As a rapidly expanding family of two-dimensional (2D) materials, MXenes have recently gained considerable attention due to their appealing properties. Here, by developing a solution-based coating method that enables transfer-free and layer-by-layer film coating, we investigate the layer-dependent nonlinear optical absorption of Ti3C2Tx films ‒ an important member of the MXene family. By using the Z-scan technique, we characterize the nonlinear absorption of the prepared MXene films consisting of different numbers of monolayers. The results show that there is a strong and layer-dependent nonlinear absorption behavior, transitioning from revisable saturable absorption (RSA) to saturable absorption (SA) as the layer number increases from 5 to 30. Notably, the nonlinear absorption coefficient β varies significantly within this range, changing from ~7.13 × 102 cm/GW to ~-2.69 × 102 cm/GW. We also characterize the power-dependent nonlinear absorption of the MXene films at various incident laser intensities, and a decreasing trend in β is observed for increasing laser intensity. These results reveal the intriguing layer-dependent nonlinear optical properties of 2D MXene films, highlighting their versatility and potential for implementing high-performance nonlinear photonic devices.

This paper highlights the contribution of utilizing ensemble deep learning with auto-encoders (AEs) for out-of-distribution data detection. The key innovation is treating ensemble UQ as a regression problem, mapping uncertainty distribution to a single model, reducing computational demands. This approach aligns well with the ensemble of AEs’ uncertainty distribution, making it valuable for resource-constrained systems and rapid decision-making in computational intelligence.

Contribution: The study discusses the relationship between interpersonal trust and team performance in an exploratory way. The empirical data is collected from students in Electrical Engineering Practice Training Course classes to evaluate their team performance. Background: Interpersonal trust within a team plays a critical role in solving problems and enhancing group performance effectiveness. Research Question: Does interpersonal trust within a team have an impact on team performance? Can interpersonal trust between teammates be improved? Design/Approach: This research presents a case study of 132 electrical engineering university students in China. The students worked in teams completing six simulation experiments in a semester. Findings: The teams always with permanent teammates had the highest level of interpersonal trust and performed the best and the teams always with tentative teammates had the lowest level of trust interpersonal trust and performed worse and worse while the teams whose members were fixed by the instructor increased their interpersonal trust quickly and performed better and better over time. Conclusions: The higher the interpersonal trust within a team, the better the team performance; low initial interpersonal trust within a team can be promoted through intervention.

On the one hand, Reconfigurable Intelligent Surfaces (RISs) emerge as a promising solution to meet the demand for higher data rates, improved coverage, and efficient spectrum utilization. On the other hand, Non-Terrestrial Networks (NTNs) offer unprecedented possibilities for global connectivity. Moreover, the NTN can also support the upsurge in the number of Internet of Things (IoT) devices by providing reliable and ubiquitous connectivity. Although NTNs have shown promising results, there are several challenges associated with their usage, such as signal propagation delays, interference, security, etc. In this article, we have discussed the possibilities of integrating RIS with an NTN platform to overcome the issues associated with NTN. Furthermore, through experimental validation, we have demonstrated that the RIS-assisted NTN can play a pivotal role in improving the performance of the entire communication system.

Emotion recognition, a process of automatic cognition of human emotions, has great potential to improve the degree of social intelligence. Among various recognition methods, Emotion recognition based on touch event’s temporal and force information receives global interests. Although previous studies have shown promise in the field of keystroke-based emotion recognition, they are limited by the need for long-term text input and the lack of high-precision force sensing technology, hindering their real-time performance and wider applicability. To address this issue, in this paper, a piezoelectric-based keystroke dynamic technique is presented for quick emotion detection. The nature of piezoelectric materials enables high-resolution force detection. Meanwhile, the data collecting procedure is highly simplified because only the password entry is needed. International Affective Digitized Sounds (IADS) are applied to elicit users’ emotions, and a PAD emotion scale is used to evaluate and label the degree of emotion induction. A Random Forest (RF) based algorithm is used in order to reduce the training dataset and improve algorithm portability. Finally, an average recognition accuracy of 79.37% of 4 emotions (happiness, sadness, fear, disgust) is experimentally achieved. The proposed technique improves the reliability and practicability of emotion recognition in realistic social systems.

