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27 pages, 9664 KiB  
Article
Bio-Inspired Motion Emulation for Social Robots: A Real-Time Trajectory Generation and Control Approach
by Marvin H. Cheng, Po-Lin Huang and Hao-Chuan Chu
Biomimetics 2024, 9(9), 557; https://doi.org/10.3390/biomimetics9090557 (registering DOI) - 15 Sep 2024
Abstract
Assistive robotic platforms have recently gained popularity in various healthcare applications, and their use has expanded to social settings such as education, tourism, and manufacturing. These social robots, often in the form of bio-inspired humanoid systems, provide significant psychological and physiological benefits through [...] Read more.
Assistive robotic platforms have recently gained popularity in various healthcare applications, and their use has expanded to social settings such as education, tourism, and manufacturing. These social robots, often in the form of bio-inspired humanoid systems, provide significant psychological and physiological benefits through one-on-one interactions. To optimize the interaction between social robotic platforms and humans, it is crucial for these robots to identify and mimic human motions in real time. This research presents a motion prediction model developed using convolutional neural networks (CNNs) to efficiently determine the type of motions at the initial state. Once identified, the corresponding reactions of the robots are executed by moving their joints along specific trajectories derived through temporal alignment and stored in a pre-selected motion library. In this study, we developed a multi-axial robotic arm integrated with a motion identification model to interact with humans by emulating their movements. The robotic arm follows pre-selected trajectories for corresponding interactions, which are generated based on identified human motions. To address the nonlinearities and cross-coupled dynamics of the robotic system, we applied a control strategy for precise motion tracking. This integrated system ensures that the robotic arm can achieve adequate controlled outcomes, thus validating the feasibility of such an interactive robotic system in providing effective bio-inspired motion emulation. Full article
(This article belongs to the Special Issue Bio-Inspired Approaches—a Leverage for Robotics)
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Figure 1
<p>(<b>a</b>) Recordable joints and the corresponding locations for human motions using the Cubmos library. (<b>b</b>) Image of the moving object motion captured with an Intel RealSense D435 camera and processed using the Cubemos framework in C#.</p>
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<p>Angle-to-angle plots of three selected motions: drinking water, raising right hand, and object lifting (The different colors represent 10 trials).</p>
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<p>Training process using the framework of CNNs.</p>
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<p>System configuration of the dual-arm robotic platform and the corresponding motion acquisition and control subsystems.</p>
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<p>(<b>a</b>) Locations of DC motors and sensors. (<b>b</b>) Block diagram of a single DC motor used for joint angular movement.</p>
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<p>Procedures of sensor fusion utilizing the Kalman filter algorithm, demonstrating the sequential steps for combining accelerometer and gyroscope measurements to estimate pitch angles with reduced noise and enhanced accuracy.</p>
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<p>Motion profiles adjusted through temporal alignment and the derived reference trajectories for the elbow and shoulder joints (object lifting). The top two figures display ten recorded motions aligned from 0 to 100%, while the lower figures present the derived reference trajectories for the motion.</p>
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<p>Derived reference trajectories of three selected motions: (<b>a</b>) object lifting, (<b>b</b>) raising the right arm, and (<b>c</b>) drinking.</p>
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<p>Adjustment of joint trajectory based on reference motions and real-world movements.</p>
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<p>(<b>a</b>) Comparison of system response between compensated system and unity feedback control result for with and without an appropriate controller and (<b>b</b>) comparison of tracking errors of both control schemes [<a href="#B24-biomimetics-09-00557" class="html-bibr">24</a>].</p>
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<p>Ten trials of compensated results of elbow and shoulder joint movements (object lifting). (<b>a</b>) Tracking performance and tracking error of elbow joint and (<b>b</b>) tracking performance and tracking error of shoulder joint.</p>
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<p>Ten trials of compensated results of elbow and shoulder joint movements (object lifting). (<b>a</b>) Tracking performance and tracking error of elbow joint and (<b>b</b>) tracking performance and tracking error of shoulder joint.</p>
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<p>Operational process of the robotic platform to mimic human arm motions.</p>
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<p>Simulated results of elbow joint with different operation durations for object lifting motion.</p>
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<p>Simulated results of shoulder joint with different operation durations for object lifting motion.</p>
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<p>Compensated results of elbow and shoulder joint movements (object lifting). (<b>a</b>) Tracking performance and tracking error of elbow joint and (<b>b</b>) tracking performance and tracking error of shoulder joint.</p>
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<p>Distribution of tracking errors of the selected motion (10 trials of object lifting).</p>
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18 pages, 4490 KiB  
Article
A Novel Modular Multilevel Converter Topology with High- and Low-Frequency Modules and Its Modulation Strategy
by Zejun Huang, Hao Bai, Min Xu, Yuchao Hou, Ruotian Yao, Yipeng Liu, Qi Guo and Chunming Tu
Electronics 2024, 13(18), 3656; https://doi.org/10.3390/electronics13183656 - 13 Sep 2024
Viewed by 290
Abstract
To resolve the issue of the difficultly in effectively balancing the output performance improvement, cost reduction, and efficiency improvement of a medium-voltage modular multilevel converter (MMC), a novel MMC (NMMC) topology based on high- and low-frequency hybrid modulation is proposed in this study. [...] Read more.
To resolve the issue of the difficultly in effectively balancing the output performance improvement, cost reduction, and efficiency improvement of a medium-voltage modular multilevel converter (MMC), a novel MMC (NMMC) topology based on high- and low-frequency hybrid modulation is proposed in this study. Each arm of the NMMC contains a high-frequency sub-module composed of a heterogeneous cross-connect module (HCCM) and N − 1 low-frequency sub-modules composed of half-bridge converters. The high-frequency bridge arm of the HCCM in this study adopts SiC MOSFET devices, while the commutation bridge arm and low-frequency sub-module of the HCCM adopt Si IGBT devices. For the NMMC topology, this study adopts a high/low-frequency hybrid modulation strategy, which gives full play to the advantages of low switching loss in SiC MOSFET devices and low on-state loss in Si IGBT devices. In addition, a specific capacitor voltage balance strategy is proposed for the HCCM, and the working state of the HCCM is analyzed in detail. Furthermore, the feasibility and effectiveness of the proposed topology, modulation strategy, and voltage balancing strategy are verified by experiments. Finally, the proposed topology is compared with the existing MMC topology in terms of device cost and operating loss, which proves that the proposed topology can better balance the cost and efficiency indicators of the device. Full article
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<p>Topology diagram of the NMMC.</p>
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<p>Modulation principle of the NMMC.</p>
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<p>PWM principle of the HCCM.</p>
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<p>Total energy change diagram of HCCM in mode I.</p>
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<p>Total energy change diagram of HCCM in mode II.</p>
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<p>Working state of the HCCM in mode I. (<b>a</b>) <span class="html-italic">S</span><sub>2</sub>, <span class="html-italic">T</span><sub>1</sub> and <span class="html-italic">T</span><sub>4</sub> are switched on. (<b>b</b>) <span class="html-italic">S</span><sub>2</sub>, <span class="html-italic">T</span><sub>2</sub> and <span class="html-italic">T</span><sub>4</sub> are switched on. (<b>c</b>) <span class="html-italic">S</span><sub>2</sub>, <span class="html-italic">T</span><sub>1</sub> and <span class="html-italic">T</span><sub>3</sub> are switched on. (<b>d</b>) <span class="html-italic">S</span><sub>2</sub>, <span class="html-italic">T</span><sub>2</sub> and <span class="html-italic">T</span><sub>3</sub> are switched on. (<b>e</b>) <span class="html-italic">S</span><sub>2</sub>, <span class="html-italic">T</span><sub>1</sub> and <span class="html-italic">T</span><sub>4</sub> are switched on. (<b>f</b>) <span class="html-italic">S</span><sub>2</sub>, <span class="html-italic">T</span><sub>2</sub> and <span class="html-italic">T</span><sub>4</sub> are switched on. (<b>g</b>) <span class="html-italic">S</span><sub>2</sub>, <span class="html-italic">T</span><sub>1</sub> and <span class="html-italic">T</span><sub>3</sub> are switched on. (<b>h</b>) <span class="html-italic">S</span><sub>2</sub>, <span class="html-italic">T</span><sub>2</sub> and <span class="html-italic">T</span><sub>3</sub> are switched on.</p>
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<p>Working state of the HCCM in mode II. (<b>a</b>) <span class="html-italic">S</span><sub>1</sub>, <span class="html-italic">T</span><sub>2</sub> and <span class="html-italic">T</span><sub>3</sub> are turned on. (<b>b</b>) <span class="html-italic">S</span><sub>1</sub>, <span class="html-italic">T</span><sub>1</sub> and <span class="html-italic">T</span><sub>3</sub> are turned on. (<b>c</b>) <span class="html-italic">S</span><sub>1</sub>, <span class="html-italic">T</span><sub>2</sub> and <span class="html-italic">T</span><sub>4</sub> are turned on. (<b>d</b>) <span class="html-italic">S</span><sub>1</sub>, <span class="html-italic">T</span><sub>1</sub> and <span class="html-italic">T</span><sub>4</sub> are turned on. (<b>e</b>) <span class="html-italic">S</span><sub>1</sub>, <span class="html-italic">T</span><sub>2</sub> and <span class="html-italic">T</span><sub>3</sub> are turned on. (<b>f</b>) <span class="html-italic">S</span><sub>1</sub>, <span class="html-italic">T</span><sub>1</sub> and <span class="html-italic">T</span><sub>3</sub> are turned on. (<b>g</b>) <span class="html-italic">S</span><sub>1</sub>, <span class="html-italic">T</span><sub>2</sub> and <span class="html-italic">T</span><sub>4</sub> are turned on. (<b>h</b>) <span class="html-italic">S</span><sub>1</sub>, <span class="html-italic">T</span><sub>1</sub> and <span class="html-italic">T</span><sub>4</sub> are turned on.</p>
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<p>Voltage balancing process of the HCCM.</p>
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<p>The output waveform.</p>
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<p>Capacitance voltage waveform.</p>
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<p>Experimental waveforms of HMMC under transient conditions.</p>
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<p>Joint simulation model of MATLAB/Simulink and PLECS.</p>
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<p>Loss comparison under different load power (<span class="html-italic">T</span><sub>j</sub> = 100 °C, <span class="html-italic">f</span><sub>eq</sub> = 20 kHz).</p>
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<p>Loss comparison at different equivalent switching frequencies (<span class="html-italic">T</span><sub>j</sub> = 100 °C, <span class="html-italic">P</span> = 1.5 MW).</p>
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18 pages, 3019 KiB  
Article
Demonstrating the Lessons Learned for Lightweighting EV Components through a Circular-Economy Approach
by Floris Teunissen and Esther van Bergen
World Electr. Veh. J. 2024, 15(9), 415; https://doi.org/10.3390/wevj15090415 - 11 Sep 2024
Viewed by 342
Abstract
LEVIS is an innovation project funded by the EU Horizon 2020 program. Its main objective is to develop lightweight multi-material solutions based on bio-based materials and carbon fiber thermoplastic composites for electric vehicle components and demonstrating the technical, operational, and economic feasibility of [...] Read more.
