Complete Acquisition Research Articles

A High-Performance Day-Age Classification and Detection Model for Chick Based on Attention Encoder and Convolutional Neural Network.

Simple SummaryMost management methods for poultry farming currently rely on human labor. Such management is labor-intensive and inefficient, especially in identifying poultry growth stages. Given the lack of a high-precision artificial method for chick’s day-age detection, a high-performance day-age classification and detection model for chicks was proposed based on artificial intelligent techniques. This method can detect and classify chicks in six different living stages from 1 to 32 day-ages, and the accuracy is 95.2%, superior to other current ones. In order to apply this method in practical scenarios, it has been deployed into an application based on the IOS system, which can recoganize the day-age of chicks by capturing real-time photos. The system is currently deployed in Rizhao City, Shandong Province, China. It helps chicken farm staff automatically detect the behavior of chickens, whose excellent working effect proves the robust availibility of the proposed method.Thanks to the boom of computer vision techniques and artificial intelligence algorithms, it is more available to achieve artificial rearing for animals in real production scenarios. Improving the accuracy of chicken day-age detection is one of the instances, which is of great importance for chicken rearing. To solve this problem, we proposed an attention encoder structure to extract chicken image features, trying to improve the detection accuracy. To cope with the imbalance of the dataset, various data enhancement schemes such as Cutout, CutMix, and MixUp were proposed to verify the effectiveness of the proposed attention encoder. This paper put the structure into various mainstream CNN networks for comparison and multiple ablation experiments. The final experimental results show that by applying the attention encoder structure, ResNet-50 can improve the accuracy of chicken age detection to 95.2%. Finally, this paper also designed a complete image acquisition system for chicken houses and a detection application configured for mobile devices.

Explaining the Length of the Due Diligence Process in Technology Acquisitions

Drawing on information economics, the theory of the firm and the organizational learning literature, we theorize about the probability of length of the due diligence process. In particular, we examine to what extent uncertainty regarding the value of a target’s resources and differences between the acquiring and target firms lengthen the acquirer’s due diligence effort. Given that we focus on the (target) technological resources we investigate the role of technological uncertainty and absorptive capacity proxying them by the number of pending patent applications of target firms and the technological distance between the two firms’ patent portfolio, respectively. Further, we argue that business relatedness (similarity) reduces the understanding gap. Using a longitudinal data on completed and non-completed technology acquisitions in the UK, we find a significantly positive effect of targets’ pending patent applications on due diligence length that is amplified by technological distance but reduced by business relatedness. Our study contributes to the M&A literature by revealing that higher information gap and uncertainty prolong the due diligence process of the acquirers in the hope of effectively evaluating prospective target firms.

Classification of multi-differentiated liver cancer pathological images based on deep learning attention mechanism

PurposeLiver cancer is one of the most common malignant tumors in the world, ranking fifth in malignant tumors. The degree of differentiation can reflect the degree of malignancy. The degree of malignancy of liver cancer can be divided into three types: poorly differentiated, moderately differentiated, and well differentiated. Diagnosis and treatment of different levels of differentiation are crucial to the survival rate and survival time of patients. As the gold standard for liver cancer diagnosis, histopathological images can accurately distinguish liver cancers of different levels of differentiation. Therefore, the study of intelligent classification of histopathological images is of great significance to patients with liver cancer. At present, the classification of histopathological images of liver cancer with different degrees of differentiation has disadvantages such as time-consuming, labor-intensive, and large manual investment. In this context, the importance of intelligent classification of histopathological images is obvious.MethodsBased on the development of a complete data acquisition scheme, this paper applies the SENet deep learning model to the intelligent classification of all types of differentiated liver cancer histopathological images for the first time, and compares it with the four deep learning models of VGG16, ResNet50, ResNet_CBAM, and SKNet. The evaluation indexes adopted in this paper include confusion matrix, Precision, recall, F1 Score, etc. These evaluation indexes can be used to evaluate the model in a very comprehensive and accurate way.ResultsFive different deep learning classification models are applied to collect the data set and evaluate model. The experimental results show that the SENet model has achieved the best classification effect with an accuracy of 95.27%. The model also has good reliability and generalization ability. The experiment proves that the SENet deep learning model has a good application prospect in the intelligent classification of histopathological images.ConclusionsThis study also proves that deep learning has great application value in solving the time-consuming and laborious problems existing in traditional manual film reading, and it has certain practical significance for the intelligent classification research of other cancer histopathological images.

