Ground failure is a major contributor to fatalities in underground mines in the US. Underground coal mines in the Northern Appalachian have weak roof rock composed of shale, which is prone to failure ...under high horizontal stress. Understanding the relationship among strength, specimen size and rock petrographic parameters is essential for developing an effective ground control plan. Size effect studies have found that rock strength varies with specimen size. This paper attempts to understand this strength variation using three specimen sizes (254-mm, 508-mm, and 762-mm). The specimen strength was measured and the major petrographic parameters affecting the strength, namely grain size, grain shape, quartz content, clay content, etc. were analyzed using X-ray diffraction (XRD) and scanning electron microscopy (SEM). The petrographic parameters were then correlated with the strength of the three differently sized specimens. The results showed that 508-mm specimen had the lowest strength. Quartz content of the 508-mm specimen was lower than that of 254-mm and 762-mm specimens. Clay content and average grain size of the 508-mm specimen were higher than those of 254-mm and 762-mm specimens. These results clearly show that grain size, quartz content and clay content contribute to strength variation observed in differently sized shale specimens.
The aperture between the marketing domain and the electroencephalography (EEG)‐based brain–computer interface (BCI) has been achieved with the inception of neuromarketing. This domain helps access ...the hidden information of the preferences and tastes of the consumers who intend to purchase. Research scholars have experimented with this emerging area in multiple aspects like designing pricing, promotions, predicting purchase‐related activities, new product development, and so on. In this study, we have proposed an innovative use of neuromarketing to build a recommendation system. This recommendation system can potentially suggest suitable products to the consumer based on the past purchase behavior. This proposal carries huge potential in converting visitors to shoppers, increasing average order value, increasing the number of items per order, designing personalized promotions, and so on. The commonality of activated brain signals has been used to build this recommendation system. This neuromarketing‐based recommendation system carries the advantage over the traditional recommendation system as this system suggests products based on the actual real‐time state of the brain during the purchase. This system successfully initiated the starting point of building a neuromarketing‐based recommendation system.
Over the years, research in neuroscience-driven marketing has progressively delved into the conscious and subconscious behaviors of consumers. Existing Electroencephalography (EEG)-based studies ...related to consumer preferences toward products are not comprehensive. Due to non-stationarity issues of EEG, a significant variance is observed in inter-trial and inter-session EEG signals of a subject, which leads to challenges in building a universal consumer preference model across diverse subjects, sessions, and tasks. Transfer learning mitigates this challenge by utilizing data or knowledge from similar subjects, sessions, or tasks to improve the learning process for a new subject, session, or task, thereby enhancing overall model performance. Moreover, high-dimensional EEG features often lead to poor classification results. Therefore, selecting meaningful or refined features is of utmost importance for classification. Therefore, we propose a robust EEG-based neuromarketing framework combining deep transfer learning, spatial attention models, and deep neural networks. The proposed framework predicts the consumer choices (in terms of “likes” and “dislikes”) for e-commerce products. Initially, the knowledge distillation is performed from the pre-trained network to the proposed model, and the model is trained on the connectivity features of EEG. Next, the attention-based features are extracted from high-level connectivity features using the spatial attention model (Convolutional Block Attention Module: CBAM). CBAM extracts the attention feature maps along channel and spatial dimensions for adaptive feature refinement. The refined features improve the classification accuracy. Finally, the attention-based features are passed to the 2D CNN-based deep learning model to evaluate consumer choices. The proposed model achieves 95.60% classification accuracy with the experimental dataset. The proposed model achieves a significant improvement of 2.60% over the existing neuromarketing-based studies.
Nowadays, vehicle theft has been increased drastically all over the world. The prevention of vehicle theft is essential to enhance vehicle security. A large number of vehicles have been stolen for ...the lack of essential foundation and administration of a secure platform. Several existing systems were developed to protect the vehicle from unauthorized access. But, they suffer from various limitations such as data security, the possibility of cybercriminals activities, leakage of personal information, and centralized System. We propose a vehicle theft detection framework using a decentralized and secure platform to increase the security level of the vehicle. Blockchain and smart contracts are used to provide the security of the stored data on the ledger and authenticate a genuine user automatically with greater accuracy. In this paper, we present 2-Step Authentication (2SA) and unauthorized access detection algorithms. The 2SA ensures the secure accessibility of the application by providing the randomly token chosen by the user. The proposed framework can provide vehicle security and owner’s privacy. In this proposed framework, more than one person can drive the vehicle authorizing by the vehicle owner without hampering stored data in the vehicle device.
