Physical and numerical investigations of target stratum selection for ground hydraulic fracturing of multiple hard roofs

Ground hydraulic fracturing plays a crucial role in controlling the far-field hard roof,making it imperative to identify the most suitable target stratum for effective control.Physical experiments are conducted based on engineering properties to simulate the gradual collapse of the roof during longwall top coal caving(LTCC).A numerical model is established using the material point method(MPM)and the strain-softening damage constitutive model according to the structure of the physical model.Numerical simulations are conducted to analyze the LTCC process under different hard roofs for ground hydraulic fracturing.The results show that ground hydraulic fracturing releases the energy and stress of the target stratum,resulting in a substantial lag in the fracturing of the overburden before collapse occurs in the hydraulic fracturing stratum.Ground hydraulic fracturing of a low hard roof reduces the lag effect of hydraulic fractures,dissipates the energy consumed by the fracture of the hard roof,and reduces the abutment stress.Therefore,it is advisable to prioritize the selection of the lower hard roof as the target stratum.

Perspectives on bone-targeted nano-drug carriers for bone tumor treatment

Bone tumour is one of most common primary cancer which exhibits cancerous osteoblastic differentiation and malignant osteoid in patients.At present,chemotherapy(pre-and post-operative)is used as a standard treatment protocol for bone tumour.However,drugs used in the treatment of bone tumour induce high toxicity to normal tissues including anaemia,neutropenia,thrombocytopenia,and heart damage which further reduce the survival rate of patients.Therefore,there is an urgent need to develop a new therapeutic approach for the treatment such that it induce maximum cell killing effect in tumor cells while sparing the healthy bone cells.In this article,some new perspectives were provided on the development of bone-targeted nano-drug carriers for bone cancer treatment.We hope such discussions wouldencourage more detailed and careful studies to support product development of bone-targeted drug carriers for bone cancer treatment.

Target Vehicle Selection Algorithm for Adaptive Cruise Control Based on Lane-changing Intention of Preceding Vehicle

To improve the ride comfort and safety of a traditional adaptive cruise control(ACC)system when the preceding vehicle changes lanes,it proposes a target vehicle selection algorithm based on the prediction of the lane-changing intention for the preceding vehicle.First,the Next Generation Simulation dataset is used to train a lane-changing intention prediction algorithm based on a sliding window support vector machine,and the lane-changing intention of the preceding vehicle in the current lane is identified by lateral position offset.Second,according to the lane-changing intention and collision threat of the preceding vehicle,the target vehicle selection algorithm is studied under three different conditions:safe lane-changing,dangerous lane-changing,and lane-changing cancellation.Finally,the effectiveness of the proposed algorithm is verified in a co-simulation platform.The simulation results show that the target vehicle selection algorithm can ensure the smooth transfer of the target vehicle and effectively reduce the longitudinal acceleration fluctuation of the subject vehicle when the preceding vehicle changes lanes safely or cancels their lane change maneuver.In the case of a dangerous lane change,the target vehicle selection algorithm proposed in this paper can respond more rapidly to a dangerous lane change than the target vehicle selection method of the traditional ACC system;thus,it can effectively avoid collisions and improve the safety of the subject vehicle.

Joint Topology Construction and Power Adjustment for UAV Networks:A Deep Reinforcement Learning Based Approach

In this paper,we investigate a backhaul framework jointly considering topology construction and power adjustment for self-organizing UAV networks.To enhance the backhaul rate with limited information exchange and avoid malicious power competition,we propose a deep reinforcement learning(DRL)based method to construct the backhaul framework where each UAV distributedly makes decisions.First,we decompose the backhaul framework into three submodules,i.e.,transmission target selection(TS),total power control(PC),and multi-channel power allocation(PA).Then,the three submodules are solved by heterogeneous DRL algorithms with tailored rewards to regulate UAVs’behaviors.In particular,TS is solved by deep-Q learning to construct topology with less relay and guarantee the backhaul rate.PC and PA are solved by deep deterministic policy gradient to match the traffic requirement with proper finegrained transmission power.As a result,the malicious power competition is alleviated,and the backhaul rate is further enhanced.Simulation results show that the proposed framework effectively achieves system-level and all-around performance gain compared with DQL and max-min method,i.e.,higher backhaul rate,lower transmission power,and fewer hop.