Application of Image Processing Sensor and Pattern Recognition in Detection of Bearing Surface Defects

In order to solve the problem that the traditional detection technology can not meet the requirements of online detection, a visual detection device for bearing inner ring defects based on image processing and pattern recognition is proposed in this paper. The device systematically designs an image acquisition device of bearing inner ring based on CCD. In the hardware scheme, the appropriate lens, camera, light source, and other related hardware are selected according to the actual needs, a complete image acquisition platform of bearing inner ring is built, and the software function design is completed. The simulation results show that the qualified rate of machine detection is 98.6%, the missed detection rate is 0, and the false detection rate is 1.4%, which are better than manual detection. Conclusion. The test results show that the bearing inner ring defect detection system can detect the surface defects of bearing inner ring quickly, stably, and reliably, and the detection efficiency and accuracy are higher than the original manual detection method, so it has a good application prospect.

Integrating 3D cameras into sterile surgical environments: A comparison of different protective materials regarding scan accuracy

Abstract This work presents a sterile concept for 3D cameras within sterile surgical environments. In the digital operating room (OR), such cameras can serve as a valuable data source for cognitive workflow assistance systems, e.g decision or mechatronic support systems. One recent example are robotic assistants for instrument handling, such as the robotic scrub nurse currently developed in the framework of the SASHA-OR research project1. In this context, we detect laparoscopic instruments and the surgical environment with a 3D camera, whereby hygienic requirements need to be met. Using a Zivid Two sensor, we generated point clouds of the laparoscopic instruments located in an instrument holder and a drop zone. We compared the effect of using different pane types and thicknesses for the sterile camera enclosure and compared the performance with and without protective pane in terms of the point cloud accuracy. When analyzing multiple pane types, polymethyl methacrylate with 0.5 mm thickness (PMMA 0.5) provided the best results. At a scan distance of 560 mm to the surface center, which is required for the complete acquisition of a laparoscopic instrument, PMMA 0.5 achieved the smallest Chamfer distance (CD) values for both the scans with the laparoscopic instruments in the instrument holder (0.23 ± 1.52 mm) and in the drop zone (0.12 ± 0.25 mm).

EFAS Score - Validation of Portuguese Version by the Score Committee of the European Foot and Ankle Society (EFAS)

BackgroundThe Score Committee of the European Foot and Ankle Society (EFAS) developed, validated, and published the EFAS Score in ten languages (English, German, French, Italian, Polish, Dutch, Swedish, Finnish, Turkish, Portuguese). From other languages under validation, the Portuguese version completed data acquisition and underwent further validation. MethodsThe Portuguese version of the EFAS Score was developed and validated in three stages: 1) item (question) identification (completed during initial validation study), 2) item reduction and scale exploration (completed during initial validation study), 3) confirmatory analyses and responsiveness of Portuguese version (completed during initial validation study in nine other languages). The data were collected pre-operatively and post-operatively at a minimum follow-up of 3 months and mean follow-up of 6 months. Item reduction, scale exploration, confirmatory analyses and responsiveness were executed using classical test theory and item response theory. ResultsThe internal consistency was confirmed in the Portuguese version (Cronbach’s Alpha 0.84). The Standard Error of Measurement (SEM) was 0.27 and is similar to other language versions. Between baseline and follow-up, 69.4% of patients showed an improvement on their EFAS score, with adequate responsiveness (effect size 0.64). ConclusionsThe Portuguese EFAS Score version was successfully validated in patients with a wide variety of foot and ankle pathologies. All score versions are freely available at www.efas.net.