LEVIS is an innovation project funded by the EU Horizon 2020 program. Its main objective is to develop lightweight multi-material solutions based on bio-based materials and carbon fiber thermoplastic composites for electric vehicle components and demonstrating the technical, operational, and economic feasibility of applying eco-design and circular-economy principles into the design process. The project demonstrates the application of these materials in four case studies: a suspension control arm, a battery box, a battery module housing, and a cross-car beam. All demonstrators achieved a 20%-to-40% reduction in component weight, but environmental assessment results varied significantly, with emissions changes ranging from an increase for suspension control arms to a 65.5% decrease for battery modules. Efficient use of materials, particularly in the battery box using hybrid solutions and bonding technologies, showed notable emissions reduction. In contrast, replacing steel with CFRPs in suspension control arms led to increased emissions, suggesting that CFRPs are more effective for replacing high-polluting materials like aluminum. Recycled carbon fibers proved more beneficial for low-polluting materials like steel. The environmental performance of technologies depends on the expected use of EVs and the electricity grid mix, with better outcomes in coal-reliant grids. Finally, no single recycling method is universally superior; the optimal method depends on the specific technologies and the energy required for recycled materials. Full article
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<p>Demo 1—suspension control arm: (<b>a</b>) benchmark product and (<b>b</b>) new design. (Reprinted with permission from Ref. [<a href="#B14-wevj-15-00415" class="html-bibr">14</a>]. Copyright 2024 LEVIS project).</p>
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<p>Demo 2A—battery box: (<b>a</b>) benchmark product and (<b>b</b>) new design. (Reprinted with permission from Ref. [<a href="#B14-wevj-15-00415" class="html-bibr">14</a>]. Copyright 2024 LEVIS project).</p>
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<p>Demo 2B—battery module: (<b>a</b>) benchmark product and (<b>b</b>) new design. (Reprinted with permission from Ref. [<a href="#B14-wevj-15-00415" class="html-bibr">14</a>]. Copyright 2024 LEVIS project).</p>
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<p>Demo 3—cross-car beam: (<b>a</b>) benchmark product and (<b>b</b>) new design. (Reprinted with permission from Ref. [<a href="#B14-wevj-15-00415" class="html-bibr">14</a>]. Copyright 2024 LEVIS project).</p>
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<p>Impact on climate change over life-cycle phases of suspension control arm demo vs. benchmark. (Reprinted with permission from Ref. [<a href="#B14-wevj-15-00415" class="html-bibr">14</a>]. Copyright 2024 LEVIS project).</p>
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<p>Impact on climate change over life-cycle phases of battery box demo vs. benchmarks. (Reprinted with permission from Ref. [<a href="#B14-wevj-15-00415" class="html-bibr">14</a>]. Copyright 2024 LEVIS project).</p>
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<p>Impact on climate change (kg CO<sub>2</sub> eq.) over all the life-cycle phases of the battery module. (Reprinted with permission from Ref. [<a href="#B14-wevj-15-00415" class="html-bibr">14</a>]. Copyright 2024 LEVIS project).</p>
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<p>Impact on climate change over life-cycle phases of cross-car beam demo vs. benchmark. (Reprinted with permission from Ref. [<a href="#B14-wevj-15-00415" class="html-bibr">14</a>]. Copyright 2024 LEVIS project).</p>
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<p>Effect of electricity grid mix on total climate-change impact of suspension control arm demo vs. benchmark. (Reprinted with permission from Ref. [<a href="#B14-wevj-15-00415" class="html-bibr">14</a>]. Copyright 2024 LEVIS project).</p>
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<p>The effect of lifespan on total climate-change impact of suspension control arm demo vs. benchmark. (Reprinted with permission from Ref. [<a href="#B14-wevj-15-00415" class="html-bibr">14</a>]. Copyright 2024 LEVIS project).</p>
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<p>Results of the end-of-life sensitivity analysis of all the demonstrators compared in percentages to their benchmark. Patterned bars represent the actual end-of-life processes used in the LEVIS project. Filled bars represent theoretical recycling processes.</p>
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10 pages, 444 KiB  
Article
Food Insecurity and Dietary Quality in African American Patients with Gastrointestinal Cancers: An Exploratory Study
by Daaimah Dratsky, Erin McGillivray, Juhi Mittal, Elizabeth A. Handorf, Giuliana Berardi, Igor Astsaturov, Michael J. Hall, Ming-Chin Yeh, Rishi Jain and Carolyn Y. Fang
Nutrients 2024, 16(18), 3057; https://doi.org/10.3390/nu16183057 - 11 Sep 2024
Viewed by 384
Abstract
African American (AA) individuals experience food insecurity at twice the rate of the general population. However, few patients are screened for these measures in the oncology setting. The primary aim of this study was to evaluate associations between food insecurity and dietary quality [...] Read more.
