bluuebunny/crossref_metadata_2025 huggingface dataset:
Strategic genetic insights and integrated approaches for successful management of blackleg in canola/rapeseed farming
Authors: Rouxel Thierry , Peng Gary , Van de Wouw Angela , Larkan Nicholas J. , Borhan Hossein , Fernando W. G. Dilantha
Published: October 2024

This review describes a triumphant narrative in the battle against the devastating plant pathogen complex, Leptosphaeria maculans and L. biglobosa, and the success of the world's second‐largest oilseed crop, canola/oilseed rape. Emphasizing global collaborations, the article explores successfully mitigating this destructive disease in canola/rapeseed production across Australia, Canada and Europe. It highlights how strategies may vary between continents to adapt to specific contexts. While initial resistance (R) genes proved effective, the evolution of the pathogen under crop‐induced disease pressure led to the breakdown of these genes. Now, growers in these regions have been equipped with new tools, allowing them to make informed decisions that help to keep the disease at generally low levels. A pivotal factor in this success has been a deepened understanding of the intricate science underlying the host–pathogen interaction. Concerted efforts of individual laboratories and collaborative initiatives have played an essential role in this success, including novel methods for disease control based on extensive research that has translated into developing highly resistant varieties, enhanced pathogen monitoring, improved cultivar recommendation and integrated management strategies. The review showcases many milestone advancements, including the cloning of numerous avirulence genes within the pathogen, characterization of specific R genes, the development of various molecular tools for monitoring both pathogen and host, the introduction of groundbreaking disease management strategies such as R gene labelling, rotation and stacking, and establishment of a universal pathogen isolate collection that facilitates the exchange of information among multiple laboratories and adds a new dimension to this triumph.
Design and Demonstration of a 3D Soft-Robotics Module Based on Rolled Dielectric Elastomer Actuators (RDEAs)
Authors: Kunze Julian , Soleti Giovanni , Bruch Daniel , Rizzello Gianluca , Motzki Paul
Published: October 2024

As robots increasingly collaborate with humans and navigate complex, unstructured environments, soft robots have emerged as an alternative to rigid ones. These robots, partially or entirely made of compliant materials, offer enhanced adaptability and safety. Common actuation methods include pneumatics, hydraulics, shape memory alloys, and dielectric elastomer actuators (DEAs).

This study introduces a bending module for a segmented soft robotics arm, actuated by rolled DEAs (RDEAs). They offer a high energy density, bandwidth, self-sensing capabilities and simple manufacturing. Our RDEAs are assembled from two sheets of 50 μm-thick silicone film with carbon-black-based electrodes, wound into a flexible roll.

The segment itself consists of two circular platforms, comprised of milled printed circuit boards (PCBs), which are connected via a custom designed low-friction ball joint. Nine RDEAs, which are actuated by a three-channel voltage amplifier, are arranged around the platform and enable controlled bending of up to 25° in all directions. A demonstrator showcases the system’s capabilities, allowing playback of prerecorded trajectories or interactive control via a game controller, which emphasizes the aspect of human-robot interaction. Future work involves stacking multiple systems to create a highly articulated soft robotics arm and adapting self-sensing and control algorithms.
Wavenumber Analysis-Based Mechanical Property Characterization for Additively Manufactured Thermoset Composites
Authors: Cai Bowen , Watts Hunter , Huberty Wayne , Shen Liang , Tian Zhenhua
Published: October 2024

Thermoset materials have begun to be applied in additive composite manufacturing due to their ability to withstand high temperatures without losing structural integrity. Meanwhile, the characterization of mechanical properties for additively manufactured composites is critical for ensuring material reliability and safety. However, traditional testing methods struggle to accurately and nondestructively characterize additively manufactured composites due to challenges posed by curing processes, microstructural variability, anisotropic properties of thermoset composites, and the risk of damaging these materials during evaluation. For characterizing the mechanical properties of additive-manufactured thermoset composites, this paper presents a novel method that combines a nondestructive PZT-LDV guided wave sensing system and a wavenumber analysis that fuses multidimensional Fourier transform with dispersion curve regression. For proof of concept, we performed an experiment using our method to measure a 3D-printed thermoset composite panel. Based on our nondestructive approach, two material properties (shear wave velocity and Poisson’s ratio) in multiple directions were successfully determined for the tested panel. We expect this research to introduce a non-contact and efficient method for characterizing various composites and monitoring their property changes after additive manufacturing.
Investigation of Different Activation Profiles During Electrical Activation of Shape-Memory Wires Regarding the Lifetime and Wire Temperature
Authors: Schmelter Tobias , Kuhlenkötter Bernd
Published: October 2024