The present paper deals with the dynamics of a stage-structured predator-prey model, with a ratio-dependent functional response including gestational delay in the predator. The prey is carrying an ...infection which affects the predator adversely. The boundedness of solutions and the stability of equilibrium points have been investigated. There is a Hopf-bifurcation arising out of the variation in the time-delay parameter. Numerical simulations of phase-plane diagrams, and bifurcation diagrams illustrate the dependence of the system on the delay -time. The effect of the disease transmission from prey to predator has also been illustrated through simulations.
Signatures have long been considered to be one of the most accepted and practical means of user verification, despite being vulnerable to skilled forgers. In contrast, EEG signals have more recently ...been shown to be more difficult to replicate, and to provide better biometric information in response to known a stimulus. In this paper, we propose combining these two biometric traits using a multimodal Siamese Neural Network (mSNN) for improved user verification. The proposed mSNN network learns discriminative temporal and spatial features from the EEG signals using an EEG encoder and from the offline signatures using an image encoder. Features of the two encoders are fused into a common feature space for further processing. A Siamese network then employs a distance metric based on the similarity and dissimilarity of the input features to produce the verification results. The proposed model is evaluated on a dataset of 70 users, comprised of 1400 unique samples. The novel mSNN model achieves a 98.57% classification accuracy with a 99.29% True Positive Rate (TPR) and False Acceptance Rate (FAR) of 2.14%, outperforming the current state-of-the-art by 12.86% (in absolute terms). This proposed network architecture may also be applicable to the fusion of other neurological data sources to build robust biometric verification or diagnostic systems with limited data size.
•A multimodal Siamese Neural Network (mSNN) is proposed to combine EEG and Signature data for user verification.•Our mSNN model learns discriminative temporal and spatial features using LSTM and CNN encoders.•Learned features are combined into a common space to build multimodal user verification.•Our mSNN model has a 98.57% classification accuracy — 99.29% True Positive Rate and False Acceptance Rate of 2.14%.•Our mSNN model demonstrates its superiority over traditional and unimodal approaches.
Emotion detection is one of the popular research topics in “Brain–Computer Interfacing” where researchers are trying to find the various emotional states of people. EEG signal is widely used for ...detecting different categories of emotions. The EEG signal is captured through multiple electrode channels, very few of them are useful for emotion detection. In our paper, a “Correlation-based subset selection” technique is introduced for dimension reduction. Then we proceed with classification process using “Higher Order Statistics” features of the reduced set of channels. However, we have classified four classes of emotions (positive, negative, angry and harmony) in our paper. The execution time of our proposed algorithm is O(n2 + 2n). The classification accuracy of this model with the reduced set of channels is 82%. Finally, we compare our proposed model with some popular emotion classification models and the result shows that our model substantially outperforms all the previous models. However, the proposed model helps physically disabled people to express their feelings with minimum time and cost-effectively.
In recent years, vehicle theft has been increasing remarkably. It is a stigma to our society. The impacts of vehicle theft have been drastically affecting the social safety and economic condition of ...the whole world because of unavailability of a proper theft detection mechanism. The few existing vehicle anti-theft systems suffer from major problems such as the leakage of personal information, centralized-based system, proper key management, and data security. In this paper, a decentralized Blockchain-based Vehicle Anti-Theft System (BVATS) is proposed to overcome these problems using smart contracts. Blockchain is a very cutting-edge decentralized technology that is well-equipped with data immutability and a secure information-sharing platform. The smart contract is a digital agreement, which can authenticate an entity automatically and stores information by verifying the predefined condition(s). This paper also explains how Blockchain can be adopted for vehicle security and provides a stepwise implementation of the proposed methodology by providing test bed results and significant critical comparison analysis with other existing systems. Using the BVATS, more than one person can be authorized to drive a vehicle without hampering the vehicle data and maintining security.
Accurate measurements of electrical conductivities and laser Raman spectra of solutions of lithium chloride (LiCl), lithium bromide (LiBr), lithium tetrafluoroborate (LiBF
4
) and lithium perchlorate ...(LiClO
4
) in tetrahydrofuran are reported. The conductivity data have been analyzed by the Fuoss-Krauss theory, yielding values for the ion-pair and triple-ion formation constants. The Raman spectra suggest the presence of a new signal of perchlorate ClO
4
−
ions in solution, whereas there is no such evidence for the other investigated anions. The observed processes have been interpreted by an Eigen multistep mechanism. For each salt, the predominant portion is found to remain in the form of ion pairs, leaving only a small fraction of triple ions.
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