Miracle-dz/UniFAR-FTUs huggingface dataset:
Classification of commonly used feed ingredients based on flow properties

The objective of this report was to classify ingredients based on their flowability. Twenty-six different feed ingredients (52 samples) were used including cereal grains, cereal by-products, oilseeds, oilseed meals, and animal-origin products. As an indication of flowability, the angle of repose was determined using a funnel test. In general, high protein oilseed meals had the lowest angle of repose, and therefore they had the highest flow ability with the exception of cottonseed meal. Corn gluten feed and wheat middlings had the highest angle of repose values (39 and 34°, respectively), and therefore they had the lowest flowabil ity. Ingredients with a range of angle of repose values between 22 and 25°, between 27 and 30°, and more than 30°, were categorized as having an easy flow, a moderate flow, and cohesive, respective -ly. The greater the protein content, the smaller the compressibility value ( r = –0.38) and the lower the angle of repose ( r = –0.42). An increase in the ether extract content of the ingredients resulted in a subsequent increase in angle of repose ( r = 0.31) and therefore a decrease in flowability (p<0.05). The angle of repose was positively correlated with compressibility and the Hausner ratio. In conclusion, oilseed meals were classified as “easy flow”, most by-products as “moderate flow”, and cereal grains as “cohesive”.
A weighted classification method based on adaptive feature selection

This paper proposes a new classification method, which can adaptively select effective features and give different classification weights according to the classification ability of features, this method effectively eliminates the influence of invalid features in classification process. For the classification problem of fault diagnosis, this method judges the fault type from the azimuth relative to the normal state in multi-dimensional space, and judges the fault degree from the ratio of distance, the method can not only give the fault category, but also give the fault probability, fault degree and whether there is a new fault type. In the process of updating the classification center, the classification method proposed in this paper solves the problem that the location of the classification center changes with the sample processing order. The effectiveness of this method is tested by the transformer vibration data (TVIB) and five data sets published in MATLAB and UCI, and compared with K-Mean, self-organizing map (SOM) methods, it is proved that considering the classification weight can effectively improve the classification accuracy, and the proposed method has strong applicability for different classification problems and good processing ability for large capacity and multi feature data.
Structural Basis for the Regiospecificity of a Lipase from Streptomyces sp. W007

The efficiency and accuracy of the synthesis of structural lipids are closely related to the regiospecificity of lipases. Understanding the structural mechanism of their regiospecificity contributes to the regiospecific redesign of lipases for meeting the technological innovation needs. Here, we used a thermostable lipase from Streptomyces sp. W007 (MAS1), which has been recently reported to show great potential in industry, to gain an insight into the structural basis of its regiospecificity by molecular modelling and mutagenesis experiments. The results indicated that increasing the steric hindrance of the site for binding a non-reactive carbonyl group of TAGs could transform the non-specific MAS1 to a α-specific lipase, such as the mutants G40E, G40F, G40Q, G40R, G40W, G40Y, N45Y, H108W and T237Y (PSI > 80). In addition, altering the local polarity of the site as well as the conformational stability of its composing residues could also impact the regiospecificity. Our present study could not only aid the rational design of the regiospecificity of lipases, but open avenues of exploration for further industrial applications of lipases.
Haptic Virtual Fixtures for Robotic Surgery

This dissertation presents a method to protect objects from touch by the robot during teleoperation. Using the point cloud obtained in real-time by a depth camera, a virtual fixture is placed around protected areas of the workspace to keep the robot at a safe distance. Upon violating the protected area, the operator receives force feedback that opposes motion inside the forbidden region. Due to the fast nature of our haptic rendering algorithm, this method can protect moving objects in a dynamic environment as it is captured in real-time by the depth camera. The methods are implemented and evaluated on a surgical robot. In addition, adaptive techniques are developed to improve the control performance of the robot a) in contact with soft tissue with nonlinear, unknown and time varying biomechanical properties, b) in the absence of end effector measurements and c) under variable cable tension. It is anticipated that virtual fixtures will increase the speed and accuracy of procedures and will reduce the operator’s fatigue.
Scalable learning of probabilistic circuits

Probabilistic circuits (PCs) are a family of tractable probabilistic models capable of answering a wide range of queries exactly and in polytime. While inference is usually straightforward, learning PCs that both obey the needed restrictions for inference tractability and exploit their expressive power has proven to be a challenge. This dissertation aims to propose fast and scalable structure learning algorithms for PCs from two different standpoints: from a logical point of view, we efficiently construct a PC that takes certain knowledge in the form of logical constraints and scalably translate them into a probabilistic circuit; from the viewpoint of data guided structure search, we propose hierarchically building PCs from random hyperplanes. We empirically show that either approach is competitive against state-of-the-art methods of the same class in both performance and scalability.
Lossless Snubber for GaN-Based Flyback Converter with Common Mode Noise Consideration