Rotary Shaft Seals: Correlation of Wear Formation at the Sealing Edge and Shaft under Various Operating Conditions

Elastomeric rotary shaft seals are a common seal type for various industrial applications. They are comparably cheap and easy to use and offer an overall good performance. But the wear behavior of these sealing systems is not fully understood and not predictable so far. If a lip seal fails, it causes high maintenance costs and environmental damage. In this article, various tribological conditions are considered. For this purpose, the sealing systems are tested with multiple operating conditions relevant to practice. Three test series are conducted with rotary shaft seals BAUM5X7 75FKM585 on plunge ground shafts. The tests are carried out with two different circumferential velocities of the shaft (4.2 and 10 m/s), three different oil sump temperatures (40, 80, and 120 °C), two different lubricant types (FVA 3 and PG 3), and five different test durations (between 100 and 625 h). For a complete acquisition of the system condition, all shaft surfaces are measured in 2D and 3D before and after the test runs and the wear of the sealing edges is evaluated with a recently developed laser line triangulation method on the complete circumference of the sealing rings. Results show large amounts of wear, depending significantly on the operating conditions. A strong correlation of the wear effects in the tribocontact area of the sealing system becomes evident. This article helps to understand and avoid critical operating conditions concerning abrasive wear.

Correlation-Based Template Tracking Of Moving Object

This paper presents the correlation-based motion estimation technique for the 3D displacement of objects. Two high-speed cameras are configured as a stereovision system and synchronized in real-time. Finger and hand motions are captured in form of digital images at 1500 fps and 2000 fps respectively. A complete motion acquisition system is calibrated to determine the intrinsic and extrinsic parameters which were later used in the correlation algorithm. The grayscale image frames acquired from the cameras are correlated using square templates of 10x10 pixels created from the reference image. The finger and hand motion are discussed with varying camera speed as a measure of brightness inconsistency. The observations in the correlation coefficient indicate that the proposed algorithm is efficient up to 20 and 50 templates for the finger and hand motion cases respectively. The correlation coefficient for finger motion was increased to 0.987 and 0.972 for the left and right cameras, respectively, while the correlation coefficient for hand motion was 0.924 and 0.898. The proposed algorithm is developed in MATLAB and validated by tracing the sinusoidal motion of a solid rectangular element from the image correlation technique and an accelerometer sensor mounted over the block.

Validation of a deep learning algorithm identifying diagnostic quality cardiac ultrasound reference views during search phase

Abstract Funding Acknowledgements Type of funding sources: Private company. Main funding source(s): DESKi Background Transthoracic echocardiography requires long and demanding training. The use of deep learning algorithms for the identification of diagnostic quality reference views opens the way for assisting low-experienced operators. However, such algorithms have only been trained and validated on retrospective data extracted from daily hospital practice. Those retrospective data are the best clips selected by experts during each exam. Thus, the performance of those algorithms has not been evaluated on complete acquisitions, including the search phase, i.e. when the operator is moving the probe to reach the optimal position. Those search phases are not recorded in clinical practice. Purpose The objective of this study was to evaluate the performance of a deep learning algorithm that identifies diagnostic quality images for 7 echocardiographic reference views on a prospective dataset including search phases. Methods A retrospective dataset containing acquisitions extracted from daily hospital practice was created. The retrospective dataset was manually annotated by experts following quality criteria that were defined for 7 main reference views: parasternal long and short axis, apical 4, 2, and 3-chamber, subcostal 4-chamber and inferior vena cava. Each frame of the acquisitions was annotated by an expert and reviewed by a second expert. A deep learning model was trained on the retrospective dataset and evaluated with 5-fold cross-validation. In a prospective dataset, for each included patient and each reference view, operators recorded the entire acquisition, including the search phase. Acquisitions were manually annotated by 2 experts following the same quality criteria. The model trained on retrospective data was evaluated on this prospective dataset against manual experts’ annotations. Additionally, 3 experts annotated a subset of the prospective dataset to evaluate the inter-observer variability. Results 481,111 frames from 1,325 patients were annotated for the retrospective dataset. On the 5-fold cross-validation, the algorithm reached an average accuracy of 89.2 ± 0.7% per frame and an average F1-score of 87.9 ± 0.9% per frame. For the prospective dataset, 70 patients were included and 143,804 frames annotated. The average accuracy was 84.8 ± 5.0% per patient, and the average F1-score was 83.1 ± 8.7% per patient. The average inter-observer agreement rates, performed on 10 patients (23,390 frames), were between 82.8 ± 4.9% and 86.3 ± 6.2%. Conclusions We propose a deep learning algorithm that automatically identifies 7 of the most common reference views with diagnostic quality while achieving performance within the range of the inter-observer variability in acquisitions including search phases.

