Proton exchange membrane (PEM) electrolyzer (EL) is regarded as a promising technology for hydrogen generation, offering load flexibility for electric grids (EGs), especially those with a high penetration of renewable energy (RE) sources. This paper proposes a PEM-focused economic dispatch strategy for EG integrated with wind-electrolysis systems. Existing strategies commonly assume a constant efficiency coefficient to model the EL, while the proposed strategy incorporates a bottom-up PEM EL model characterized by a part-load efficiency curve, which accurately represents the nonlinear hydrogen production performance, capturing efficiency variations at different loads. To model this, it first establishes a 0D electrochemical model to derive the polarization curve. Next, it accounts for the hydrogen and oxygen crossover phenomena, represented by the Faraday efficiency, to correct the stack efficiency curve. Finally, it includes the power consumption of ancillary equipment to obtain the nonlinear part-load system efficiency. This strategy is validated using the PJM-5 bus test system with coal-fired generators (CFGs) and is compared with a simple EL model using constant efficiency under three scenarios. The results show that the EL modeling method significantly influences both the dispatch outcome and the economic performance. Sensitivity analyses on coal and hydrogen prices indicate that, for this case study, the proposed strategy is economically advantageous when the coal price is below 121.6 $/tonne. Additionally, the difference in total annual operating cost between using the efficiency curve anda constant efficiency to model becomes apparent when the hydrogen price ranges from 2.9 to 5.4 $/kg.

Virtual reality (VR) is emerging as a promising tool in the treatment of mental disorders, particularly as a complement to cognitive-behavioral therapy (CBT). It enables patients to experience anxiety-provoking situations—such as public speaking or flying—in a controlled, immersive environment that feels realistic enough to trigger emotional and physical reactions.

A key application of VR is in exposure therapy, where patients gradually confront their fears. Unlike traditional settings, VR allows therapists to recreate difficult or impractical scenarios in a safe, repeatable way while monitoring and adjusting treatment in real time. This makes it especially effective for anxiety-related disorders, including phobias, social anxiety, and PTSD.

VR also helps reduce avoidance behaviors by offering a less intimidating starting point for patients who may struggle to face real-life situations. However, its effectiveness in treating other conditions, such as depression or addiction, remains less clear due to limited research.

Despite its advantages, VR has limitations, including costs, access to technology, and the need for proper therapist training. It may also cause side effects like dizziness in some users. Importantly, VR cannot replace real-life experiences; successful therapy still depends on applying learned skills in everyday situations.

Overall, VR is likely to play a significant role in the future of hybrid therapy models, combining traditional methods with digital tools to enhance treatment outcomes.

Research finds soil inorganic carbon rise offsets organic carbon loss, masking ecosystem degradation beneath stable total carbon pools.

The security of digital signatures has been a focus of research in emerging area. Threshold signature is a technique designed for distributed scenarios, allowing the signing key to be divided into multiple shares. This approach can resist single point failures of device and ensure key security. However, most existing surveys on threshold signature schemes are either not comprehensive or cover only a limited range of signature types.

Sub-headline: BIT researchers introduce Malcom to tackle cross-domain encrypted traffic detection using self-supervised learning.

Deep neural networks (DNNs) are demonstrated to be vulnerable to adversarial examples. Adversarial training is mainstrem method to improve adversarial robustness of DNNs, which augments the training set with adversarial examples and adopts adversarial regularization loss to improve the robustness of DNNs. Existing adversarial training methods are facing the challenge to balance the accuracy and robustness.