Automatic Rotation Research Articles

Effects of High-intensity Continuous Ultrasound on Infrapatellar Fat Pad Stiffness and Gliding in Healthy Participants: A Randomized, Single-blind, Placebo-controlled Crossover Trial

ObjectiveUltrasound therapy effectively treats a joint range of motion limitation and pain originating from soft tissue in knee osteoarthritis (OA). Few interventional studies have focused on the infrapatellar fat pad (IFP), and the effects of high-intensity continuous ultrasound (HICUS) on IFP stiffness and gliding have not been investigated. Therefore, we aimed to determine the effects of HICUS on IFP stiffness and gliding. MethodsThis single-blind, randomized, controlled crossover study involved 24 healthy participants. The HICUS and placebo conditions were applied to the knee joint on 2 different days. HICUS was performed (1 MHz, 2.5 W/cm², duty cycle 100%, 5 min) using an ultrasound machine equipped with an applicator and adsorption-type fixed automatic rotation irradiation function. The main outcomes were IFP stiffness and gliding measured at 10 degrees and 120 degrees knee flexion. Gliding was measured as the difference between the patellar-tendon tibial angles at 10 degrees and 120 degrees knee flexion. Measurements were performed before (T1), immediately after (T2), and 15 min after (T3) treatment. ResultsTwo-way repeated measures analysis of variance showed a significant interaction of IFP stiffness at 10 degrees knee flexion; post-test results showed that HICUS decreased stiffness at T2 and T3. There was no significant difference at 120 degrees. A significant interaction of gliding was observed under the HICUS condition. Post-tests showed that HICUS significantly improved gliding at T2 and T3. ConclusionHICUS is a simple, safe intervention for improving IFP stiffness and gliding in healthy participants, with sustained effects. Further studies are needed to evaluate its efficacy in patients.

EpidermaQuant: Unsupervised Detection and Quantification of Epidermal Differentiation Markers on H-DAB-Stained Images of Reconstructed Human Epidermis.

The integrity of the reconstructed human epidermis generated in vitro can be assessed using histological analyses combined with immunohistochemical staining of keratinocyte differentiation markers. Technical differences during the preparation and capture of stained images may influence the outcome of computational methods. Due to the specific nature of the analyzed material, no annotated datasets or dedicated methods are publicly available. Using a dataset with 598 unannotated images showing cross-sections of in vitro reconstructed human epidermis stained with DAB-based immunohistochemistry reaction to visualize four different keratinocyte differentiation marker proteins (filaggrin, keratin 10, Ki67, HSPA2) and counterstained with hematoxylin, we developed an unsupervised method for the detection and quantification of immunohistochemical staining. The pipeline consists of the following steps: (i) color normalization; (ii) color deconvolution; (iii) morphological operations; (iv) automatic image rotation; and (v) clustering. The most effective combination of methods includes (i) Reinhard's normalization; (ii) Ruifrok and Johnston color-deconvolution method; (iii) proposed image-rotation method based on boundary distribution of image intensity; and (iv) k-means clustering. The results of the work should enhance the performance of quantitative analyses of protein markers in reconstructed human epidermis samples and enable the comparison of their spatial distribution between different experimental conditions.

Continuous Integration for New Service Deployment and Service Validation Script for Vault

Modern cloud-native applications demand robust security measures to safeguard sensitive data such as passwords, API keys, and encryption keys. Managing these secrets securely within Kubernetes clusters presents a significant challenge. In response, this project proposes a comprehensive solution leverag- ing HashiCorp Vault, Kubernetes, and Docker to enhance secret management and strengthen overall security posture.HashiCorp Vault serves as a centralized secrets management tool, provid- ing encryption, access control, and auditing functionalities. By integrating Vault with Kubernetes, secrets can be dynamically generated, securely stored, and automatically injected into ap- plication pods at runtime. This approach reduces the exposure of sensitive information within containerized environments and mitigates the risk of unauthorized access. The project architecture involves deploying Vault within the Kubernetes cluster, utilizing Docker containers for seamless encapsulation and portability. Kubernetes’ native integrations with Vault, such as the Kubernetes Auth method and the Vault Agent Injector, streamline the authentication and authorization processes, ensuring secure communication between applications and Vault. The project involves deploying Vault in Kubernetes for secrets management, ensuring High Availability. It focuses on generating, storing, and managing secrets securely, leveraging Vault’s dynamic secrets engine for automatic rotation. Integration with Kubernetes employs authentication methods like Service Accounts and RBAC for granular access control. Dockerization ensures application consistency and portability, with Vault Agent containers enabling seamless secret injection. Security best practices, including least privilege access and encryption, are prioritized, along with regular auditing and monitoring. Overall, the project aims to establish a robust secrets management solution within Kubernetes while empha- sizing resilience, security, and compliance in handling sensitive information. Index Terms—Docker, DevOps, CI/CD, Automation, Secrets, Kubernetes, Vault, Security

Automatic Vertebral Rotation Angle Measurement of 3D Vertebrae Based on an Improved Transformer Network.