How does liability of origin influence cross-border acquisition completion? Evidence from Chinese firms

PurposeThe environment of international business (IB) and the capabilities of emerging market multinational enterprises (EMNEs) as well as their home countries have changed significantly, leading to some new features of liability of origin (LOR). This paper aims to extend the LOR literature by particularly focusing on the LOR of Chinese multinational enterprises (MNEs) and by taking into account the heterogeneity among industries and across individual MNEs.Design/methodology/approachBased on the stereotype content model and organizational legitimacy perspective, this study explores how LOR influences Chinese MNEs’ cross-border acquisition completions. Several hypotheses were tested by using a binary logistic regression model with panel data techniques based on data of 780 Chinese MNEs’ acquisition deals between 2008 and 2018.FindingsThe results of this study show that when the competence dimension of China’s LOR is perceived as high in the host country, Chinese MNEs are less likely to complete cross-border acquisitions. Moreover, deals are less likely to be completed when the warmth dimension of China’s LOR is perceived to be low. Global experience and the foreign-listed status of individual Chinese MNEs can alter the relationship between the LOR and deal completions.Originality/valueThis study advances and enriches the LOR research. It shows that a high level of competence in the home country has led to LOR for Chinese MNEs rather than the low level of competence proposed by existing LOR studies; and the LOR for Chinese MNEs is also determined by the perceived low level of warmth in the home country resulting from the geopolitical conflicts between two countries. In addition, the LOR suffered by EMNEs could vary based on certain industry- and firm-level characteristics. The findings of this study provide important practical implications for emerging economy governments and for firms intending to go abroad.

Using Image Feature Extraction to Identification of Ancient Ceramics Based on Partial Differential Equation

This paper presents an in-depth study and analysis of the image feature extraction technique for ancient ceramic identification using an algorithm of partial differential equations. Image features of ancient ceramics are closely related to specific raw material selection and process technology, and complete acquisition of image features of ancient ceramics is a prerequisite for achieving image feature identification of ancient ceramics, since the quality of extracted area-grown ancient ceramic image feature extraction method is closely related to the background pixels and does not have generalizability. In this paper, we propose a deep learning-based extraction method, using Eased as a deep learning support platform, to extract and validate 5834 images of 272 types of ancient ceramics from kilns, celadon, and Yue kilns after manual labelling and training learning, and the results show that the average complete extraction rate is higher than 99%. The implementation of the deep learning method is summarized and compared with the traditional region growth extraction method, and the results show that the method is robust with the increase of the learning amount and has generalizability, which is a new method to effectively achieve the complete image feature extraction of ancient ceramics. The main content of the finite difference method is to use the ratio of the difference between the function values of two adjacent points and the distance between the two points to approximate the partial derivative of the function with respect to the variable. This idea was used to turn the problem of division into a problem of difference. Recognition of ancient ceramic image features was realized based on the extraction of the overall image features of ancient ceramics, the extraction and recognition of vessel type features, the quantitative recognition of multidimensional feature fusion ornamentation image features, and the implementation of deep learning based on inscription model recognition image feature classification recognition method; three-layer B/S architecture web application system and cross-platform system language called as the architectural support; and database services, deep learning packaging, and digital image processing. The specific implementation method is based on database service, deep learning encapsulation, digital image processing, and third-party invocation, and the service layer fusion and relearning mechanism is proposed to achieve the preliminary intelligent recognition system of ancient ceramic vessel type and ornament image features. The results of the validation test meet the expectation and verify the effectiveness of the ancient ceramic vessel type and ornament image feature recognition system.

