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Abstract Complex temporal bone anatomy complicates operations; thus, surgeons must engage in practice to mitigate risks, improving patient safety and outcomes. However, existing training methods often involve prohibitive costs and ethical problems. Therefore, we developed an educational mastoidectomy simulator, considering mechanical properties using 3D printing. The mastoidectomy simulator was modeled on computed tomography images of a patient undergoing a mastoidectomy. Infill was modeled for each anatomical part to provide a realistic drilling sensation. Bone and other anatomies appear in assorted colors to enhance the simulator’s educational utility. The mechanical properties of the simulator were evaluated by measuring the screw insertion torque for infill specimens and cadaveric temporal bones and investigating its usability with a five-point Likert-scale questionnaire completed by five otolaryngologists. The maximum insertion torque values of the sigmoid sinus, tegmen, and semicircular canal were 1.08 ± 0.62, 0.44 ± 0.42, and 1.54 ± 0.43 N mm, displaying similar-strength infill specimens of 40%, 30%, and 50%. Otolaryngologists evaluated the quality and usability at 4.25 ± 0.81 and 4.53 ± 0.62. The mastoidectomy simulator could provide realistic bone drilling feedback for educational mastoidectomy training while reinforcing skills and comprehension of anatomical structures.

Abstract Wireless energy transfer (WET) based on ultrasound‐driven generators with enormous beneficial functions, is technologically in progress by the valuation of ultrasonic metamaterials (UMMs) in science and engineering domains. Indeed, novel metamaterial structures can develop the efficiency of mechanical and physical features of ultrasound energy receivers (US‐ETs), including ultrasound‐driven piezoelectric and triboelectric nanogenerators (US‐PENGs and US‐TENGs) for advantageous applications. This review article first summarizes the fundamentals, classification, and design engineering of UMMs after introducing ultrasound energy for WET technology. In addition to addressing using UMMs, the topical progress of innovative UMMs in US‐ETs is conceptually presented. Moreover, the advanced approaches of metamaterials are reported in the categorized applications of US‐PENGs and US‐TENGs. Finally, some current perspectives and encounters of UMMs in US‐ETs are offered. With this objective in mind, this review explores the potential revolution of reliable integrated energy transfer systems through the transformation of metamaterials into ultrasound‐driven active mediums for generators.

Convolutional neural networks (CNNs) have gained recognition for their remarkable performance across various tasks. However, the sheer number of parameters and the computational demands pose challenges, particularly on edge devices with limited processing power. In response to these challenges, this paper presents a novel approach aimed at enhancing the efficiency of deep learning models. Our method introduces the concept of accuracy and efficiency coefficients, offering a fine-grained control mechanism to balance the trade-off between network accuracy and computational efficiency. At our core is the Rewarded Meta-Pruning algorithm, guiding neural network training to generate pruned model weight configurations. The selection of this pruned model is based on approximations of the final model’s parameters, and it is precisely controlled through a reward function. This reward function empowers us to tailor the optimization process, leading to more effective fine-tuning and improved model performance. Extensive experiments and evaluations underscore the superiority of our proposed method when compared to state-of-the-art techniques. We conducted rigorous pruning experiments on well-established architectures such as ResNet-50, MobileNetV1, and MobileNetV2. The results not only validate the efficacy of our approach but also highlight its potential to significantly advance the field of model compression and deployment on resource-constrained edge devices.

Abstract An innovative autonomous resonance‐tuning (ART) energy harvester is reported that utilizes adaptive clamping systems driven by intrinsic mechanical mechanisms without outsourcing additional energy. The adaptive clamping system modulates the natural frequency of the harvester's main beam (MB) by adjusting the clamping position of the MB. The pulling force induced by the resonance vibration of the tuning beam (TB) provides the driving force for operating the adaptive clamp. The ART mechanism is possible by matching the natural frequencies of the TB and clamped MB. Detailed evaluations are conducted on the optimization of the adaptive clamp tolerance and TB design to increase the pulling force. The energy harvester exhibits an ultrawide resonance bandwidth of over 30 Hz in the commonly accessible low vibration frequency range (

Convolutional neural networks (CNNs) have shown decent performance in a variety of computer vision tasks. However, these network configurations are largely hand-crafted, which leads to inefficiency in the constructed network. Various other algorithms have been proposed to address this issue, but the inefficiencies resulting from human intervention have not been addressed. Our proposed EvolveNet algorithm is a task-agnostic evolutionary search algorithm that can find optimal depth and width scales automatically in an efficient way. The optimal configurations are not found using grid search, and are instead evolved from an existing network. This eliminates inefficiencies that emanate from hand-crafting, thus reducing the drop in accuracy. The proposed algorithm is a framework to search through a large search space of subnetworks until a suitable configuration is found. Extensive experiments on the ImageNet dataset demonstrate the superiority of the proposed method by outperforming the state-of-the-art methods.

