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Aggregating person-level data across multiple clinical study sites is often constrained by privacy regulations, necessitating the development of decentralized modeling approaches in biomedical research. To address this requirement, a federated nonlinear regression algorithm based on the Choquet integral has been introduced for outcome prediction. This approach avoids reliance on prior statistical assumptions about data distribution and captures feature interactions, reflecting the non-additive nature of biomedical data characteristics. This work represents the first theoretical application of Choquet integral regression to multisite longitudinal trial data within a federated learning framework. The Multiple Imputation Choquet Integral Regression with LASSO (MIChoquet-LASSO) algorithm is specifically designed to reduce overfitting and enable variable selection in federated learning settings. Its performance has been evaluated using synthetic datasets, publicly available biomedical datasets, and proprietary longitudinal randomized controlled trial data. Comparative evaluations were conducted against benchmark methods, including ordinary least squares (OLS) regression and Choquet-OLS regression, under various scenarios such as model misspecification and both linear and nonlinear data structures in non-federated and federated contexts. Mean squared error was used as the primary performance metric. Results indicate that MIChoquet-LASSO outperforms compared models in handling nonlinear longitudinal data with missing values, particularly in scenarios prone to overfitting. In federated settings, Choquet-OLS underperforms, whereas the federated variant of the model, FEDMIChoquet-LASSO, demonstrates consistently better performance. These findings suggest that FEDMIChoquet-LASSO offers a reliable solution for outcome prediction in multisite longitudinal trials, addressing challenges such as missing values, nonlinear relationships, and privacy constraints while maintaining strong performance within the federated learning framework. 

Digital Twin (DT) technology offers real-time monitoring, simulation, optimization, and precise forecasting. However, its theoretical framework and practical implementation, particularly in digital random controlled trials (RCT), remain underdeveloped, limiting its full potential. Therefore, we seek to develop an application of digital twins with the virtual reality using data from four longitudinal RCT in Massachusetts. As case studies, we create digital twins of two individuals from these RCT. This application of digital twins provides an avenue for a more personalized healthcare experience for patients, the enhancement of medical simulations, and a visualization for predictive analytics. In the future, we will move from virtual reality to extended reality with AI-generated digital twin models. 

Not AvailableThis work presents a digital twin framework based on smart rings. The compact design of smart rings with a composite sensor enables a digital twin as a viable option for mobile health monitoring, analysis and prediction of biomarkers over time. Built upon sensor data from a smart ring such as Photoplethysmogram (PPG), peripheral oxygen saturation (SpO2), physical motion, and others, a digital twin can provide continuous predictive insights. This framework enables the detection of anomalies in heart rate, monitoring of sleep patterns, evaluation of blood oxygen levels, and assessment of stress. Additionally, it integrates these findings into visual representations, enhancing the understanding of health outcomes. 

Beamforming techniques are considered as essential parts to compensate severe path losses in millimeter-wave (mmWave) communications. In particular, these techniques adopt large antenna arrays and formulate narrow beams to obtain satisfactory received powers. However, performing accurate beam alignment over narrow beams for efficient link configuration by traditional standard defined beam selection approaches, which mainly rely on channel state information and beam sweeping through exhaustive searching, imposes computational and communications overheads. And, such resulting overheads limit their potential use in vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communications involving highly dynamic scenarios. In comparison, utilizing out-of-band contextual information, such as sensing data obtained from sensor devices, provides a better alternative to reduce overheads. This paper presents a deep learning-based solution for utilizing the multi-modality sensing data for predicting the optimal beams having sufficient mmWave received powers so that the best V2I and V2V line-of-sight links can be ensured proactively. The proposed solution has been tested on real-world measured mmWave sensing and communication data, and the results show that it can achieve up to 98.19% accuracies while predicting top-13 beams. Correspondingly, when compared to existing been sweeping approach, the beam sweeping searching space and time overheads are greatly shortened roughly by 79.67% and 91.89%, respectively which confirm a promising solution for beamforming in mmWave enabled communications. 

Obesity is a common, serious, and costly chronic disease in the United States, a risk factor for several major cancers, linked to higher rates of illness and death. It is thus a critical issue that needs attention from health care professionals and the public alike. We use a novel approach to target nonstandard variations to better understand the variables associated with weight loss. We introduce a new methodology using the Choquet Integral with fuzzy measure, an approach that accounts for interactions between measured features. The Choquet Integral has limited sourced applications to the biomedical field despite widespread use in theoretical mathematics and economics. Our technique applies it to health data to show a robust method to target and optimize weight loss parameters. We identify data versus noise, optimally choose a reduced version of the powerset for computability purposes, and identify the sub-additive cooperative learning bound of the Choquet Integral. We show that the proposed technique targets heretofore unknown variations in predictive weight loss studies with broad potential applications. 

Connected and autonomous vehicles (CAVs) will revolutionize tomorrow’s intelligent transportation systems, being considered promising to improve transportation safety, traffic efficiency, and mobility. In fact, envisioned use cases of CAVs demand very high throughput, lower latency, highly reliable communications, and precise positioning capabilities. The availability of a large spectrum at millimeter-wave (mmWave) band potentially promotes new specifications in spectrum technologies capable of supporting such service requirements. In this article, we specifically focus on how mmWave communications are being approached in vehicular standardization activities, CAVs use cases and deployment challenges in realizing the future fully connected settings. Finally, we also present a detailed performance assessment on mmWave-enabled vehicle-to-vehicle (V2V) cooperative perception as an example case study to show the impact of different configurations. 

Occlusion is a critical problem in the Autonomous Driving System. Solving this problem requires robust collaboration among autonomous vehicles traveling on the same roads. However, transferring the entirety of raw sensors' data among autonomous vehicles is expensive and can cause a delay in communication. This paper proposes a method called Realtime Collaborative Vehicular Communication based on Bird's-Eye-View (BEV) map. The BEV map holds the accurate depth information from the point cloud image while its 2D representation enables the method to use a novel and well-trained image-based backbone network. Most importantly, we encode the object detection results into the BEV representation to reduce the volume of data transmission and make real-time collaboration between autonomous vehicles possible. The output of this process, the BEV map, can also be used as direct input to most route planning modules. Numerical results show that this novel method can increase the accuracy of object detection by cross-verifying the results from multiple points of view. Thus, in the process, this new method also reduces the object detection challenges that stem from occlusion and partial occlusion. Additionally, different from many existing methods, this new method significantly reduces the data needed for transfer between vehicles, achieving a speed of 21.92 Hz for both the object detection process and the data transmission process, which is sufficiently fast for a real-time system. 

Digital technology has huge potentials in transforming clinical trial research. One common issue in digital clinical trials for long-term behavioral treatments is incomplete longitudinal data, as subjects’ behavior changes over time. In this paper, we aim to improve the fuzzy clustering accuracy and stability of digital clinical trials by intelligently searching for the optimal fuzzifier, which is the key to identify the optimal number of overlapped clusters for incomplete longitudinal data. Our findings showed that integrating optimal fuzzifier searching with cluster validation can streamline the clustering process, thus enabling the intelligent fuzzy clustering procedure.