Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring the static acoustic force sensitivity using AFM micro-cantilever under single- and bimodal-frequency excitation

We present here a numerical calculation for sensitivity comparison of single- and bimodal-excitation of micro-cantilever (owing rectangular shape) on the measurement of static acoustic force. We model the micro-cantilever as a point mass in our simulations similar to forced harmonic oscillator with damping. In bimodal operation, the micro-cantilever is excited at its first two flexural modes. Results of amplitude and phase shift obtained through bimodal-frequency excitation are compared with the ones obtained using single-frequency excitation scheme. Oscillation observables are calculated in both excitation schemes with respect to magnitudes of excitation forces at fundamental and second eigenmode. Influence of driving force for micro-cantilever actuation on amplitude and phase shift is explored so that optimum excitation parameters can be provided for highest observable sensitivity. Moreover, we relate our findings with virial and energy dissipation to understand the physics behind observed dynamics. Our results clearly indicates that phase sensitivity at the fundamental eigenmode to static acoustic force could be enhanced using multi-frequency excitation scheme. In addition, results of virial and dissipated power at second eigenmode point out the existence of interaction of flexural modes in the presence of static acoustic force in bimodal-frequency excitation scheme. Therefore, our approach can be used to increase sensitivity of dynamically excited micro-cantilever for measuring ultra-low (pN) acoustic forces.
A Target Detection and Evaluation Method for Safety Protection Equipment

The existing safety detection methods are not sufficient to achieve real-time accurate detection, and cannot perform real-time safety assessment. Taking the construction site as an example, this paper proposes a safety equipment wear detection method based on the joint constraint of linear distance and angle, and proposes the idea of establishing a knowledge database of safety protection equipment wear behavior, and proposes a safety factor evaluation model. In this paper, convolutional neural networks are used to identify the construction personnel and safety equipment in two basic poses, and the linear distance and deflection angle of the regional center point coordinates are used to jointly determine the wearing status of the safety equipment. The generated result matches the safety information knowledge base to obtain the final test results. We obtained the real-time safety factor of the construction personnel by correlating the detection results with the actual scene. Finally, the method used in the article is summarized and prospected.
A Pose Transformation Estimation Method Based on Feature Extraction and Matching in Ceramic Green Body Bonding Process

Bonding the ceramic green-bodies of the seat ring to the cup body is an important step in the production process of the toilet. This paper proposes a pose transformation estimation method based on feature extraction and matching in ceramic green body bonding process. This method uses a Local Feature Statistics Histogram (LFSH) to extract the features of the bonding surfaces and find the corresponding point sets. Then, a Sample Consensus Initial Aligment (SAC-IA) algorithm combined with LFSH features is used to perform coarse registration calculation. Finally, using the Iterative Cloest Point (ICP) algorithm, the result of the coarse registration is used as the initial value to complete the fine registration and obtain the pose transformation matrix. Before registration, the relative poses of the two point clouds are randomly transformed. This method is tested on the point cloud data of the cup body and the seat ring. Experimental results show that the role of SAC-IA can increase the speed of registration compared to using ICP alone.
A dual-view alignment-based domain adaptation network for fault diagnosis

Domain adaptation is a major area of interest in intelligent equipment maintenance and fault diagnosis in recent years. Traditional machine/deep-learning-based fault diagnosis methods assume that the source and target domains share the same distribution, which may fail and lead to catastrophic damages. Many domain adaptation-based fault diagnosis methods have been proposed to address the domain shift problem. However, most of them only align global domain distributions and ignore class relationships between domains, which leads to a decline in diagnostic performance. To overcome this deficiency, a dual-view alignment-based domain adaptation network (DVADAN) for fault diagnosis is proposed in this paper. Specifically, the proposed dual-view alignment, consisting of a global (marginal) alignment constructed with maximum mean discrepancy and a local (conditional) alignment calculating the class-centers by Wasserstein distance, is developed to reduce domain distribution discrepancy. Extensive experiments on two test rigs validated the effectiveness of the proposed DVADAN and showed its superiority over state-of-art fault diagnosis methods.
Segmentation of Pulmonary Nodules Based on BBClstm Unet

