BACKGROUND: Integrative use of multi-omics data can enhance genomic prediction, yet its application remains challenged by the high cost, temporal specificity, and instability of transcriptomic signals across developmental stages. To address these limitations, it is crucial to utilize small, high-quality multi-omics datasets to efficiently identify stable, major-effect SNPs that can be applied to larger populations with genomic data alone. We propose AbGP (Attention-based Genomic Prediction), a framework designed to extract these robust genomic features. RESULTS: Using a discovery population of Huaxi cattle (HX_A, n = 218) with matched genotype and transcriptome data, AbGP employed a self-attention mechanism to identify a compact, high-value subset of SNPs (top 1.25%). The model’s predictive power was validated in a large, independent population (HX_B, n = 1496), where it significantly outperformed GBLUP and machine learning baselines for economic traits. CONCLUSION: AbGP effectively distills complex multi-omics information into a small subset of key SNPs that capture essential non-linear genetic architectures. This approach improves prediction accuracy and model stability, facilitating practical deployment in Huaxi cattle breeding.

Placenta Accreta Spectrum (PAS) is a rare but highly dangerous obstetric disease. Early and accurate PAS diagnosis is critical for maternal health. Traditional PAS diagnosis relies on experienced doctors by analyzing the cesarean history and Magnetic Resonance Imaging (MRI) data. However, district-level hospitals often lack the expertise and resources for accurate PAS diagnosis. To address these challenges, we establish the first MRI-based PAS dataset, which includes both fine-grained segmentation and classification annotations. Meanwhile, diagnosing PAS can be significantly enhanced by segmenting lesion areas from MRI images of the uterus. To achieve automatic PAS diagnosis, we propose 3DSAMba, a novel feature learning framework for effective lesion segmentation. More specifically, we first design a 3D Segment Anything Model (SAM) and incorporate medical domain information into the model through an efficient adapter mechanism. In addition, we introduce a Multi-Level Aggregation Mamba (MLAM) to aggregate feature maps across different levels and a Fusion State Space Model (FSSM) to fuse multi-scale features from both the encoder and decoder. Finally, we apply segmentation masks to the original MRI images through element-wise multiplication, effectively isolating lesion areas for more accurate PAS diagnosis. Extensive experiments validate that our framework significantly improves the PAS diagnostic performance. To facilitate further research in PAS diagnosis, we have released the dataset and source code at https://github.com/Drchip61/PASD.

Ridesharing service, as a sustainable transportation mode, has gained great interest in both industry and academic fields. Existing TNC ridesharing services provide prescriptive solutions without coordinating riders’ interactions and intentions on route choices. As a result, ridesharing services often end with ride-hailing services, and they are still under a relatively low usage rate. Motivated by this view, this study developed a real-time coordinated ridesharing route choice mechanism (CSM) for guiding riders’ ridesharing route choices to fill this research and application gap. Specifically, this study modeled this CSM as a pure-strategy atomic fare-sharing game based on the assumption that every rider is selfish and tries to choose the best route to minimize his/her travel fare among multiple feasible candidate routes. An existing tree-generation algorithm was used to find the candidate routes for each rider. This study proved the existence of a Nash Equilibrium in this game by constructing a potential function and proving this game is a potential game. This study further developed a sequential updated distributed algorithm and proved its convergence to explore an equilibrium solution of the CSM. To address the scalability issue, this study created a coalition formation approach based on ridesharing potential, to separate riders into ridesharing coalitions and then independently implement the CSM for each ridesharing coalition. Our experiments illustrate that the coalition approach scales down the problem size of each CSM and dramatically improves the computation efficiency while maintaining the same level of system performance. More importantly, the CSM can significantly promote riders’ usage of the ridesharing service by greatly saving their travel fares while satisfying their trip requirements.

Obtaining future traffic state evolution information is critical to traffic control algorithms design and further to intelligent transportation systems. However, accurately predicting traffic state evolution is not an easy task, although the traffic prediction-related study attracted a lot of attention. This study develops a macroscopic traffic flow model-integrated deep learning framework ($\mathbf{MTFD}$) for the high-resolution temporal-spatial traffic state dynamic propagation on a road network, integrating temporal-spatial traffic dependency, traffic flow theory, and data analysis techniques. First, traffic state propagation on every road section is mathematically described by the$\mathbf{CTM}$model given traffic initial and boundary conditions. Next, a temporal-spatial traffic dependency attention ($\mathbf{TSTD}$) recurrent neural network is developed to predict boundary conditions factoring the traffic temporal-spatial dependency. Also, this paper develops a graph theory-based method to capture the temporal-spatial traffic dependency among the traffic on neighboring road sections. Last, the extended Kalman Filter ($\mathbf{EKF}$) is introduced to adjust the predicted traffic state at an intersection to satisfy the conservation law. The numerical experiments illustrate that the proposed method predicts the traffic state evolution in a freeway network within 30 minutes with accuracy varying from 75%-95%. It has a better performance compared to the tested baseline models (APTN, Graph CNN-LSTM, and so on). The experimental results also illustrate that factoring traffic dependency and integrating data assimilation techniques can improve prediction accuracy.