Optimal ship imaging for shore-based ISAR using DCF estimation

The optimal imaging time selection of ship targets for shore-based inverse synthetic aperture radar （ISAR） in high sea conditions is investigated. The optimal imaging time includes opti- mal imaging instants and optimal imaging duration. A novel method for optimal imaging instants selection based on the estimation of the Doppler centroid frequencies （DCFs） of a series of images obtained over continuous short durations is proposed. Combined with the optimal imaging duration selection scheme using the image contrast maximization criteria, this method can provide the ship images with the highest focus. Simulated and real data pro- cessing results verify the effectiveness of the proposed imaging method.

Review of studies on target acquisition in virtual reality based on the crossing paradigm

Crossing is a fundamental paradigm for target selection in human-computer interaction systems.This paradigm was first introduced to virtual reality(VR)interactions by Tu et al.,who investigated its performance in comparison to pointing,and concluded that crossing is generally no less effective than pointing and has unique advantages.However,owing to the characteristics of VR interactions,there are still many factors to consider when applying crossing to a VR environment.Thus,this review summarizes the main techniques for object selection in VR and crossing-related studies.Then,factors that may affect crossing interactions are analyzed from the perspectives of the input space and visual space.The aim of this study is to provide a reference for future studies on target selection based on the crossing paradigm in virtual reality.

Influence of multi-modality on moving target selection in virtual reality

Background Owing to recent advances in virtual reality(VR)technologies,effective user interaction with dynamic content in 3D scenes has become a research hotspot.Moving target selection is a basic interactive task in which the user performance research in tasks is significant to user interface design in VR.Different from the existing static target selection studies,the moving target selection in VR is affected by the change in target speed,angle and size,and lack of research on some key factors.Methods This study designs an experimental scenario in which the users play badminton under the condition of VR.By adding seven kinds of modal clues such as vision,audio,haptics,and their combinations,five kinds of moving speed and four kinds of serving angles,and the effect of these factors on the performance and subjective feelings in moving target selection in VR,is studied.Results The results show that the moving speed of the shuttlecock has a significant impact on the user performance.The angle of service has a significant impact on hitting rate,but has no significant impact on the hitting distance.The acquisition of the user performance by the moving target is mainly influenced by vision under the combined modalities;adding additional modalities can improve user performance.Although the hitting distance of the target is increased in the trimodal condition,the hitting rate decreases.Conclusion This study analyses the results of user performance and subjective perception,and then provides suggestions on the combination of modality clues in different scenarios.

SELECTION AND THEIR ANTITUMOR ACTIVITY OF ANTISENSE OLIGONUCLEOTIDES TARGETING MESSENGER RNA OF VASCULAR ENDOTHELIAL GROWTH FACTOR RECEPTOR 2

Objective： To select the antisense oligonucleotides （asONs） which hybridize with the mRNA of vascular endothelial growth factor receptor2 （VEGFR2, also named as kinase insert domain-containing receptor：KDR） in an effective and specific way, and to investigate their antitumor activity in MCF-7 cells. Methods： The effective antisense oligonucleotides were chosen by computer prediction combined with oligonucleotide microarrays. The inhibition effect on MCF-7 cells proliferation was measured by MTT; and VEGFR2 expression was surveyed by Western-blotting and RT- PCR. Results： Using predicting secondary structure of VEGFR2 mRNA with RNA folding program, computer prediction designed 30 antisense oligonucleotide probes that were directed to local loose regions of RNA structure. In 30 probes, 4（4/30, 13.33%） antisense oligonucleotides showed strong hybridization intensities in oligonucleotide microarrays test and were selected. All these antisense oligonucleotides targeting 4 different sites of VEGFR2 mRNA lowered the level of VEGFR2 mRNA and protein present in MCF-7 cells. Proliferation of MCF-7 cells was reduced by 4 antisense oligonucleotides, respectively, in which asON1 was the most effective, with the inhibitory rates being 53.06% at 0.8 I.tmol/L. Conclusion： Combination of computer prediction with oligonucleotide microarrays is an effective way in selecting optimal antisense oligonucleotides. The antisense oligonucleotides showed good correlation between their antitumor activity and the hybridization intensities. The antisense oligonucleotides targeting VEGFR2 mRNA demonstrated prominent antitumor role in vitro.