The previous lossless snubber for flyback converters, although with a small number of components, simple control and high power conversion efficiency, has no consideration for electromagnetic interference (EMI). This paper proposes a lossless snubber converter with a novel drain voltage rising slew rate control for the main switch of the flyback converter. Compared to traditional topologies, the proposed snubber topology has two advantages: first, only an additional inductor is used. Second, the proposed method can reduce EMI emissions significantly. Finally, a prototype of the proposed snubber is built and verified with two existing similar snubbers.
Management strategy for open innovation in Korean biochip industry

Due to the need to access external knowledge for new product development (NPD), open innovation has been widely used in the biochip industry. Since current resources owned by single firm do not have all the capabilities, NPD in biochip requires strong interdisciplinarity, wide diversity of technological knowledge, and integration capabilities. In the present study, management strategy for open innovation is investigated for NPD in the Korean biochip industry. Open innovation is classified in three steps: (1) switching phase about starting open innovation in the NPD initial stage; (2) implementation phase about open innovation management in the NPD middle stage; and (3) transition phase about change to close innovation in the NPD final stage. Three models for three phases are developed and then tested by carrying out surveys in the Korean biochip firms. In addition, the transition phase model is evaluated in the Korean bio-pharmaceutical firms. The switching phase model suggests that research capability and external trust are the main variables that affect switching cost, which relates to the perception of advantage of open innovation. The implementation phase model suggests that technological novelty affects degree of openness, which, in turn, relates to technological capability and firm performance. Furthermore, institutional-, environmental-, and firm characteristics affect the depth- and breadth of open innovation activity. The transition phase model suggests that knowledge connected with product innovation has an impact on the open-close transition tendency, which relates to perception of advantage of close innovation. Based on the results for three phases of open innovation, we propose the management strategy for open innovation during NPD. Therefore, based on the results of analyses of the proposed model, we can evaluate the factors that affect open innovation activity and develop an appropriate management strategy of open innovation for NPD of biochip.
Wind creates a natural bubble curtain mitigating porpoise avoidance during offshore pile driving

During offshore wind farms construction, abundance in harbour porpoise (Phocoena phocoena) is known to be negatively affected. From 2011 to 2013, extensive passive acoustic monitoring was conducted during research projects accompanying the construction of two wind farms in the German North Sea. Using C-PODs, we studied the effect ranges of pile driving disturbance on acoustic porpoise detections to test how these may change with different wind speeds. We found that disturbance radii highly depended on the prevailing wind speed during construction with further reaching effects at lower wind speed. Disturbance effects reached to 16 km at wind speed of 2m/s and to 10km at wind speed of 5m/s. With increasing wind speed, more air bubbles in the upper water layer may lead to greater mitigation of piling noise, thereby reducing disturbance radii for porpoises. Alternatively, increasing wind may increase high frequency noise due to sediment movement in the water, which could decrease the signal to noise ratio of .
Quantitative evaluation of morphological characteristics of road coarse aggregates based on image processing technology

To quantitatively characterize and evaluate the morphological features of coarse aggregate particles, 2D images of coarse aggregate particles were processed by using Image Pro-Plus software in this paper. Through image enhancement, correction, segmentation, and retrieval, six morphological indicators such as axial coefficient, rectangularity, roundness, roughness, angular parameters, and fractal dimension were obtained. Further statistical analysis of the distribution pattern of each morphological characteristic parameter was carried out in a large sample to provide a basis for the quality control of coarse aggregates processing. What’s more, further statistical analysis of the distribution pattern of each morphological characteristic parameter was carried out in a large sample to provide a basis for the quality control of coarse aggregates processing. The axial coefficient of coarse aggregates of different particle sizes, decreases with increasing particle size. The roundness decreases with increasing particle size, and the fractal dimension decreases with increasing particle size; the axial coefficient and rectangularity can be used to characterize the shape of coarse aggregates without the influence of angles. Roughness and fractal dimension can be used to characterize coarse aggregate angles independent of shape; angular parameters, roughness or angular parameters, and fractal dimension can be chosen when characterizing angles only. This study can provide a fast and efficient quantitative basis for the optimization of the coarse aggregate processing and process control of incoming quality.
Parasites of the invasive Chinese sleeper Percсottus glenii (Actinopterygii: Odontobutidae) in the region of the first introduction of the Carpathian population

