential to provide generalizable and mechanistic predictions of ecosystem function from data of species distributions and traits. The traits that are selected should both respond to environmental factors and influence ecosystem functioning. Invertebrate mouthpart traits fulfill these criteria, but are seldom collected, lack standardized measurement protocols, and have infrequently been investigated in response to environmental factors. We surveyed isopod species that consume plant detritus, and tree communities in 58 plots across primary and secondary forests in Singapore. We measured body dimensions (body size traits), pereopod and antennae lengths (locomotory traits), dimensions of mandible structures (morphological mouthpart traits), and mechanical advantages generated by mandible shape (mechanical mouthpart traits) for six isopod species found in these plots and investigated if these traits respond to changes in tree community composition, tree diversity, and forest structure. Morphological mouthpart traits responded to a tree compositional gradient reflecting forest recovery degree. Mouthpart features associated with greater consumption of litter (broader but less serrated/rugose lacinia mobilis [an important cutting and chewing structure on the mandible]) were most prevalent in abandoned plantation and young secondary forests containing disturbance-associated tree species. Feeding strategies associated with fungi grazing (narrower and more serrated/rugose lacinia mobilis) were most prevalent in late secondary forests containing later successional tree species. Since morphological mouthpart traits likely also predict consumption and excretion rates of isopods, these traits advance our understanding of environment-trait-ecosystem functioning relationships across contrasting tropical forest plots that vary in composition, disturbance history, and post-disturbance recovery.