A hybrid neural network based on KF-SA-Transformer for SOC prediction of lithium-ion battery energy storage systems

With the widespread application of energy storage stations, BMS has become an important subsystem in modern power systems, leading to an increasing demand for improving the accuracy of SOC prediction in lithium-ion battery energy storage systems. Currently, common methods for predicting battery SOC include the Ampere-hour integration method, open circuit voltage method, and model-based prediction techniques. However, these methods often have limitations such as single-variable research, complex model construction, and inability to capture real-time changes in SOC. In this paper, a novel prediction method based on the KF-SA-Transformer model is proposed by combining model-based prediction techniques with data-driven methods. By using temperature, voltage, and current as inputs, the limitations of single-variable studies in the Ampere-hour integration method and open circuit voltage method are overcome. The Transformer model can overcome the complex modeling process in model-based prediction techniques by implementing a non-linear mapping between inputs and SOC. The presence of the Kalman filter can eliminate noise and improve data accuracy. Additionally, a sparse autoencoder mechanism is integrated to optimize the position encoding embedding of input vectors, further improving the prediction process. To verify the effectiveness of the algorithm in predicting battery SOC, an open-source lithium-ion battery dataset was used as a case study in this paper. The results show that the proposed KF-SA-Transformer model has superiority in improving the accuracy and reliability of battery SOC prediction, playing an important role in the stability of the grid and efficient energy allocation

Primary adenoid cystic carcinoma of the skin: a case report

A 75-year-old male complained of a demarcated plaque on his left upper arm for 13 years. The patient underwent a surgery 7 years prior to the appearance of the palque. Dermatological examination revealed a dark red elevated plaque of 3.0 cm×3.5 cm×0.5 cm in size on the extensor side of the left upper arm. The plaque was firm and mild tenderness, with uniform color and poor mobility. Histology showed that the tumor was composed of myoepithelial and glandular epithelial cells arranged in cribriform and glandular pattern, with glandular and pseudoglandular lumens. Some areas appeared differentiation to sebaceous glands, infiltrative growth pattern and nerve invasion. Immunohistochemistry showed that tumor cells were CK-pan (glandular epithelial+), CK5/6(myoepithelial+), CD117(glandular epithelial+), CK7(glandular epithelial+), EMA(glandular epithelial+), P63(myoepithelial+), Ki-67 (15% positive rate within hotspot area). A diagnosis of primary cutaneous adenoid cystic carcinoma was made. Surgical excision was given, and no recurrence was observed during a 6-month follow-up

Real-Time Forecast of SMAP L3 Soil Moisture Using Spatial–Temporal Deep Learning Model with Data Integration

Soil moisture (SM) has significant impacts on the Earth’s energy and water cycle system. Remote sensing, such as the Soil Moisture Active Passive (SMAP) mission, has delivered valuable estimations of global surface soil moisture. However, it has a 2~3 days revisit time leading to gaps between SMAP areas. To achieve accurate and comprehensive real-time forecast of SM, we propose a spatial–temporal deep learning model based on the Convolutional Gated Recursive Units with Data Integration (DI_ConvGRU) to capture the spatial and temporal variation in SM simultaneously by modeling the influence of adjacent SM values in space and time. Experiments show that the DI_ConvGRU outperforms the ConvGRU with Linear Interpolation (interp_ConvGRU) and the Long Short-Term Memory with Data Integration (DI_LSTM). The best performance (Bias = 0.0132 m3/m3, ubRMSE = 0.022 m3/m3, R = 0.977) has been achieved through the use of spatial–temporal deep learning model and Data Integration term. In comparison with interp_ConvGRU and DI_LSTM, DI_ConvGRU has improved the model performance in 74.88% and 68.99% of the regions according to RMSE, respectively. The predictability of SM depends highly on SM memory characteristics. DI_ConvGRU can provide accurate spatial–temporal forecast for SM with missing data, making them potentially useful for applications such as filling observational gaps in satellite data

Image-guided combination chemotherapy and photodynamic therapy using a mitochondria-targeted molecular probe with aggregation-induced emission characteristics.

Subcellular targeted cancer therapy and in situ monitoring of therapeutic effect are highly desirable for clinical applications. Herein, we report a series of probes by conjugating zero (TPECM-2Br), one (TPECM-1TPP) and two (TPECM-2TPP) triphenylphosphine (TPP) ligands to a fluorogen with aggregation-induced emission (AIE) characteristics. The probes are almost non-emissive as molecularly dissolved species, but they can light up in cell cytoplasm or mitochondria. TPECM-2TPP is found to be able to target mitochondria, depolarize mitochondria membrane potential and selectively exert potent chemo-cytotoxicity on cancer cells. Furthermore, it can efficiently generate singlet oxygen with strong photo-toxicity upon light illumination, which further enhances its anti-cancer effect. On the other hand, TPECM-1TPP can also target mitochondria and generate singlet oxygen to trigger cancer cell apoptosis, but it shows low cytotoxicity in dark. Meanwhile, TPECM-1TPP can report the cellular oxidative stress by visualizing the morphological changes of mitochondria. However, TPECM-2Br does not target mitochondria and shows no obvious anticancer effect either in dark or under light illumination. This study thus highlights the importance of molecular probe design, which yields a new generation of subcellular targeted molecular theranostic agents with multi-function, such as cancer cell imaging, chemotherapy, photodynamic therapy, and in situ monitoring of the therapeutic effect in one go