African American (AA) individuals experience food insecurity at twice the rate of the general population. However, few patients are screened for these measures in the oncology setting. The primary aim of this study was to evaluate associations between food insecurity and dietary quality in AA patients with gastrointestinal (GI) malignancies. The secondary aim was to evaluate differences in dietary quality and the level of food insecurity between the participants at Temple University Hospital (TUH) vs. Fox Chase Cancer Center (FCCC). A single-arm, cross-sectional study was conducted, in which 40 AA patients with GI malignancies were recruited at FCCC and TUH between February 2021 and July 2021. Participants completed the US Adult Food Security Survey Module to assess the level of food security (food secure vs. food insecure). An electronic food frequency questionnaire (VioScreenTM) was administered to obtain usual dietary intake. Diet quality was calculated using the Healthy Eating Index 2015 (HEI-2015). Dietary quality and food insecurity were summarized using standard statistical measures. Overall, 6 of the 40 participants (15%) reported food insecurity, and the mean HEI-2015 score was 64.2. No association was observed between dietary quality and food insecurity (p = 0.29). However, we noted that dietary quality was significantly lower among patients presenting at TUH (mean HEI-2015 = 57.8) compared to patients at FCCC (mean HEI-2015 = 73.5) (p < 0.01). Food insecurity scores were also significantly higher in the TUH population vs. the FCCC population (p < 0.01). Full article
(This article belongs to the Section Nutritional Immunology)
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<p>Associations between diet quality and US Adult Food Security Survey Module Scores, and diet quality and study site, for 40 African American patients with gastrointestinal cancers. <sup>a</sup> Healthy Eating Index-2015, a measure of diet quality. <sup>b</sup> Food security scores measured by the US Adult Food Security Survey Module (10 items survey, range 0–10). Raw scores of 0–2 indicate the participant is food secure and raw scores 3–10 indicate food insecure. In this study, participants with scores of 3 or greater were categorized as food insecure/having food insecurity [<a href="#B24-nutrients-16-03057" class="html-bibr">24</a>]. <sup>c</sup> TUH = Temple University Hospital and FCCC = Fox Chase Cancer Center. * Statistically significant difference between study sites.</p>
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19 pages, 7597 KiB  
Article
Ergonomic Assessment of Key Biomechanical Factors in Patient Lifting: Results from a Cross-Sectional Study
by Karolis Senvaitis, Aušra Adomavičienė, Alina Tomaševič, Radvilė Kernagytė, Ada Petrauskaitė and Kristina Daunoravičienė
Appl. Sci. 2024, 14(17), 8076; https://doi.org/10.3390/app14178076 - 9 Sep 2024
Viewed by 408
Abstract
This study includes an ergonomic evaluation of patient lifting motion performed by healthcare specialists. This analysis focuses on the neck, shoulder, and elbow, as these are statistically significant areas with insufficient research data. Data collection was conducted using the Movella Xsens system as [...] Read more.
This study includes an ergonomic evaluation of patient lifting motion performed by healthcare specialists. This analysis focuses on the neck, shoulder, and elbow, as these are statistically significant areas with insufficient research data. Data collection was conducted using the Movella Xsens system as a standard 17 IMU (inertia measurement unit) marker set. A total of 44 test subjects participated, resulting in 396 measurements. A mathematical model was presented, including the main expressions and a three-dimensional moment arm of the shoulder calculation determining both the moment and accumulated moment. The patient load profile was measured in the experiment and parametrically integrated into the mathematical model. Ergonomic limits were calculated and presented, showing that during the lifting motion, the neck exceeds its ergonomic limit by 66%, the shoulders by 49%, and the elbow by 76%. The accumulated moment can vary by up to 23% depending on different evaluated techniques or data cross-sections. The model was verified by comparing it with data from other experiments, and recommendations were presented based on the findings, along with suggestions for future research development in the area. Full article
(This article belongs to the Section Applied Biosciences and Bioengineering)
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<p>Flow chart of study procedure.</p>
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<p>The experiment lifting procedure environment and schematical view: (<b>A</b>)—patient transfer motion capture setup; (<b>B</b>)—patient load evaluation: (<b>B.1</b>)—patient reaction force estimation; and (<b>B.2</b>)—patient leftover reaction force estimation [<a href="#B20-applsci-14-08076" class="html-bibr">20</a>].</p>
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<p>Neck moment (<b>A</b>) and shoulder moment (<b>B</b>) calculation principal schematics. Purple outline represents patients body.</p>
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<p>Unified forearm and hand center of mass schematics.</p>
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<p>Mathematical scheme of projected shoulder distance expression (<b>A</b>) and three-dimensional trigonometry expression (<b>B</b>) [<a href="#B20-applsci-14-08076" class="html-bibr">20</a>]. Purple outline represents patient’s body.</p>
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<p>Patient load for test subject measurement results and load profile definition. Different movement phases are identified in the graph. (<b>A</b>)—patient lift motion; (<b>B</b>)—patient move-while-lifting motion; (<b>C</b>)—patient put-down motion. Colored area shows ±MAD.</p>
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<p>Average neck moment values during different data cross-sections with statistical significance: (<b>A</b>)—test subject age; (<b>B</b>)—test subject physical readiness; (<b>C</b>)—test subject gender. Gray colored area shows ±MAD.</p>
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<p>Average arm moment values during different scenarios with statistical significance. Gray colored area shows ±MAD.</p>
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<p>Average neck and arm moment values, with ergonomic limit and identified motion phases: (<b>A</b>)—patient lift motion phase; (<b>B</b>)—patient move-while-lifting motion phase; (<b>C</b>)—patient put-down motion phase; (<b>D</b>)—neck moment; (<b>E</b>)—shoulder moment; (<b>F</b>)—elbow moment. Gray colored area shows ±MAD.</p>
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<p>Neck, shoulder, and elbow mean value comparison with experiment model: (<b>A</b>)—neck 15° flexion scenario, (<b>B</b>)—neck 30° flexion scenario, (<b>C</b>)—neck 45° flexion scenario, (<b>D</b>)—shoulder verification with 14.3 Nm baseline, (<b>E</b>)—elbow verification with 5.8 Nm baseline, (<b>F</b>)—shoulder verification with 22.5 Nm baseline. Colored area around average value shows ±SD.</p>
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13 pages, 960 KiB  
Article
Occupational Health Problems among Cambodian Dentists: A Cross-Sectional Study
by Rodrigo Mariño, Rithvitou Horn, Moniroth Seat, Konitha Hong and Sokpheakta Hen
Epidemiologia 2024, 5(3), 592-604; https://doi.org/10.3390/epidemiologia5030041 - 5 Sep 2024
Viewed by 343
Abstract
Dental practitioners, as part of their work, are exposed to a variety of hazards. This highlights the ongoing need for attention to occupational health in the dental field. A cross-sectional study was organised to investigate the range, prevalence, and associated factors for occupational [...] Read more.
Dental practitioners, as part of their work, are exposed to a variety of hazards. This highlights the ongoing need for attention to occupational health in the dental field. A cross-sectional study was organised to investigate the range, prevalence, and associated factors for occupational health problems related to dental practice among Cambodian dentists. Participants underwent a face-to-face interview to explore dentists work-related health problems; 106 Cambodian dentists participated in this study, of which 68.9% were male. Ages ranged from 29 to 71 years, averaging 36.1 years, with the majority (77.4%) in the 29–40 age group. They had 5 to 18 years of practice experience, and worked an average of 52.2 h per week. Commonly reported health issues included back pain (88.7%), headaches (81.1%), shoulder pain (78.3%), arm/hand pain (57.5%), and eye problems (48.1%). Additionally, 38.7% of participants felt stressed and 19.8% depressed. Some reported suicidal thoughts and taking medication for depression. Despite these challenges, 91.5% enjoyed practicing dentistry. These findings highlight the need for interventions and strategies to address the physical and mental well-being of Cambodian dentists. By addressing these issues, steps can be taken to enhance the working conditions and professional satisfaction of dental professionals, ultimately benefiting both the practitioners and their patients. Full article
11 pages, 1388 KiB  
Article
Clinical, Radiographic, and Biomechanical Evaluation of the Upper Extremity in Patients with Osteogenesis Imperfecta
by Katharina Oder, Fabian Unglaube, Sebastian Farr, Andreas Kranzl, Alexandra Stauffer, Rudolf Ganger, Adalbert Raimann and Gabriel T. Mindler
J. Clin. Med. 2024, 13(17), 5174; https://doi.org/10.3390/jcm13175174 - 31 Aug 2024
Viewed by 443
Abstract
Introduction: Osteogenesis imperfecta (OI) is a hereditary disorder primarily caused by mutations in type I collagen genes, resulting in bone fragility, deformities, and functional limitations. Studies on upper extremity deformities and associated functional impairments in OI are limited. This cross-sectional study aimed to [...] Read more.
