Current Efficiency of Low Temperature Aluminum Electrolysis Studied by Neural Network

A prediction model for Current Efficiency (CE) of low temperature aluminum electrolysis (LTAE) with the low molar ratioelectfolyte of Na3AIF6-AIF3 - CaF2-MgF2-LiF -Al2O3 system was investigated based on artificial neural network principles. The nonlinearmapping between CE of LATE and various electrolytic conditions was obtained from a number of experimental data and used to predictCE of LATE. The trsined neural networks possessed high precision and resulted in a good predicting effect. As a result, attificial neuralnetworks as a new cooperating and predicting technology provide a new approach to the further studies on low temperature aluminumelectrolysis.

Guest Editorial for Special Issue on Cognitive Computing for Collaborative Robotics

Cognitive Computing breaks the boundary between two separate fields,neuroscience and computer science.It paves the way for machines to have reasoning abilities which is analogous to human.The research field of cognitive computing is interdisciplinary,and uses knowledge and methods from many areas such as psychology,biology,signal processing,physics,information theory,mathematics,and statistics.The development of cognitive computing will keep cross-fertilizing these research areas.However,in collaborative robotics applications there still remain many open problems for using cognitive computing theories.Technologies like computational cognition and perception(CCP)and computational neuroscience(CN)are driving as the best tools for upgrading the robots with near human intelligence,which can be intended to physically interact with humans in a shared workspace.

Separate Least Mean Square Based Equalizer with Joint Optimization for Multi-CAP Visible Light Communication

Visible light communication(VLC) is expected to be a potential candidate of the key technologies in the sixth generation(6G) wireless communication system to support Internet of Things(IoT) applications. In this work, a separate least mean square(S-LMS) equalizer is proposed to compensate lowpass frequency response in VLC system. Joint optimization is employed to realize the proposed S-LMS equalizer with pre-part and post-part by introducing Lagrangian. For verification, the performance of VLC system based on multi-band carrier-less amplitude and phase(m-CAP) modulation with S-LMS equalizer is investigated and compared with that without equalizer,with LMS equalizer and with recursive least squares(RLS)-Volterra equalizer. Results indicate the proposed equalizer shows significant improved bit error ratio(BER) performance under the same conditions. Compared to the RLS-Volterra equalizer, SLMS equalizer achieves better performance under low data rate or high signal noise ratio(SNR) conditions with obviously lower computational complexity.

DRRS-BC:Decentralized Routing Registration System Based on Blockchain

The border gateway protocol(BGP)has become the indispensible infrastructure of the Internet as a typical inter-domain routing protocol.However,it is vulnerable to misconfigurations and malicious attacks since BGP does not provide enough authentication mechanism to the route advertisement.As a result,it has brought about many security incidents with huge economic losses.Exiting solutions to the routing security problem such as S-BGP,So-BGP,Ps-BGP,and RPKI,are based on the Public Key Infrastructure and face a high security risk from the centralized structure.In this paper,we propose the decentralized blockchain-based route registration framework-decentralized route registration system based on blockchain(DRRS-BC).In DRRS-BC,we produce a global transaction ledge by the information of address prefixes and autonomous system numbers between multiple organizations and ASs,which is maintained by all blockchain nodes and further used for authentication.By applying blockchain,DRRS-BC perfectly solves the problems of identity authentication,behavior authentication as well as the promotion and deployment problem rather than depending on the authentication center.Moreover,it resists to prefix and subprefix hijacking attacks and meets the performance and security requirements of route registration.

Fast Scene Reconstruction Based on Improved SLAM

Simultaneous location and mapping(SLAM)plays the crucial role in VR/AR application,autonomous robotics navigation,UAV remote control,etc.The traditional SLAM is not good at handle the data acquired by camera with fast movement or severe jittering,and the efficiency need to be improved.The paper proposes an improved SLAM algorithm,which mainly improves the real-time performance of classical SLAM algorithm,applies KDtree for efficient organizing feature points,and accelerates the feature points correspondence building.Moreover,the background map reconstruction thread is optimized,the SLAM parallel computation ability is increased.The color images experiments demonstrate that the improved SLAM algorithm holds better realtime performance than the classical SLAM.