Parasites of the invasive Chinese sleeper (Perccottus glenii) were studied in five different localities at the site of the first introduction of the Carpathian population of this fish. We recorded eight taxa of parasites in Lviv water bodies. The monogenean Gyrodactylus perccotti and the cestode Nippoteania perccotti were recorded in all five lakes studied. These parasites represent Far East species introduced together with their host. Also the copepod Neoergasilus japonicus is a Far East parasite introduced to Europe with its aquaculture vector. Differences between the surveyed bodies of water were mainly related to their local seasonal conditions and watershed characteristics. The current data confirm the presence of co-introduced populations of G. perccotti and N. perccotti, which probably affects the future invasiveness of their host. Due to low acquisition of local parasites, we cannot confirm any significant effect of invasive fish on local parasite assemblages in this region. However, the presence of the non-indigenous copepod, N. japonicus, requires additional attention in the future.
Synthetic microseismic datasets

We designed and created synthetic microseismic datasets for testing the accuracy of different source location algorithms. Three–component microseismograms with sources and receivers embedded in horizontally layered velocity structures were generated either by ray-tracing (RT) and convolution, or by 2D finite-difference (FD) modeling. The RTmodeled seismograms include direct and head-wave arrivals, but no reflected arrivals. The ray-tracing method can approximate some effects of TTI anisotropy in the layers. Traces from FD modeling include reflected as well as direct and head wave arrivals, but they do not include effects due to anisotropy. Gaussian noise and harmonic noise are added to the synthetic microseismograms to simulate different signal-to-noise levels, and the modeled data are stored in SEG2 format to conform to field-recorded data files.
Automatic identification of home IOT devices for traffic management in satellite networks

The widespread emergence of Internet-connected devices raises concerns about Internet of Things (IoT) traffic management in a consumer satellite network. For efficient IoT traffic management of such networks, detecting a device's semantic type is extremely important. This paper covers the challenges and solutions in identifying devices based on signatures of traffic flowing between locally connected devices and over-the-air sessions from devices behind Very Small Aperture Terminals (VSATs). Machine Learning (ML) techniques are used to predict device characteristics, and then IoT traffic can be carried via satellite channels according to the rule defined for each device type. For the first time, device-based IoT traffic classification is applied to a satellite network that greatly differs from terrestrial networks due to its resource-constrained return link. A supervised machine learning, Random Forest Classification algorithm is applied to multiple continuous datasets, and then resulting estimators are combined to produce a model using which the type and behaviour of an IoT device are predicted. Test results show an encouraging accuracy. The paper proposes a forward-looking federated learning model training that would produce better accuracy where a server in a central location trains a global learning model using machine learning parameters received from multiple edge VSATs.
Enhanced Nonuniform Constellations for High-Capacity Communications With Low-Complexity Demappers

Non-uniform constellations (NUCs) successfully utilize geometrical shaping to approach the Shannon capacity, but increase demapping complexity. In this paper, enhanced non-uniform constellations for low-complexity and high-capacity communications are investigated and addressed in two steps: low-complexity demappers for fixed constellations and the constrained design of enhanced NUCs. First, simplified demappers for NUCs are studied, and modified virtual point searching (VPS)-based demappers are proposed. Theoretical analysis and numerical results show that the proposed modified VPS-based demappers reduce demapping complexity with negligible performance degradation. Secondly, two shaping constraint schemes are proposed to NUC design for the purpose of low complexity and high capacity. NUCs optimized under the proposed constraints outperform the uniform constellations, and the proposed demappers have good applicability to the optimized NUCs. The proposed NUC design constraint schemes and simplified demappers enable solutions to achieve low-complexity high-capacity communication.
Emergence of Machine Learning Techniques in Ultrasonic Guided Wave-based Structural Health Monitoring

Identification of damage in its early stage can have a great contribution in decreasing the maintenance costs and prolonging the life of valuable structures. Although conventional damage detection techniques have a mature background, their widespread application in industrial practice is still missing. In recent years the application of Machine Learning (ML) algorithms have been more and more exploited in structural health monitoring systems (SHM). Because of the superior capabilities of ML approaches in recognizing and classifying available patterns in a dataset, they have demonstrated a significant improvement in traditional damage identification algorithms. This review study focuses on the use of machine learning (ML) approaches in Ultrasonic Guided Wave (UGW)-based SHM, in which a structure is continually monitored using permanent sensors. Accordingly, multiple steps required for performing damage detection through UGWs are stated. Moreover, it is outlined that the employment of ML techniques for UGW-based damage detection can be subtended into two main phases: (1) extracting features from the data set, and reducing the dimension of the data space, (2) processing the patterns for revealing patterns, and classification of instances. With this regard, the most frequent techniques for the realization of those two phases are elaborated. This study shows the great potential of ML algorithms to assist and enhance UGW-based damage detection algorithms.
Neurogenesis Is Increased in Human Neural Stem Cells by Aβ40 Peptide

