A Non-Anatomical Graph Structure for isolated hand gesture separation in continuous gesture sequences

Continuous Hand Gesture Recognition (CHGR) has been extensively studied by researchers in the last few decades. Recently, one model has been presented to deal with the challenge of the boundary detection of isolated gestures in a continuous gesture video [17]. To enhance the model performance and also replace the handcrafted feature extractor in the presented model in [17], we propose a GCN model and combine it with the stacked Bi-LSTM and Attention modules to push the temporal information in the video stream. Considering the breakthroughs of GCN models for skeleton modality, we propose a two-layer GCN model to empower the 3D hand skeleton features. Finally, the class probabilities of each isolated gesture are fed to the post-processing module, borrowed from [17]. Furthermore, we replace the anatomical graph structure with some non-anatomical graph structures. Due to the lack of a large dataset, including both the continuous gesture sequences and the corresponding isolated gestures, three public datasets in Dynamic Hand Gesture Recognition (DHGR), RKS-PERSIANSIGN, and ASLVID, are used for evaluation. Experimental results show the superiority of the proposed model in dealing with isolated gesture boundaries detection in continuous gesture sequence

Strategies to Improve the Electrochemical Performance of Aluminum Anodes in Primary Alkaline Aluminum-Air Batteries

Passivation layers and self-corrosion developed on the Aluminum anodes in alkaline Aluminum-air batteries can significantly reduce the widespread application and capacity density of the batteries. Most corrosion prevention techniques focus on changing the electrode and electrolyte, including electrode alloying, additives for the electrolyte, and nonaqueous electrolytes. This thesis concentrates on some novel approaches like application of ultrasonic waves, as well as cold working to prevent the formation of passivation layers and hydrogen evolution. Furthermore, a conventional strategy of adding different inhibitors, including cerium chloride, a hybrid inhibitor of sodium vanadate and nanoclay is investigated. Experimental analyses were performed using ultrasonication generators, and a frequency range of 28 - 50 kHz and a power range of 20 - 120 W were provided. These variables were combined and examined to determine how ultrasonic frequency and power affected the performance of the Al anode. For the OCP measurements, the ultrasonic irradiation was continually turned on and off to evaluate the impact of ultrasonication with non-ultrasonic circumstances on the potential change. In addition, it was confirmed that the potential shifts toward more adverse values when ultrasonication was used. According to the findings, the anode exposed to ultrasonic waves exhibited greater corrosion resistance, a lower current density of corrosion, and more negative corrosion potential than the anode not exposed to them. Furthermore, the use of ultrasonic waves increased anodic efficiency and decreased hydrogen development on the Al anode surface. Another strategy was employed for commercially AA1100 and AA7050 to reduce the thickness of the samples by 10%, 25%, 50%, 90%, and 95%, using cold working on a rolling machine. It was found that cold-worked anodes had a more negative corrosion potential and a lower corrosion current density, which suggested a higher level of electrochemical activity and improved anti-corrosion behaviour. When the amount of cold working increased, the rate of self-corrosion decreased. The amount of the drop was greater for the alloy AA7050 than for AA1100, demonstrating the inhibitory impact of the MgO and ZnO layers formed on the surface of AA7050 after immersion in KOH solution. Electrolyte additives such as cerium chloride, sodium vanadate and nanoclay at different concentrations were used to examine the anticorrosion inhibitory effect of the additives on the prevention of self-corrosion of Al anodes. It was figured out that the efficiency of the Al anode increased from 43.8% to 76.1% as the cerium chloride concentration rose, and the capacity density rose from 1294 to 2244 mAh.g-1. The outcomes demonstrated that the addition of vanadate, nanoclay, or a blend of the two significantly decreased Al anode corrosion. However, compared to 57.6% for vanadium and 69.8% for nanoclay, the hybrid additive\u27s 72.6% inhibitory efficiency was the highest. With the help of a hybrid inhibitor, the anode\u27s anodic efficiency and capacity density were increased to 81.4% and 2426 mAh.g-1, respectively

