Robust Allocation of Reserve Policies for a Multiple-Cell Based Power System

This paper applies a robust optimization technique for coordinating reserve allocations in multiple-cell based power systems. The linear decision rules (LDR)-based policies were implemented to achieve the reserve robustness, and consist of a nominal power schedule with a series of linear modifications. The LDR method can effectively adapt the participation factors of reserve providers to respond to system imbalance signals. The policies considered the covariance of historic system imbalance signals to reduce the overall reserve cost. When applying this method to the cell-based power system for a certain horizon, the influence of different time resolutions on policy-making is also investigated, which presents guidance for its practical application. The main results illustrate that: (a) the LDR-based method shows better performance, by producing smaller reserve costs compared to the costs given by a reference method; and (b) the cost index decreases with increased time intervals, however, longer intervals might result in insufficient reserves, due to low time resolution. On the other hand, shorter time intervals require heavy computational time. Thus, it is important to choose a proper time interval in real time operation to make a trade off

Astraea: Self-balancing Federated Learning for Improving Classification Accuracy of Mobile Deep Learning Applications

Federated learning (FL) is a distributed deep learning method which enables multiple participants, such as mobile phones and IoT devices, to contribute a neural network model while their private training data remains in local devices. This distributed approach is promising in the edge computing system where have a large corpus of decentralized data and require high privacy. However, unlike the common training dataset, the data distribution of the edge computing system is imbalanced which will introduce biases in the model training and cause a decrease in accuracy of federated learning applications. In this paper, we demonstrate that the imbalanced distributed training data will cause accuracy degradation in FL. To counter this problem, we build a self-balancing federated learning framework call Astraea, which alleviates the imbalances by 1) Global data distribution based data augmentation, and 2) Mediator based multi-client rescheduling. The proposed framework relieves global imbalance by runtime data augmentation, and for averaging the local imbalance, it creates the mediator to reschedule the training of clients based on Kullback-Leibler divergence (KLD) of their data distribution. Compared with FedAvg, the state-of-the-art FL algorithm, Astraea shows +5.59% and +5.89% improvement of top-1 accuracy on the imbalanced EMNIST and imbalanced CINIC-10 datasets, respectively. Meanwhile, the communication traffic of Astraea can be 82% lower than that of FedAvg.Comment: Published as a conference paper at IEEE 37th International Conference on Computer Design (ICCD) 201

Chemokines as Potential Biomarkers for PTSD in Military Population

Post-traumatic stress disorder (PTSD) is a serious mental health concern worldwide among civilians and military personnel. Gaps in our understanding of its biological basis create significant obstacles for accurate diagnosis and assessment of therapeutic interventions. In light of this, investigation of biological factors associated with possible molecular cues of inflammation or neuroimmune disorders, could provide new surrogate markers for PTSD or PTSD treatment response. Analyses to date in deployed military personnel have suggested that sets of chemokines may be useful as biomarkers for PTSD acquired in military operations. Specifically, studies to date suggest that CCL2, CCL15, CCL22, CCL25, CXCL2, and CXCL12 are associated with PTSD onset, while CCL13, CCL20, and CXCL6 are correlated to PTSD risk; CX3CL1 are associated with resilience; CCL3; CXCL11, and CXCL16 are associated with stress response. CCL11, CCL13, CCL20, and CCL25 are correlated with the severity of PTSD symptoms. This chapter reviews the current understanding of potential chemokine markers for PTSD, and the potential chemokines associated with PTSD onset, risk, resilience, as well as stress responses in service members. Although the proposed biomarkers require further validation, these findings may lead to additional knowledge for the education and development of diagnostic and therapeutic approaches for PTSD, not only benefiting military personnel, but civilians as well

Loss‐of‐Function Genetic Screening Identifies Aldolase A as an Essential Driver for Liver Cancer Cell Growth Under Hypoxia

Background and aims: Hypoxia is a common feature of the tumor microenvironment (TME), which promotes tumor progression, metastasis, and therapeutic drug resistance through a myriad of cell activities in tumor and stroma cells. While targeting hypoxic TME is emerging as a promising strategy for treating solid tumors, preclinical development of this approach is lacking in the study of HCC. Approach and results: From a genome-wide CRISPR/CRISPR-associated 9 gene knockout screening, we identified aldolase A (ALDOA), a key enzyme in glycolysis and gluconeogenesis, as an essential driver for HCC cell growth under hypoxia. Knockdown of ALDOA in HCC cells leads to lactate depletion and consequently inhibits tumor growth. Supplementation with lactate partly rescues the inhibitory effects mediated by ALDOA knockdown. Upon hypoxia, ALDOA is induced by hypoxia-inducible factor-1α and fat mass and obesity-associated protein-mediated N6 -methyladenosine modification through transcriptional and posttranscriptional regulation, respectively. Analysis of The Cancer Genome Atlas shows that elevated levels of ALDOA are significantly correlated with poor prognosis of patients with HCC. In a screen of Food and Drug Administration-approved drugs based on structured hierarchical virtual platforms, we identified the sulfamonomethoxine derivative compound 5 (cpd-5) as a potential inhibitor to target ALDOA, evidenced by the antitumor activity of cpd-5 in preclinical patient-derived xenograft models of HCC. Conclusions: Our work identifies ALDOA as an essential driver for HCC cell growth under hypoxia, and we demonstrate that inhibition of ALDOA in the hypoxic TME is a promising therapeutic strategy for treating HCC