Assesing teaching assistant programmes as a tool facilitating the learning process of Physiology / Avaliação do modelo de monitoria como ferramenta facilitadora para o processo de aprendizagem em Fisiologia

A teaching assistant programme is an extracurricular activity in which an advanced student (the assistant) is guided and supervised by the lecturer to provide support to the curricular unit. The aim of the present study was to asses the contribution of teaching assistant programmes based on problem cases towards the acquisition of a medicine course content. The study was cross-sectional and involved 153 students divided into two groups: the present and the absent at teaching assistant sessions. All the participants responded to standardised questionnaires that have been used to analyse their perception of content acquisition, their active participation in learning and the contribution of teaching assistant sessions. The results showed that the perception of complete content acquisition among the present group was at 38.4%, while for the absent group only at 29.7%. The negative perception of content acquisition was at 13.6 % for the absent group and at 6.1% for the present group. Within the present group 73.5% of the students studied the topics covered in lectures within two weeks. For the absent group this number fell to 49.4%. In addition, 86% of the present group said that the teaching assistant sessions contributed towards their learning. As the main reason they pointed to the opportunity to answer questions. On the whole, the results suggest that teaching assistant programmes based on problem cases can be effective in facilitating the perception of learning. The reason appears to be a more intense involvement of the participants in a more active study routine.

Investigation of the Fatigue Stress of Orthotropic Steel Decks Based on an Arch Bridge with the Application of the Arlequin Method.

Orthotropic steel decks are widely used in the construction of steel bridges. Although there are many fatigue-evaluation methods stipulated by codes, unexpected fatigue cracks are still detected in some bridges. To justify whether the local finite element model commonly used in fatigue investigations on orthotropic decks can correctly instruct engineering practices, the Arlequin framework is applied in this paper to determine the full fatigue stress under traffic loads. The convergence on and validity of this application for orthotropic decks are checked. Results show that the Arlequin model for deck-fatigue analysis established in this paper tends to be an efficient method for complete fatigue stress acquisition, whereby the vulnerable sites of orthotropic steel decks under traffic loads are defined. Vehicles near the flexible components, such as hangers or cables, can have adverse effects on the fatigue durability of decks. Additionally, the total number of vehicles and their arrangement concentration also affect fatigue performance. Complex traffic conditions cannot be fully loaded in local models. Regardless of the gross bridge mechanics and deck deformation, the fatigue stress range is underestimated by about 30–40%. Such a difference in fatigue assessment seems to explain the premature cracks observed in orthotropic steel decks.

Prince explores divestments in order to complete Ferro acquisition

Prince explores divestments in order to complete Ferro acquisition

Fear learning, avoidance, and generalization are more context-dependent for adults than adolescents

This study examined developmental differences in contextual and perceptual generalization of fear and avoidance learning. Adults (N = 39) and adolescents (N = 44) completed differential fear acquisition wherein each conditional stimulus (CS) appeared in a background context. In the dangerous context, one stimulus (CS+) predicted an aversive sound, and the other stimulus (CS-) did not. In the safe context, the aversive sound was never administered with either CS. During fear generalization, participants were presented with three generalization stimuli (GSs), ranging on a perceptual continuum from threat to safety stimuli, in both contexts. Participants then completed avoidance conditioning and avoidance generalization phases, allowing them to actively avoid the upcoming aversive sound by pressing an avoidance button. Developmental differences emerged in threat perception, physiological arousal, avoidance behavior, and eye movements during contextual fear learning and generalization. Adolescents showed less discrimination between stimuli and contexts than adults, resulting primarily from their elevated fear responses to safety and generalized stimuli. Developmental differences in fear learning should be further explored in future research, as they could explain why adolescence is a sensitive developmental period for anxiety.

Study on Grey Benchmark Adjustment and Visual Measurement of Cigarette Combustion

The color of cigarette paper and its ash after combustion is one of the most intuitive evaluations for consumers. In order to solve the problem that the judgment of machine vision is not unified and there is no standard reference, an objective and accurate method of cigarette combustion gray benchmark adjustment and visual measurement based on ceramic stick was are provided in this paper. Through multi-faceted simultaneous shooting of a single cigarette in the state of static combustion or smoking, real-time image tracking acquisition is carried out to complete multi-faceted full vision acquisition of cigarette sample combustion. The experimental results show that there are some standard deviations in the three angles, and the method of taking the mean value reflects the gray of cigarette combustion to the greatest extent. Using ceramic bar as reference to calibrate cigarette gray, real-time acquisition and data processing can accurately and objectively determine and compare the test samples, which could be applied for scientific statistics and research. The uniform lighting design of multiple LED light sources ensures the quality of the collected images.