Introduction: Osteogenesis imperfecta (OI) is a hereditary disorder primarily caused by mutations in type I collagen genes, resulting in bone fragility, deformities, and functional limitations. Studies on upper extremity deformities and associated functional impairments in OI are limited. This cross-sectional study aimed to evaluate upper extremity deformities and functional outcomes in OI. Methods: We included patients regardless of their OI subtypes with a minimum age of 7 years. Radiographic analysis of radial head dislocation, ossification of the interosseous membrane, and/or radioulnar synostosis of the forearm were performed, and deformity was categorized as mild, moderate, or severe. Clinical evaluation was performed using the Quick Disabilities of Arm, Shoulder, and Hand (qDASH) questionnaire and shoulder-elbow-wrist range of motion (ROM). Three-dimensional motion analysis of the upper limb was conducted using the Southampton Hand Assessment Procedure (SHAP). The SHAP quantifies execution time through the Linear Index of Function (LIF) and assesses the underlying joint kinematics using the Arm Profile Score (APS). Additionally, the maximum active Range of Motion (aRoM) was measured. Results: Fourteen patients aged 8 to 73 were included. Radiographic findings revealed diverse deformities, including radial head dislocation, interosseous membrane ossification, and radioulnar synostosis. Six patients had mild, six moderate, and two severe deformities of the upper extremity. Severe deformities and radial head dislocation correlated with compromised ROM and worse qDASH scores. The qDASH score ranged from 0 to 37.5 (mean 11.7). APS was increased, and LIF was reduced in OI-affected persons compared with non-affected peers. APS and LIF also varied depending on the severity of bony deformities. aRoM was remarkably reduced for pro-supination. Conclusion: Patients with OI showed variable functional impairment from almost none to severe during daily life activities, mainly depending on the magnitude of deformity in the upper extremity. Larger multicenter studies are needed to confirm the results of this heterogeneous cohort. Level of evidence: Retrospective clinical study; Level IV. Full article
(This article belongs to the Special Issue Challenges in Hand and Upper Limb Surgery)
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<p>APS in relation to the underlying bony deformities. Gray: mean of non-affected persons with one and two times standard deviation; *: mean; -: median; small box: one times standard deviation; wide box: interquartile range; n: number of extremities; mean ± standard deviation.</p>
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<p>LIF in relation to the underlying bony deformities. Gray: mean of non-affected persons with one and two times standard deviation; *: mean; -: median; small box: one times standard deviation; wide box: interquartile range; n: number of extremities; mean ± standard deviation.</p>
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<p>Pro-supination aRoM in relation to the bony deformities. Gray: mean of non-affected persons with one and two times standard deviation; *: mean; -: median; small box: one times standard deviation; wide box: interquartile range; n: number of extremities; mean ± standard deviation.</p>
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<p>Differences between OI-affected and non-affected persons for all SHAP tasks. The black bars indicate tasks that require more forearm supination/pronation.</p>
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14 pages, 4926 KiB  
Article
Eight-Bar Elbow Joint Exoskeleton Mechanism
by Giorgio Figliolini, Chiara Lanni, Luciano Tomassi and Jesús Ortiz
Robotics 2024, 13(9), 125; https://doi.org/10.3390/robotics13090125 - 23 Aug 2024
Viewed by 394
Abstract
This paper deals with the design and kinematic analysis of a novel mechanism for the elbow joint of an upper-limb exoskeleton, with the aim of helping operators, in terms of effort and physical resistance, in carrying out heavy operations. In particular, the proposed [...] Read more.
This paper deals with the design and kinematic analysis of a novel mechanism for the elbow joint of an upper-limb exoskeleton, with the aim of helping operators, in terms of effort and physical resistance, in carrying out heavy operations. In particular, the proposed eight-bar elbow joint exoskeleton mechanism consists of a motorized Watt I six-bar linkage and a suitable RP dyad, which connects mechanically the external parts of the human arm with the corresponding forearm by hook and loop velcro, thus helping their closing relative motion for lifting objects during repetitive and heavy operations. This relative motion is not a pure rotation, and thus the upper part of the exoskeleton is fastened to the arm, while the lower part is not rigidly connected to the forearm but through a prismatic pair that allows both rotation and sliding along the forearm axis. Instead, the human arm is sketched by means of a crossed four-bar linkage, which coupler link is considered as attached to the glyph of the prismatic pair, which is fastened to the forearm. Therefore, the kinematic analysis of the whole ten-bar mechanism, which is obtained by joining the Watt I six-bar linkage and the RP dyad to the crossed four-bar linkage, is formulated to investigate the main kinematic performance and for design purposes. The proposed algorithm has given several numerical and graphical results. Finally, a double-parallelogram linkage, as in the particular case of the Watt I six-bar linkage, was considered in combination with the RP dyad and the crossed four-bar linkage by giving a first mechanical design and a 3D-printed prototype. Full article
(This article belongs to the Section Neurorobotics)
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<p>Ten-bar exoskeleton elbow joint mechanism.</p>
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<p>Ten-bar mechanism.</p>
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<p>Watt I six-bar linkage: (<b>a</b>) vector loops; (<b>b</b>) ICs.</p>
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<p>Crossed four-bar linkage: (<b>a</b>) vector loop; (<b>b</b>) ICs.</p>
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<p>Ten-bar mechanism: ICs.</p>
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<p>Ten-bar mechanism: result for a crank angle <span class="html-italic">θ</span><sub>2</sub> = 255° of <span class="html-italic">A</span><sub>0</sub><span class="html-italic">A</span> (Blue and magenta colors indicate the eight-bar elbow joint exoskeleton mechanism and the crossed four-bar linkage, respectively).</p>
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<p>Ten-bar mechanism: result for a crank angle <span class="html-italic">θ</span><sub>2</sub> = 290° of <span class="html-italic">A</span><sub>0</sub><span class="html-italic">A.</span>(Blue and magenta colors indicate the eight-bar elbow joint exoskeleton mechanism and the crossed four-bar linkage, respectively).</p>
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<p>Ten-bar elbow joint exoskeleton mechanism: (<b>a</b>) kinematic sketch; (<b>b</b>) application.</p>
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<p>Ten-bar linkage for the upper-limb exoskeleton: result for a crank angle <span class="html-italic">θ</span><sub>2</sub> = 300° of <span class="html-italic">A</span><sub>0</sub><span class="html-italic">A</span>. (Blue and magenta colors indicate the eight-bar elbow joint exoskeleton mechanism and the crossed four-bar linkage, respectively).</p>
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<p>Result for a crank angle <span class="html-italic">θ</span><sub>2</sub> = 252° of <span class="html-italic">A</span><sub>0</sub><span class="html-italic">A</span> for ten-bar linkage (Blue and magenta colors indicate the eight-bar elbow joint exoskeleton mechanism and the crossed four-bar linkage, respectively).</p>
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<p>Ten-bar mechanism and 3D-printed prototype for different configurations of crank angles <span class="html-italic">θ</span><sub>2</sub>: (<b>a</b>) 225°; (<b>b</b>) 240°; (<b>c</b>) 252°; (<b>d</b>) 270°; (<b>e</b>) 300°; (<b>f</b>) 315°.</p>
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<p>Whole sequence of the ten-bar mechanism closing motion.</p>
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26 pages, 13382 KiB  
Article
Construction and Characteristic Analysis of Dynamic Stress Coupling Simulation Models for the Attitude-Adjustable Chassis of a Combine Harvester
by Xiaoyu Chai, Jinpeng Hu, Tianle Ma, Peng Liu, Maolin Shi, Linjun Zhu, Min Zhang and Lizhang Xu
Agronomy 2024, 14(8), 1874; https://doi.org/10.3390/agronomy14081874 - 22 Aug 2024
Viewed by 372
Abstract
The combine harvester equipped with attitude-adjustment functionality significantly enhances its adaptability to complex terrain but often struggles to maintain the reliability of its mechanisms. Therefore, investigating the dynamic load characteristics of the attitude-adjustment mechanism becomes imperative. This article employed the DEM–FMBD (Discrete Element [...] Read more.