Piezoelectric materials are a class of materials that can generate an electric charge when subjected to mechanical stress, or vice versa. These materials have a wide range of applications, from sensors and actuators to energy-harvesting devices and medical implants. Recently, there has been growing interest in using 3D printing to fabricate piezoelectric materials with complex geometries and tailored properties. Three-dimensional printing allows for the precise control of the material’s composition, microstructure, and shape, which can significantly enhance piezoelectric materials’ performance. Three-dimensional printing has emerged as a promising technique for fabricating piezoelectric materials with tailored properties and complex geometries. The development of high-performance piezoelectric materials using 3D printing could have significant implications for various applications, including sensors, energy harvesting, and medical devices. In this review paper, 3D printing methods for piezoelectric materials, their advantages and disadvantages, representative piezoelectric ceramics, and examples of 3D printing are presented. Furthermore, the applications utilizing these materials are summarized.

This paper presents an algorithm for solving the power flow (PF) problem of droop-regulated AC microgrids (DRACMs) operating in isolated mode. These systems are based on radial distribution networks without having a slack bus to facilitate conventional computations. Moreover, distributed generation units have to distribute the power and voltage regulation among themselves as a function of operating frequency and voltage droop rather than having a slack bus that regulates voltage and power demands. Based on the conventional backward/forward sweep algorithm (BFS), the proposed method is a derivative-free PF algorithm. To manage the absence of a slack bus in the system, the BFS algorithm introduces new loops, equations, and self-adaptation procedures to its computation procedures. A comparison is presented between the proposed BFS algorithm and other state-of-the-art PF algorithms, as well as PSCAD/EMTDC. In comparison to existing algorithms, the proposed approach is fast, simple, accurate, and easy to implement, and it can be considered an effective tool for planning and analyzing islanded DRACMs.

With the advances in Unmanned Aerial Vehicles (UAVs) technology, aerial images with huge variations in the appearance of objects and complex backgrounds have opened a new direction of work for researchers. The task of semantic segmentation becomes more challenging when capturing inherent features in the global and local context for UAV images. In this paper, we proposed a transformer-based encoder-decoder architecture to address this issue for the precise segmentation of UAV images. The inherent feature representation of the UAV images is exploited in the encoder network using a self-attention-based transformer framework to capture long-range global contextual information. A Token Spatial Information Fusion (TSIF) module is proposed to take advantage of a convolution mechanism that can capture local details. It fuses the local contextual details about the neighboring pixels with the encoder network and makes semantically rich feature representations. We proposed a decoder network that processes the output of the encoder network for the final semantic level prediction of each pixel. We demonstrate the effectiveness of this architecture on UAVid and Urban Drone datasets, where we achieved mIoU of 61.93% and 73.65%, respectively.

Visual perception is a critical task for autonomous driving. Understanding the driving environment in real time can assist a vehicle in driving safely. In this study, we proposed a multi-task learning framework for simultaneous traffic object detection, drivable area segmentation, and lane line segmentation in an efficient way. Our network encoder extracts features from an input image and three decoders at multilevel branches handle specific tasks. The decoders share the feature maps with more similar tasks for joint semantic understanding. Multiple loss functions are automatically weighted summed to learn multiple objectives simultaneously. We demonstrate the effectiveness of this framework on a BerkeleyDeepDrive100K (BDD100K) dataset. In the experiment, the proposed method outperforms the competing multi-task and single-task methods in terms of accuracy and maintains a real-time inference at more than 37 frames per second.

Autonomous driving is a safety-critical application that requires a high-level understanding of computer vision with real-time inference. In this study, we focus on the computational efficiency of an important factor by improving the running time and performing multiple tasks simultaneously for practical applications. We propose a fast and accurate multi-task learning-based architecture for joint segmentation of drivable area, lane line, and classification of the scene. An encoder–decoder architecture efficiently handles input frames through shared representation. A comprehensive understanding of the driving environment is improved by generalization and regularization from different tasks. The proposed method learns end-to-end through multi-task learning on a very challenging Berkeley Deep Drive dataset and shows its robustness for three tasks in autonomous driving. Experimental results show that the proposed method outperforms other multi-task learning approaches in both speed and accuracy. The computational efficiency of the method was over 93.81 fps at inference, enabling execution in real-time.