Searching for Novel Targets to Control Wheat Head Blight Disease—I-Protein Identification, 3D Modeling and Virtual Screening

Fusarium head blight (FHB) is a destructive disease of wheat and other cereals. FHB occurs in Europe, North America and around the world causing significant losses in production and endangers human and animal health. In this article, we provide the strategic steps for the specific target selection for the phytopathogen system wheat-Fusarium graminearum. The economic impact of FHB leads to the need for innovation. Currently used fungicides have been shown to be effective over the years, but recently cereal infecting Fusaria have developed resistance. Our work presents a new perspective on target selection to allow the development of new fungicides. We developed an innovative approach combining both genomic analysis and molecular modeling to increase the discovery for new chemical compounds with both safety and low environmental impact. Our protein targets selection revealed 13 candidates with high specificity, essentiality and potentially assayable with a favorable accessibility to drug activity. Among them, three proteins: trichodiene synthase, endoglucanase-5 and ERG6 were selected for deeper structural analyses to identify new putative fungicides. Overall, the bioinformatics filtering for novel protein targets applied for agricultural purposes is a response to the demand for chemical crop protection. The availability of the genome, secretome and PHI-base allowed the enrichment of the search that combined experimental data in planta. The homology modeling and molecular dynamics simulations allowed the acquisition of three robust and stable conformers. From this step, approximately ten thousand compounds have been virtually screened against three candidates. Forty-five top-ranked compounds were selected from docking results as presenting better interactions and energy at the binding pockets and no toxicity. These compounds may act as inhibitors and lead to the development of new fungicides.

Application of Dempster-Shafer theory in mineral resource potential mapping

The Dempster-Shafer theory has been successfully applied to mineral resource potential mapping in GIS environmental. In this applied form, basic probability assignment and combined basic probability assignment are applied to measuring map pattem and map pattem combination, respectively; and the environment composed of the only two singleton sets （deposit set and non-deposit set）, is used for expressing the entire map area. For a subarea in which the certain map pattern combination exists, the combined basic probability assignment corresponding to the map pattern combination existing in this subarea, expresses the belief of inferring the subarea belonging to the deposit set from the evidence that the corresponding map pattern combination existing in the subarea. Thus, it may be served as a statistical index measuring the relative mineral resource potentials of the subarea. And it may be determined like 1） dividing the map area into a series of small equal-sized grid cells and then select the training sample set composed of the well-known grid cells or the entire grid cells; 2） estimating the basic probability assignments corresponding to each map pattern fromthe training sample set; 3） determining the map pattern combination existing in each cell, and then appling the Dempster＇s Rule of Combination to integrating the all basic probability assignments corresponding to the map patterns existing in the cell into the combined basic probability assignment. Mineral resource potential mapping with the Dempster-Shafer theory is demonstrated on a case study to select mineral resource targets. The experimental results manifest that the model can be compared with the weights of evidence model in the effectiveness of mineral resource target selection.

Trajectory prediction model for crossing-based target selection

Background Crossing-based target selection motion may attain less error rates and higher interactive speed in some cases.Most of the research in target selection fields are focused on the analysis of the interaction results.Additionally,as trajectories play a much more important role in crossing-based target selection compared to the other interactive techniques,an ideal model for trajectories can help computer designers make predictions about interaction results during the process of target selection rather than at the end of the whole process.Methods In this paper,a trajectory prediction model for crossing based target selection tasks is proposed by taking the reference of a dynamic model theory.Results Simulation results demonstrate that our model performed well with regard to the prediction of trajectories,endpoints and hitting time for target-selection motion,and the average error of trajectories,endpoints and hitting time values were found to be 17.28%,2.73mm and 11.50%,respectively.

Make Leapfrogging Development By Concentrating Resource On Selected Targets

The Chinese Academy of Sci-ences (CAS) has begun introducingthe trial implementation of theKnowledge Innovation Program(KIP)in an all-round manner just as,at the beginning of the new century,the state is about to implement itsTenth Five-Year Plan for thecountry’s ongoing economic andsocial development. This will

Bispecific antibodies in cancer therapy:Target selection and regulatory requirements

In recent years,the development of bispecific antibodies(bsAbs)has been rapid,with many new structures and target combinations being created.The boom in bsAbs has led to the successive issuance of industry guidance for their development in the US and China.However,there is a high degree of similarity in target selection,which could affect the development of diversity in bsAbs.This review presents a classification of various bsAbs for cancer therapy based on structure and target selection and examines the advantages of bsAbs over monoclonal antibodies(mAbs).Through database research,we have identified the preferences of available bsAbs combinations,suggesting rational target selection options and warning of potential wastage of medical resources.We have also compared the US and Chinese guidelines for bsAbs in order to provide a reference for their development.

