Search Results (891)

The long-range migration of monarch butterflies, extended over 4000 km, is not well understood. Monarchs experience varying density conditions during migration, ranging as high as 3000 m, where the air density is much lower than at sea level. In this study, we test the hypothesis that the aerodynamic performance of monarchs improves at reduced density conditions by considering the fluid–structure interaction of chordwise flexible wings. A well-validated, fully coupled Navier–Stokes/structural dynamics solver was used to illustrate the interplay between wing motion, aerodynamics, and structural flexibility in forward flight. The wing density and elastic modulus were measured from real monarch wings and prescribed as inputs to the aeroelastic framework. Our results show that sufficient lift is generated to offset the butterfly weight at higher altitudes, aided by the wake-capture mechanism, which is a nonlinear wing–wake interaction mechanism, commonly seen for hovering animals. The mean total power, defined as the sum of the aerodynamic and inertial power, decreased by 36% from the sea level to the condition at 3000 m. Decreasing power with altitude, while maintaining the same equilibrium lift, suggests that the butterflies generate lift more efficiently at higher altitudes. Full article

Parkinson’s disease (PD) affects a large proportion of elderly people. Symptoms include tremors, slow movement, rigid muscles, and trouble speaking. With the aging of the developed world’s population, this number is expected to rise. The early detection of PD and avoiding its severe consequences require a precise and efficient system. Our goal is to create an accurate AI model that can identify PD using human voices. We developed a transformer-based method for detecting PD by retrieving dysphonia measures from a subject’s voice recording. It is uncommon to use a neural network (NN)-based solution for tabular vocal characteristics, but it has several advantages over a tree-based approach, including compatibility with continuous learning and the network’s potential to be linked with an image/voice encoder for a more accurate multi modal solution, shifting SOTA approach from tree-based to a neural network (NN) is crucial for advancing research in multimodal solutions. Our method outperforms the state of the art (SOTA), namely Gradient-Boosted Decision Trees (GBDTs), by at least 1% AUC, and the precision and recall scores are also improved. We additionally offered an XgBoost-based feature-selection method and a fully connected NN layer technique for including continuous dysphonia measures, in addition to the solution network. We also discussed numerous important discoveries relating to our suggested solution and deep learning (DL) and its application to dysphonia measures, such as how a transformer-based network is more resilient to increased depth compared to a simple MLP network. The performance of the proposed approach and conventional machine learning techniques such as MLP, SVM, and Random Forest (RF) have also been compared. A detailed performance comparison matrix has been added to this article, along with the proposed solution’s space and time complexity. Full article

Biological principles draw attention to service robotics because of similar concepts when robots operate various tasks. Bioinspired perception is significant for robotic perception, which is inspired by animals’ awareness of the environment. This paper reviews the bioinspired perception and navigation of service robots in indoor environments, which are popular applications of civilian robotics. The navigation approaches are classified by perception type, including vision-based, remote sensing, tactile sensor, olfactory, sound-based, inertial, and multimodal navigation. The trend of state-of-art techniques is moving towards multimodal navigation to combine several approaches. The challenges in indoor navigation focus on precise localization and dynamic and complex environments with moving objects and people. Full article

In an emergency situation, fast and efficient logistics and distribution are essential for minimizing the impact of a disaster and for safeguarding property. When selecting a distribution center location, time satisfaction needs to be considered, in addition to the general cost factor. The improved jellyfish search algorithm (CIJS), which simulates the bionics of jellyfish foraging, is applied to solve the problem of an emergency logistics and distribution center site selection model considering time satisfaction. The innovation of the CIJS is mainly reflected in two aspects. First, when initializing the population, the two-level logistic map method is used instead of the original logistic map method to improve the diversity and uniform distribution of the population. Second, in the jellyfish search process, a Cauchy strategy is introduced to determine the moving distance of internal motions, which improves the global search capability and prevents the search from falling into local optimal solutions. The superiority of the improved algorithm was verified by testing 20 benchmark functions and applying them to site selection problems of different dimensions. The performance of the CIJS was compared to that of heuristic algorithms through the iterative convergence graph of the algorithm. The experimental results show that the CIJS has higher solution accuracy and faster solution speed than PSO, the WOA, and JS. Full article