The combine harvester equipped with attitude-adjustment functionality significantly enhances its adaptability to complex terrain but often struggles to maintain the reliability of its mechanisms. Therefore, investigating the dynamic load characteristics of the attitude-adjustment mechanism becomes imperative. This article employed the DEM–FMBD (Discrete Element Method–Flexible Multibody Dynamics) bidirectional coupling simulation method to establish a multibody dynamic model of a tracked combine harvester. The study delved into the interaction mechanism and dynamic stress response characteristics between the tracked chassis and the complex terrain under various height adjustments, lateral adjustment angles, longitudinal adjustment angles, and different field-ridge crossing methods. Finally, the accuracy of the coupled simulation model was validated through a constructed stress detection system. The research findings revealed that the displacement and tilt angle deviation of the hydraulic cylinders utilized to execute the chassis adjustment actions in the constructed coupled simulation model was less than 5%, and the deviation between the simulation results and the actual maximum dynamic stress under multiple working conditions ranged from 7% to 15%. This verification confirmed the effectiveness of the DEM–FMBD coupled simulation method. Under different adjustment conditions, the maximum stress position was consistently distributed in the same area of the left-front and left-rear rotating arms. The primary and secondary effects of the various parts of the adjustment mechanism on the overall reliability of the chassis were as follows: left front > right front > left rear > right rear. By implementing the middle height with the adjustment strategy, the dynamic stress extreme value of the adjustment mechanism can be effectively reduced by 21.98%, thereby enhancing the structural stability of the chassis. Full article
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<p>4LZ-4.0 tracked combine harvester attitude-adjustment system.</p>
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<p>Composition of the chassis attitude-adjustment mechanism.</p>
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<p>Principle of chassis attitude-adjustment based on the planar linkage mechanism. (<b>a</b>) Lateral adjustment principle; (<b>b</b>) Longitudinal adjustment principle.</p>
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<p>Measurement of the hydraulic cylinder displacement and the vehicle inclination angle data. (<b>a</b>) Lateral adjustment; (<b>b</b>) Longitudinal adjustment.</p>
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<p>Measurement data on the lateral adjustment angle, the overall lifting height, and the cylinders’ displacement.</p>
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<p>Measurement data in the longitudinal adjustment angle and the cylinders’ displacement.</p>
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<p>MBD model of the combine harvester.</p>
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<p>FMBD model of the attitude-adjustment mechanism.</p>
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<p>Adjustment process of the <span class="html-italic">FMBD</span> model during the working cycle.</p>
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<p>Completed soil particle bed.</p>
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<p>FMBD–DEM bidirectional coupling process.</p>
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<p>FMBD–DEM coupled simulation models. (<b>a</b>) Track–horizontal ground; (<b>b</b>) Track–horizontal slope; (<b>c</b>) Track–vertical slope; (<b>d</b>) Track–field ridge.</p>
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<p>Mounting method of strain gauges.</p>
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<p>DH5902N dynamic stress detection system.</p>
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<p>Stress–membership function curve.</p>
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<p>Stress cloud diagram of the attitude-adjustment mechanism at different adjustment heights. (<b>a</b>) Adjustment height by 0 mm; (<b>b</b>) Adjustment height by 25 mm; (<b>c</b>) Adjustment height by 50 mm; (<b>d</b>) Adjustment height by 75 mm; (<b>e</b>) Adjustment height by 100 mm.</p>
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<p>Dynamic stress curves of each mechanism under the lowest adjustment height.</p>
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<p>Maximum stress values of each mechanism under different adjustment heights.</p>
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<p>Reliability of various chassis mechanisms and the overall chassis under different adjustment heights.</p>
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<p>Stress cloud diagram of each mechanism under different longitudinal adjustment angles. (<b>a</b>) Longitudinal adjustment of −3°; (<b>b</b>) Longitudinal adjustment of −1.5°; (<b>c</b>) Longitudinal adjustment of 0°; (<b>d</b>) Longitudinal adjustment of 2.5°; (<b>e</b>) Longitudinal adjustment of 5°.</p>
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<p>Maximum stress curve of each mechanism at a forward tilt angle of 5°.</p>
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<p>Extreme stress values of various mechanisms under different longitudinal adjustment angles.</p>
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<p>Reliability of each mechanism and overall under different longitudinal adjustment angles.</p>
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<p>Stress cloud diagram of each mechanism under different lateral adjustment angles. (<b>a</b>) Lateral adjustment of −5°; (<b>b</b>) Lateral adjustment of −2.5°; (<b>c</b>) Lateral adjustment of 0°; (<b>d</b>) Lateral adjustment of 2.5°; (<b>e</b>) Lateral adjustment of 5°.</p>
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<p>Stress cloud diagram of each mechanism under different lateral adjustment angles. (<b>a</b>) Lateral adjustment of −5°; (<b>b</b>) Lateral adjustment of −2.5°; (<b>c</b>) Lateral adjustment of 0°; (<b>d</b>) Lateral adjustment of 2.5°; (<b>e</b>) Lateral adjustment of 5°.</p>
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<p>Dynamic stress curves of each mechanism at a lateral angle of 5°.</p>
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<p>Extreme stress values of various mechanisms under different lateral adjustment angles.</p>
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<p>Reliability of various mechanisms and overall under different lateral adjustment angles.</p>
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<p>Stress cloud diagram of each mechanism at Mode 4. (<b>a</b>) 10.2 s; (<b>b</b>) 12.4 s; (<b>c</b>) 14.1 s.</p>
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<p>Stacking diagram of dynamic stress of the regulating mechanism under four different crossing modes. (<b>a</b>) Dynamic stress of the left-front rotating arm; (<b>b</b>) Dynamic stress of the left-rear rotating arm.</p>
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<p>Overall reliability of the adjustment mechanism and the chassis under four different crossing modes.</p>
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<p>Testing site for dynamic stress.</p>
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15 pages, 7182 KiB  
Article
Characterization of New Wheat-Thinopyrum intermedium Derivative Lines with Superior Genes for Stripe Rust and Powdery Mildew Resistance
by Zhihui Yu, Guangrong Li, Zhiqiang Zheng, Hongjin Wang and Zujun Yang
Plants 2024, 13(16), 2333; https://doi.org/10.3390/plants13162333 - 22 Aug 2024
Viewed by 422
Abstract
The wild species Thinopyrum intermedium (genome JJJSJSStSt) serves as a valuable germplasm resource providing novel diseases resistance and agronomically important genes for wheat improvement. Two wheat-Th. intermedium partial amphiploids, TAI7045 (2n = 56) and 78784 (2n = 56), [...] Read more.
The wild species Thinopyrum intermedium (genome JJJSJSStSt) serves as a valuable germplasm resource providing novel diseases resistance and agronomically important genes for wheat improvement. Two wheat-Th. intermedium partial amphiploids, TAI7045 (2n = 56) and 78784 (2n = 56), exhibit high resistance to stripe rust and powdery mildew, and their chromosome constitutions have been characterized. With the aim to transfer novel resistance genes from Th. intermedium, the crosses of common wheat line MY11 with TAI7045 and 78784 were produced, and their individual F2-F5 progenies were characterized using sequential non-denaturing fluorescence in situ hybridization (ND-FISH) and molecular markers. We identified a set of wheat-Th. intermedium addition lines, involving the chromosomes 1St-JS, 2St, 2St-JS, 3St, 4J, 4St, 5St, 5J.St, 6JS.J, and 7JS. Above all, the stable wheat-Th. intermedium small segmental translocation lines with chromosomes 4DS.4DL-4StL-4DL-4JL and 4DS.4DL-4StL-4DL were selected. Combining data from specific marker amplification and resistance evaluation, we mapped the gene(s) for resistance to powdery mildew and stripe rust in the 233.56–329.88 Mb region of the long arm of the 4St chromosome from the reference Th. intermedium genome. The new wheat-Th. intermedium introgressions will be used as novel germplasm for breeding purposes. Full article
(This article belongs to the Section Plant Genetics, Genomics and Biotechnology)
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<p>Sequential ND-FISH patterns of wheat-<span class="html-italic">Th. intermedium</span> partial amphiploid TAI7045 (<b>a</b>,<b>b</b>,<b>e</b>, <b>f</b>) and 78784 (<b>c</b>,<b>d</b>,<b>g</b>,<b>h</b>) with multiple probes. The probes Oligo-B11 (green) + Oligo-D (red) (<b>a</b>,<b>c</b>), Oligo-pSc119.2-1 (green) and Oligo-pTa535-1 (red) (<b>b</b>,<b>d</b>), Oligo-7v108 (green) + Oligo-Dv86 (red) (<b>e</b>,<b>g</b>), and Oligo-v03-86 (green) + Oligo-Ae369 (red) (<b>f</b>,<b>h</b>) are presented, respectively. White arrows indicate the recombinant chromosomes in wheat background, while yellow arrows indicate the translocation chromosomes between wheat and <span class="html-italic">Th. intermedium</span>. The <span class="html-italic">Th. intermedium</span> chromosomes and recombinant chromosomes of TAI7045 and 78784 are shown (<b>i</b>). The period “.” means that the breakpoint of the chromosome translocation events was located in the region of the centromere, and the hyphen “-” means that the breakpoint was located in the other regions. Bars represent 10 μm.</p>
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<p>Number of <span class="html-italic">Thinopyrum</span> chromosomes in F<sub>2</sub> generation of two crosses, TAI7045/MY11 (<b>left</b>) and 78784/MY11 (<b>right</b>).</p>
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<p>Sequential ND-FISH of the lines WT4D-1 (<b>a</b>–<b>d</b>) and WT4D-2 (<b>e</b>–<b>h</b>) by multiple probes. The probes Oligo-B11 (green) + Oligo-D (red) (<b>a</b>,<b>e</b>), Oligo-pSc119.2-1 (green) + Oligo-pTa535-1 (red) (<b>b</b>,<b>f</b>), Oligo-7v108 (green) + Oligo-Dv86 (red) (<b>c</b>,<b>g</b>), and Oligo-v03-86 (green) + Oligo-Ae369 (red) (<b>d</b>,<b>h</b>). The wheat-<span class="html-italic">Th. intermedium</span> translocation chromosomes are shown by arrows and the cut-and-paste chromosomes at the top right. Bars represent 10 μm.</p>
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<p>PCR profiling of molecular markers in wheat-<span class="html-italic">Th. intermedium</span> lines. M, molecular marker; lane 1: a wheat cultivar Chinese Spring (CS); lane 2: MY11; lane 3: TAI7045; lane 4: WT4D-1; lane 5: 78784; lane 6: WT4D-2; lane 7: nullisomic-4D tetrasomic-4B of CS; lane 8: nullisomic-4A tetrasomic-4D of CS; lane 9: X24C10 (4J/4B substitution line); lane 10: WT78-4 (4St addition line). The chromosome 4A- and 4D-specific bands are indicated in red, and the yellow arrows and blue arrows indicate the chromosome 4J-specific bands and chromosome 4St-specific bands, respectively.</p>
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<p>Physical map of the translocation chromosomes 4D-Th, WT4D-1 and WT4D-2 in comparison with 4D chromosomes. The blue molecular markers and Oligo probes indicate the deletion of 4D chromosome fragments. The 4St-specific marker C10-56 indicates the putative breakpoint of the translocation. The chromosome fragments with blue and orange backgrounds represent the introgression of chromosomes 4St and 4J, respectively.</p>
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<p>Physical location of wheat-<span class="html-italic">Th. intermedium</span> lines using rust resistance survey and molecular marker map. Stripe rust response (<b>a</b>) and powdery mildew response (<b>b</b>). The 34 4St-specific markers were screened and blasted to located on the 4StL of <span class="html-italic">Th. intermedium</span> genome sequence of V3.1 (<b>c</b>). The chromosome fragments with blue and orange backgrounds represent the introgression of chromosomes 4St and 4J, respectively.</p>
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11 pages, 2639 KiB  
Article
Relationships between Anthropometric and Strength Profiles of Streetlifting Athletes
by Giuseppe Rosaci, Davide Latini, Sandro Bartolomei and Federico Nigro
Appl. Sci. 2024, 14(16), 7172; https://doi.org/10.3390/app14167172 - 15 Aug 2024
Viewed by 577
Abstract
The aim of this study was to investigate the anthropometric characteristics of streetlifting athletes in the different body weight categories and to develop specific equations to predict the individual performance in the different exercises included in competitive programs (chin-up, dip, muscle-up and squat). [...] Read more.