Diabetes mellitus, particularly type-2 diabetes, remains a prevalent health issue, raising concerns due to its associated risk of complications. Among these, cardiovascular complications pose a significant threat, exhibiting high morbidity and mortality rates. Health screening plays a pivotal role in stratifying the risk levels of diabetes patients, facilitating proactive measures to prevent the progression of complications. As such, the primary objective of this study is to develop a predictive model system for assessing cardiovascular risk in diabetes patients. Our study used the Cardiovascular Disease dataset and conducts experiments with various supervised machine learning algorithms, such as Naive Bayes, decision tree, random forest, AdaBoost, and XGBoost. The results reveal that ensemble learning algorithms based on boosting, particularly AdaBoost and XGBoost, outperform other supervised machine learning methods. However, even with the best performance achieved using the dataset, the accuracy stands at 0.71, and the F-1 score is 0.69, which is still acceptable for screening purposes. Although these results provide valuable insights, indicating individuals at higher risk for cardiovascular complications in diabetes, further improvements are needed to enhance early prevention strategies.

[ACCEPTED FOR PUBLICATION IN THE DAIS JOURNAL OF SCIENCE 2023] Intercity travel optimization has always been a goal of every government. Such an optimization would allow efficient and economical development. Optimizing travel would facilitate transfer of ideas and resources and promote economic growth. As such, travel can be optimized on two fronts: cost of arranging for that travel and the ease of travel i.e., the length/time of travel for a person to go from any one city to any other. This study proposes a methodology of optimizing travel pathways in any circumstance through genetic algorithm metaheuristics and network theory representations. This study demonstrates the same to optimize inter-city travel between the 25 cities with the highest population in India.

A lightweight cylindrical Frequency Selective Surface (FSS) using high-pass inductive mesh grid is proposed as a radiofrequency (RF) shield for solenoid coils at 2.84 MHz working in a portable MRI system with a transversal B0-field averaged at 67 mT. Using a solenoid coil of 60 mm diameter as an example, the proposed FSS shield shows good shielding effectiveness at different wall-to-wall distances between the coil and the shield (5-25 mm). It also shows less compromise in B1-sensitivity when compared to the coil with a copper shield. The effects of the FSS shield were examined numerically and validated experimentally. When the wall-to-wall distance is 5 mm, the solenoid coil with the proposed FSS shield is compact, lightweight, and shows B1-sensitivity of more than 50% higher than the copper counterparts with comparable shieding effectiveness. Compared to an unshielded coil, it shows a wider bandwidth which benefits the excitation of an MRI system that has less homogeneous fields and a smaller inductance which indicates less trapping of energy and thus can enable different pulse sequence programming by taking this approach. This study shows that an FSS shield with inductive mesh grids around a solenoid coil is a promising approach for compact shielding without a big shielding box. It will contribute to the compactness of a portable MRI system.

A computer program was built to model a random walk that represents utilization of a limited set of items by a group of servers for  their customers. An example is the use of ventelators (items) for very sick patients (customers)  by hospitals (servers). Some hospitals amy run out of ventilators while others may have extras if a common pool of ventilators is not employed. The simulation showed a 5% improvement in one of the cases studied. A theoretical model was developed that displayed very accurate prediction of the simulation results.

We present a novel theory of the microstructure of deep neural networks. Using a theoretical framework called \textit{statistical teleodynamics}, which is a conceptual synthesis of statistical thermodynamics and potential game theory, we predict that all highly connected layers of deep neural networks have a universal microstructure of connection strengths that is distributed lognormally ($LN (\mu, \sigma$)). Furthermore, under ideal conditions, the theory predicts that $\mu$ and $\sigma$ are the same for all layers in all networks. This is shown to be the result of an arbitrage equilibrium where all connections compete and contribute the same effective utility towards the minimization of the overall loss function. These surprising predictions are shown to be supported by empirical data from six large-scale deep neural networks in real life. We also discuss how these results can be exploited to reduce the amount of data, time, and computational resources needed to train large deep neural networks.