To solve the problem of insufficient segmentation accuracy caused by the failure of UNET model to make full use of the weight relationship and semantic information between coding layers in lung nodule image segmentation, a lung nodule segmentation method based on BBClstm-Unet is proposed. In this method, parallel attention module is used to complete the information coding of a larger range of pulmonary nodules image and reset the weight of sampling correlation channel information. Bconvlstm structure is used to combine feature mapping, and forward and backward output sampling are fused to obtain the final segmentation result. At the same time, hybrid loss function is used to alleviate the class imbalance problem. The experimental results show that the average dice value of BBClstm-Unet network on luna16 data set reaches 90.83%, which is better than the original UNET network.
A neural network for prediction of high intensity resonance modes in magnetic multilayers

The use of magnetic materials as building blocks for frequency applications makes it possible to fabricate micrometer and nanometer high frequency devices. Moreover, devices with multiple high intensity modes for multiband devices can be designed by using magnetic multilayers. However, as the number of layers increases the multilayer becomes more complex, making it very difficult to find optimal configurations due to a big number of possible configurations. Fortunately, over the past decade a surge in the applicability and accessibility of machine learning algorithms and neural networks has been observed, which allow to analyse big quantities of data in search of complex patterns not always evident to humans. In this work, a theoretical model is used to generate approximately 10 × 106 data points, which in turn are used to train a neural network to calculate the number of high intensity resonance modes of three ferromagnetically coupled magnetic layers with an accuracy of over 99.8%. The neural network is then used to identify a configuration of the multilayer which provides the maximum number of high-intensity modes, and comparisons with the theoretical model are presented. Finally, the correlations between parameter were calculated over 600 million of data points, and clear guidelines for obtention of two high intensity resonance modes were identified. These results provide a simple way to find a configuration of the trilayer that have a high number of high intensity modes, thus greatly simplifying the design process of magnetic multi-band frequency devices.
New EOR Technology: Simultaneous Gas Alternating Gas (SGAG) injection

A new oil recovery method known as Simultaneous Gas Alternating Gas (SGAG) injection that is based on the principle of the conventional Simultaneous Water Alternating Gas (SWAG) injection has been proposed in this study. The performance was observed via a simulation process based on data from an oil field of a shale oil reservoir. The effectiveness of SGAG was evaluated through a sensitivity analysis, and by observing its effect on oil recovery factor and ultimate oil recovery. The results of the sensitivity analysis show that (1) SGAG technique will improve the ultimate recovery factor compared to continuous gas injection; (2) Based on gravity segregation between two gases, oil recovery is enhanced; and (3) Because the produced CH4 can be reinjected and based on the availability of CO2, the possible SGAG combination of (CH4 + CO2) is more economically and feasible for a tank of Shale Oil reserve. This study provides a reliable basis for minimizing operating costs per unit CO2 injection volume and insight into cost-effectively enhanced oil recovery (EOR) technique.
Research on Improved Intelligent Generative Dialogue Algorithm Based on Knowledge Graph

The rise of a new generation of artificial intelligence technology, represented by deep learning, has promoted the vigorous development of natural language processing technology. As a typical application of natural language processing technology, human-machine intelligent dialogue system, coupled with its commercial value in the fields of voice assistants and chat robots, has become a hot topic in the current academic and industrial circles. In the past, the responses generated by intelligent dialogue robots is single and universal, and even the content is inappropriate. Therefore, this study proposes to use the knowledge graph as the background knowledge when the dialogue model generates the responses[1]. In order to meet the needs of users more closely, it also proposes to introduce the user information participating in the dialogue and the dialogue scene into the model. The model is trained and evaluated on the DuRecDial public dataset, and the optimized model is compared with the original model. The experimental results show that the model with these two modules has better effect than the original model, especially in the generation index, the F1, BLEU2 and DIST-2 indexes have been improved by 0.91%, 0.5% and 0.9% respectively.
Novel design of an effective pneumatic magnetostrictive patch transducer based on the ultrasonic guided wave for application of fast pipe health inspection