Incorporating the genotypic and phenotypic of the correlated traits into the multi-trait model can significantly improve the prediction accuracy of the target trait in animal and plant breeding, as well as human genetics. However, in most cases, the phenotypic information of the correlated and target trait of the individual to be evaluated was null simultaneously, particularly for the newborn. Therefore, we propose a machine learning framework, MAK, to improve the prediction accuracy of the target trait by constructing the multi-target ensemble regression chains and selecting the assistant trait automatically, which predicted the genomic estimated breeding values of the target trait using genotypic information only. The prediction ability of MAK was significantly more robust than the genomic best linear unbiased prediction, BayesB, BayesRR and the multi trait Bayesian method in the four real animal and plant datasets, and the computational efficiency of MAK was roughly 100 times faster than BayesB and BayesRR.

Carpooling service obtains a significant interest in both industry and academic fields for its potential to improve mobility and save travel costs. However, the existing approaches for exploring online carpooling routes for riders struggle with the trade-off between system optimality and computation efficiency. This study is motivated to develop a semi-centralized ride-matching strategy (SCM) to address this difficulty. Specifically, we model the carpooling service problem (CSP) by a mixed-integer programming (CSP-MIP), which explores an optimal carpooling solution for a large-scale of riders to minimize system service time by using a small CAV fleet to serve all the riders. To address the computation difficulty, we first analyze and quantify the carpooling chances (C2) among riders according to their trip features. According to the C2, we decompose a large-scale CSP-MIP involving all riders in a network into small sub-MIPs, each including a small number of riders in a carpooling community, without over sacrificing the system optimality. For each sub-MIP, we develop a network flow algorithm combined with a greedy ride-matching model to explore a local optimal solution. The experiments built upon the Hardee network demonstrated that the SCM could solve the CSP efficiently. It significantly improves the computation efficiency while maintaining the system performance as it’s compared to the existing approaches.

The recent emergence of new technologies and systems such as connected and automated vehicles (CAVs), novel incentive and routing platforms, and shared mobility services is making a significant impact on traffic flow in road networks. The rapid development of these innovations, powered by new capabilities in data collection, communication, and vehicle autonomy raises both great opportunities and new challenges for managing and controlling the transportation network efficiently. It is thus imperative to integrate the emerging systems into a dynamic transportation network analysis, and to develop new methodologies, which coherently integrate dynamic traffic models with increasingly available data, and methods for large-scale computation. Consequently, they call for new theories, models, computational methods, and application scenarios to study dynamic transportation networks with the emerging technologies as essential components.

CAV platooning technology has received considerable attention, driven by the next generation smart transportation systems. This paper considers nonlinear vehicle dynamics and develops fully distributed optimization based CAV platooning control schemes via the platoon centered MPC approach for a possibly heterogeneous CAV platoon. The nonlinear vehicle dynamics leads to major difficulties in distributed algorithm development and control analysis. Specifically, the underlying MPC optimization problem is nonconvex and densely coupled. Further, the closed loop dynamics becomes a time-varying nonlinear system with non-vanishing external perturbations, making stability analysis rather complicated. To overcome these difficulties, we formulate the underlying MPC optimization problem as a locally coupled, albeit nonconvex, optimization problem and develop a sequential convex programming based fully distributed scheme for a general MPC horizon. Such a scheme can be effectively implemented for real-time computing using operator splitting methods. To analyze the closed loop stability, we apply various tools from global implicit function theorems, stability of linear time-varying systems, and Lyapunov theory for input-to-state stability to show that the closed loop system is locally input-to-state stable uniformly in all small coefficients pertaining to the nonlinear dynamic effects. Numerical tests on a heterogeneous CAV platoon in a real traffic condition illustrate the effectiveness of the proposed method.