A Conditional Generative Chatbot using Transformer Model

A Chatbot serves as a communication tool between a human user and a machine to achieve an appropriate answer based on the human input. In more recent approaches, a combination of Natural Language Processing and sequential models are used to build a generative Chatbot. The main challenge of these models is their sequential nature, which leads to less accurate results. To tackle this challenge, in this paper, a novel end-to-end architecture is proposed using conditional Wasserstein Generative Adversarial Networks and a transformer model for answer generation in Chatbots. While the generator of the proposed model consists of a full transformer model to generate an answer, the discriminator includes only the encoder part of a transformer model followed by a classifier. To the best of our knowledge, this is the first time that a generative Chatbot is proposed using the embedded transformer in both generator and discriminator models. Relying on the parallel computing of the transformer model, the results of the proposed model on the Cornell Movie-Dialog corpus and the Chit-Chat datasets confirm the superiority of the proposed model compared to state-of-the-art alternatives using different evaluation metrics

Multi-Modal Deep Hand Sign Language Recognition in Still Images Using Restricted Boltzmann Machine

In this paper, a deep learning approach, Restricted Boltzmann Machine (RBM), is used to perform automatic hand sign language recognition from visual data. We evaluate how RBM, as a deep generative model, is capable of generating the distribution of the input data for an enhanced recognition of unseen data. Two modalities, RGB and Depth, are considered in the model input in three forms: original image, cropped image, and noisy cropped image. Five crops of the input image are used and the hand of these cropped images are detected using Convolutional Neural Network (CNN). After that, three types of the detected hand images are generated for each modality and input to RBMs. The outputs of the RBMs for two modalities are fused in another RBM in order to recognize the output sign label of the input image. The proposed multi-modal model is trained on all and part of the American alphabet and digits of four publicly available datasets. We also evaluate the robustness of the proposal against noise. Experimental results show that the proposed multi-modal model, using crops and the RBM fusing methodology, achieves state-of-the-art results on Massey University Gesture Dataset 2012, American Sign Language (ASL). and Fingerspelling Dataset from the University of Surrey's Center for Vision, Speech and Signal Processing, NYU, and ASL Fingerspelling A datasets

Spoken Persian digits recognition using deep learning

Classification of isolated digits is a fundamental challenge for many speech classification systems. Previous works on spoken digits have been limited to the numbers 0 to 9. In this paper, we propose two deep learning-based models for spoken digit recognition in the range of 0 to 599. The first model is a Convolutional Neural Network (CNN) model that uses the Mel spectrogram obtained from the audio data. The second model uses the recent advances in deep sequential models, especially the Transformer model followed by a Long Short-Term Memory (LSTM) Network and a classifier. Moreover, we also collected a dataset, including audio data by a contribution of 145 people, covering the numerical range from 0 to 599. The experimental results on the collected dataset indicate a validation accuracy of 98.03%

Functional Outcomes of Temporomandibular Joint Ankylosis Treatments: A 10-year cohort study

Introduction : Ankylosis of the temporomandibular joint (TMJ) is a disabling condition due to the fusion of joint to the base of skull and results in mouth opening limitation. Several surgical techniques have been described for treatment of this condition but no consensus has been reached. This study sought to assess the success of treatment with regard to long-term functional improvement and rate of complications in ankylosis patients during a 10-year period. Materials and Methods: Patients who underwent unilateral or bilateral condylectomy without joint reconstruction during 2001-2011 in the Maxillofacial Surgery Department of Shariati Hospital were evaluated in this historical cohort study. The patients were recalled to ensure the accuracy of information in their medical records and were clinically examined. Improvement in their joint function and rate of complications were evaluated. Data were analyzed using Wilcoxon Signed rank test, multivariate tests, Mauchly's sphericity test and McNemar’s test. Results: A total of 27 subjects (13 males and 14 females) with a mean age of 34.8 years and 6.1 years mean duration of follow-up were evaluated. The results of observation showed that trauma was the most common cause of ankylosis (63%). The most common type of ankyloses was fibrous (55.6%) and 55.6% of the patients had bilateral ankylosis. Maximum mouth opening (MMO), the amount of lateral movement and open bite significantly improved after the operation (P<0.001). Frontal, zygomatic and buccal nerves had been injured in 4, 4 and 3 patients, respectively during the operation. Conclusion: Condylectomy without reconstruction significantly improves the TMJ function in patients with TMJ ankylosis with regard to MMO, the amount of lateral movement, maintenance of occlusion and the skeletal form