The Arithmetic Optimization Algorithm (AOA) is a meta-heuristic algorithm inspired by mathematical operators, which may stagnate in the face of complex optimization issues. Therefore, the convergence and accuracy are reduced. In this paper, an AOA variant called ASFAOA is proposed by integrating a double-opposite learning mechanism, an adaptive spiral search strategy, an offset distribution estimation strategy, and a modified cosine acceleration function formula into the original AOA, aiming to improve the local exploitation and global exploration capability of the original AOA. In the proposed ASFAOA, a dual-opposite learning strategy is utilized to enhance population diversity by searching the problem space a lot better. The spiral search strategy of the tuna swarm optimization is introduced into the addition and subtraction strategy of AOA to enhance the AOA’s ability to jump out of the local optimum. An offset distribution estimation strategy is employed to effectively utilize the dominant population information for guiding the correct individual evolution. In addition, an adaptive cosine acceleration function is proposed to perform a better balance between the exploitation and exploration capabilities of the AOA. To demonstrate the superiority of the proposed ASFAOA, two experiments are conducted using existing state-of-the-art algorithms. First, The CEC 2017 benchmark function was applied with the aim of evaluating the performance of ASFAOA on the test function through mean analysis, convergence analysis, stability analysis, Wilcoxon signed rank test, and Friedman’s test. The proposed ASFAOA is then utilized to solve the wireless sensor coverage problem and its performance is illustrated by two sets of coverage problems with different dimensions. The results and discussion show that ASFAOA outperforms the original AOA and other comparison algorithms. Therefore, ASFAOA is considered as a useful technique for practical optimization problems. Full article

This study aimed to determine the reinforcing effect of two weight ratios of Gum Arabic (GA) natural biopolymer, i.e., 0.5% and 1.0% in the powdered composition of glass ionomer luting cement. GA powder was oxidized and GA-reinforced GIC in 0.5 and 1.0 wt.% formulations were prepared in rectangular bars using two commercially available GIC luting materials (Medicem and Ketac Cem Radiopaque). The control groups of both materials were prepared as such. The effect of reinforcement was evaluated in terms of microhardness, flexural strength (FS), fracture toughness (FT), and tensile strength (TS). The internal porosity and water contact angle formation on the study samples were also evaluated. Film thickness was measured to gauge the effect of micron-sized GA powder in GA–GIC composite. Paired sample t-tests were conducted to analyze data for statistical significance (p < 0.05). The experimental groups of both materials containing 0.5 wt.% GA–GIC significantly improved FS, FT, and TS compared to their respective control groups. However, the microhardness significantly decreased in experimental groups of both cements compared to their respective control groups. The addition of GA powder did not cause a significant increase in film thickness and the water contact angle of both 0.5 and 1.0 wt.% GA–GIC formulations were less than 90^o. Interestingly, the internal porosity of 0.5 wt.% GA–GIC formulations in both materials were observed less compared to their respective control groups. The significantly higher mechanical properties and low porosity in 0.5 wt.% GA–GIC formulations compared to their respective control group indicate that reinforcing GA powder with 0.5 wt.% in GIC might be promising in enhancing the mechanical properties of GIC luting materials. Full article

This study proposes a design approach and the development of a low-power planar biped robot named YU-Bibot. The kinematic structure of the robot consists of six independently driven axes, and it weighs approximately 20 kg. Based on biomimetics, the robot dimensions were selected as the average anthropomorphic dimensions of the human lower extremities. The optimization of the mechanical design and actuator selection of the robot was based on the results of parametric simulations. The natural human walking gait was mimicked as a walking pattern in these simulations. As a result of the optimization, a low power-to-weight ratio of 30 W/kg was obtained. The drive system of the robot joints consists of servo-controlled brushless DC motors with reduction gears and additional bevel gears at the knee and ankle joints. The robot features spring-supported knee and ankle joints that counteract the robot’s weight and compensate for the backlash present in these joints. The robot is constrained to move only in the sagittal plane by using a lateral support structure. The robot’s feet are equipped with low-cost, force-sensitive resistor (FSR)-type sensors for monitoring ground contact and zero-moment point (ZMP) criterion. The experimental results indicate that the proposed robot mechanism can follow the posture commands accurately and demonstrate locomotion at moderate stability. The proposed parametric natural gait simulation-based design approach and the resulting biped robot design with a low power/weight ratio are the main contributions of this study. Full article