The aim of this study was to investigate the anthropometric characteristics of streetlifting athletes in the different body weight categories and to develop specific equations to predict the individual performance in the different exercises included in competitive programs (chin-up, dip, muscle-up and squat). A total of 79 athletes (60 men and 19 women; age: 26.1 ± 6.4 y; body mass: 72.7 ± 13.2 kg; height: 171.7 ± 8.9 cm) were tested in accordance with the Italian National championships. Athletes were tested for anthropometry and body composition before the competition, and the performance in each lift was registered. A partial correlation of 0.47 and 0.60 was detected between arm girth and chin-up and dip performance, respectively. On the contrary, body fat was negatively correlated with the same exercises (r = −0.42). Squat performance appeared mainly determined by fat-free mass and thigh cross-sectional area, while body fat did not affect the performance in this exercise. The prediction equations developed were based on anthropometric and body composition parameters and showed near-perfect correlations with the participants’ competitive results (R2 between 0.66 and 0.90). The normative data presented in this investigation and the prediction equations developed may help coaches and practitioners in athlete evaluation and comprehension of the key factor of streetlifting performance. Full article
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<p>Somatochart of the streetlifting athletes’ somatotypes for sex and body weight categories. BW = Body Weight.</p>
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20 pages, 16326 KiB  
Article
Multiplatform Computer Vision System to Support Physical Fitness Assessments in Schoolchildren
by José Sulla-Torres, Bruno Santos-Pamo, Fabrizzio Cárdenas-Rodríguez, Javier Angulo-Osorio, Rossana Gómez-Campos and Marco Cossio-Bolaños
Appl. Sci. 2024, 14(16), 7140; https://doi.org/10.3390/app14167140 - 14 Aug 2024
Viewed by 447
Abstract
Currently, the lack of physical activity can lead to health problems, with the increase in obesity in children between 8 and 18 years old being of particular interest because it is a formative stage. One of the aspects of trying to solve this [...] Read more.
Currently, the lack of physical activity can lead to health problems, with the increase in obesity in children between 8 and 18 years old being of particular interest because it is a formative stage. One of the aspects of trying to solve this problem is the need for a standardized, less subjective, and more efficient method of evaluating physical condition in these children compared to traditional approaches. Objective: Develop a multiplatform based on computer vision technology that allows the evaluation of the physical fitness of schoolchildren using smartphones. Methodology: A descriptive cross-sectional study was carried out on schoolchildren aged 8 to 18 years of both sexes. The sample was 228 schoolchildren (128 boys and 108 girls). Anthropometric measurements of weight, height, and waist circumference were evaluated. Body mass index (BMI) was calculated. Four physical tests were evaluated: flexibility (sit and reach), horizontal jump (explosive strength), biceps curl (right arm strength resistance), and sit-ups (abdominal muscle resistance). With the information collected traditionally and by filming the physical tests, a computer vision system was developed to evaluate physical fitness in schoolchildren. Results: The implemented system obtained an acceptable level of precision, reaching 94% precision in field evaluations and a percentage greater than 95% in laboratory evaluations for testing. The developed mobile application also obtained a high accuracy percentage, greater than 95% in two tests and close to 85% in the remaining two. Finally, the Systematic Software Quality Model was used to determine user satisfaction with the presented prototype. Regarding usability, a satisfaction level of 97% and a reliability level of 100% was obtained. Conclusion: Compared to traditional evaluation and computer vision, the proposal was satisfactorily validated. These results were obtained using the Expanded Systematic Software Quality Model, which reached an “advanced” quality level, satisfying functionality, usability, and reliability characteristics. This advance demonstrates that the integration of computer vision is feasible, highly effective in the educational context, and applicable in the evaluations of physical education classes. Full article
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<p>View of body tracking on Unity while the bicep curl test is running.</p>
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<p>Frontal distribution of the test.</p>
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<p>Joint map provided by LightBuzz.</p>
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<p>Vector representation of the sit-up test.</p>
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<p>Vector representation of the biceps curls test.</p>
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<p>Example of sit-up test evaluation.</p>
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<p>Example of biceps flexion test evaluation.</p>
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<p>Example of horizontal jump test evaluation without impulse.</p>
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<p>Flexibility bending test evaluation example.</p>
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<p>Mobile application initial interfaces.</p>
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<p>Mobile application execution test interfaces.</p>
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<p>Abdominal flexion test by control and software.</p>
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<p>Biceps curl test by control and software.</p>
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<p>Flexibility test by control and software.</p>
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<p>Horizontal jump test without impulse by control and software.</p>
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11 pages, 645 KiB  
Article
Migration Challenges and Their Impact on the Primary Healthcare System—A Qualitative Research
by Olga Partyka, Monika Pajewska, Aleksandra Czerw, Katarzyna Sygit, Oleh Lyubinets, Tomasz Banaś, Krzysztof Małecki, Elżbieta Grochans, Szymon Grochans, Anna Cybulska, Daria Schneider-Matyka, Elżbieta Cipora, Mateusz Kaczmarski, Krzysztof Sośnicki, Grażyna Dykowska, Zofia Sienkiewicz, Łukasz Strzępek, Ewa Bandurska, Weronika Ciećko, Jarosław Drobnik, Piotr Pobrotyn, Aleksandra Sierocka, Michał Marczak and Remigiusz Kozlowskiadd Show full author list remove Hide full author list
Healthcare 2024, 12(16), 1607; https://doi.org/10.3390/healthcare12161607 - 12 Aug 2024
Viewed by 823
Abstract
In 2020 it is estimated that 281 million people were international migrants. Migrants constitute a potentially vulnerable population in terms of facing discrimination, poor living and housing conditions, and insufficient access to healthcare services. Due to the armed conflict in Ukraine in 2022, [...] Read more.
In 2020 it is estimated that 281 million people were international migrants. Migrants constitute a potentially vulnerable population in terms of facing discrimination, poor living and housing conditions, and insufficient access to healthcare services. Due to the armed conflict in Ukraine in 2022, almost 10 million people crossed the Polish border within a year of the outbreak of the conflict. The objective of this paper is to present the use of primary healthcare services by people migrating from Ukraine to Poland and identify the barriers in access to healthcare by this group of persons. This study used a qualitative research technique in the form of an expert interview using individual in-depth interviews (IDI). The study group consisted of professionally active primary healthcare providers (doctors, nurses, and facility managers) in Poland. Research was carried out in the areas regarding the availability of healthcare, the potential threats and challenges, and possible system solutions. The results showed that the most common cause for doctor’s appointments among migrants are respiratory infections, including COVID-19. Many cases were related to back pain, mainly resulting from the physical work of the patients. Additionally, some barriers to access and the provision of healthcare services for patients from Ukraine were identified. The majority (75%) of respondents indicated language as a significant barrier when providing services. Based on the study results, we recommend creating a dedicated website and telephone hotline for this group of persons as well as the use of traditional media to distribute information about access to healthcare services. It is also essential to focus on assistance for older people, since they may experience more difficulties with language and navigating the healthcare system. Full article
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<p>Challenges, barriers, and methods of using services by patients from Ukraine based on the authors’ research of healthcare providers.</p>
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32 pages, 11560 KiB  
Article
Global Stability Behavior of Pre-Cast Cable-Stiffened Steel Columns
by Ying Zhao, Junxiu Hu, Shushuang Song, Tianhao Zhang, Pengcheng Li and Gang Xiong
Buildings 2024, 14(8), 2485; https://doi.org/10.3390/buildings14082485 - 12 Aug 2024
Viewed by 461
Abstract
Cable-stiffened steel columns (CSSC) have a high load-carrying capacity and strong stability compared to ordinary steel columns. In practical engineering, the connection between the crossarm and main column of a CSSC is usually welded. However, the welding-residual stress adversely affects the steel column. [...] Read more.