The measurement of vertebral rotation angles serves as a crucial parameter in spinal assessments, particularly in understanding conditions such as idiopathic scoliosis. Historically, these angles were calculated from 2D CT images. However, such 2D techniques fail to comprehensively capture the intricate three-dimensional deformities inherent in spinal curvatures. To overcome the limitations of manual measurements and 2D imaging, we introduce an entirely automated approach for quantifying vertebral rotation angles using a three-dimensional vertebral model. Our method involves refining a point cloud segmentation network based on a transformer architecture. This enhanced network segments the three-dimensional vertebral point cloud, allowing for accurate measurement of vertebral rotation angles. In contrast to conventional network methodologies, our approach exhibits notable improvements in segmenting vertebral datasets. To validate our approach, we compare our automated measurements with angles derived from prevalent manual labeling techniques. The analysis, conducted through Bland-Altman plots and the corresponding intraclass correlation coefficient results, indicates significant agreement between our automated measurement method and manual measurements. The observed high intraclass correlation coefficients (ranging from 0.980 to 0.993) further underscore the reliability of our automated measurement process. Consequently, our proposed method demonstrates substantial potential for clinical applications, showcasing its capacity to provide accurate and efficient vertebral rotation angle measurements.

Smart Drone Surveillance System Based on AI and on IoT Communication in Case of Intrusion and Fire Accident

Research on developing a smart security system is based on Artificial Intelligence with an unmanned aerial vehicle (UAV) to detect and monitor alert situations, such as fire accidents and theft/intruders in the building or factory, which is based on the Internet of Things (IoT) network. The system includes a Passive Pyroelectric Infrared Detector for human detection and an analog flame sensor to sense the appearance of the concerned objects and then transmit the signal to the workstation via Wi-Fi based on the microcontroller Espressif32 (Esp32). The computer vision models YOLOv8 (You Only Look Once version 8) and Cascade Classifier are trained and implemented into the workstation, which is able to identify people, some potentially dangerous objects, and fire. The drone is also controlled by three algorithms—distance maintenance, automatic yaw rotation, and potentially dangerous object avoidance—with the support of a proportional–integral–derivative (PID) controller. The Smart Drone Surveillance System has good commands for automatic tracking and streaming of the video of these specific circumstances and then transferring the data to the involved parties such as security or staff.

Automatic axial vertebral rotation estimation on radiographs for adolescent idiopathic scoliosis by deep learning

PurposeAdolescent idiopathic scoliosis (AIS) is a three-dimensional spine deformity governed by lateral curvature and axial vertebral rotation (AVR). Estimating AVR is important for treatment decisions and the prediction of AIS progression. However, manual AVR measurements have intra-observer and inter-observer errors, and therefore this paper proposes an automatic AVR measurement method to address the low precision issue of manual measurement. MethodWe develop an improved feature extraction module for vertebral landmark detection and pedicle segmentation. The improved coordinate convolution layer combined Polarized Self-Attention mechanism is applied in the feature extraction module to help the High-resolution Network to extract coordinate information. Based on vertebral landmark detection and pedicle segmentation, we propose an automatic AVR measurement algorithm to estimate the AVR. ResultsThe mean radial error (MRE) of vertebral landmark detection is 2.70 mm pixels, and the dice coefficient of pedicle segmentation is 72.45%. Compared with the original model, the MRE decreases by 2.13 mm, and the dice coefficient improves by 4.61%. For the AVR measurement results, the testing set contains 37 spine radiographs, including 481 vertebrae and 962 AVR measurements (481 vertebrae by two observers). The average measurement error is lower than 5°, which is within the standard of clinical measurement error. ConclusionThe results demonstrate that the proposed network performs well in vertebral landmark detection and pedicle segmentation, and the proposed AVR measurement is adopted for clinic diagnosis of AIS. SignificanceOur method achieves automatic AVR measurement, reducing the error introduced by manual measurement and improving the efficiency of orthopedists.

Automatic 3D reconstruction of an anatomically correct upper airway from endoscopic long range OCT images.