For a progressive and economically viable use of Shape-Memory Alloys (SMA), it is necessary to further investigate the fatigue behavior and its influence on the lifetime of SMA. In addition to material-scientific research findings, an understanding of the influences in the field of application of SMA is necessary to increase its reliability. Especially when used as an actuator with electrical activation, high numbers of cycles are often required. One of the numerous influences on the lifetime is the type of activation of the wire, which can be carried out electrically or thermally. In the case of electrical activation, the shape of the activation profile can be varied. In addition to the step response, other profiles such as triangular profiles can be run, although in this work the focus is only on linear profiles. This varies the speed of the energy input and thus the activation behavior, which may influence the lifetime. In this publication, different activation profiles are examined regarding the thermal influence on the wire. For this purpose, these profiles are tested one after the other while the wire is monitored with a thermal imaging camera, and the respective wire temperature is recorded. The maximum temperatures reached for each activation profile are then correlated with the lifetime achieved and the stroke generated. To ensure comparability of the activation profiles, the electrical energy input is kept constant across the different activation profiles. To achieve this, the integrals of the electrical energy for the profiles are determined and calculated in advance. This results in two adaptable factors for keeping the applied energy constant for the different profiles. These factors are the maximum current and the duration of activation, whereby only one factor can be kept constant at a time. The test series are therefore carried out twice, whereby the applied energy is kept constant in both series, once with a constant activation duration of the profiles and once with a constant maximum current. Together with the reached lifetime data of the individual activation profiles, conclusions are to be drawn regarding the suitability of a respective profile for use in the field of actuators and a basic understanding of the relationship between activation profile, wire temperature, and lifetime of the wire is to be achieved.
ECCube – Small Scale Elastocaloric Cooling Demonstrator
Authors: Ehl Lukas , Scherer Nicolas , Zimmermann David , Molitor Philipp , Trofimenko Ivan , Louia Franziska , Motzki Paul
Published: October 2024

The excellent elastocaloric (EC) properties of Nickel-Titanium (NiTi) enable the development of sustainable and highly efficient heating and cooling systems. To showcase the realization of elastocalorics in a functional system an elastocaloric can cooler demonstrator with fixed cooling chamber is developed. The demonstrator system is based on a rotary drive design and tensile loading of Smart Material Alloys (SMAs) wire bundles, which has been presented in. In the developed demonstrator prototype, the chamber can hold a can with the size of ø 52 × 134 mm. For optimized cooling of this volume, the development process is supported by simulation. A temperature drop of around 10 K is targeted. The general specifications of the demonstrator are set to an outer system diameter of 150 mm, carrying 12 bundles of 30 SMA wires of 200 μm diameter and a length of 150 mm. In operation, the rotational frequency is set to 0.5 Hz. To use the centered space for the cooling chamber, a novel concept for air circulation is developed. By circulating the cold airflow within the cooling chamber, the inside air temperature continuously drops with each cycle. The paper gives a detailed description of the design process from simulation-based development to assembly and first measurements of an elastocaloric can cooler demonstration system.
New Concept of a Bidirectional SMA-Actuated Stepper Drive for a Hand Exoskeleton
Authors: Carabello Alina , Pagel Kenny , Drossel Welf-Guntram
Published: October 2024

The development of an exoskeleton for the hand poses a particular challenge due to the limited installation space and complicated movement sequences. The antagonistic arrangement of two tendon systems enables a relatively natural flexion and extension of the finger with actuators. For this purpose, electromagnetic, pneumatic, or hydraulic principles, and smart materials are conventionally used. Nevertheless, these concepts are distinguished by their augmented mass and extensive spatial requirements, alongside occasional high energy consumption. Stepper drives are a promising alternative to conventional actuators, as they are characterized by both high functional density and low energy consumption.

In the following, the kinematic of a novel shape memory alloy (SMA)-actuated stepper driver for use in exoskeletons is described and analyzed. The kinematic concept is based on a bidirectional ratchet mechanism that can be used to wound and unwound the tendons on a pully for active flexion or extension of the affected finger. The mechanism can be driven bidirectionally or kept in a stable state of equilibrium without energy using two antagonistically arranged SMA actuators. Upon activation of the actuator, the lever undergoes deflection, causing the ratchet to rotate by one tooth in the direction of deflection. Upon cooling of the actuator, the system reverts to its initial equilibrium state, with the control wheel maintaining its position. This process can be iterated as necessary, with the cable being wound up or unwound accordingly. Activation of the antagonistic actuator results in a reversal of the rotation direction. The gear analysis indicates that the kinematics enable the overcoming of a finger force of approximately 25 N.
Multiphysical Design of a Chaos Generator
Authors: Marschner Uwe , Feldmann Ute , Bakardjiev Petko , Rost Gustav
Published: October 2024

In this paper, an equivalent mechanical chaotic signal generator, based on Chua’s circuit, is analyzed using the isomorphy between electrical and mechanical systems using the structure-true “2nd” analogy. It enables a timefficient behavioral system analysis with the help of well-established network methods without the need for extensive experintal setups. The goal is to identify parameter stets of an equivalent mechanical system leading to a chaotic1 behavior and vizualize the behavior of such a system by means of a realized electromechanical setup.
Spectral Analysis of Mechanical Reservoir Computing With ReLU Spring Networks
Authors: Nelson Daniel , Kiyabu Steven , Vincent Timothy , Gillman Andrew , Criner Amanda , Buskohl Philip R.
Published: October 2024

