Blockchain Smart Contracts for Grid Connection Management in Achieving Net Zero Energy Systems

Energy systems are transitioning towards a decentralized and decarbonized paradigm with the integration of distributed renewable energy sources. Blockchain smart contracts have the increasing potential to facilitate the transition of energy systems due to the natures of automation, standardization, and selfenforcement. This paper proposes a Blockchain smart contracts based platform to manage the grid connection for both large scale generation companies and individual prosumers (both producers and consumers). Through evaluating the capacity margin and carbon intensity for each substation or feeder in power networks, the incurred connection fee and low carbon incentive are formulated for incentivizing the local energy balance and connection of renewable energy sources. Case studies testify the effectiveness for encouraging the low carbon grid connection. Energy systems are transitioning towards a decentralized and decarbonized paradigm with the integration of distributed renewable energy sources. Blockchain smart contracts have the increasing potential to facilitate the transition of energy systems due to the natures of automation, standardization, and selfenforcement. This paper proposes a Blockchain smart contracts based platform to manage the grid connection for both large scale generation companies and individual prosumers (both producers and consumers). Through evaluating the capacity margin and carbon intensity for each substation or feeder in power networks, the incurred connection fee and low carbon incentive are formulated for incentivizing the local energy balance and connection of renewable energy sources. Case studies testify the effectiveness for encouraging the low carbon grid connection

Biomedical Entity Recognition by Detection and Matching

Biomedical named entity recognition (BNER) serves as the foundation for numerous biomedical text mining tasks. Unlike general NER, BNER require a comprehensive grasp of the domain, and incorporating external knowledge beyond training data poses a significant challenge. In this study, we propose a novel BNER framework called DMNER. By leveraging existing entity representation models SAPBERT, we tackle BNER as a two-step process: entity boundary detection and biomedical entity matching. DMNER exhibits applicability across multiple NER scenarios: 1) In supervised NER, we observe that DMNER effectively rectifies the output of baseline NER models, thereby further enhancing performance. 2) In distantly supervised NER, combining MRC and AutoNER as span boundary detectors enables DMNER to achieve satisfactory results. 3) For training NER by merging multiple datasets, we adopt a framework similar to DS-NER but additionally leverage ChatGPT to obtain high-quality phrases in the training. Through extensive experiments conducted on 10 benchmark datasets, we demonstrate the versatility and effectiveness of DMNER.Comment: 9 pages content, 2 pages appendi

Transactive Energy and Flexibility Provision in Multi-microgrids using Stackelberg Game

Aggregating the demand side flexibility is essential to complementing the inflexible and variable renewable energy supply in achieving low carbon energy systems. Sources of demand side flexibility, e.g., dispatchable generators, storages, and flexible loads, can be structured in a form of microgrids and collectively provided to utility grids through transactive energy in local energy markets. This paper proposes a framework of local energy markets to manage the transactive energy and facilitate the flexibility provision. The distribution system operator aims to achieve local energy balance by scheduling the operation of multi-microgrids and determining the imbalance prices. Multiple microgrid traders aim to maximise profits for their prosumers through dispatching flexibility sources and participating in localised energy trading. The decision making and interactions between a distribution system operator and multiple microgrid traders are formulated as the Stackelberg game-theoretic problem. Case studies using the IEEE 69-bus distribution system demonstrate the effectiveness of the developed model in terms of facilitating the local energy balance and reducing the dependency on the utility grids

P21cip-Overexpression in the Mouse β Cells Leads to the Improved Recovery from Streptozotocin-Induced Diabetes

Under normal conditions, the regeneration of mouse β cells is mainly dependent on their own duplication. Although there is evidence that pancreatic progenitor cells exist around duct, whether non-β cells in the islet could also potentially contribute to β cell regeneration in vivo is still controversial. Here, we developed a novel transgenic mouse model to study the pancreatic β cell regeneration, which could specifically inhibit β cell proliferation by overexpressing p21cip in β cells via regulation of the Tet-on system. We discovered that p21 overexpression could inhibit β-cell duplication in the transgenic mice and these mice would gradually suffer from hyperglycemia. Importantly, the recovery efficiency of the p21-overexpressing mice from streptozotocin-induced diabetes was significantly higher than control mice, which is embodied by better physiological quality and earlier emergence of insulin expressing cells. Furthermore, in the islets of these streptozotocin-treated transgenic mice, we found a large population of proliferating cells which expressed pancreatic duodenal homeobox 1 (PDX1) but not markers of terminally differentiated cells. Transcription factors characteristic of early pancreatic development, such as Nkx2.2 and NeuroD1, and pancreatic progenitor markers, such as Ngn3 and c-Met, could also be detected in these islets. Thus, our work showed for the first time that when β cell self-duplication is repressed by p21 overexpression, the markers for embryonic pancreatic progenitor cells could be detected in islets, which might contribute to the recovery of these transgenic mice from streptozotocin-induced diabetes. These discoveries could be important for exploring new diabetes therapies that directly promote the regeneration of pancreatic progenitors to differentiate into islet β cells in vivo