Endoscopic airway optical coherence tomography (OCT) is a non-invasive and high resolution imaging modality for the diagnosis and analysis of airway-related diseases. During OCT imaging of the upper airway, in order to reliably characterize its 3D structure, there is a need to automatically detect the airway lumen contour, correct rotational distortion and perform 3D airway reconstruction. Based on a long-range endoscopic OCT imaging system equipped with a magnetic tracker, we present a fully automatic framework to reconstruct the 3D upper airway model with correct bending anatomy. Our method includes an automatic segmentation method for the upper airway based on dynamic programming algorithm, an automatic initial rotation angle error correction method for the detected 2D airway lumen contour, and an anatomic bending method combined with the centerline detected from the magnetically tracked imaging probe. The proposed automatic reconstruction framework is validated on experimental datasets acquired from two healthy adults. The result shows that the proposed framework allows the full automation of 3D airway reconstruction from OCT images and thus reveals its potential to improve analysis efficiency of endoscopic OCT images.

Using machine learning to automatically measure axial vertebral rotation on radiographs in adolescents with idiopathic scoliosis.

Adolescent idiopathic scoliosis is a 3D lateral spinal curvature coupled with axial vertebral rotation (AVR). Measuring AVR during clinic is important because it affects treatment options and predicts the risk of scoliosis progression. However, manual measurements are time consuming and have high inter-rater and intra-rater errors. This study aimed to develop a machine learning algorithm based on convolutional neural networks (CNNs) to automatically calculate AVR on posteroanterior radiographs using three different segmentations including spinal column, individual vertebra, and pedicles. Separate labeling and training processes were performed on each of the developed segmentation algorithms. The final machine learning software was tested on 221 vertebrae from 17 spinal radiographs. An experienced rater with over 25 years of experience measured the 221 vertebral rotations manually. By comparing the manual and the fully automatic measurements, 81% (178/221) of the automatic measurements were within the clinical acceptance error (±5°). The mean absolute difference and the standard deviation between the manual and automatic measurements was 4.3°±5.7°. Based on the Bland-Altman plot, the manual and automatic measurements had a strong correlation and no bias. The error did not relate to the severity of the rotation. This method is fully automatic, and the result is comparable to others.

Application of Intelligent Technology on Tunnel Steel Structure Factory

At present, the construction of tunnel steel structure factories has been common, but most of them are still on the low end. In addition, the manual processing modes face certain problems, such as high waste rate, low efficiency, and discrete welding of steel components. To address these problems, this paper introduces an intelligent management cloud platform, aiming to realize unmanned control methods and construct an intelligent tunnel steel component processing plant. There are four main production lines in the intelligent steel structure factory. First, an intelligent production line of a tunnel grille arch effectively improves the production efficiency, production quality, and dust removal effect of the grille arch through the automatic welding technology of a reinforcement mechanism-based welding robot. Second, a processing line of a steel pipe for tunnel construction improves the overall processing efficiency, saves costs, and reduces work intensity by using an intelligent numerical control system, a pneumatic clamping device, a plasma cutting hole assembly, and a 360-degree automatic rotation system. Third, an automatic feeding platform of section steel arch production line adopts the six-axis robot sensor clamping connecting plate positioning and six-axis robot sensing three-plane automatic welding system, which not only compensates for the defects of manual production but also realizes the reduction, increases efficiency, and improves welding quality and precision. Fourth, an automatic steel mesh production line saves much labor and enhances site management and production efficiency through vertical and horizontal reinforcement pay-off racks. Overall, in this study, the construction goals of informatization, intellectualization, and personnel downsizing of the tunnel steel structure processing have been achieved.

Robot arm grasping using learning-based template matching and self-rotation learning network

Applying deep neural network models to robot-arm grasping tasks requires the laborious and time-consuming annotation of a large number of representative examples in the training process. Accordingly, this work proposes a two-stage grasping model, in which the first stage employs learning-based template matching (LTM) algorithm for estimating the object position, and a self-rotation learning (SRL) network is then proposed to estimate the rotation angle of the grasping objects in the second stage. The LTM algorithm measures similarity between the feature maps of the search and template images which are extracted by a pre-trained model, while the SRL network performs the automatic rotation and labelling of the input data for training purposes. Therefore, the proposed model does not consume an expensive human-annotation process. The experimental results show that the proposed model obtains 92.6% when testing on 2400 pairs of the template and target images. Moreover, in performing practical grasping tasks on a NVidia Jetson TX2 developer kit, the proposed model achieves a higher accuracy (88.5%) than other grasping approaches on a split of Cornell-grasp dataset.