Nonlinear dynamics are a pervasive phenomenon in natural and synthetic mechanical systems, which can be leveraged for novel control of vibrations and elastic wave propagation. A mechanical system with high dimensionality and nonlinear dynamics can perform information processing on the physical stimuli that act upon the system. This information processing (known as physical reservoir computing) results from the dimensional expansion that occurs in the dynamic system’s state as it reacts to the input. The enriched signal contains both nonlinear transformations as well as memory of the original input, which can be leveraged for machine learning and control applications. In this study, a two-dimensional network of nonlinear springs is studied for its capabilities as a mechanical reservoir computer. The reservoir is acted upon by a driving input force, resulting in a nonlinear, high dimensional response from the network of rectified linear unit (ReLU) springs. These ReLU springs possess bilinear force-displacement curves that resemble the leaky ReLU activation function used in neural networks. A spectral analysis is applied to understand both the dynamics and the computational capabilities of the spring reservoirs with different nonlinearities. Specifically, the frequency content of each reservoir was compared against that of the input signal and the output target. Reservoir computing performance of fitting the target output function improved when the spectral content of the system dynamics aligned with the spectral content of the target. We further analyzed the relationship between noise and number of readouts to investigate potential trade-offs in the selection and quantity of readouts.
Physics-Informed Neural Networks (PINNs)-Based Solution of the Lamb Wave Dispersion Equation for Isotropic Plates
Authors: Liu Hongye , Wang Rong , Huang Ziqi , Sun Maoxun , Li Jiali , Wang Zhide , Tian Zhenhua
Published: October 2024

Dispersion characterization is crucial for nondestructive testing (NDT) and structural health monitoring (SHM). To understand the propagation of dispersive Lamb waves in isotropic plates, this paper presents physics-informed neural networks (PINNs) to calculate the frequency-wave number domain dispersion curves of Lamb waves propagating in aluminum plates. Considering the physical properties, boundary conditions, and wave equations of isotropic metal plates, the dispersion equation for the propagation of Lamb waves in an aluminum plate is derived. Then a deep neural network is constructed using PINNs to obtain the solution of the wave equation, which enables the network to satisfy both data fitting and physical constraints by fusing the priori information of the dispersion equation. To verify the accuracy of the PINNs algorithm, the solutions are compared with those of the Legendre orthogonal polynomial expansion method. The results of this study reveal that the PINNs-based approach has the ability to solve the dispersion relations of Lamb waves in isotropic plates. In our future research, we will extend the PINNs-based algorithm to the solving of wave equations of guided waves in complex structures such as anisotropic composites and arbitrary cross-sectioned waveguides.
Analytical Modeling of a Generalized Multi-Cell Lithium-Ion Battery Multimorph Actuator
Authors: Herrod Austin , LeBlanc Bryan , Gonzalez Cody
Published: October 2024

Actuators are fundamental components of modern technology. With the proper inclusion of silicon in a composite anode, a lithium-ion battery can be adapted for use as a self-powered actuator. Lithium-ion battery actuators have the potential to yield quieter and self-powered actuation, which can lead to the development and possible revolutionization of modern actuators.

This paper further generalizes the analytical modeling of a lithium-ion battery actuator composed of any number of cathode-anode pairs (CAP) or complete lithium-ion battery cells. Relevant applications include actuator cases which require precise, constant, or silent actuation. Some suggested use cases include underwater soft robotics, and rehabilitative equipment such as active compression sleeves or exoskeleton-based robotics.

Two distinct cases in the literature, 13- and 33-layers, have predicted the actuator metrics of a lithium-ion battery actuator, such as free deflection and blocked force [1–3]. However, these models were not sufficiently generalized to show these metrics for a battery actuator beyond a 2-CAP or 6-CAP case without explicitly providing parameters for a particular case. This research generalizes the pattern required to generate the parameters of the 13-layer and 33-layer case studies and creates a generalized code to predict the actuator metrics of a lithium-ion battery actuator composed of (NCAP) number of cathode-anode pairs. The improved simulations account for such characteristics and properly visualize the requested battery actuator’s properties. The modeling of multiple case studies will enable further study for maximizing metrics such as free deflection, blocked force (or zero tip deflection), and actuator energy (or the ability to do useful work).