Image-guided combination chemotherapy and photodynamic therapy using a mitochondria-targeted molecular probe with aggregation-induced emission characteristics

10.1039/c5sc00826cCHEMICAL SCIENCE684580-458

Grosmannia tibetensis, a new ophiostomatoid fungus associated with Orthotomicus sp. (Coleoptera) in Tibetan subalpine forests

Few ophiostomatoid fungi have been reported from the margin of the Tibetan Plateau and none have been found in the central portion of the region. In a survey of ophiostomatoid fungi associated with spruce bark beetles in Tibetan subalpine forests, numerous strains of Leptographium s. l. (Ophiostomataceae) were isolated from Orthotomicus sp. (Coleoptera: Scolytinae) and its galleries infesting Picea likiangensis var. balfouriana. Morphological characters and phylogenetic analysis based on multiple DNA sequence data (ITS2-partial LSU rDNA region, beta-tubulin and transcription elongation factor-1a genes) revealed a new species in the “Grosmannia penicillata complex”, which is proposed as G. tibetensis. The species is characterized by both Leptographium and Pesotum asexual states, which is unique in the “G. penicillata complex”. Additionally, sequences of the tubC paralogue gene were found combining with tub2 sequences in many species of the “G. penicillata complex”, resulting in incongruent trees. This is the first report of tubulin paralogue genes in ophiostomatoid fungi. Gene duplication and losses make betatubulin a potentially challenging locus for use as a molecular marker for tracing speciation

The Catalytic Behavior for Ni-W Electrode Modified by Carbon Ion Beam Implantion

镍基溅射钨注碳电极的催化性能张季爽，吕瑶姣，李青莲（湖南大学化学化工系环境工程系，长沙４１００８２）沈报恩（杭州大学化学系，杭州３１００２８）析氢反应研究涉及到氢能、燃料电池、氯碱工业等的能源开发及节能技术，并具有重要的理论意义￣［１～５］．本工作研...The hydrogen evolution activity in acidic and basic mediums for Ni-W electrodemodified by carbon ion beam implantation was investigated.The experimental results show thiselectrode has superior catalytic activity for the hydrogen evolution reaction than that of the unmodifiedNi-electrode.Long period testing of electrolysis under high current density show that this electrode hasalonger useful life hence it is a practical electrode material.The temperature effect of electrocatalyticproperty was also inverstigated.The experimental regularities are explained on the basis of the formation of surface m uItialloys of Ni-W-WC.作者联系地址：湖南大学化学化工系环境工程系，杭州大学化学系，西安２１３研究所Author's Address: Department of Chemistry and Chemical Engineering,Hunan University,Changsha 410082Shen BaoenDepartment of Chemistry,Hangzhou University,Hangzhou 31002

Individual nanostructure optimization in donor and acceptor phases to achieve efficient quaternary organic solar cells

Fullerene derivative (PC71BM) and high crystallinity molecule (DR3TBDTT) are employed into PTB7-Th:FOIC based organic solar cells (OSCs) to cooperate an individual nanostructure optimized quaternary blend. PC71BM functions as molecular adjuster and phase modifier promoting FOIC forming "head-to-head" molecular packing and neutralizing the excessive FOIC crystallites. A multi-scale modified morphology is present thanks to the mixture of FOIC and PC71BM while DR3TBDTT disperses into PTB7-Th matrix to reinforce donors crystal-linity and enhance domain purity. Morphology characterization highlights the importance of individually optimizated nanostructures for donor and acceptor, which contributes to efficient hole and electron transport toward improved carrier mobilities and suppressed non-geminated recombination. Therefore, a power conversion efficiency of 13.51% is realized for a quaternary device which is 16% higher than the binary device (PTB7-Th:FOIC). This work demonstrates that utilizing quaternary strategy for simultaneous optimization of donor and acceptor phases is a feasible way to realize high efficient OSCs.Funding Agencies|Ministry of science and technologyMinistry of Education, Culture, Sports, Science and Technology, Japan (MEXT) [2016YFA0200700]; NSFCNational Natural Science Foundation of China [21704082, 21875182, 21534003]; China Postdoctoral Science FoundationChina Postdoctoral Science Foundation [2017M623162]; 111 project 2.0 [BP2018008]; Office of Science, Office of Basic Energy Sciences, of the U.S. Department of EnergyUnited States Department of Energy (DOE) [DE-AC02-05CH11231]</p

Extracellular-vesicle-packaged S100A11 from osteosarcoma cells mediates lung premetastatic niche formation by recruiting gMDSCs

Summary: The premetastatic niche (PMN) contributes to lung-specific metastatic tropism in osteosarcoma. However, the crosstalk between primary tumor cells and lung stromal cells is not clearly defined. Here, we dissect the composition of immune cells in the lung PMN and identify granulocytic myeloid-derived suppressor cell (gMDSC) infiltration as positively associated with immunosuppressive PMN formation and tumor cell colonization. Osteosarcoma-cell-derived extracellular vesicles (EVs) activate lung interstitial macrophages to initiate the influx of gMDSCs via secretion of the chemokine CXCL2. Proteomic profiling of EVs reveals that EV-packaged S100A11 stimulates the Janus kinase 2/signal transducer and activator of transcription 3 signaling pathway in macrophages by interacting with USP9X. High level of S100A11 expression or circulating gMDSCs correlates with the presentation of lung metastasis and poor prognosis in osteosarcoma patients. In summary, we identify a key role of tumor-derived EVs in lung PMN formation, providing potential strategies for monitoring or preventing lung metastasis in osteosarcoma