Quantitative magnetic resonance imaging and tumor forecasting of breast cancer patients in the community setting.

This protocol describes a complete data acquisition, analysis and computational forecasting pipeline for employing quantitative MRI data to predict the response of locally advanced breast cancer to neoadjuvant therapy in a community-based care setting. The methodology has previously been successfully applied to a heterogeneous patient population. The protocol details how to acquire the necessary images followed by registration, segmentation, quantitative perfusion and diffusion analysis, model calibration, and prediction. The data collection portion of the protocol requires ~25 min of scanning, postprocessing requires 2-3 h, and the model calibration and prediction components require ~10 h per patient depending on tumor size. The response of individual breast cancer patients to neoadjuvant therapy is forecast by application of a biophysical, reaction-diffusion mathematical model to these data. Successful application of the protocol results in coregistered MRI data from at least two scan visits that quantifies an individual tumor's size, cellularity and vascular properties. This enables a spatially resolved prediction of how a particular patient's tumor will respond to therapy. Expertise in image acquisition and analysis, as well as the numerical solution of partial differential equations, is required to carry out this protocol.

Aplicaciones de la centrífuga geotecnica en la enseñanza de la mecánica de suelos aplicada

This paper presents the characteristics of the new geotechnical centrifuge of the Escuela Colombiana de Ingeniería Julio Garavito, including the installed capacities, future applications, and possibilities that offer the new centrifuge as an important tool for teaching and learning in geotechnics. This centrifuge has a radio of 610mm and a maximum acceleration of 200 gravities. A very complete acquisition system with a capacity of 12 sensors to measure different properties is installed. Also, the centrifuge has five oedometric consolidometers as a complementary tool to prepared fine soils in the construction boxes. The possibilities in teaching soil mechanics and applied soil mechanics using the new centrifuge are multiple and are based on the visualization of failure surfaces and deformations produced by different geotechnical structures.

Intelligent Interpretation of the Geometric Properties of Rock Mass Discontinuities Based on an Unmanned Aerial Vehicle

The geometric properties of rock mass discontinuities are essential for the evaluation of the safety of rock masses. Numerous studies have recently been performed on the extraction of discontinuity information. However, most methods are characterized by poor data collection and processing efficiency. This paper presents a UAV-based methodology for the accurate and complete acquisition of rock surface data, as well as the automatic extraction of discontinuity information. Moreover, a program called Random Sample Consensus (RANSAC) Discontinuity Detection (RDD) is developed to extract discontinuity information based on the proposed method. The conclusions of this research are as follows. 1) RANSAC Discontinuity Detection (RDD) can identify the feature point set of discontinuities from a raw point cloud, and can calculate the discontinuity orientation. 2) The boundary of a discontinuity can be precisely depicted using the improved Graham scan algorithm. 3) The orientations of marked discontinuities extracted by RDD are compared with those extracted by the three-point method in CloudCompare. The differences in the orientations extracted by the two methods are found to be less than 3° for flat discontinuities and only about 4.87° for rough discontinuities, which are within a reasonable error range in practical engineering applications. Therefore, the feasibility of the proposed method is verified.

New Sensor for Medium- and High-Voltage Measurement

Measurements of medium and high voltages in a power grid are normally performed with large and bulky voltage transformers or capacitive dividers. Besides installation problems, these devices operate in a relatively narrow frequency band, which limits their usability in modern systems that are saturated with power electronic devices. A sensor that can be installed directly on a wire and can operate without a galvanic connection to the ground may be used as an alternative voltage measurement device. This type of voltage sensor can complement current sensors installed on a wire, forming a complete power acquisition system. This paper presents such a sensor. Our sensor is built using two dielectric elements with different permeability coefficients. A finite element method simulation is used to estimate the parameters of a constructed sensor. Besides simulations, a laboratory model of a sensor was built and tested in a medium-voltage substation. Our results provide a proof of concept for the presented sensor. Some errors in voltage reconstruction have been traced to an oversimplified data acquisition and transmission system, which has to be improved during the further development of the sensor.