MATLAB is used to create an algorithm that can provide the actuator metrics and order for any size matrix of N-CAPs, and subsequently determine the metrics associated with a change in the State of Charge (SOC) of the battery actuator. The developed N-CAP functions in this paper can be used to predict and validate the performance of a lithium-ion battery actuator through modeling of useful actuator metrics.
Programming of Nonlinear Heterogeneous Metamaterial for Shock and Vibration
Authors: Zhao Qianyu , Tao Hongcheng , Gibert James M.
Published: October 2024

A digitally encoded mechanical metamaterial utilizing selective insertion of rigid elements into an elastomeric matrix achieves programmable stiffness based on geometric patterns. Beyond the linear behavior, interests are developed towards the nonlinear stiffness region to extend the application of such metamaterials from vibration isolation to shock mitigation. A 12-unit-cell prototype is constructed and experimentally characterized under compression and drop tests, yielding the stress-strain curves and energy absorption capabilities of all independent geometric patterns identified, respectively, with validation by finite-element analysis. The versatility of the metamaterial is illustrated via a categorization of its pattern-dependent responses.
Scalable fabrication of vertically arranged Bi2Se3 crossbar arrays for memristive device applications
Authors: Ki Seung jun , Lee Shiwoo , Chen Mingze , Liang Xiaogan
Published: October 2024

Despite the unique advantages of the memristive switching devices based on two-dimensional (2D) transition metal dichalcogenides, scalable growth technologies of such 2D materials and wafer-level fabrication remain challenging. In this work, we present the gold-assisted large-area physical vapor deposition (PVD) growth of Bi2Se3 features for the scalable fabrication of 2D-material-based crossbar arrays of memristor devices. This work indicates that gold layers, prepatterned by photolithography processes, can catalyze PVD growth of few-layer Bi2Se3 with 100-folds larger crystal grain size in comparison with that grown on bare Si/SiO2 substrates. We also present a fluid-guided growth strategy to improve growth selectivity of Bi2Se3 on Au layers. Through the experimental and computational analyses, we identify two key processing parameters, i.e., the distance between Bi2Se3 powder and the target substrate and the distance between the leading edges of the substrate and the substrate holder with a hollow interior, which plays a critical role in realizing large-scale growth. By optimizing these growth parameters, we have successfully demonstrated cm-scale highly-selective Bi2Se3 growth on crossbar-arrayed structures with an in-lab yield of 86%. The whole process is etch- and plasma-free, substantially minimizing the damage to the crystal structure and also preventing the formation of rough 2D-material surfaces. Furthermore, we also preliminarily demonstrated memristive devices, which exhibit reproducible resistance switching characteristics (over 50 cycles) and a retention time of up to 106 s. This work provides a useful guideline for the scalable fabrication of vertically arranged crossbar arrays of 2D-material-based memristive devices, which is critical to the implementation of such devices for practical neuromorphic applications.
The Effects of Cupping Therapy and Stretching on Lumbar Spine Range of Motion Using Bubble Inclinometry
Authors: Glovach Heather L. , Wilkenfeld David A. , Ostrowski Jennifer L.
Published: October 2024

Low back pain is a leading cause of disability. Cupping can be effective for improving motion in those with back pain; however, little is known about its efficacy compared with other interventions. This study evaluated differences among cupping, stretching, and cupping with stretching on lumbar flexion, extension, and lateral flexion. Although the interventions increased lumbar spine motion, there was no significant difference between groups. Cupping with stretching appears to result in greater motion changes; however, the overall increases were small. Cupping does not appear to increase lumbar spine motion more than stretching or cupping combined with stretching. While cupping may improve low back pain through other means, it does not appear to significantly impact lumbar spine motion in asymptomatic individuals.
Factors Informing Training Load During the Transition From Club to National Football Teams
Authors: Gould Joshua J , Clements Ewan , Ehrmann Fabian , Duffield Rob
Published: October 2024

Purpose: This study examined the relationship of precamp match exposure, travel demands, perceptual wellness, and musculoskeletal screening with ensuing training load (TL) during national-team (NT) duties for international footballers (soccer). Methods: Match minutes and days since last match prior to NT arrival were collected for match exposure. Travel duration and time-zone difference were included as travel demands. Perceptual fatigue, soreness, sleep quality, and stress scores were collected via self-report questionnaire on arrival to NT. Musculoskeletal testing on day 1 of NT included knee to wall and hip internal and external rotation. Relative TL across the first 3 days of NT camp was measured via total distance, high-speed running distance, very high-speed running distance, and accelerations and decelerations per minute. Linear mixed models determined the influence of independent variables on respective TL metrics, with the player included as a random effect. Results: Total distance, high-speed running, very high-speed running, and decelerations per minute were all highest on day 3 (P < .001). Small relationships existed (R2 < .15) between decreased total distance per minute and increased soreness (P = .037), while decreased high-speed running per minute was associated with increased fatigue (P = .017). Small associations existed between decreased accelerations per minute, increased hip internal rotation (P < .001), fatigue (P = .033), and knee to wall (P = .016), while decreased decelerations per minute were associated with increased soreness (P = .001), knee to wall (P = .024), and days between recent match and arrival (P = .003). Conclusions: Very small relationships existed between fatigue, soreness, musculoskeletal screening, and prior match exposure with TL. The larger influence of camp day suggests that deliberate periodization of training is a major factor for prescription on arrival into NT duties.
The Role of Social Support in Concussion Rehabilitation: A Prospective Mixed Methods Study of Canadian University Athletes’ Return to Sport
Authors: Jewell Carley B. , Caron Jeffrey G. , Pope J. Paige , Rathwell Scott
Published: October 2024