Pipelines are widely used to transport water, oil, and gas. Severe corrosion of pipes can cause leakage of natural gas and result in an explosion or even more severe disasters. Magnetostrictive sensors based on ultrasonic guided waves have been validated as feasible for non-destructive testing of pipes with advantages of long-range inspection, high signal to noise ratio, with high testing efficiency. Using a patch made from a highly magnetostrictive material dramatically enhances the performance of these sensors; however, mounting this patch of smart material on a pipe requires using strong epoxy glue. Plus, applying the glue on the pipe and waiting for it to dry is time-consuming. After completing the test, leaving the pipe’s smart material could be hazardous and initiate electrochemical corrosion. Thus, smart material must be removed after the test. Nonetheless, it is complicated to remove smart material from the pipe’s surface after the glue dries. Considering such complications, as a solution to this problem, in this paper, the novel design of the pneumatic magnetostrictive patch transducer (PMPT) is proposed that benefits from the advantages of the patch in the magnetostrictive sensor but also eliminates such drawbacks. The performance of PMPT was validated by theoretical analysis and experimental investigation. The flexible, fast feature and portable configuration of the proposed PMPT make it easy to be applied on in-service pipes in different diameters with corrosion surfaces or coating.
Sparse based Latent Fingerprint Enhancement & Recognition using Minutiae Matching

The fingerprints that are obtained from the crime scenes are normally smudged,overlapped and distorted in nature. These fingerprints are known as latent fingerprints. These fingerprints normally possess unclear ridge structures which makes them really difficult to be matched from the known database of fingerprints. It is vital to recognise the owner of these fingerprints inorder to establish a proper law enforcement. So it is very important to enhance these latent fingerprints inorder to identify them by the recognition algorithm. This enhancement or denoising can be achieved by using sparse representation. Initially the total variation model is used to disintegrate the fingerprint to texture component and cartoon component. Generally the texture component holds most of the relevant information and the cartoon component holds the noisy data. Thus the cartoon component can be discarded and the texture component is further enhanced by applying Gabor dictionary learning algorithm along with sparse reconstruction. This enhanced fingerprint is then passed to the minutiae matching algorithm. It searches for the similar fingerprint from the police database on the basis of the minutiae points and coordinates. If the minutiae points in the fingerprint matches with the minutiae points of a fingerprint from the database then it would show the details of the fingerprint from the datbase. Thus the owner of the latent fingerprint can be identified.
Automated Image Captioning Using Deep Learning

Computer vision has become ubiquitous in our society, with applications in several fields. In this project, we focus on one of the visual recognition facets of computer vision, i.e image captioning. We analyze Image Cationing using deep neural networks based caption generation method. Image as input and the method can output short english sentence describes the content in the image. We used RNN, CNN, LSTM for generating suitable captions according to image. Flicker8k dataset we used for training and testing purpose.
Compact Multilayer Substrate Integrated Waveguide Dual-band Filtering Rat-race Coupler Based on Fan-shaped Cavities

─ A new type of multilayer dual-band filtering rat-race coupler based on fan shaped substrate integrated waveguide (SIW) cavity is first proposed in this paper. Resonant frequencies of SIW fan-shaped cavity (SIFC) have been derived to construct a resonant cell. Magnetic and electric coupling between SIFCs are realized through multiple rectangular and circular slots located on metal layer, respectively. Specifically, the multiple rectangular slots are beneficial to increase coupling bandwidth and achieve better amplitude and phase balance. Moreover, the bandwidth ratio and center frequency of two pass-bands can be flexibly controlled by adjusting the size of band-stop resonators. The detailed analysis and the design method based on coupling matrix have been introduced to realize a dual-band filtering rat-race coupler. The new type of component should be able to provide at least three functions simultaneously, including in-phase and out-ofphase power dividing, filtering, and dual-band operation. Compared with other filtering couplers, the proposed design exhibits good dual-band filtering responses, high Q factor, better isolation, amplitude balance, as well as 0° and 180° phase differences. Index Terms ─ Dual-band, filtering rat-race coupler, multilayer, substrate integrated fan-shaped cavity (SIFC).
Differentiable Architecture Pruning for Transfer Learning

