Handing stability and ride comfort, basic indexes to evaluate vehicle performance, usually cannot be guaranteed simultaneously. Given the contradiction between the two indexes, a new type of suspension – balanced suspension, has attracted wide attentions for years. Balanced suspensions are a device that converts the movement of a wheel into force at the other wheels through a mechanical or hydraulic structure, which might improve ride comfort/handling stability while maintaining the handling stability/ride comfort. As the hydraulically interconnected suspension and hydro-pneumatic suspension show disadvantages of high cost and high installation space requirement, a specific balanced suspension which is connected through a mechanical structure is presented and thoroughly analyzed in this paper. The balanced suspension connects the front and rear suspension motion of the vehicle by means of the lever mechanical connection structure to obtain the comprehensive performance of enhanced vehicle’s ride comfort and handling stability. The half-vehicle mathematical model for ride comfort and the multi-body dynamics model for handling stability are established for the comparison and analysis of the dynamic performance of vehicle when the balanced suspension on and off. In addition, experimental tests of the modified vehicle prototypes when the balanced suspension on and off on the ride comfort and handling stability are conducted. Similar with the simulation results, experimental tests show that the handling stability is nearly unchanged while the ride comfort improves about 15.9% when the balanced suspension is on.

Glaucoma is the leading cause of irreversible vision loss, affecting more than 70 million individuals worldwide. Circulatory disturbances of aqueous humor (AH) have long been central pathological contributors to glaucomatous lesions. Thus, targeting the AH outflow is a promising approach to treat glaucoma. However, the epigenetic mechanisms initiating AH outflow disorders and the targeted treatments remain to be developed. Studying glaucoma patients, we identified GDF7 (Growth Differentiation Factor 7) hypomethylation as a crucial event in the onset of AH outflow disorders. Regarding the underlying mechanism, the hypomethylated GDF7 promoter was responsible for the increased GDF7 production and secretion in POAG. Excessive GDF7 protein promoted trabecular meshwork (TM) fibrosis through BMPR2/Smad signaling and up-regulated pro-fibrotic genes, α-smooth muscle actin (α-SMA) and fibronectin (FN). GDF7 protein expression formed a positive feedback loop in GTM. This positive feedback loop was dependent on activated TET (ten-eleven translocation) enzyme, which kept GDF7 promoter region hypomethylated. The phenotypic transition in TM fortified the AH outflow resistance, thus elevating the intraocular pressure (IOP) and attenuating the nerve fiber layer. This methylation dependent mechanism is also confirmed by a machine-learning model in silico with a specificity of 84.38% and a sensitivity of 89.38%. In rhesus monkeys, we developed GDF7 neutralization therapy to inhibit TM fibrosis and consequent AH outflow resistance that contributes to glaucoma. The neutralization therapy achieved high-efficiency control of the IOP (from 21.3±0.3 to 17.6±0.2 mmHg), a three-fold improvement in the outflow facility (from 0.1 to 0.3 μl/min·mmHg), and protection of nerve fibers. This study provides new insights into the epigenetic mechanism of glaucoma and proposes an innovative GDF7 neutralization therapy as a promising intervention.

Large Language Models (LLMs) are increasingly deployed in web-based medical advice applications, offering scalable and accessible healthcare solutions. However, their outputs often reflect demographic biases, raising concerns about fairness and equity for vulnerable populations. In this work, we propose FairMed, a framework designed to mitigate biases in LLM-generated medical advice through fine-tuning and prompt engineering strategies. We evaluate FairMed using language-based and content-level metrics across demographic groups on publicly available (MedQA), synthetic (Synthea), and private (CBHS) datasets. Experimental results demonstrate consistent improvements over Llama3 - Med42, as well as over the zero-shot prompting baseline. For instance, in sentiment analysis for gender groups using MedQA, FairMed with Descriptive Prompting reduces the Statistical Parity Difference (SPD) from 0.0902 to 0.0658, improves the Disparate Impact Ratio from 1.1916 to 1.1566, and decreases the Kullback-Leibler Divergence from 0.0045 to 0.0024. Similarly, in directive language evaluation for gender groups using Synthea, SPD improves from 0.1056 to nearly zero, achieving near-perfect parity. On the CBHS dataset, FairMed with Descriptive Prompting increases Diagnostic Recommendation Divergence (DRD) for race groups from 0.9530 to 0.9848, indicating improved group-specific tailoring, while reducing the Action Disparity Index (ADI) from 0.0857 to 0.0469 and Referral Frequency Parity (RFP) from 0.0791 to 0.0511, reflecting enhanced fairness. These findings highlight FairMed's effectiveness in addressing demographic disparities and promoting equitable healthcare guidance through web technologies. This framework contributes to building trustworthy and inclusive systems for delivering medical advice by ensuring fairness in sensitive applications.

Social networks represent complex ecosystems where the interactions between users or groups play a pivotal role in information dissemination, opinion formation, and social interactions. Effectively harnessing event sequence data within social networks to unearth interactions among users or groups has persistently posed a challenging frontier within the realm of point processes. Current deep point process models face inherent limitations within the context of social networks, constraining both their interpretability and expressive power. These models encounter challenges in capturing interactions among users or groups and often rely on parameterized extrapolation methods when modeling intensity over non-event intervals, limiting their capacity to capture intricate intensity patterns, particularly beyond observed events. To address these challenges, this study proposes modifications to Transformer Hawkes processes (THP), leading to the development of interpretable Transformer Hawkes processes (ITHP). ITHP inherits the strengths of THP while aligning with statistical nonlinear Hawkes processes, thereby enhancing its interpretability and providing valuable insights into interactions between users or groups. Additionally, ITHP enhances the flexibility of the intensity function over non-event intervals, making it better suited to capture complex event propagation patterns in social networks. Experimental results, both on synthetic and real data, demonstrate the effectiveness of ITHP in overcoming the identified limitations. Moreover, they highlight ITHP's applicability in the context of exploring the complex impact of users or groups within social networks. Our code is available at https://github.com/waystogetthere/Interpretable-Transformer- Hawkes-Process.git.