Cross-Domain Identity-based Matchmaking Encryption

Recently, Ateniese et al. (CRYPTO 2019) proposed a new cryptographic primitive called matchmaking encryption (ME), which provides fine-grained access control over encrypted data by allowing both the sender and receiver to specify access control policies. The encrypted message can be decrypted correctly if and only if the attributes of the sender and receiver simultaneously meet each other\u27s specified policies. In current ME, when users from different organizations need secret communication, they need to be managed by a single-authority center. However, it is more reasonable if users from different domains obtain secret keys from their own authority centers, respectively. Inspired by this, we extend ME to cross-domain scenarios. Specifically, we introduce the concept of the cross-domain ME and instantiate it in the identity-based setting (i.e., cross-domain identity-based ME). Then, we first formulate and design a cross-domain identity-based ME (IB-ME) scheme and prove its privacy and authenticity in the random oracle model. Further, we extend the cross-domain IB-ME to the multi-receiver setting and give the formal definition, concrete scheme and security proof. Finally, we analyze and implement the schemes, which confirms the efficiency feasibility

Possible Luttinger liquid behavior of edge transport in monolayer transition metal dichalcogenide crystals.

In atomically-thin two-dimensional (2D) semiconductors, the nonuniformity in current flow due to its edge states may alter and even dictate the charge transport properties of the entire device. However, the influence of the edge states on electrical transport in 2D materials has not been sufficiently explored to date. Here, we systematically quantify the edge state contribution to electrical transport in monolayer MoS2/WSe2 field-effect transistors, revealing that the charge transport at low temperature is dominated by the edge conduction with the nonlinear behavior. The metallic edge states are revealed by scanning probe microscopy, scanning Kelvin probe force microscopy and first-principle calculations. Further analyses demonstrate that the edge-state dominated nonlinear transport shows a universal power-law scaling relationship with both temperature and bias voltage, which can be well explained by the 1D Luttinger liquid theory. These findings demonstrate the Luttinger liquid behavior in 2D materials and offer important insights into designing 2D electronics

A new model based on gamma-glutamyl transpeptidase to lymphocyte ratio and systemic immune-inflammation index can effectively predict the recurrence of hepatocellular carcinoma after liver transplantation

BackgroundLiver transplantation (LT) is one of the most effective treatment modalities for hepatocellular carcinoma (HCC), but patients with HCC recurrence after LT always have poor prognosis. This study aimed to evaluate the predictive value of the gamma-glutamyl transpeptidase-to-lymphocyte ratio (GLR) and systemic immune-inflammation index (SII) in terms of HCC recurrence after LT, based on which we developed a more effective predictive model.MethodsThe clinical data of 325 HCC patients who had undergone LT were collected and analyzed retrospectively. The patients were randomly divided into a development cohort (n = 215) and a validation cohort (n = 110). Cox regression analysis was used to screen the independent risk factors affecting postoperative recurrence in the development cohort, and a predictive model was established based on the results of the multivariate analysis. The predictive values of GLR, SII and the model were evaluated by receiver operating characteristic (ROC) curve analysis, which determined the cut-off value for indicating patients’ risk levels. The Kaplan-Meier survival analysis and the competing-risk regression analysis were used to evaluate the predictive performance of the model, and the effectiveness of the model was verified further in the validation cohort.ResultsThe recurrence-free survival of HCC patients after LT with high GLR and SII was significantly worse than that of patients with low GLR and SII (P<0.001). Multivariate Cox regression analysis identified GLR (HR:3.405; 95%CI:1.954-5.936; P<0.001), SII (HR: 2.285; 95%CI: 1.304-4.003; P=0.004), tumor number (HR:2.368; 95%CI:1.305-4.298; P=0.005), maximum tumor diameter (HR:1.906; 95%CI:1.121-3.242; P=0.017), alpha-fetoprotein level (HR:2.492; 95%CI:1.418-4.380; P=0.002) as independent risk factors for HCC recurrence after LT. The predictive model based on these risk factors had a good predictive performance in both the development and validation cohorts (area under the ROC curve=0.800, 0.791, respectively), and the performance of the new model was significantly better than that of single GLR and SII calculations (P<0.001). Survival analysis and competing-risk regression analysis showed that the predictive model could distinguish patients with varying levels of recurrence risk in both the development and validation cohorts.ConclusionsThe GLR and SII are effective indicators for evaluating HCC recurrence after LT. The predictive model based on these indicators can accurately predict HCC recurrence after LT and is expected to guide preoperative patient selection and postoperative follow-up