Amyloid-β 40 peptides [Aβ1-40 (Aβ40)] are present within amyloid plaques in the brains of patients with Alzheimer’s disease (AD). Even though Aβ peptides are considered neurotoxic, they can mediate many biological processes, both in adult brains and throughout brain development. However, the physiological function of these Aβ peptides remains poorly understood, and the existing data are sometimes controversial. Here, we analyze and compare the effects of monomeric Aβ40 on the biology of differentiating human neural stem cells (human NSCs). For that purpose, we used a model of human NSCs called hNS1. Our data demonstrated that Aβ40 at high concentrations provokes apoptotic cellular death and the damage of DNA in human NSCs while also increasing the proliferation and favors neurogenesis by raising the percentage of proliferating neuronal precursors. These effects can be mediated, at least in part, by β-catenin. These results provide evidence of how Aβ modulate/regulate human NSC proliferation and differentiation, suggesting Aβ40 may be a pro-neurogenic factor. Our data could contribute to a better understanding of the molecular mechanisms involved in AD pathology and to the development of human NSC-based therapies for AD treatment, since these results could then be used in diagnosing the disease at early stages and be applied to the development of new treatment options.
An Improved Ant Colony Algorithm for Urban Bus Network Optimization Based on Existing Bus Routes

Adding new lines on the basis of the existing public transport network is an important way to improve public transport operation networks and the quality of urban public transport service. Aiming at the problem that existing routes are rarely considered in the previous research on public transportation network planning, a public transportation network optimization method based on an ant colony optimization (ACO) algorithm coupled with the existing routes is proposed. First, the actual road network and existing bus lines were abstracted with a graph data structure, and the integration with origin–destination passenger flow data was completed. Second, according to the ACO algorithm, combined with the existing line structure constraints and ant transfer rules at adjacent nodes, new bus-line planning was realized. Finally, according to the change of direct passenger flow in the entire network, the optimal bus-line network optimization scheme was determined. In the process of node transfer calculation, the algorithm adopts the Softmax strategy to realize path diversity and increase the path search range, while avoiding premature convergence and falling into local optimization. Moreover, the elite ant strategy increases the pheromone release on the current optimal path and accelerates the convergence of the algorithm. Based on existing road network and bus lines, the algorithm carries out new line planning, which increases the rationality and practical feasibility of the new bus-line structure.
Fatigue Detection Using Machine Learning Methods

The development of motor market during the last few years has already shown the inevitable trend of artificial intelligence. Fatigue detection, among other functional requirements, is more and more urgently demanded for modern vehicles. This paper focuses on recent studies and results regarding fatigue detection using machine learning methods. We divided machine learning into classic machine learning algorithms and deep learning using neutral network algorithms. We make comparisons among different algorithms and conclude their drawbacks and advantages. Hopefully, this paper will help to find effective machine learning approaches for improvement and feasible recommendations on fatigue detection.
Analysis and Design Procedures of CMOS OTAs Based on Settling Time

Analysis of generic single-pole, two-pole, and three-pole operational transconductance amplifiers (OTAs) is carried out based on settling time. The most important design metrics of the open-loop frequency response, such as the stability margins and the gain-bandwidth product (GBW) are related to the settling time of single-, two- and three-stage OTAs in closed-loop configuration, enabling to present a design procedure for each OTA based on the settling time specifications. Transistor-level design examples are provided for each case to validate the described settling-based design strategies.
Photoionization cross sections of ultracold 88Sr in 1P1 and 3S1 states at 390 nm and the resulting blue-detuned magic wavelength optical lattice clock constraints.

We present the measurements of the photoionization cross sections of the excited 1P1 and 3S1 states of ultracold 88Sr atoms at 389.889 nm wavelength, which is the magic wavelength of the 1S0-3P0 clock transition. The photoionization cross section of the 1P1 state is determined from the measured ionization rates of 88Sr in the magneto-optical trap in the 1P1 state to be 2.20(50)×10-20 m2, while the photoionization cross section of 88Sr in the 3S1 state is inferred from the photoionization-induced reduction in the number of atoms transferred through the 3S1 state in an operating optical lattice clock to be 1.38(66) ×10-18 m2. Furthermore, the resulting limitations of employing a blue-detuned magic wavelength optical lattice in strontium optical lattice clocks are evaluated. We estimated photoionization induced loss rates of atoms at 389.889 nm wavelength under typical experimental conditions and made several suggestions on how to mitigate these losses. In particular, the large photoionization induced losses for the 3S1 state would make the use of the 3S1 state in the optical cycle in a blue-detuned optical lattice unfeasible and would instead require the less commonly used 3D1,2 states during the detection part of the optical clock cycle.