RNA-binding protein RALY reprogrammes mitochondrial metabolism via mediating miRNA processing in colorectal cancer

Objective: Dysregulated cellular metabolism is a distinct hallmark of human colorectal cancer (CRC). However, metabolic programme rewiring during tumour progression has yet to be fully understood. Design: We analysed altered gene signatures during colorectal tumour progression, and used a complex of molecular and metabolic assays to study the regulation of metabolism in CRC cell lines, human patient-derived xenograft mouse models and tumour organoid models. Results: We identified a novel RNA-binding protein, RALY (also known as hnRNPCL2), that is highly associated with colorectal tumour aggressiveness. RALY acts as a key regulatory component in the Drosha complex, and promotes the post-transcriptional processing of a specific subset of miRNAs (miR-483, miR-676 and miR-877). These miRNAs systematically downregulate the expression of the metabolism-associated genes (ATP5I, ATP5G1, ATP5G3 and CYC1) and thereby reprogramme mitochondrial metabolism in the cancer cell. Analysis of The Cancer Genome Atlas (TCGA) reveals that increased levels of RALY are associated with poor prognosis in the patients with CRC expressing low levels of mitochondrion-associated genes. Mechanistically, induced processing of these miRNAs is facilitated by their N6-methyladenosine switch under reactive oxygen species (ROS) stress. Inhibition of the m6A methylation abolishes the RALY recognition of the terminal loop of the pri-miRNAs. Knockdown of RALY inhibits colorectal tumour growth and progression in vivo and in organoid models. Conclusions: Collectively, our results reveal a critical metabolism-centric role of RALY in tumour progression, which may lead to cancer therapeutics targeting RALY for treating CRC

Model Studies on Load-Settlement Characteristics of Coarse-Grained Soil Treated with Geofiber and Cement

This study aims to verify the effectiveness of fiber reinforcing with and without cement on settlement controlling of subgrade models, and to investigate the effect of fiber reinforcement on the load-settlement behavior of subgrade models. To this end, laboratory subgrade model tests were conducted under different static vertical loads. Three subgrade models composed of different fillers were constructed in a rigid concrete tank, and the internal earth pressures and settlements at different depths were measured through an earth pressure cell and settlement plate. Results show that the fiber-reinforced model keeps a slight difference to the unreinforced model in terms of earth pressure distribution under lower applied surface pressure. However, the earth pressure at various locations under each surface pressure was obviously lower than that of the other two models due to the combined effect of fiber and cement. In addition, for the unreinforced subgrade model, the 60 cm settlement domain was restricted within 40 cm depth through fiber-cement and fiber reinforcing, and the total settlement under 100 kPa was decreased by 48.5% and 30.8%, respectively. Moreover, reinforced models present with different settlement deformation features. The inflection points, after which the rate of settlement decreased with increasing applied surface pressure, were observed in the pressure-settlement curves. Under 200 kPa, the fiber-cement and fiber reinforcement decreased the total settlement of the unreinforced model by 61.4% and 34.7%, respectively. The greater applied surface pressure, the more efficient was fiber-cement reinforcing in settlement controlling

World Decarbonization through Global Electricity Interconnections

The challenge of worldwide energy decarbonization is crucial to ensure sustainable development. The achievement of decarbonization encompasses not only a considerable exploitation of renewable energy sources, but also a paradigm shift in final energy uses towards their massive electrification. Electrification based on Global Energy Interconnections (GEI) is one of the possible pathways towards decarbonization in energy systems. In this paper, we critically discuss the idea of decarbonization through global interconnections in an ‘electricity based’ world, contrasting it against the typically desirable attributes for energy in terms of security, efficiency, sustainability, and affordability. We provide a comparative analysis of global interconnection with other internationally proposed visions of future energy scenarios. The analysis shows that the GEI option could be particularly beneficial from an environmental point of view; however, it requests deep and relevant modifications in the energy markets and regulations, in which a common framework based on the cooperation among different countries is needed

Soil Classification and Site Variability Analysis Based on CPT—A Case Study in the Yellow River Subaquatic Delta, China

The Yellow River Delta is located at the junction of the Yellow River and Bohai. The impact function from the river and the dynamics of the ocean tides make the soil composition and distribution in this area substantially complicated. In order to test the distribution and variation of the soil layers in the Yellow River Delta, the soil layers in the test area were classified and the variation was calculated using the cone penetration test (CPT). The following conclusions were drawn: (1) the soil in the measured area is mainly composed of sensitive fine-grained soil, accounting for about 70% of all soil types, and the content of sensitive fine-grained soil in the far-sea position is higher than that in the offshore position in the direction perpendicular to the coastline. (2) It has a high vertical variability index (VVI) at the near-shore location, above 45%, and a low vertical variability at the far coast, generally below 20%. (3) The horizontal variability index (HVI) changes significantly near the coast, and it remains below 45% in the test area