The exploration and categorization of essential and synthetic lethality genes hold significant importance in seeking effective and targeted therapies for diverse ailments. This endeavor hinges upon genetic minimal cut sets (gMCSs), which also find utility in metabolic engineering. There have been various methods suggested for calculating gMCSs. Still, with the emergence of numerous new models and their growing intricacy, it has become vital to introduce new algorithms in this field. This paper presents a new algorithmic approach for computing gMCSs, which utilizes linear programming techniques to improve temporal efficiency. The key concept of the method is to use a k-representative subset to replace the target set with a smaller one.

Continuous monitoring and prediction of Length of Stay (LoS) for critically ill patients admitted to Intensive Care Units (ICU) can help anticipate on their needs, thereby reducing risks of adverse events occurring, efficiently allocate resources and improve care. In this work, four different sequence-to-sequence deep learning models (Long-Short Term Memory, Gated Recurrent Units, Temporal Convolution Network and the Transformer network) were implemented to predict hourly remaining LoS, using 271 input features extracted from 20,489 ICU stays. Input sequences for model training were created using two approaches: (i) by stacking data one hour at a time and (ii) by using a sliding window of length $l$ shifted one hour at a time. To enhance model explainability, both the models’ latent space visualization and feature importance ranking were used. Compared against baseline models, the sequential models reported the best performance, reducing the prediction error by almost 3 days. Moreover, the sliding window approach showed improvement both in terms of performance and training time whilst requiring less resources. The latent space visualization of each model using t-SNE revealed that, with only 6 hours of data, the models are able to learn early clinical patterns that separate short ($0<\text{LoS}\leq3$ days) from medium ($3<\text{LoS}<7$ days) and long ($7\leq\text{LoS}<+\infty$ days) stays, and, the models are able to capture existing correlations between long LoS and risk of mortality, the latter not used during modeling. The benefit of this approach in a true hospital setting, besides LoS prediction, is that early clustering enables the early identification of long stays with shown existing risk of death, to whom prompt allocation of focus and adequate resources can be delivered.

Speech-driven gesture generation is an emerging field within virtual human creation. However, a significant challenge lies in accurately determining and processing the multitude of input features (such as acoustic, semantic, emotional, personality, and even subtle unknown features). Traditional approaches, reliant on various explicit feature inputs and complex multimodal processing, constrain the expressiveness of resulting gestures and limit their applicability. To address these challenges, we present Persona-Gestor, a novel end-to-end generative model designed to generate highly personalized 3D full-body gestures solely relying on raw speech audio. The model combines a fuzzy feature extractor and a non-autoregressive Adaptive Layer Normalization (AdaLN) transformer diffusion architecture. The fuzzy feature extractor harnesses a fuzzy inference strategy that automatically infers implicit, continuous fuzzy features. These fuzzy features, represented as a unified latent feature, are fed into the AdaLN transformer. The AdaLN transformer introduces a conditional mechanism that applies a uniform function across all tokens, thereby effectively modeling the correlation between the fuzzy features and the gesture sequence. This module ensures a high level of gesture-speech synchronization while preserving naturalness. Finally, we employ the diffusion model to train and infer various gestures. Extensive subjective and objective evaluations on the Trinity, ZEGGS, and BEAT datasets confirm our model's superior performance to the current state-of-the-art approaches. \Persona-Gestor improves the system's usability and generalization capabilities, setting a new benchmark in speech-driven gesture synthesis and broadening the horizon for virtual human technology. Supplementary videos and code can be accessed at https://zf223669.github.io/Diffmotion-v2-website/

A document by Wazib Ansar . Click on the document to view its contents.

Background: Although early rehabilitation is important following a stroke, severely affected patients have limited options for intensive rehabilitation as they are often bedridden. To create a system for early rehabilitation of lower extremities in severely affected patients, we have combined the robotic manipulator ROBERT® and EMG-triggered FES and developed a novel user-driven Assist- As-Needed (AAN) control approach. The method is based on a state machine that can detect user movement capability and provide different levels of assistance, as required by the patient (no support, FES only, and simultaneous FES and mechanical support). Methods: To technically validate the system, we tested 10 able-bodied participants who were instructed to perform specific behaviors to trigger the desired system states while conducting knee extension and ankle dorsal flexion exercise. In addition, the system was tested on two stroke patients to establish the clinical feasibility. Results: The technical validation showed that the state machine correctly detected the participants’ behavior and activated the target AAN state in more than 96% of the exercise repetitions. The clinical feasibility test showed that the system successfully recognized the patients’ movement capacity and activated assistive states according to their needs, providing the minimal level of support required to perform the exercise successfully. Conclusions: The system was technically validated and preliminarily proven clinically feasible. The present study shows that the novel system can be used to deliver exercises with a high number of repetitions while engaging the participants’ residual capabilities through an effective AAN strategy.