褐藻胶裂解酶基因的克隆表达与酶学性质

随着大型褐藻生产燃料乙醇以及褐藻寡糖重大药用价值的发现,褐藻胶裂解酶成为国内外多个领域的研究重点。文中对解藻酸弧菌上与褐藻胶降解相关的5个基因分别进行克隆表达,通过SDS-PAGE和酶活性定量测定,发现该基因簇中的4个基因有降解褐藻胶活性。对酶活最高的rAlgV3进行了诱导条件的优化、酶蛋白纯化及酶性质研究,发现优化诱导条件后重组酶rAlgV3的酶活由2.34×10~4 U/L上升为1.68×10~5 U/L,比优化前提高了7.3倍;对酶性质进行表征发现该酶在4–70℃均有活性,最适反应温度为40℃,在4–20℃酶相对稳定;该酶在pH 6.5-9.0环境下均有较高的酶活,最适pH为8.0;pH稳定性好,在pH 4.5–9.5环境下可以稳定存在;适量的NaCl浓度和Fe^(2+)、Fe^(3+)等离子具有促进酶活的作用,SDS和Cu^(2+)离子可明显抑制酶活力。对该酶的底物特性的研究发现,该酶不仅可以降解褐藻胶中的Poly-M片段,也能降解Poly-G片段,具有广泛底物特性;其降解海藻酸钠主要释放二糖和三糖,是一种内切酶。该酶对于第三代燃料乙醇的发展及褐藻寡糖的生产具有重要作用。

Protective role of berberine on ulcerative colitis through modulating enteric glial cells-intestinal epithelial cells-immune cells interactions

Ulcerative colitis(UC)manifests as an etiologically complicated and relapsing gastrointestinal disease.The enteric nervous system(ENS)plays a pivotal role in rectifying and orchestrating the inflammatory responses in gut tract.Berberine,an isoquinoline alkaloid,is known as its antiinflammatory and therapeutic effects in experimental colitis.However,little research focused on its regulatory function on ENS.Therefore,we set out to explore the pathological role of neurogenic inflammation in UC and the modulating effects of berberine on neuro-immune interactions.Functional defects of enteric glial cells(EGCs),with decreased glial fibrillary acidic protein(GFAP)and increased substance P expression,were observed in DSS-induced murine UC.Administration of berberine can obviously ameliorate the disease severity and restore the mucosal barrier homeostasis of UC,closely accompanying by maintaining the residence of EGCs and attenuating inflammatory infiltrations and immune cells overactivation.In vitro,berberine showed direct protective effects on monoculture of EGCs,bone marrowderived dendritic cells(BMDCs),T cells,and intestinal epithelial cells(IECs)in the simulated inflammatory conditions.Furthermore,berberine could modulate gut EGCs-IECs-immune cell interactions in the co-culture systems.In summary,our study indicated the EGCs-IECs-immune cell interactions might function as a crucial paradigm in mucosal inflammation and provided an infusive mechanism of berberine in regulating enteric neurogenic inflammation.

Self-organization model on receptive field of neuron with asymmetric time window of synaptic modification

The spatial-temporal response properties of some simple neurons in visual pathway arise basically prior to birth. In the absence of visual experience, how do these neurons develop in visual system? Based on Wimbauer network with delay, a four-layer feed-forward network model is proposed, which is characterized by modifying the Hebb learning rule through introducing the asymmetric time window of synaptic modification found recently in neurobiology. The model can not only generate by self-organization more diversified spatial-temporal response characteristics of neu-ronal receptive field than earlier models but also provide some explanations for the possible mechanism underlying the development of receptive fields of contrast polarity sensitive neurons found in visual system of vertebrate. Thus the proposed model may be more widely applicable than Linsker model and Wimbauer model.

An Efficient Multidimensional Fusion Algorithm for IoT Data Based on Partitioning

The Internet of Things （IoT） implies a worldwide network of interconnected objects uniquely addressable, via standard communication protocols. The prevalence of IoT is bound to generate large amounts of multisource, heterogeneous, dynamic, and sparse data. However, IoT offers inconsequential practical benefits without the ability to integrate, fuse, and glean useful information from such massive amounts of data. Accordingly, preparing us for the imminent invasion of things, a tool called data fusion can be used to manipulate and manage such data in order to improve process efficiency and provide advanced intelligence. In order to determine an acceptable quality of intelligence, diverse and voluminous data have to be combined and fused. Therefore, it is imperative to improve the computational efficiency for fusing and mining multidimensional data. In this paper, we propose an efficient multidimensional fusion algorithm for IoT data based on partitioning. The basic concept involves the partitioning of dimensions （attributes）, i.e., a big data set with higher dimensions can be transformed into certain number of relatively smaller data subsets that can be easily processed. Then, based on the partitioning of dimensions, the discernible matrixes of all data subsets in rough set theory are computed to obtain their core attribute sets. Furthermore, a global core attribute set can be determined. Finally, the attribute reduction and rule extraction methods are used to obtain the fusion results. By means of proving a few theorems and simulation, the correctness and effectiveness of this algorithm is illustrated.