Q-Learning-Based Target Selection for Bearings-Only Autonomous Navigation

This paper presents a Q-learning-based target selection algorithm for spacecraft autonomous navigation using bearing observations of known visible targets.For the considered navigation system,the position and velocity of the spacecraft are estimated using an extended Kalman filter(EKF)with the measurements of inter-satellite line-of-sight(LOS)vectors obtained via an onboard star camera.This paper focuses on the selection of the appropriate target at each observation period for the star camera adaptively,such that the performance of the EKF is enhanced.To derive an effective algorithm,a Q-function is designed to select a proper observation region,while a U-function is introduced to rank the targets in the selected region.Both the Q-function and the U-function are constructed based on the sequence of innovations obtained from the EKF.The efficiency of the Q-learning-based target selection algorithm is illustrated via numerical simulations,which show that the presented algorithm outperforms the traditional target selection strategy based on a Cramer-Rao bound(CRB)in the case that the prior knowledge about the target location is inaccurate.

Ready-to-implement low-carbon retrofit of coal-fired power plants in China:Optimal scenarios selection based on sludge and photovoltaic utilization

Currently the flexible demand for high proportion penetration of renewable energy depends on coal-fired units(CFUs),and the large-scale phase-out of CFUs in a short time is not realistic in China.Due to urban expansion,approximately 458 Chinese coal-fired power plants(CFPPs)are now located in cities.Limited by space,urban CFUs face difficulty in becoming equipped with carbon capture and storage systems.This presents a sizeable challenge for the low-carbon transition of urban CFPPs and carbon neutral processes.Here,we present a ready-to-implement method to reduce the carbon emission of CFPPs in limited space:roof photovoltaic-assisted power generation combined with sludge cocombustion for coal-fired power generation systems(PVSCs).We also consider nonurban CFPPs with the method of roof photovoltaic-assisted power generation(PVs)only.Based on remaining life cycle analysis,we find that the PVSCs could save 28.47 Mt of coal,reduce CO_(2)emissions by 69.76 Mt,treat 125.70 Mt of sludge,and also generate 12.08 billion RMB worth of electricity revenue per year.In addition,our scenario analysis shows that PVSCs are more profitable when choosing an urban CFU with a remaining life of more than 12 years and while the sludge treatment subsidy is set at 100 RMB t
1.Under strict and lenient CFU decommissioning policies,CFUs with a remaining life of between 19 and 30 years and between 13 and 24 years should be selected for PVs,respectively.Thus,we conclude that PVSCs can not only generate economic benefits but also facilitate carbon reduction and solid waste treatment.

The role of accounting conservatism in M&A target selection

Mergers and acquisitions(M&As)are among the most important investment activities for companies,but they contain great risks.We investigate the role of accounting conservatism in M&A target selection and risk.We find that for risk-averse reasons,firms with high accounting conservatism are likely to acquire profitable targets and avoid loss-making targets.When such firms acquire loss-making targets,the conservatism’s risk-control role reduces M&A risk and increases M&A performance,but only when control of the target is transferred and the acquirer has high long-term debt and low management power.Furthermore,accounting conservatism reduces risk by increasing the maturity match between cash flow and debt.Our results suggest that accounting conservatism plays not only a risk-averse role but also a riskcontrol role,providing new evidence for the usefulness of accounting conservatism in M&A decisions.

Cancer stem cells, stemness markers and selected drug targeting: metastatic colorectal cancer and cyclooxygenase-2/prostaglandin E2 connection to WNT as a model system

Few studies have reported on the analyses of drugs targeting enriched populations of cancer stem cells (CSCs) as a means for identifying potent anti-CSC agents. This review evaluates recent information on the identification and functions of specific CSC surface markers, with particular emphasis on colorectal cancers and the screening of drugs to eliminate such cells. Many of these CSC markers are found commonly expressed on CSCs from different cancer types as well as embryonic stem cells. These markers are often related to hypoxic activation of the WNT/b-catenin pathway, cyclooxygenase-2/prostaglandin E signalling and their relationship to LGR5. By effectively using drugs that inhibit these pathways to kill the CSC population, or otherwise forcing them out of dormancy into active cell division, cancers should become more susceptible to chemotherapy. Such combinational therapies targeting both CSCs and proliferating tumor cells should greatly improve upon the current basis for treatment.

Natural small-molecule sappanone A selectively targets IMPDH2 to inhibit neuroinflammation

With the support by the National Natural Science Foundation of China,a study by the research group led by Prof.Tu Pengfei(屠鹏飞)from the School of Pharmaceutical Sciences,Peking University demonstrates that natural small-molecule sappanone A directly targets IMPDH2to block its activity,