Sea cucumber manual monitoring and fishing present various issues, including high expense and high risk. Meanwhile, compared to underwater bionic robots, employing autonomous underwater robots for sea cucumber monitoring and capture also has drawbacks, including low propulsion efficiency and significant noise. Therefore, this paper is concerned with the design of a robotic manta ray for sea cucumber recognition, localization, and approach. First, the developed robotic manta ray prototype and the system framework applied to real-time target search are elaborated. Second, by improved YOLOv5 object detection and binocular stereo-matching algorithms, precise recognition and localization of sea cucumbers are achieved. Thirdly, the motion controller is proposed for autonomous 3D monitoring tasks such as depth control, direction control, and target approach motion. Finally, the capabilities of the robot are validated through a series of measurements. Experimental results demonstrate that the improved YOLOv5 object detection algorithm achieves detection accuracies ([email protected]) of 88.4% and 94.5% on the URPC public dataset and self-collected dataset, respectively, effectively recognizing and localizing sea cucumbers. Control experiments were conducted, validating the effectiveness of the robotic manta ray’s motion toward sea cucumbers. These results highlight the robot’s capabilities in visual perception, target localization, and approach and lay the foundation to explore a novel solution for intelligent monitoring and harvesting in the aquaculture industry. Full article

The formation of bone in a bone defect is accomplished by osteoblasts, while the over activation of fibroblasts promotes fibrosis. However, it is not clear how the extracellular matrix stiffness of the bone-regeneration microenvironment affects the function of osteoblasts and fibroblasts. This study aim to investigate the effect of bone-regeneration microenvironment stiffness on cell adhesion, cell proliferation, cell differentiation, synthesizing matrix ability and its potential mechanisms in mechanotransduction, in pre-osteoblasts and fibroblasts. Polyacrylamide substrates mimicking the matrix stiffness of different stages of the bone-healing process (15 kPa, mimic granulation tissue; 35 kPa, mimic osteoid; 150 kPa, mimic calcified bone matrix) were prepared. Mouse pre-osteoblasts MC3T3-E1 and mouse fibroblasts NIH3T3 were plated on three types of substrates, respectively. There were significant differences in the adhesion of pre-osteoblasts and fibroblasts on different polyacrylamide substrates. Runx2 expression increased with increasing substrate stiffness in pre-osteoblasts, while no statistical differences were found in the Acta2 expression in fibroblasts on three substrates. OPN expression in pre-osteoblasts, as well as Fn1 and Col1a1 expression in fibroblasts, decreased with increasing stiffness. The difference between the cell traction force generated by pre-osteoblasts and fibroblasts on substrates was also found. Our results indicated that substrate stiffness is a potent regulator of pre-osteoblasts and fibroblasts with the ability of promoting osteogenic differentiation of pre-osteoblasts, while having no effect on myofibroblast differentiation of fibroblasts. Full article

With the rapid development of enabling technologies like VR and AR, we human beings are on the threshold of the ubiquitous human-centric intelligence era. 6G is believed to be an indispensable cornerstone for efficient interaction between humans and computers in this promising vision. 6G is supposed to boost many human-centric applications due to its unprecedented performance improvements compared to 5G and before. However, challenges are still to be addressed, including but not limited to the following six aspects: Terahertz and millimeter-wave communication, low latency and high reliability, energy efficiency, security, efficient edge computing and heterogeneity of services. It is a daunting job to fit traditional analytical methods into these problems due to the complex architecture and highly dynamic features of ubiquitous interactive 6G systems. Fortunately, deep learning can circumvent the interpretability issue and train tremendous neural network parameters, which build mapping relationships from neural network input (status and specific requirements of a 6G application) to neural network output (settings to satisfy the requirements). Deep learning methods can be an efficient alternative to traditional analytical methods or even conquer unresolvable predicaments of analytical methods. We review representative deep learning solutions to the aforementioned six aspects separately and focus on the principles of fitting a deep learning method into specific 6G issues. Based on this review, our main contributions are highlighted as follows. (i) We investigate the representative works in a systematic view and find out some important issues like the vital role of deep reinforcement learning in the 6G context. (ii) We point out solutions to the lack of training data in 6G communication context. (iii) We reveal the relationship between traditional analytical methods and deep learning, in terms of 6G applications. (iv) We identify some frequently used efficient techniques in deep-learning-based 6G solutions. Finally, we point out open problems and future directions. Full article

The detection of multi-class small objects poses a significant challenge in the field of computer vision. While the original YOLOv5 algorithm is more suited for detecting full-scale objects, it may not perform optimally for this specific task. To address this issue, we proposed MC-YOLOv5, an algorithm specifically designed for multi-class small object detection. Our approach incorporates three key innovations: (1) the application of an improved CB module during feature extraction to capture edge information that may be less apparent in small objects, thereby enhancing detection precision; (2) the introduction of a new shallow network optimization strategy (SNO) to expand the receptive field of convolutional layers and reduce missed detections in dense small object scenarios; and (3) the utilization of an anchor frame-based decoupled head to expedite training and improve overall efficiency. Extensive evaluations on VisDrone2019, Tinyperson, and RSOD datasets demonstrate the feasibility of MC-YOLOv5 in detecting multi-class small objects. Taking VisDrone2019 dataset as an example, our algorithm outperforms the original YOLOv5L with improvements observed across various metrics: mAP50 increased by 8.2%, mAP50-95 improved by 5.3%, F1 score increased by 7%, inference time accelerated by 1.8 ms, and computational requirements reduced by 35.3%. Similar performance gains were also achieved on other datasets. Overall, our findings validate MC-YOLOv5 as a viable solution for accurate multi-class small object detection. Full article