We propose a new gradient-based approach for extracting sub-architectures from a given large model. Contrarily to existing pruning methods, which are unable to disentangle the network architecture and the corresponding weights, our architecture-pruning scheme produces transferable new structures that can be successfully retrained to solve different tasks. We focus on a transfer-learning setup where architectures can be trained on a large data set but very few data points are available for fine-tuning them on new tasks. We define a new gradient-based algorithm that trains architectures of arbitrarily low complexity independently from the attached weights. Given a search space defined by an existing large neural model, we reformulate the architecture search task as a complexity-penalized subset-selection problem and solve it through a two-temperature relaxation scheme. We provide theoretical convergence guarantees and validate the proposed transfer-learning strategy on real data.
Knee bracing vs Taping as an Adjunct to Rehabilitative Exercise in Patellofemoral Pain Syndrome Management among Basketball Players: A Prospective Study

Patellofemoral pain syndrome (PFPS) is a common source of knee problems found mostly in physically active young adults. Causative factors are multifactorial, including lower extremity weakness, especially quadriceps muscles. Conservative treatment of PFPS consists mainly of knee bracing and knee taping combined with physical therapy. This study was aimed to analyse the outcome comparison between knee bracing and knee taping in combination with rehabilitative exercise for PFPS management. A prospective study was performed involving twenty-five bas- ketball players suffering from PFPS, aged 19–30 years old with regular practice sessions. The subjects were each grouped randomly into a taping or a bracing group. Follow-up evaluations in term of Kujala patellofemoral score were done in the 1 st , 2 nd , and 4 th weeks after treatment. Unpaired t-test and chi-square were used to analyse the difference between both groups in pre- and post-intervention. Baseline characteristics of each group did not dif- fer significantly (p>0.05). Both groups had significantly higher functional scores after the 1 st , 2 nd , and 4 th weeks of intervention (p<0.05). Significantly higher functional scores were found in the bracing group on the 2 nd (p=0.013) and 4 th weeks (p=0.002). No significant differences were found in functional scores between both groups in the 1 st week (p=0.142). While both methods were efficacious, knee bracing is more effective compared to knee taping in improving functional outcomes as an adjunct to rehabilitative exercise in PFPS management.
Properties of polypropylene fibers using the green chemical orotic acid as nucleating agent

It has been reported in the technical literature that orotic acid can be used in order to induce improved crystallization of biodegradable and biocompatible polymers like poly(L-lactic acid), polyhydroxybutyrate and poly(hydroxybutyrate-co-hydroxyhexaonat). The expected advantage of the changed crystalline structure is a reinforcing effect of the polymers. A lot of papers reported about the application of inorganic and organic agents for acceleration of heterogeneous nucleation. This study reports on an attempt to use orotic acid as appropriate non-toxic nucleating agent for improving mechanical properties of isotactic polypropylene. Special attention is given to demonstrate the effect of nucleation in a typical melt spinning process in order to improve the mechanical properties. The effects were demonstrated using rheology, thermal analysis and tensile testing.
Suppression of Spruce Needle Blight in Coastal Alaska