Context: Social support is an important consideration during athletes’ rehabilitation following a concussion. Yet, its influence during the return-to-sport (RTS) strategy has been underexplored. Design: Concussed athletes’ experiences with social support during RTS were explored prospectively using an explanatory sequential mixed methods design. Methods: University rugby, basketball, and track and field athletes (N = 8) from a single Canadian institution, 19–23 years of age (mean = 20.63, SD = 1.51), completed concentric circles maps, the Perceived Available Support in Sport Questionnaire, and 2 semistructured interviews during their RTS. We analyzed concentric circles maps and questionnaire data descriptively and interview data using codebook thematic analysis. Results: Athletes identified 16 different social support agents. The 5 most important agents during RTS were athletic therapists, student therapists, head coaches, significant others, and teammates/friends. Significant others provided the most emotional (mean = 2.82, SD = 1.85) and esteem (mean = 2.63, SD = 1.85) support, and the athletic therapist (mean = 1.82, SD = 1.35) and head coaches (mean = 0.93, SD = 1.19) provided the most informational and tangible support. We generated 4 themes from athletes’ interviews: social support behaviors, contextual factors, concussion and RTS factors, and psychological readiness factors. Conclusions: The number of social support agents present, frequency of support types, and demonstration of support behaviors decreased across the concussion RTS strategy. Contextual (ie, toughness), concussion (ie, visibility), and RTS (ie, prognosis uncertainty) factors influenced athletes’ perceptions of social support agents’ support behaviors. Results add to our limited understanding of athletes’ social support during RTS following a concussion and suggest exploring the integration of behavior-specific (eg, checking in, providing reassurance, and demonstrating compassion) social support strategies to promote concussion rehabilitation and readiness to RTS.
Longitudinal OCTA vessel density loss in macula and optic nerve head in healthy, glaucoma suspect and established glaucoma eyes
Authors: Wu Jo-Hsuan , Moghimi Sasan , Nishida Takashi , Adelpour Mohsen , Kamalipour Alireza , Gunasegaran Gopikasree , Zangwill Linda M , Weinreb Robert N
Published: October 2024

Background/aims

To examine longitudinal optical coherence tomography angiography (OCTA) changes in macula and optic nerve head (ONH) in healthy, glaucoma suspect (GS) and primary open-angle glaucoma (POAG) eyes.

Methods

Healthy, GS and POAG eyes from Diagnostic Innovations in Glaucoma Study with ≥2 years follow-up and four visits of macular/ONH OCTA imaging were included. Rates of macular wiVD (whole-image vessel density) and ONH wiCD (whole-image capillary density) changes were calculated for each diagnosis group using join mixed-effect modelling. Differences in wiVD/wiCD change rates across diagnoses were examined through pairwise comparison. Relationships of baseline 24-2 visual field (VF) mean deviation (MD) with wiVD/wiCD change rates were evaluated for POAG eyes.

Results

There were 36, 57 and 79 eyes (25, 38 and 50 subjects) in healthy, GS and POAG groups, respectively. Rates of wiVD (range:−0.72 to −0.92 %/year) and wiCD (range:−0.28 to −0.66 %/year) loss were different from zero in all groups (p<0.05). The rates of wiCD loss differed across all diagnosis groups (p<0.001), while wiVD change rates did not increase when comparing healthy to GS eyes (p=0.167). Baseline VF MD showed a significant but modest correlation with the rates of both wiVD and wiCD loss (p<0.05), and the correlation with wiCD change rate was slightly stronger (R2=0.27 vs 0.16).

Conclusions

In glaucoma, there is earlier microvasculature loss in the ONH than in the macula. Moreover, ONH VD loss shows a slightly stronger association with baseline VF than macular VD. Observing ONH VD loss with OCTA may help to monitor early glaucoma, which should be confirmed by future larger studies.
Participation, barriers, facilitators and bullying experiences of trans people in sport and fitness: findings from a national community survey of trans people in Australia
Authors: Bailey Sasha , Trevitt Benjamin , Zwickl Sav , Newell Beau , Staples Emma , Storr Ryan , Cheung Ada S
Published: October 2024

Objectives

This study aimed to assess participation rates of transgender and gender diverse (trans) people in sport/fitness activities, compare mental health outcomes for trans people participating in sport/fitness with those who do not and explore internal/external barriers and bullying experiences faced by trans people in sport/fitness contexts.

Methods

A cross-sectional online survey open to trans people aged ≥16 years living in Australia was conducted between February and April 2023 and it assessed rates of sport/fitness participation, barriers to participation and bullying experiences through multiple-choice questions. Mental health measures comprised the Kessler Psychological Distress Scale (K10) and self-reported history of self-harm and suicidality. Covariate-adjusted binary logistic regression models tested associations between interpersonal factors, sport/fitness variables and mental health outcomes.