In this paper, the ballistic performance of a multilayered composite inspired by the structural characteristics of nacre is numerically investigated using finite element (FE) simulations. Nacre is a natural composite material found in the shells of some marine mollusks, which has remarkable toughness due to its hierarchical layered structure. The bioinspired nacre-like composites investigated here were made of five wavy aluminum alloy 7075-T651 (AA7075) layers composed of ~1.1-mm thick square tablets bonded together with toughened epoxy resin. Two composite configurations with continuous layers (either wavy or flat) were also studied. The ballistic performance of the composite plates was compared to that of a bulk monolithic AA7075 plate. The ballistic impact was simulated in the 300–600 m/s range using two types of spherical projectiles, i.e., rigid and elastoplastic. The results showed that the nacre plate exhibited improved ballistic performance compared to the bulk plate and the plates with continuous layers. The structural design of the nacre plate improved the ballistic performance by producing a more ductile failure and enabling localized energy absorption via the plastic deformation of the tablets and the globalized energy dissipation due to interface debonding and friction. All the plate configurations exhibited a better ballistic performance when impacted by an elastoplastic projectile compared to a rigid one, which is explained by the projectile plastic deformation absorbing some of the impact energy and the enlarged contact area between the projectile and the plates producing more energy absorption by the plates. Full article

This paper presents the development, modeling, and control of L03, an underactuated 3D bipedal robot with symmetrical hips and straight legs. This innovative design requires only five actuators, two for the legs and three for the hips. This paper is divided into three parts: (1) mechanism design and kinematic analysis; (2) trajectory planning for the center of mass and foot landing points based on the Divergent Component of Motion (DCM), enabling lateral and forward walking capabilities for the robot; and (3) gait stability analysis through prototype experiments. The primary focus of this study is to explore the application of underactuated symmetrical designs and determine the number of motors required to achieve omnidirectional movement of a bipedal robot. Our simulation and experimental results demonstrate that L03 achieves simple walking with a stable and consistent gait. Due to its lightweight construction, low leg inertia, and straight-legged design, L03 can achieve ground perception and gentle ground contact without the need for force sensors. Compared to existing bipedal robots, L03 closely adheres to the characteristics of the linear inverted pendulum model, making it an invaluable platform for future algorithm research. Full article

Hopping locomotion has the potential to enable small-scale robots to maneuver lands quickly while overcoming obstacles bigger than themselves. To make this possible, in this paper, we propose a novel design of a high-power linear actuator for a small-scale hopper. The key design principle of the linear actuator is to use a power spring and an active clutch. The power spring provides a near constant torque along the wide range of output displacement. The active clutch controls the moving direction and operation timing of the linear actuator, which enables the hopper to take off at the right timing. As a result, the hopper has a size of 143 mm, a mass of 45.9 g, and hops up to 0.58 m. Full article

Barium titanate (BaTiO₃) piezoelectric ceramic may be a potential alternative for promoting osseointegration due to its piezoelectric properties similar to bone electric potentials generated in loading function. In this sense, the aim of this in vitro study was to evaluate the cellular response of human osteoblasts and gingival fibroblasts as well as the impact on S. oralis when in contact with BaTiO₃ functionalized zirconia implant surfaces with piezoelectric properties. Zirconia discs with BaTiO₃ were produced and contact poling (piezo activation) was performed. Osteoblasts (hFOB 1.19), fibroblasts (HGF hTERT) and S. oralis were culture on discs. Cell viability and morphology, cell differentiation markers, bacterial adhesion and growth were evaluated. The present study suggests that zirconia composite surfaces with the addition of piezoelectric BaTiO₃ are not cytotoxic to peri-implant cells. Also, they seem to promote a faster initial osteoblast differentiation. Moreover, these surfaces may inhibit the growth of S. oralis by acting as a bacteriostatic agent over time. Although the piezoelectric properties do not affect the cellular inflammatory profile, they appear to enable the initial adhesion of bacteria, however this is not significant over the entire testing period. Furthermore, the addition of non-poled BaTiO₃ to zirconia may have a potential reduction effect on IL-6 mediated-inflammatory activity in fibroblasts. Full article