Accurate trajectory prediction is vital for various applications, including autonomous vehicles. However, the complexity and limited transparency of many prediction algorithms often result in black-box models, making it challenging to understand their limitations and anticipate potential failures. This further raises potential risks for systems based on these prediction models. This study introduces the Performance Monitor for Black-Box Trajectory Prediction Model (PMTM) to address this challenge. The PMTM estimates the performance of black-box trajectory prediction models online, enabling informed decision-making. The study explores various methods’ applicability to the PMTM, including anomaly detection, machine learning, deep learning, and ensemble, with specific monitors designed for each method to provide real-time output representing prediction performance. Comprehensive experiments validate the PMTM’s effectiveness, comparing different monitoring methods. Results show that the PMTM effectively achieves promising monitoring performance, particularly excelling in deep learning-based monitoring. It achieves improvement scores of 0.81 and 0.79 for average prediction error and final prediction error monitoring, respectively, outperforming previous white-box and gray-box methods. Furthermore, the PMTM’s applicability is validated on different datasets and prediction models, while ablation studies confirm the effectiveness of the proposed mechanism. Hybrid prediction experiments further demonstrate the value of the proposed monitor from an application perspective.

Road traffic crashes in urban areas pose significant safety and efficiency challenges. This paper presents an approach, merging case studies and literature reviews, to mitigate urban road traffic crashes and enhance safety. The study commences with an extensive literature review, delving into existing research on road traffic crashes. This review establishes the groundwork for comprehending urban road safety solutions, insights into ongoing research trends, and guiding case study selection. Three case studies parallel analogous cities where Intelligent Transportation Systems and other smart city initiatives targeting road safety succeeded. Each case dissects safety interventions, accomplishments, and lessons learned. Scrutinizing these studies aims to unveil trends underpinning a holistic road safety strategy. From literature and case studies, a comprehensive road safety strategy emerges. It leverages advanced technology, data insights, and proactive measures to enhance road safety, curtail accidents, and optimize urban traffic. This research yields an all-encompassing road safety strategy for policymakers, planners, and safety practitioners to elevate urban road safety. It promotes secure, efficient, technologically advanced urban areas, endorsing sustainable transportation, elevating residentsâ\euro™ lives, and positioning cities as urban mobility leaders. Furthermore, this study contributes to the academic literature on road safety and urban transportation. Through case studies and literature reviews, it supplements knowledge about effective road traffic crash mitigation strategies. These insights have the potential to guide future research, advancing road safety and urban transportation domains.

In the contemporary digital age, the healthcare sector is inundated with a plethora of data, emanating from various sources such as Electronic Health Records (EHRs), wearable devices, and telemedicine platforms. The management, security, and efficient utilization of this data present a formidable challenge, especially considering the stringent regulatory and ethical standards governing healthcare data. This research explores an innovative concoction, blending the robustness of JavaBeans with the immutable and decentralized nature of blockchain technology, aiming to optimize healthcare data management. The study delves into the development of a secure, scalable, and interoperable data management system that ensures the confidentiality, integrity, and availability of healthcare data, while also facilitating seamless data sharing among authorized entities. Through the integration of Java and blockchain technology, this research seeks to navigate through the complexities of healthcare data management, providing a platform that not only adheres to regulatory compliance but also enhances the quality of healthcare delivery. The proposed system leverages the object-oriented and platform-independent features of Java, coupled with the secure and transparent attributes of blockchain, to establish a healthcare data management system that is secure, user-friendly, and adaptable to the evolving healthcare landscape. This research encompasses the systems design, development, implementation, and evaluation, providing insights into its efficacy, challenges encountered, and potential for future adaptations and scalability in the ever-dynamic healthcare domain.