A novel integrated microfluidic chip for on-demand electrostatic droplet charging and sorting

On-demand droplet sorting is extensively applied for the efficient manipulation and genome-wide analysis of individual cells.However,state-of-the-art microfluidic chips for droplet sorting still suffer from low sorting speeds,sample loss,and labor-intensive preparation procedures.Here,we demonstrate the development of a novel microfluidic chip that integrates droplet generation,on-demand electrostatic droplet charging,and high-throughput sorting.The charging electrode is a copper wire buried above the nozzle of the microchannel,and the deflecting electrode is the phosphate buffered saline in the microchannel,which greatly simplifies the structure and fabrication process of the chip.Moreover,this chip is capable of high-frequency droplet generation and sorting,with a frequency of 11.757 kHz in the drop state.The chip completes the selective charging process via electrostatic induction during droplet generation.On-demand charged microdroplets can arbitrarilymove to specific exit channels in a three-dimensional(3D)-deflected electric field,which can be controlled according to user requirements,and the flux of droplet deflection is thereby significantly enhanced.Furthermore,a lossless modification strategy is presented to improve the accuracy of droplet deflection or harvest rate from 97.49% to 99.38% by monitoring the frequency of droplet generation in real time and feeding it back to the charging signal.This chip has great potential for quantitative processing and analysis of single cells for elucidating cell-to-cell variations.

Joint Optimization for on-Demand Deployment of UAVs and Spectrum Allocation in UAVs-Assisted Communication

To improve the efficiency and fairness of the spectrum allocation for ground communication assisted by unmanned aerial vehicles(UAVs),a joint optimization method for on-demand deployment and spectrum allocation of UAVs is proposed,which is modeled as a mixed-integer non-convex optimization problem(MINCOP).An algorithm to estimate the minimum number of required UAVs is firstly proposed based on the pre-estimation and simulated annealing.The MINCOP is then decomposed into three sub-problems based on the block coordinate descent method,including the spectrum allocation of UAVs,the association between UAVs and ground users,and the deployment of UAVs.Specifically,the optimal spectrum allocation is derived based on the interference mitigation and channel reuse.The association between UAVs and ground users is optimized based on local iterated optimization.A particle-based optimization algorithm is proposed to resolve the subproblem of the UAVs deployment.Simulation results show that the proposed method could effectively improve the minimum transmission rate of UAVs as well as user fairness of spectrum allocation.

Diversified Business Model of On-Demand Cooking:An Analysis Based on the Business Model Canvas

With the acceleration of the pace of social life and the emergence of the convenience economy,the requirements for on-demand cooking services have increased due to the increasing demand for food quality and the health concerns associated with takeaway food,as well as the lack of cooking skills.This paper aims to analyze the business model of on-demand cooking service platforms in China with the theory of Business Model Canvas proposed by Alexander Osterwalder.By exploring the advantages and disadvantages of the business model of on-demand door-to-door cooking,the study seeks to provide relevant recommendations.

Trust-based Intelligent Scheme for Mitigating Black Hole Attacks in IoT

Internet of Things(IoT)networks are characterized by a multitude of wireless,interconnected devices that can dynamically join or exit the network without centralized administration or fixed infrastructure for routing.While multipath routing in IoT networks can improve data transmission reliability and load balancing by establishing multiple paths between source and destination nodes,these networks are susceptible to security threats due to their wireless nature.Traditional security solutions developed for conventional networks are often ill-suited to the unique challenges posed by IoT environments.In response to these challenges,this paper proposes the integration of the Ad hoc On-demand Multipath Distance Vector(AOMDV)routing protocol with a trust model to enhance network performance.Key findings from this research demonstrate the successful fusion of AOMDV with a trust model,resulting in tangible improvements in network performance.The assessment of trustworthiness bolsters both security and routing capabilities in IoT networks.The trust model plays a crucial role in mitigating black hole attacks in IoT networks by evaluating the trustworthiness of nodes and helping in the identification and avoidance of malicious nodes that may act as black holes.Simulation results validate the efficacy of the proposed trust-based routing mechanism in achieving its objectives.Trust plays a pivotal role in decision-making and in the creation of secure distribution systems.By assessing the trustworthiness of nodes,both network security and routing efficiency can be enhanced.The effectiveness of the proposed trust-based routing mechanism is scrutinized through simulations,offering insights into its potential advantages in terms of improved network security and routing performance in the context of the IoT.

Combinations of Transportation Policies to Promote BRT Usage Using Artificial Society Model

Various transportation systems have been developed in recent years.In this study,an artificial society model is developed to examine the combination of transportation policies in urban areas.In this model,each trip maker selects the primary and terminal transportation modes.An artificial society model is applied to the southeastern region of Osaka City,Japan.The effects of introducing BRT(bus rapid transit,primary transportation)and on-demand buses(terminal transportation)are investigated.The results confirm that BRT is used by a certain number of users.An increase in the use of BRT will increase the amount of walking,thus resulting in a healthy city.However,on-demand buses are rarely used as terminal transportation.Additionally,the development of bicycle parking stations near BRT stops is shown to be effective in the northern section of the BRT route.