Targeting PDE4 as a promising therapeutic strategy in chronic ulcerative colitis through modulating mucosal homeostasis

Phosphodiesterase-4(PDE4) functions as a catalyzing enzyme targeting hydrolyzation of intracellular cyclic adenosine monophosphate(c AMP) and inhibition of PDE4 has been proven to be a competitive strategy for dermatological and pulmonary inflammation. However, the pathological role of PDE4 and the therapeutic feasibility of PDE4 inhibitors in chronic ulcerative colitis(UC) are less clearly understood. This study introduced apremilast, a breakthrough in discovery of PDE4 inhibitors,to explore the therapeutic capacity in dextran sulfate sodium(DSS)-induced experimental murine chronic UC. In the inflamed tissues, overexpression of PDE4 isoforms and defective c AMP-mediating pathway were firstly identified in chronic UC patients. Therapeutically, inhibition of PDE4 by apremilast modulated c AMP-predominant protein kinase A(PKA)-c AMP-response element binding protein(CREB)signaling and ameliorated the clinical symptoms of chronic UC, as evidenced by improvements on mucosal ulcerations, tissue fibrosis, and inflammatory infiltrations. Consequently, apremilast maintained a normal intestinal physical and chemical barrier function and rebuilt the mucosal homeostasis by interfering with the cross-talk between human epithelial cells and immune cells. Furthermore, we found that apremilast could remap the landscape of gut microbiota and exert regulatory effects on antimicrobial responses and the function of mucus in the gut microenvironment. Taken together, the present studyrevealed that intervene of PDE4 provided an infusive therapeutic strategy for patients with chronic and relapsing UC.

Elevated Kir2.1/nuclear N2ICD defines a highly malignant subtype of non-WNT/SHH medulloblastomas

Medulloblastoma(MB)is one of the most common childhood malignant brain tumors(WHO grade IV),traditionally divided into WNT,SHH,Group 3,and Group 4 subgroups based on the transcription profiles,somatic DNA alterations,and clinical outcomes.Unlike WNT and SHH subgroup MBs,Group 3 and Group 4 MBs have similar transcriptomes and lack clearly specific drivers and targeted therapeutic options.The recently revised WHO Classification of CNS Tumors has assigned Group 3 and 4 to a provisional non-WNT/SHH entity.In the present study,we demonstrate that Kir2.1,an inwardly-rectifying potassium channel,is highly expressed in non-WNT/SHH MBs,which promotes tumor cell invasion and metastasis by recruiting Adam10 to enhance S2 cleavage of Notch2 thereby activating the Notch2 signaling pathway.Disruption of the Notch2 pathway markedly inhibited the growth and metastasis of Kir2.1-overexpressing MB cell-derived xenograft tumors in mice.Moreover,Kir2.1^(high)/nuclear N2ICD^(high)MBs are associated with the significantly shorter lifespan of the patients.Thus,Kir2.1^(high)/nuclear N2ICD^(high)can be used as a biomarker to define a novel subtype of non-WNT/SHH MBs.Our findings are important for the modification of treatment regimens and the development of novel-targeted therapies for non-WNT/SHH MBs.

LWD-3D:Lightweight Detector Based on Self-Attention for 3D Object Detection

Lightweight modules play a key role in 3D object detection tasks for autonomous driving,which are necessary for the application of 3D object detectors.At present,research still focuses on constructing complex models and calculations to improve the detection precision at the expense of the running rate.However,building a lightweight model to learn the global features from point cloud data for 3D object detection is a significant problem.In this paper,we focus on combining convolutional neural networks with selfattention-based vision transformers to realize lightweight and high-speed computing for 3D object detection.We propose lightweight detection 3D(LWD-3D),which is a point cloud conversion and lightweight vision transformer for autonomous driving.LWD-3D utilizes a one-shot regression framework in 2D space and generates a 3D object bounding box from point cloud data,which provides a new feature representation method based on a vision transformer for 3D detection applications.The results of experiment on the KITTI 3D dataset show that LWD-3D achieves real-time detection(time per image<20 ms).LWD-3D obtains a mean average precision(mAP)75%higher than that of another 3D real-time detector with half the number of parameters.Our research extends the application of visual transformers to 3D object detection tasks.