Results

Of 664 respondents (median age 32 years), around one-third (32.8%) regularly participated in sport/fitness. Common internal barriers to participation included anxiety about others’ reactions (63.9%), body dissatisfaction/dysphoria (56.5%) and fears about feeling accepted/affirmed by others (54.7%). Respondents commonly reported experiencing inadequate bathroom/changing facilities (44.4%), exclusionary rules and regulations surrounding gender (36.7%), and invasive or uncomfortable policies or procedures (23.3%). Over one-third (34.4%) of trans people had experienced gender-based bullying or exclusion. Regular sport/fitness participation was associated with significantly lower K10 scores (mean difference=−4.4, 95% CI−5.8, –2.9, p<0.001) and a 40% reduction in odds of recent thoughts of self-harm and suicide (aOR 0.6, 95% CI 0.5, 0.9, p=0.01; aOR 0.6, 95%CI 0.4, 0.9, p=0.02).

Conclusion

Trans people face significant barriers to sport/fitness despite experiencing significant mental health and personal benefits from participation. Affirming and including trans people in sport and fitness should be a key priority for sport and fitness federations, organisations and policy-makers.
Malnutrition in infants aged 6-23 months in China’s poorest rural counties from 2016 to 2021: cross sectional study
Authors: Huo Junsheng , Huang Yangmu , Sun Jing , Huang Jian , Dong Jingya , Sun Yinuo , Feng Xing Lin
Published: October 2024

Objectives

To assess trends and differences in child malnutrition by population subgroups among infants aged 6-23 months in China’s poorest rural counties.

Design

Six consecutive cross sectional surveys were conducted annually.

Setting

The study was conducted in 116 counties in 19 provinces from 2016 to 2021, representing China’s 832 poorest counties.

Participants

A total of 210 088 participants were selected through a multistage cluster sampling procedure; all participants were infants aged 6-23 months.

Main outcome measures

Prevalence of anaemia, stunting, wasting, overweight, and growth status in children (measured by length-for-age and weight-for-length z scores).

Results

Four main malnutrition forms were prevalent in 2016: anaemia (prevalence 18.3%), stunting (7.5%), wasting (4.7%), and overweight (3.1%). The prevalence of any two coexisting malnutrition forms was low. All four forms of malnutrition decreased from 2016 to 2021. Anaemia decreased by more than half, with an annual reduction rate of 9.11% (95% confidence interval (CI) 4.83% to 13.20%). Stunting was reduced by over a third, with an annual reduction rate of 10.44% (7.56% to 13.22%), which is faster than the World Health Organization’s target of 3.9%. Differences in child growth by county gross domestic product quarters were small and decreased over time, but growth differences related to education persisted. Infants whose mothers completed education up to primary school level had approximately twice the risk of stunting (adjusted rate ratio 2.29 (95% CI 1.87 to 2.81)) and wasting (1.73 (1.40 to 2.13)) compared with children whose mothers had an education level of a college degree or above. Boys had poorer growth and higher anaemia than did girls. For all outcomes, differences related to sex and education were greatest at 6 months of age.

Conclusions

Education related inequalities in growth of infants persists, with these differences particularly affecting children whose mothers completed education only up to primary school level.
Development and validation of a prognostic model to predict relapse in adults with remitted depression in primary care: secondary analysis of pooled individual participant data from multiple studies
Authors: Moriarty Andrew S , Paton Lewis W , Snell Kym I E , Archer Lucinda , Riley Richard D , Buckman Joshua E J , Chew Graham Carolyn A , Gilbody Simon , Ali Shehzad , Pilling Stephen , Meader Nick , Phillips Bob , Coventry Peter A , Delgadillo Jaime , Richards David A , Salisbury Chris , McMillan Dean
Published: October 2024

Background

Relapse of depression is common and contributes to the overall associated morbidity and burden. We lack evidence-based tools to estimate an individual’s risk of relapse after treatment in primary care, which may help us more effectively target relapse prevention.

Objective

The objective was to develop and validate a prognostic model to predict risk of relapse of depression in primary care.

Methods

Multilevel logistic regression models were developed, using individual participant data from seven primary care-based studies (n=1244), to predict relapse of depression. The model was internally validated using bootstrapping, and generalisability was explored using internal–external cross-validation.

Findings

Residual depressive symptoms (OR: 1.13 (95% CI: 1.07 to 1.20), p<0.001) and baseline depression severity (OR: 1.07 (1.04 to 1.11), p<0.001) were associated with relapse. The validated model had low discrimination (C-statistic 0.60 (0.55–0.65)) and miscalibration concerns (calibration slope 0.81 (0.31–1.31)). On secondary analysis, being in a relationship was associated with reduced risk of relapse (OR: 0.43 (0.28–0.67), p<0.001); this remained statistically significant after correction for multiple significance testing.

Conclusions

We could not predict risk of depression relapse with sufficient accuracy in primary care data, using routinely recorded measures. Relationship status warrants further research to explore its role as a prognostic factor for relapse.