Practical Implementation of Reflectors for Wi-Fi Antennas

Abstract This article presents a practical approach towards the use of reflectors for Wi-Fi antennas. First, a custom Wi-Fi antenna was simulated in CST Studio software. Using simulation, we have analysed the effects of several types of reflectors on the antenna parameters. The most effective solution (antenna plus corner reflector) was manufactured and mounted on the specially designed ensemble. The designed ensemble enables automatic rotation of the reflector around the antenna. Simulation results were finally confronted with experimental measurements to validate the data. Our findings show that the presence of the reflector significantly increases the gain in the main radiation lobe and several other radiating directions.

Dynamical clustering interrupts motility-induced phase separation in chiral active Brownian particles.

One of the most intriguing phenomena in active matter has been the gas-liquid-like motility-induced phase separation (MIPS) observed in repulsive active particles. However, experimentally, no particle can be a perfect sphere, and the asymmetric shape, mass distribution, or catalysis coating can induce an active torque on the particle, which makes it a chiral active particle. Here, using computer simulations and dynamic mean-field theory, we demonstrate that the large enough torque of circle active Brownian particles in two dimensions generates a dynamical clustering state interrupting the conventional MIPS. Multiple clusters arise from the combination of the conventional MIPS cohesion, and the circulating current caused disintegration. The nonvanishing current in non-equilibrium steady states microscopically originates from the motility "relieved" by automatic rotation, which breaks the detailed balance at the continuum level. This suggests that no equilibrium-like phase separation theory can be constructed for chiral active colloids even with tiny active torque, in which no visible collective motion exists. This mechanism also sheds light on the understanding of dynamic clusters observed in a variety of active matter systems.

Automatic Cell Rotation Based on Real-Time Detection and Tracking

Cell rotation has great significance for cell manipulation, which is applied to intracytoplasmic sperm injection, preimplantation genetic screening and diagnosis, somatic cell nuclear transfer, etc. In this letter, an automatic cell rotation method is proposed based on real-time detection and tracking of the polar body. Image segmentation and visual tracking are combined to detect the polar body accurately and robustly during cell rotation. Both out-of-plane and in-plane rotation are realized by utilizing a pair of standard glass micropipettes. In experiments, two kinds of oocytes, which are quite different in appearance, are used to validate the system. For mouse oocytes, the robotic system achieved a 98% success rate of out-of-plane rotation and a 93% success rate of in-plane rotation. For porcine oocytes, the robotic system achieved an 80% success rate of out-of-plane rotation and a 93% success rate of in-plane rotation. The average time of cell rotation is approximately 9.1s for mouse oocytes and 12.8s for porcine oocytes. The experiment results demonstrate that the proposed method is effective, stable and high speed for automatic cell rotation in 3D.

Performance of agricultural tractor with and without automatic transmission and engine rotation management

HIGHLIGHTS Electronic gear selection and autonomous engine rotation management bring benefits to tractor driving. The electronic management responded positively to the increase in the traction load. The use of electronic management software should be more widespread among farmers and researchers.

Establishment of an integrated automated embryonic manipulation system for producing genetically modified mice

Genetically modified mice are commonly used in biologic, medical, and drug discovery research, but conventional microinjection methods used for genetic modification require extensive training and practical experience. Here we present a fully automated system for microinjection into the pronucleus to facilitate genetic modification. We first developed software that automatically controls the microinjection system hardware. The software permits automatic rotation of the zygote to move the pronucleus to the injection pipette insertion position. We also developed software that recognizes the pronucleus in 3-dimensional coordinates so that the injection pipette can be automatically inserted into the pronucleus, and achieved a 94% insertion rate by linking the 2 pieces of software. Next, we determined the optimal solution injection conditions (30 hPa, 0.8–2.0 s) by examining the survival rate of injected zygotes. Finally, we produced transgenic (traditional DNA injection and piggyBac Transposon system) and knock-in (genomic editing) mice using our newly developed Integrated Automated Embryo Manipulation System (IAEMS). We propose that the IAEMS will simplify highly reproducible pronuclear stage zygote microinjection procedures.

A new energy-saving and convenient dump truck system

Traditional lift dump trucks complete unloading by raising the center of gravity of the carriage, which often requires too much extra energy input and is inefficient. This paper improves the structure of the traditional dump truck and proposes a new dump truck system that integrates energy saving and convenience. The system as a whole includes conveyor belt system, driving system, automatic turntable system and solar thin film battery system. Based on theoretical calculations and modeling, the transmission efficiency and energy saving of the traditional dump truck and the new system are compared. The results show that the new system has higher transmission efficiency, lower energy consumption, and can achieve different directions through automatic rotation unloading, which also very well verified the superiority of the system.