Cable-stiffened steel columns (CSSC) have a high load-carrying capacity and strong stability compared to ordinary steel columns. In practical engineering, the connection between the crossarm and main column of a CSSC is usually welded. However, the welding-residual stress adversely affects the steel column. In this study, pre-cast CSSCs, with a pinned connection between the crossarm and main column, are presented. The new type of pre-cast CSSCs avoid the welding-residual and are easy to disassemble. A model test and numerical analysis of its global stability behavior under eccentric compression is conducted. Based on the analysis, the buckling modes of these columns are defined and a method for determining the governing imperfection in a nonlinear buckling analysis is proposed. The effects of slenderness ratio, cross-arm length, cable diameter, and other parameters on the load-carrying capacities of the columns are investigated using the proposed method. The results of this study can be used as a reference for the engineering designs and specifications of pre-cast CSSCs. Full article
(This article belongs to the Special Issue Research on Industrialization and Intelligence in Building Structures)
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<p>Pre-cast CSSC model.</p>
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<p>Material properties experiment of crossarms and columns [<a href="#B29-buildings-14-02485" class="html-bibr">29</a>].</p>
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<p>Scheme for the cable system property experiment.</p>
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<p>Stress versus strain curves of the cable systems.</p>
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<p>Measurement method for initial geometric imperfection [<a href="#B29-buildings-14-02485" class="html-bibr">29</a>].</p>
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<p>Initial out-of-straight of pre-cast CSSCs: (<b>a</b>) 250 mm; (<b>b</b>) 200 mm and 150 mm.</p>
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<p>Measured initial pretension forces of the cables: (<b>a</b>) 250 mm; (<b>b</b>) 200 mm and 150 mm.</p>
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<p>Steel column loading scheme: (<b>a</b>) layout of the measuring points; (<b>b</b>) test setup.</p>
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<p>Comparison between various types of columns [<a href="#B29-buildings-14-02485" class="html-bibr">29</a>]: (<b>a</b>) load versus displacement curve; (<b>b</b>) buckling mode.</p>
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<p>Load–deflection curves of the pre-cast CSSCs: (<b>a</b>) 250-0-T; (<b>b</b>) 250-20-T; (<b>c</b>) 250-0-0.5T; (<b>d</b>) 250-20-0.5T.</p>
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<p>Load–deflection curves of the pre-cast CSSCs: (<b>a</b>) 250-0-T; (<b>b</b>) 250-20-T; (<b>c</b>) 250-0-0.5T; (<b>d</b>) 250-20-0.5T.</p>
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<p>Buckling modes of the pre-cast CSSCs.</p>
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<p>Load–strain curves for different points on the columns: (<b>a</b>) 250-0-T; (<b>b</b>) 250-20-T; (<b>c</b>) 250-0-0.5T; (<b>d</b>) 250-20-0.5T.</p>
Full article ">Figure 13
<p>Cable force–displacement curves of the main column: (<b>a</b>) 250-0-T; (<b>b</b>) 250-20-T; (<b>c</b>) 250-0-0.5T; (<b>d</b>) 250-20-0.5T.</p>
Full article ">Figure 13 Cont.
<p>Cable force–displacement curves of the main column: (<b>a</b>) 250-0-T; (<b>b</b>) 250-20-T; (<b>c</b>) 250-0-0.5T; (<b>d</b>) 250-20-0.5T.</p>
Full article ">Figure 14
<p>Load–deflection curves of the pre-cast CSSCs: (<b>a</b>) 200-0-T; (<b>b</b>) 200-20-T; (<b>c</b>) 150-0-T; (<b>d</b>) 150-20-T.</p>
Full article ">Figure 15
<p>Buckling modes of the columns.</p>
Full article ">Figure 16
<p>Load–strain curves for different points on the steel columns: (<b>a</b>) 200-0-T; (<b>b</b>) 200-20-T; (<b>c</b>) 150-0-T; (<b>d</b>) 150-20-T.</p>
Full article ">Figure 16 Cont.
<p>Load–strain curves for different points on the steel columns: (<b>a</b>) 200-0-T; (<b>b</b>) 200-20-T; (<b>c</b>) 150-0-T; (<b>d</b>) 150-20-T.</p>
Full article ">Figure 17
<p>Force–displacement curves of the cables: (<b>a</b>) 200-0-T; (<b>b</b>) 200-20-T; (<b>c</b>) 150-0-T; (<b>d</b>) 150-20-T.</p>
Full article ">Figure 17 Cont.
<p>Force–displacement curves of the cables: (<b>a</b>) 200-0-T; (<b>b</b>) 200-20-T; (<b>c</b>) 150-0-T; (<b>d</b>) 150-20-T.</p>
Full article ">Figure 18
<p>Finite element model: (<b>a</b>) model diagram; (<b>b</b>) finite element mesh.</p>
Full article ">Figure 19
<p>Pre-cast CSSC model under eccentric load.</p>
Full article ">Figure 20
<p>Typical buckling modes of the pre-casted CSSCs under eccentric loads: (<b>a</b>) Mode 1; (<b>b</b>) Mode 2.</p>
Full article ">Figure 21
<p>Changes in linear buckling loads (<math display="inline"><semantics> <mrow> <msup> <mi>P</mi> <mi>c</mi> </msup> </mrow> </semantics></math>) with changes in the cross-arm length for different cable diameters: (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mi>φ</mi> <mi>s</mi> </msub> <mo>=</mo> <mn>1.6</mn> <mrow> <mo> </mo> <mi>mm</mi> </mrow> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mi>φ</mi> <mi>s</mi> </msub> <mo>=</mo> <mn>3.2</mn> <mrow> <mo> </mo> <mi>mm</mi> </mrow> </mrow> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <mrow> <msub> <mi>φ</mi> <mi>s</mi> </msub> <mo>=</mo> <mn>4.8</mn> <mrow> <mo> </mo> <mi>mm</mi> </mrow> </mrow> </semantics></math>; (<b>d</b>) <math display="inline"><semantics> <mrow> <msub> <mi>φ</mi> <mi>s</mi> </msub> <mo>=</mo> <mn>6.4</mn> <mrow> <mo> </mo> <mi>mm</mi> </mrow> </mrow> </semantics></math>; (<b>e</b>) <math display="inline"><semantics> <mrow> <msub> <mi>φ</mi> <mi>s</mi> </msub> <mo>=</mo> <mn>8.0</mn> <mrow> <mo> </mo> <mi>mm</mi> </mrow> </mrow> </semantics></math>.</p>
Full article ">Figure 22
<p>Initial deformations of the main column for different imperfection directions: (<b>a</b>) +x-direction; (<b>b</b>) −x-direction.</p>
Full article ">Figure 23
<p>Influence of imperfection shape for different eccentricity ratios: (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>ε</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <mi>ε</mi> <mo>=</mo> <mn>0.5</mn> </mrow> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <mrow> <mi>ε</mi> <mo>=</mo> <mn>1.0</mn> </mrow> </semantics></math>; (<b>d</b>) <math display="inline"><semantics> <mrow> <mi>ε</mi> <mo>=</mo> <mn>1.5</mn> </mrow> </semantics></math>; (<b>e</b>) <math display="inline"><semantics> <mrow> <mi>ε</mi> <mo>=</mo> <mn>2.0</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 24
<p>Influence of the imperfection shape for different cross-arm lengths: (<b>a</b>) <span class="html-italic">a</span>/<span class="html-italic">L</span> = 0.05; (<b>b</b>) <span class="html-italic">a</span>/<span class="html-italic">L</span> = 0.075; (<b>c</b>) <span class="html-italic">a</span>/<span class="html-italic">L</span> = 0.1; (<b>d</b>) <span class="html-italic">a</span>/<span class="html-italic">L</span> = 0.125; (<b>e</b>) <span class="html-italic">a</span>/<span class="html-italic">L</span> = 0.15.</p>
Full article ">Figure 25
<p>Influence of the imperfection shape for different cable diameters: (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mi>φ</mi> <mi>s</mi> </msub> <mo>=</mo> <mn>1.6</mn> <mrow> <mo> </mo> <mi>mm</mi> </mrow> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mi>φ</mi> <mi>s</mi> </msub> <mo>=</mo> <mn>3.2</mn> <mrow> <mo> </mo> <mi>mm</mi> </mrow> </mrow> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <mrow> <msub> <mi>φ</mi> <mi>s</mi> </msub> <mo>=</mo> <mn>4.8</mn> <mrow> <mo> </mo> <mi>mm</mi> </mrow> </mrow> </semantics></math>; (<b>d</b>) <math display="inline"><semantics> <mrow> <msub> <mi>φ</mi> <mi>s</mi> </msub> <mo>=</mo> <mn>6.4</mn> <mrow> <mo> </mo> <mi>mm</mi> </mrow> </mrow> </semantics></math>; (<b>e</b>) <math display="inline"><semantics> <mrow> <msub> <mi>φ</mi> <mi>s</mi> </msub> <mo>=</mo> <mn>8.0</mn> <mrow> <mo> </mo> <mi>mm</mi> </mrow> </mrow> </semantics></math>.</p>
Full article ">Figure 26
<p>Load–compression curves of the structures for different slenderness ratios: (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>ε</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <mi>ε</mi> <mo>=</mo> <mn>0.5</mn> </mrow> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <mrow> <mi>ε</mi> <mo>=</mo> <mn>1.0</mn> </mrow> </semantics></math>; (<b>d</b>) <math display="inline"><semantics> <mrow> <mi>ε</mi> <mo>=</mo> <mn>1.5</mn> </mrow> </semantics></math>; (<b>e</b>) <math display="inline"><semantics> <mrow> <mi>ε</mi> <mo>=</mo> <mn>2.0</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 26 Cont.