Fabrication and optimization of superhydrophobic ZnO-SA/PVC/PVP nanocomposite membrane distillation for highly saline RO brine recovery

The induced phase separation method was used to fabricate polyvinyl chloride (PVC) flat sheets for membrane distillation (MD) of RO brine feed by using dimethylformamide (DMF) and water as solvent and nonsolvent, respectively. Polyvinylpyrrolidone (PVP) and zinc oxide (ZnO) nanoparticles were utilized to improve the membrane structure and modify pore surfaces. The Taguchi experimental design approach was employed to investigate the impacts of concentrations of PVP and ZnO nanoparticles on the membrane's structural characteristics and performance. SEM, XRD, and FT-IR were used to characterize the surface and cross-sectional morphology, as well as the presence of crystalline phases and cross-linked organic groups, respectively. The water contact angle was measured to determine the wettability of the surface membrane and the impact of ZnO nanoparticles on its hydrophobicity. The membrane synthesis and MD process parameters were optimized for a Persian Gulf feed brine to obtain a maximum contact angle of 148°, under 80 °C and 12 L.min-1 circulating feed water, and resulted in high salt rejection (96.4%) and proper permeability water flux (4.2 L.m-2h-1)

Salvurmin A and Salvurmin B, Two Ursane Triterpenoids of Salvia urmiensis Induce Apoptosis and Cell Cycle Arrest in Human Lung Carcinoma Cells

Background: Ursane triterpenoids could be considered as novel multi-target therapeutic anti-cancer agents. Salvurmin A and Salvurmin B are novel cytotoxic ursane triterpenoids isolated from the aerial parts of Salvia urmiensis, an endemic plant species of Iran. Methods: In this study, we assessed cytotoxicity of these compounds against two human cancer cell lines and one human normal cell line and investigated its mechanism via apoptosis and cell cycle arrest. Results: Salvurmin A and B showed the most cytotoxic effect on A549 cells compared to other studied cancer cells. IC50 values for Salvurmin A and B against A549 cells were 35.6 ± 1.5 and 19.2 ± 0.8 µM, respectively. Based on annexin V staining, both of these compounds significantly induced apoptosis in A549 cells. Moreover, these two compounds significantly increased cell accumulation in G2/M and decreased the number of cells in G0/G1 phases in A549 cells in a dose-dependent manner. Conclusion: Based on the results Salvurmin B can be considered as potential candidate for further studies against human lung carcinoma

Face recognition using fine-tuning of Deep Convolutional Neural Network and transfer learning

Deep learning is one of the most important scopes of the Machine Learning that includes some important architectures. Deep Convolutional Neural Network is one of the attractive architectures that uses in digital image processing. In this paper, we use the Alexnet model for face recognition from input images. We fine-tune the Alexnet model by converting one or two fully connected layers to convolutional layers as well as using the suitable filters. To improve the robustness of the model in coping with the situations that some parts of the input images damaged, we use five crops of the input images including five pixel areas. Furthermore, to visualize the output of each layer, we use the Deconvolution technique in our method. The output of some convolutional and activation layers has been shown. Using this technique, we obtain the Heat-map of the image. To show the results, we use the LFW and Caltech faces datasets. After pre-processing the images of datasets, we compare the results of the Alexnet model in two states: before fine-tuning and after fine-tuning. The results show the recognition accuracy improvement of the fine-tuned models on input images