To evaluate the efficacy of disease suppression, three fungicides were applied to 72 young Sitka spruce trees near Juneau, Alaska in an area of endemic infection by the needle blight fungus, Lirula macrospora. Treatments were applied to three branches shortly after spruce needles emerged in spring and, on one-half of the trees, again during shoot elongation. Trees treated with each of the three fungicides had significantly fewer dead needles one year after infection than did trees treated with the control. Needle death did not differ significantly among the three fungicides or on trees that were sprayed once compared to trees sprayed twice. None of the three fungicides caused observable injury to spruce tissues, nor significantly altered the shoot growth of sprayed trees. These results suggest that the disease can be controlled by applying chemical treatment at the correct time in spring shortly after bud break of spruce.
EVALUATIO OF I VITRO A TIOXIDA T ACTIVITY OF CITRUS LIMETTA A D CITRUS MAXIMA O REACTIVE OXYGE A D ITROGE SPECIES

Citrus limetta and Citrus maxima belong to the genus Citrus (Rutaceae) and are rich in flavonoids, vitamin C, folate, dietary fibre, carotenoids etc. Different parts of both plants are responsible for the prevention of cancer, other degenerative diseases, antiinflammatory activity and antidiabetic activity etc. The present study was aimed to evaluate the in vitro antioxidant activity of the methanol extracts of Citrus limetta fruit peel (MECL) and Citrus maxima (MECM) leaves in different models such as DPPH, superoxide radical and nitric oxide radical scavenging activity and inhibition of lipid peroxidation. The total phenolic compounds present in the extracts were estimated by Folin-Ciocalteu’s reagent. Both the extracts have shown good free radical scavenging activity in a dose dependent manner. The IC50 values for MECM were found to be 5.5 μg/ml, 79.16 μg/ml, 174. 97μg/ml and 111. 5μg/ml respectively for DPPH, superoxide radical, nitric oxide radical inhibition and lipid peroxidation assays. The IC50 values for DPPH, superoxide radical, nitric oxide radical inhibition and lipid peroxidation assays of MECL were 7.36 μg/ml, 9.36 μg/ml, 136.45 μg/ml and 111 μg/ml respectively. Measurement of total phenolic compounds by FolinCiocalteu’s reagent indicated that 1 mg extract contains 126.85 and 116.7 μg equivalent of pyrocatechol in MECL and MECM respectively. The present study reports significant in vitro antioxidant activity of the test plants.
Epstein–Barr virus nuclear antigen-1 is useful as therapeutic efficacy marker in serum but not in saliva of nasopharyngeal cancer patients who underwent radiotherapy

Introduction Nasopharyngeal carcinoma (NPC) is a multifactorial disease with genetic, viral, environmental and lifestyle-related risk factors. Epstein–Barr virus (EBV) can promote the oncogenic transformation of an infected cell into malignant. EBV encodes many stimulating products including Epstein–Barr virus nuclear antigen-1 (EBNA-1) which plays a key role in the regulation of gene expression and replication of the genome in the latent period of infection. EBNA-1 in serum and tumour tissue of NPC patients correlates with NPC prognosis. Moreover, the presence of EBV DNA in serum samples from NPC patients’ blood circulation can be used as an early marker in the diagnosis of NPC. Objective The objective of this study was to find effective methods for monitoring the progress of NPC patients undergoing radiotherapy and therapeutic efficacy by observing the changes in EBV DNA in serum and saliva. Methodology The pre-experimental design compared blood and saliva taken from a pre-test and post-test group of NPC patients before and after radiation therapy. The concentration of EBV DNA was measured in the serum and saliva after amplification using quantitative polymerase chain reaction (qPCR) with compatible primers for the EBNA-1 gene. The data were statistically analysed by paired T-test. Results Highly significant (p = 0.0001) increase in cycle threshold qPCR and decrease in the mean concentration of EBV DNA (p = 0.0001) were observed in serum samples, but no significant changes were observed in saliva. Conclusions The results suggest that EBV DNA in serum can be used as the gold standard and a marker for monitoring the response to radiation therapy in NPC patients, whereas the examination of EBV DNA from saliva samples is not accurate and thus, not appropriate.