Clinical implications

Until we can accurately stratify patients according to risk of relapse, a universal approach to relapse prevention may be most beneficial, either during acute-phase treatment or post remission. Where possible, this could be guided by the presence or absence of known prognostic factors (eg, residual depressive symptoms) and targeted towards these.

Trial registration number

NCT04666662.
Why robot embodiment matters: questions of disability, race and intersectionality in the design of social robots
Authors: Paterson Mark
Published: October 2024

A growing minority of those with disabilities are people of color (POC), with, for example, autism diagnosis rates now higher for children of color than for white children in the USA. This trend underscores the need for assistive technologies, especially socially assistive robots, to be designed with intersectional users in mind. Outside of Japan, most robots are designed with white synthetic skin and able-bodied features, failing to reflect the diverse users they are meant to assist. This paper explores the concept of the "engineering imaginary," the historical and cultural influences that shape these designs, and which tend to limit robot embodiment to white, able-bodied forms. Drawing on work from scholars like Lucy Suchman, Jennifer Rhee, Neda Atanasoski and Kalindi Vora, the paper critiques this engineering bias. A key historical moment in the production of the engineered imaginary of artificial humans is provided by Ovid’s myth of Pygmalion and its influence on representations across literature, film, and then robotics. Furthermore, the physicality of the robot, and its role in the production of nonverbal communication (NVC) for more inclusive interaction with humans is explored, seeing these as steps toward what some roboticists are calling Artificial Empathy (AE). Through case studies like Bestic, Bina48, and HuggieBot 3.0, the paper explores what I callthe poverty of the engineering imaginary, how current robotics design fails to properly address issues of race, gender, and disability. Ultimately, the paper argues for more inclusive robot designs that accommodate diverse bodies and social dynamics, questioning the pervasive norm of white, able-bodied robotic embodiment.
Time dependent black holes and gravitational wave in Einstein–Gauss–Bonnet theory with two scalar fields
Authors: Nashed G. G. L.
Published: October 2024

In this paper, we investigate the time dependent black holes in the frame of Einstein–Gauss–Bonnet theory having two scalar fields and investigate the propagation of the gravitational wave (GW). In the reconstructed models, there often appear ghosts, which could be eliminated by imposing some constraints. We investigate the behavior of high-frequency gravitational waves by examining the effects of varying Gauss–Bonnet coupling during their propagation. The speed of propagation changes due to the coupling during the black hole formation process. The propagation speed of gravitational waves differs when they enter the black hole compared to when they exit.
3D Digital Modeling Technology for Thermal Power Plant Boilers Based on Digital Twins
Authors: Zhu Wei , Lu Xinsheng
Published: October 2024

With the rapid advancement of Digital Twins (DTs) technologies, they have emerged as viable solutions for enhancing the control and optimization of thermal power combustion processes. This paper presents a pioneering approach that leverages DTs to revolutionize the combustion process control system within thermal power generating units. Focused on bridging the physical and digital realms, our research establishes DTs of the combustion process control system (CPCS), situated within the Networked Control System Laboratory (NCSLab). We propose a novel five-dimensional (5D) DTs model encompassing the physical, data, service, connection, and virtual models, thereby facilitating a comprehensive fusion of physical and digital information. By analyzing the demands and technical intricacies of the combustion process control system, we design and implement a virtual 3D model, enriching the visualization and analytical capabilities of the DT environment. Furthermore, through controlled experiments on the combustion process and rigorous comparison with offline simulations, we validate the efficacy and feasibility of our approach. Importantly, our research opens avenues for future exploration, particularly in harnessing industrial real-time data to drive the virtual 3D model. Through the integration of Artificial Intelligence (AI) techniques such as Deep Learning, we elevate the capabilities of our DT framework. This enhanced predictive analytics, dynamic optimization, human–machine interaction, and virtual model realism. This research contributes to the advancement of DT technology in the context of thermal power plants and holds promise for driving innovation and efficiency across diverse industrial sectors.
FOSTERING USER INVOLVEMENT IN COLLABORATIVE INNOVATION SPACES: INSIGHTS FROM LIVING LABS
Authors: HUANG JUDY HONG , IAKOVLEVA TATIANA A. , BESSANT JOHN
Published: October 2024

Innovation studies have shown a growing interest in collaborative approaches, particularly user co-creation within innovation spaces. This qualitative study delves into the intricate dynamics of user involvement in fostering user-centric innovation within living labs. Through interviews with 22 informants across 14 countries from 2021 to 2022, this study presents a comprehensive framework outlining four pivotal phases of involvement: recruitment, motivation, co-creation, and relation. Through a nuanced exploration of the critical factors shaping each, the research illuminates ongoing debates and consensus surrounding the orchestration of the co-creation process and the vital role of living labs in cultivating innovations. By offering insights into the co-creation process with users, the study underscores the approaches adopted by living labs to achieve sustainable outcomes in user-centric innovation. These findings provide valuable knowledge that can aid practitioners in navigating challenges related to user engagement, thereby advancing user-centric innovation initiatives.
Per Segment Plane Sweep Line Segment Intersection on the GPU
Authors: Frye Roger , McKenney Mark
Published: October 2024