The increased market penetration of route guidance tools–relaying real-time traffic information to drivers–can have damaging effects on transportation networks, including traffic congestion oscillation resulting from the overreaction phenomenon and the inability to control system performance. To address these issues, this study leverages V2I communication capabilities to integrate strategic real-time traffic information perturbation into an online, in-vehicle coordinated routing mechanism for connected vehicles using a mixed-strategy congestion game (CRM-M-IP). Under the CRM-M-IP, the routing decisions of all vehicles are coordinated to prevent overreaction. Additionally, the routing decisions for all vehicles are based on strategically perturbed traffic information (a convex combination between average and marginal link travel times), to ensure that the selfish route choices made by users also help improve system performance. We prove that low information perturbation levels can lead to high system performance gains with correspondingly low individual user optimality losses. From numerical experiments conducted on the Sioux Falls network, we observe that the CRM-M-IP leads to a system performance improvement greater than 3%, and average individual travel time reduction up to 3.5% as compared to the case with no perturbation. Moreover, we find that the average individual user optimality loss resulting from information perturbation is less than 2%. However, we find that when perturbation is high, some users can experience losses approaching 30%—illustrating the need to not over-perturb to ensure compliance of drivers.

Nowadays, advances in high-throughput sequencing benefit the increasing application of genomic prediction (GP) in breeding programs. In this research, we designed a Cosine kernel-based KRR named KCRR to perform GP. This paper assessed the prediction accuracies of 12 traits with various heritability and genetic architectures from four populations using the genomic best linear unbiased prediction (GBLUP), BayesB, support vector regression (SVR), and KCRR. On the whole, KCRR performed stably for all traits of multiple species, indicating that the hypothesis of KCRR had the potential to be adapted to a wide range of genetic architectures. Moreover, we defined a modified genomic similarity matrix named Cosine similarity matrix (CS matrix). The results indicated that the accuracies between GBLUP_kinship and GBLUP_CS almost unanimously for all traits, but the computing efficiency has increased by an average of 20 times. Our research will be a significant promising strategy in future GP.

Aggregate statistics are becoming increasingly commonplace for mobile sensing applications which crowdsources data from individual users. In order to relieve user's concerns for privacy leakage, privacy preserving mechanisms have to be applied to enable the aggregator to compute aggregate statistics without learning each individual data. Although the aggregator will not know the value of the data, it is necessary to ensure the (semantic) validity of the data contributed by users. In this work, we design a privacy-preserving protocol for an aggregator to compute corrected aggregated statistics over users' data that can both preserve user's privacy and verify the semantic validity of the data. We evaluated our protocol on real-world dataset and demonstrated the efficiency of our protocol.

Vehicle-to-vehicle (V2V) communication networks, as one of the core components of connected vehicle systems, have been granted many promising applications to address traffic mobility, safety, and sustainability. However, only a limited amount of work has been completed to understand the fundamental properties of information propagation in such systems, while comprehensively considering traffic and communication reality. Motivated by this view, this proposed research develops analytical formulations to estimate information propagation time delay via a V2V communication network formed on a one-way or two-way road segment with multiple lanes. Distinguished to previous efforts, the proposed study carefully involves several critical communication and traffic flow features in reality, such as wireless communication interference, intermittent information transmission, and dynamic traffic flow. Moreover, this study elaborately analyzes the interactions between information and traffic flow under sparse and congested traffic flow conditions. The numerical experiments based on Next-Generation Simulation field data illustrate that the proposed analytical formulations are able to provide very good estimation, with the relative error less than 5%, for the information propagation time delay on a one-way or two-way road segment under various traffic conditions. The proposed work can be further extended to characterize information propagation time delay and coverage over local transportation networks.

We study how to achieve optimal network capacity in the most energy-efficient manner over a general large-scale wireless network, say, a multi-hop multi-radio multi-channel (MR-MC) network. We develop a multi-objective optimization framework for computing the resource allocation that leads to optimal network capacity with minimal energy consumption. Our framework is based on a linear programming multi-commodity flow (MCF) formulation augmented with scheduling constraints over multi-dimensional conflict graph (MDCG). The optimization problem however involves finding all independent sets (ISs), which is NP-hard in general. Novel delayed column generation (DCG) based algorithms are developed to effectively solve the optimization problem. The DCG-based algorithms have significant advantages of low computation overhead and achieving high energy efficiency, compared to the common heuristic algorithm that randomly searches a large number of ISs to use. Extensive numerical results demonstrate the energy efficiency improvement by the proposed energy-efficient optimization techniques, over a wide range of networking scenarios.