In multi/many-objective evolutionary algorithms (MOEAs), to alleviate the degraded convergence pressure of Pareto dominance with the increase in the number of objectives, numerous modified dominance relationships were proposed. Recently, the strengthened dominance relation (SDR) has been proposed, where the dominance area of a solution is determined by convergence degree and niche size (θ¯). Later, in controlled SDR (CSDR), θ¯ and an additional parameter (k) associated with the convergence degree are dynamically adjusted depending on the iteration count. Depending on the problem characteristics and the distribution of the current population, different situations require different values of k, rendering the linear reduction of k based on the generation count ineffective. This is because a particular value of k is expected to bias the dominance relationship towards a particular region on the Pareto front (PF). In addition, due to the same reason, using SDR or CSDR in the environmental selection cannot preserve the diversity of solutions required to cover the entire PF. Therefore, we propose an MOEA, referred to as NSGA-III*, where (1) a modified SDR (MSDR)-based mating selection with an adaptive ensemble of parameter k would prioritize parents from specific sections of the PF depending on k, and (2) the traditional weight vector and non-dominated sorting-based environmental selection of NSGA-III would protect the solutions corresponding to the entire PF. The performance of NSGA-III* is favourably compared with state-of-the-art MOEAs on DTLZ and WFG test suites with up to 10 objectives.

In the general population, serial imaging is recommended over anticoagulant therapy for below-knee deep vein thrombosis (BKDVT). However, no clinical trial in Asian patients with spinal cord injury and BKDVT has been performed. Therefore, we evaluated the natural course of BKDVT in patients with acute spinal cord injury. We retrospectively analyzed inpatients with spinal cord injury with BKDVT between 2016 and 2020. All patients underwent inpatient rehabilitation treatment and duplex ultrasonographic examination of both the lower extremities at follow-up. After screening 172 patients with acute spinal cord injury for deep vein thrombosis using duplex ultrasound, 27 patients with below-the-knee deep vein thrombosis were included in this study. The mean lower-extremity motor score (median, interquartile range) was 66.0, 54.0–74.5. Sixteen patients received a non-vitamin K antagonist oral anticoagulant (NOAC) for anticoagulation. None of the patients had proximal propagation according to the follow-up duplex ultrasonography. BKDVT disappearance was not significantly different between the NOAC treatment and non-treatment groups. Asian patients with spinal cord injury have a low incidence of venous thromboembolism and favorable natural history of BKDVT. We recommend serial imaging over anticoagulant therapy for BKDVT in these patients.

This study aimed to determine whether bypass circulation was present in lymphedema and its effect. This was a retrospective, cross-sectional study. Patients who underwent unilateral breast cancer surgery with axillary lymph node dissection were recruited and underwent single-photon emission tomography/computed tomography (SPECT/CT). SPECT/CT was performed to detect the three-dimensional locations of radio-activated lymph nodes. Patients with radioactivity in anatomical locations other than axillary lymph nodes were classified into a positive group. All patients received complete decongestive therapy (CDT). Exclusion criteria were as follows: History of bilateral breast cancer surgery, cervical lymph node dissection history, and upper extremity amputation. The difference in the upper extremity circumference (cm) was measured at four points: Mid-point of the upper arm, elbow, and 10 and 15 cm below the elbow. Twenty-nine patients were included in this study. Fifteen patients (51.7%) had bypass lymphatic systems on the affected side, six (20.7%) had a bypass lymphatic system with axillary lymph nodes on the unaffected side, and 11 (37.9%) showed new lymphatic drainage. The positive group showed significantly less swelling than the negative group at the mid-arm, elbow, and 15 cm below the elbow. Bypass lymphatic circulation had two patterns: Infraclavicular lymph nodes and supraclavicular and/or cervical lymph nodes. Changes in lymph drainage caused by surgery triggered the activation of the superficial lymphatic drainage system to relieve lymphedema. Superficial lymphatic drainage has a connection through the deltopectoral groove.

A full-sized model for the horizontally oriented metal cask containing 21 spent fuel assemblies has been considered to evaluate the internal natural convection behavior within a dry shield canister (DSC) filled with helium as a working fluid. A variety of two-dimensional CFD numerical investigations using a turbulent model have been performed to evaluate the heat transfer characteristics and the velocity distribution of natural convection inside the canister. The present numerical solutions for a range of Rayleigh number values (3× 106∼3× 107) and a working fluid of air are further validated by comparing with the experimental data from previous work, and they agreed well with the experimental results. The predicted temperature field has indicated that the peak temperature is located in the second basket from the top along the vertical center line by effects of the natural convection. As the Rayleigh number increases, the convective heat transfer is dominant and the heat transfer due to the local circulation becomes stronger. The heat transfer characteristics show that the Nusselt numbers corresponding to 1.5×106 < Ra < 1.0×107 are proportional to 0.5 power of the Rayleigh number, while the Nusselt numbers for 1.0×107 < Ra < 8.0×107 are proportional to 0.27 power of the Rayleigh number. These results agreed well with the trends of the experimental data for Ra>1.0×107.