<p>Load–compression curves of the structures for different slenderness ratios: (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>ε</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <mi>ε</mi> <mo>=</mo> <mn>0.5</mn> </mrow> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <mrow> <mi>ε</mi> <mo>=</mo> <mn>1.0</mn> </mrow> </semantics></math>; (<b>d</b>) <math display="inline"><semantics> <mrow> <mi>ε</mi> <mo>=</mo> <mn>1.5</mn> </mrow> </semantics></math>; (<b>e</b>) <math display="inline"><semantics> <mrow> <mi>ε</mi> <mo>=</mo> <mn>2.0</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 27
<p>Load–compression curves of the pre-casted CSSCs for different eccentricity ratios: (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>ε</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <mi>ε</mi> <mo>=</mo> <mn>0.5</mn> </mrow> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <mrow> <mi>ε</mi> <mo>=</mo> <mn>1.0</mn> </mrow> </semantics></math>; (<b>d</b>) <math display="inline"><semantics> <mrow> <mi>ε</mi> <mo>=</mo> <mn>1.5</mn> </mrow> </semantics></math>; (<b>e</b>) <math display="inline"><semantics> <mrow> <mi>ε</mi> <mo>=</mo> <mn>2.0</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 27 Cont.
<p>Load–compression curves of the pre-casted CSSCs for different eccentricity ratios: (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>ε</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <mi>ε</mi> <mo>=</mo> <mn>0.5</mn> </mrow> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <mrow> <mi>ε</mi> <mo>=</mo> <mn>1.0</mn> </mrow> </semantics></math>; (<b>d</b>) <math display="inline"><semantics> <mrow> <mi>ε</mi> <mo>=</mo> <mn>1.5</mn> </mrow> </semantics></math>; (<b>e</b>) <math display="inline"><semantics> <mrow> <mi>ε</mi> <mo>=</mo> <mn>2.0</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 28
<p>Ultimate load-carrying capacities of the columns for different initial imperfection sizes.</p>
Full article ">Figure 29
<p>Strength reduction coefficients of the pre-casted CSSCs.</p>
Full article ">Figure 30
<p>Load–compression curves of the columns for different initial prestress levels: (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>ε</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <mi>ε</mi> <mo>=</mo> <mn>0.5</mn> </mrow> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <mrow> <mi>ε</mi> <mo>=</mo> <mn>1.0</mn> </mrow> </semantics></math>; (<b>d</b>) <math display="inline"><semantics> <mrow> <mi>ε</mi> <mo>=</mo> <mn>1.5</mn> </mrow> </semantics></math>; (<b>e</b>) <math display="inline"><semantics> <mrow> <mi>ε</mi> <mo>=</mo> <mn>2.0</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 31
<p>Load–displacement curves of the columns for different cross-arm lengths: (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>ε</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <mi>ε</mi> <mo>=</mo> <mn>0.5</mn> </mrow> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <mrow> <mi>ε</mi> <mo>=</mo> <mn>1.0</mn> </mrow> </semantics></math>; (<b>d</b>) <math display="inline"><semantics> <mrow> <mi>ε</mi> <mo>=</mo> <mn>1.5</mn> </mrow> </semantics></math>; (<b>e</b>) <math display="inline"><semantics> <mrow> <mi>ε</mi> <mo>=</mo> <mn>2.0</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 32
<p>Load–compression curves of columns for different cable diameters: (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>ε</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <mi>ε</mi> <mo>=</mo> <mn>0.5</mn> </mrow> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <mrow> <mi>ε</mi> <mo>=</mo> <mn>1.0</mn> </mrow> </semantics></math>; (<b>d</b>) <math display="inline"><semantics> <mrow> <mi>ε</mi> <mo>=</mo> <mn>1.5</mn> </mrow> </semantics></math>; (<b>e</b>) <math display="inline"><semantics> <mrow> <mi>ε</mi> <mo>=</mo> <mn>2.0</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 32 Cont.
<p>Load–compression curves of columns for different cable diameters: (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>ε</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <mi>ε</mi> <mo>=</mo> <mn>0.5</mn> </mrow> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <mrow> <mi>ε</mi> <mo>=</mo> <mn>1.0</mn> </mrow> </semantics></math>; (<b>d</b>) <math display="inline"><semantics> <mrow> <mi>ε</mi> <mo>=</mo> <mn>1.5</mn> </mrow> </semantics></math>; (<b>e</b>) <math display="inline"><semantics> <mrow> <mi>ε</mi> <mo>=</mo> <mn>2.0</mn> </mrow> </semantics></math>.</p>
Full article ">
9 pages, 241 KiB  
Article
The Association between Anthropometric Measurements and Body Composition with Hand Grip Strength among the Elderly Population in Indonesia
by Nina Kemala Sari, Stepvia Stepvia and Muhana Fawwazy Ilyas
J. Clin. Med. 2024, 13(16), 4697; https://doi.org/10.3390/jcm13164697 - 10 Aug 2024
Viewed by 522
Abstract
Background/Objectives: Hand grip strength (HGS) is a crucial measure for evaluating muscle function and general physical ability, and it may be associated with several diseases. Previous studies have demonstrated inconsistent associations between anthropometric measurement and body composition with HGS. This study aims [...] Read more.
Background/Objectives: Hand grip strength (HGS) is a crucial measure for evaluating muscle function and general physical ability, and it may be associated with several diseases. Previous studies have demonstrated inconsistent associations between anthropometric measurement and body composition with HGS. This study aims to investigate the association between anthropometric measurement and body composition with HGS in the elderly population residing in Indonesia. Methods: This is a cross-sectional study on older adults aged between 60 and 82 years who live in the community. Anthropometric parameters assessed in this study comprised the body mass index (BMI), mid-upper arm circumference (MUAC), calf circumference (CC), and waist circumference (WC). Subsequently, body composition measurements, including fat mass (FM), fat-free mass (FFM), muscle mass (MM), skeletal muscle mass (SMM), and the appendicular skeletal mass index (ASMI), were assessed using a body composition analyzer. Last, the measurement of HGS was conducted using a hand dynamometer. Results: A total of 109 participants were involved in this study. Our study demonstrates a significant association between anthropometric parameters, namely CC and HGS. Subsequently, several body composition parameters, including FFM, SMM, ASMI, and MM in the four extremities, are also significantly associated with HGS. However, in a multivariate analysis, only CC and FFM were able to significantly predict HGS. Conclusions: Improving CC and maintaining FFM may enhance muscle strength in older adults. This suggests that targeted exercise and nutrition programs could increase muscle mass and strength, thereby mitigating age-related decline and improving quality of life. Full article
(This article belongs to the Section Clinical Rehabilitation)
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