Polygon overlay operations are used for various purposes such as GIS searches and queries, VLSI and basic geometric operations of intersection, union and difference. There have been recent research articles presenting algorithms using the GPU to perform line segment intersection for geometric operations. We present two parallel algorithms implemented on the GPU that focus on the active list portion of the traditional serial plane sweep algorithm. The first algorithm uses a single block of threads to simulate the active list data structure in hardware; this algorithm is slow due to GPU thread block size limitations and synchronization points, but demonstrates favorable time complexity. The second algorithm uses dynamic parallelism to remove synchronization and scales to utilize available GPU hardware (single GPU). We perform experiments on both synthetic and real world data sets. The presented results show improvement in execution time with respect to recent algorithms, and low memory usage compared to recent algorithms. We achieve speedups of up to 38.8 over the serial sweep line algorithm on real world data.
MemoriaNova: Optimizing Memory-Aware Model Inference for Edge Computing
Authors: Zhang Renjun , Zhang Tianming , Cai Zinuo , Li Dongmei , Ma Ruhui , Rajkumar Buyya
Published: October 2024

In recent years, deploying deep learning models on edge devices has become pervasive, driven by the increasing demand for intelligent edge computing solutions across various industries. From industrial automation to intelligent surveillance and healthcare, edge devices are being leveraged for real-time analytics and decision-making. Existing methods face two challenges when deploying machine learning models on edge devices. The first challenge is handling the execution order of operators with a simple strategy, which can lead to a potential waste of memory resources when dealing with directed acyclic graph structure models. The second challenge is that they usually process operators of a model one by one to optimize the inference latency, which may lead to the optimization problem getting trapped in local optima.

We present MemoriaNova, comprising BTSearch and GenEFlow, to solve these two problems. BTSearch is a graph state backtracking algorithm with efficient pruning and hashing strategies designed to minimize memory overhead during inference and enlarge latency optimization search space. GenEFlow, based on genetic algorithms, integrates latency modeling and memory constraints to optimize distributed inference latency. This innovative approach considers a comprehensive search space for model partitioning, ensuring robust and adaptable solutions. We implement BTSearch and GenEFlow and test them on eleven deep-learning models with different structures and scales. The results show that BTSearch can reach 12% memory optimization compared with the widely used random execution strategy. At the same time, GenEFlow reduces inference latency by 33.9% in distributed systems with four-edge devices.
Proposal Semantic Relationship Graph Network for Temporal Action Detection
Authors: Su Shaowen , Zhang Yan , Gan Minggang
Published: October 2024

Temporal action detection, a critical task in video activity understanding, is typically divided into two stages: proposal generation and classification. However, most existing methods overlook the importance of information transfer among proposals during classification, often treating each proposal in isolation, which hampers accurate label prediction. In this article, we propose a novel method for inferring semantic relationships both within and between action proposals, guiding the fusion of action proposal features accordingly. Building on this approach, we introduce the Proposal Semantic Relationship Graph Network (PSRGN), an end-to-end model that leverages intra-proposal semantic relationship graphs to extract cross-scale temporal context and an inter-proposal semantic relationship graph to incorporate complementary neighboring information, significantly improving proposal feature quality and overall detection performance. This is the first method to apply graph structure learning in temporal action detection, adaptively constructing the inter-proposal semantic graph. Extensive experiments on two datasets demonstrate the effectiveness of our approach, achieving state-of-the-art (SOTA). Code and results are available at
http://github.com/Riiick2011/PSRGN
.
Diffusion-driven Incomplete Multimodal Learning for Air Quality Prediction
Authors: Fan Jinxiao , Qi Mengshi , Liu Liang , Ma Huadong
Published: October 2024

Predicting air quality using multimodal data is crucial to comprehensively capture the diverse factors influencing atmospheric conditions. Therefore, this study introduces a multimodal learning framework that integrates outdoor images with traditional ground-based observations to improve the accuracy and reliability of air quality predictions. However, aligning and fusing these heterogeneous data sources poses a formidable challenge, further exacerbated by pervasive data incompleteness issues in practice. In this article, we propose a novel incomplete multimodal learning approach (iMMAir) to recovery missing data for robust air quality prediction. Specifically, we first design a shallow feature extractor to capture modal-specific features within the latent representation space. Then we develop a conditional diffusion-driven recovery module to mitigate the distribution gap between the recovered and true data. This module further incorporates two conditional constraints of temporal correlation and semantic consistency for effective modal completion. Finally, we reconstruct incomplete modalities and fuse available data using a multimodal transformer network to predict the air quality. To alleviate the modality imbalance problem, we employ an adaptive gradient modulation strategy to adjust the optimization of each modality. Experimental results demonstrate that iMMAir significantly reduces prediction errors, outperforming baseline models by an average of 5.6% and 2.5% in air quality regression and classification tasks. Our source code and data are available at
https://github.com/pestasu/IMMAir
.