Automatic Yaw Rotation of Plates on Roller Tables

Abstract Yaw rotations of plates are frequently required in heavy-plate rolling between roll passes in longitudinal and lateral plate direction. The primary objective are fast and accurate plate rotations to ensure a high throughput. This paper deals with automatic 90◦ yaw rotations of plates on a roller table with shouldered rolls spinning in opposite direction. A two-degrees-of-freedom control strategy is derived based on a mathematical model. The model also captures the friction forces between the roller table and the plate, which are essentially required for the rotation. Unknown parameters of the model are identified based on measurements from an industrial plant. Such measurements also serve for model validation. An infrared camera is used to track the current position of the plate on the roller table. Based on these measurements, a state observer estimates non-measurable process variables. Finally, the estimator and control design is tested in simulations based on the validated model.

Research on the Regulation of the New Development Bank’s Internal Governance

The Articles of Agreement on the New Development Bank is not only the Charter of the New Development Bank of the BRICs, but also a new rule of global financial governance. Its internal governance rules resemble its skeleton, supporting the decision-making and operation of the entire New Development Bank. On the basis of drawing lessons from the agreements of the existing international development financial institutions, there are many innovative practices in the internal governance rules of the New Development Bank. However, at the beginning, there are still some imperfections in the internal governance of the new development bank. Its legitimacy, accountability, and beneficial coordination among the three are outstanding, which are mainly manifested in: First, the New Development Bank has established a basic internal organizational structure of the board of directors, board of governors and presidents.Second, the lack of representativeness of directors, presidents and vice presidents weakens the legitimacy of the bank. Third, the rotation system of the president of BRICS countries affects the bank’s aid continuation of policy. Fourth, the voting mechanism of equal rights in the five countries reduces the efficiency of the bank, and the two-state veto system has led to a lack of leaders in decision-making rules.Under the current post-epidemic background, the New Development Bank, as the first global development financial institution established by developing countries, is playing an active role by providing emergency assistance loans and other means, especially the economic recovery of emerging market countries. In view of this, it is necessary to make a systematic and innovation analysis on the internal governance of the New Development Bank. The regulation of the internal governance of the New Development Bank should be carried out from the following aspects: First, to establish a coordinating supervisory agency, strengthen the coordination, supervision and mutual restriction of the existing mechanism of the New Development Bank, and implement a director system combining non-resident and resident directors. Second, to improve the representativeness of directors and presidents, and establish the election system of rotating presidents and the procedure of no-confidence cases. It includes optimizing the composition of rotating presidents, extending the period of rotation, establishing the procedure for no-confidence motions against rotating presidents, and adjusting the automatic rotation system for rotating presidents. Finally, to establish a more reasonable voting mechanism that weights basic voting rights and equity voting rights and an interest compensation mechanism of non-founding countries that replaces voting rights with interests.

INDFORG: Industrial Forgery Detection Using Automatic Rotation Angle Detection and Correction

Internet and other online media networks have emerged as the most important platforms for the sharing of digital information. However, the readily available editing tools provide an easy way for adversaries to manipulate the data and affect decision-making in various industrial applications. This malicious modification of the content, which has reduced the credibility of information delivery, is a commonly prevalent issue and hence needs serious attention. It also initiates an extreme need for industrial cyber–physical systems (ICPS), which can compare the transferred and received images for correct orientation to ensure that it conveys meaningful information and assists in correct decision-making in industrial automation. In this article, we propose “INDFORG”, which employs a novel and highly accurate automatic rotation angle detection and correction algorithm (ARADC) for intelligent detection of forgery in industrial images. ARADC uses basic geometrical concepts, such as Pythagorean theorem and intensity correlation computation and works without any digital signature or watermark. It performs accurately even under several simultaneous signal-processing manipulations. The proposed framework detects the rotation angles blindly with a 99% accuracy rate for rotation up to ±89°. Experimental results prove that the proposed algorithm is highly efficient compared to various state-of-the-art approaches and is a preferred ICPS for trustworthy media delivery in industrial automation.

Mixed‐integer quadratic programming for automatic walking footstep placement, duration, and rotation

SummaryThis paper presents a mixed‐integer model predictive controller for walking. In the proposed scheme, mixed‐integer quadratic programs (MIQP) are solved online to simultaneously decide center of mass jerks, footsteps positions, durations, and rotations while respecting actuation, geometry, and contact constraints. Most walking controllers require preplanned footstep rotations to avoid dealing with the nonlinearity introduced by foot rotation decision. The main contribution of this work is an optimization formulation where feet rotations are automatically planned to attain a reference speed rotation. Finally, simulation results are shown to present and discuss the capabilities of the proposed formulation.