Comparative study of on-demand and daily use of sertraline in treatment of premature ejaculation:A randomized clinical trial

Objective:The intravaginal ejaculatory latency time(IELT)may increase less in on-demand compared to daily intake,but may fulfill a suitable treatment for specific patients.We decided to compare the efficacy and safety of on-demand and daily use of sertraline in order to find the most effective and least complicated method in treatment of premature ejaculation(PE).Methods:This study was parallel or concurrent control randomized clinical trial.Two hundred and forty patients with PE diagnosed by urologist in the two groups of 120 from July 2017 to February 2019 enrolled in the study.In the first group,it is prescribed 50 mg sertraline each 12 h daily and the second group received 50 mg 4 h before coitus for 4 and 8 weeks.The IELT before treatment and during all coitus after treatment were recorded by the patient’s wife with a stopwatch.Results:Mean IELT before,4 and 8 weeks after treatment in two groups were:On-demand group 101.62±65.44 s,208.75±128.02 s and 265.87±145.70 s;daily use group 102.50-81.22 s,276.87±181.08 s and 353.75±176.45 s,respectively.The ejaculation time increased significantly in both groups(p<0.05).However,increase in ejaculation time in daily use group was significantly higher than the on-demand group in 4 weeks(p=0.036),especially in 8 weeks(p=0.009).The percent of side effects in daily use group(26.7%)was higher than on-demand group(20%)(p<0.05).Drowsiness,diarrhea and vertigo were significantly higher in the daily use than on-demand(p<0.05).Conclusions:On-demand and daily use of sertraline are effective and usually have no serious complications,but the on-demand method is considerably more tolerable.In patients who did not tolerate to daily use of this drug,on-demand could be used as a salvage therapy.

Network coding-aware cache replacement policy in on-demand 0broadcast environments

Network coding has been proved to be an effective technique in improving the performance of data broadcast systems because clients requesting different data items can be served simultaneously in one broadcast. Previous studies showed that its efficiency is highly related to the content of clients' cache. However, existing data broadcast systems do not take network coding information into account when making cache replacement decisions. In this paper, we propose two networks coding-aware cache replacement policies called DLRU and DLRU-CP to supplement network coding assisted data broadcast in on-demand broadcast environments. In DLRU, both data access and decoding contribution are taken into account to make replacement decisions. DLRU-CP is based on DLRU but allows clients to retrieve decodable data items that have not been requested yet. The performance gain of our proposed cache replacement policies over traditional cache replacement policy is shown in the simulation results, which demonstrate conclusively that the proposed policies can effectively reduce the overall response time.

Ants-Based On-Demand Routing Algorithm for Mobile Ad Hoc Networks

An ants-based on-demand routing algorithm （AORA） specialized for mobile ad hoc networks is proposed. AORA measures the network＇s traffic information including delivery time, route energy etc. by the continuous delivery of data packets, then calculates the compositive parameter for each route which can be seen as the stigmity and uses it to choose the comparatively optimal route in real time. To adjust the weight of each traffic information, the algorithm can meet the different demand of the network＇s user. Multipath source self repair routing （MSSRR） algorithm and dynamic source routing （DSR） can be seen as the special samples of AORA. The routing overhead is not increased in this algorithm. By using simulation, it can be seen that the performance of AORA is better than that of DSR in all scenarios obviously, especially the delivery fraction is increased by more than 100 96.

On-Demand Service in Cloud Computing

Cloud computing provides a new paradigm for hardware and software infrastructure design as well as planning and usage of information systems. It offers flexible, efficient, inexpensive, and quality services. This paper proposes an on-demand service system using the cloud computing architecture and analyzes important issues such as organization, management, and monitoring of distributed service resources; context-aware on-demand service modeling, on-demand automated service composition in large-scale networks, and service system analysis based on complex system theory. Continuous Operating Reference Station （CORS） of a geo-spatial information system is taken as an example, and its architecture is analyzed from the perspective of cloud computing. Some fundamental questions are raised about its service.

Topology Control for Ad-Hoc Networks: A Comprehensive Review for Table Driven and On-Demand Routing Protocols

Routing on ad-hoc network has become a major research issue among the networking communities due to its increasing complexity and the surge of challenging problems. One major factor contributing to this tendency is that every terminal of an ad-hoc network is also functioning as a network router. In this paper we provide a comprehensive review about the principles and mechanisms of routing protocols used in ad-hoc networks. For comparison purposes, we discuss some relevant technical issues of two well-known routing strategies, namely On-Demand (Proactive routing) and Table-Driven (Reactive routing). In particular, focus our attention on two major and well-known routing protocols: AODV (Ad-hoc On-Demand Distance Vector Protocol) and OLSR (Optimized Link State Routing Protocol). Our study has no intention to suggest any definite solution for any ad-hoc network, because it is the case depending on dictated by the nature and varying factors of networks. Instead, we demonstrate our major perception and describe general models that may assist us while modeling a given network.

On-demand:新“游戏”?新“模式”?

IBM蓄意要将"On demand"发展成为未来第三代电子商务的新"模式"。尽管这一理想蓝图已经在业界引起极大反响,但更多的声音却表示怀疑:这究竟是一场新游戏,还是从根本上改变电子商务运作的新模式?

Design and Experimental Testing of an Electric Field-Driven Droplet Injection Device

The properties of droplets produced by existing on-demand injection systems are typically determined by the nozzle diameter,i.e.,only droplets with size larger than this diameter can be obtained.To solve this problem,a system for electricfield-driven droplet injection and deposition was developed,and the related performances were compared with those of a standard pneumatic system.The results show that the diameter of droplets gen-erated accordingly can be significantly smaller than the nozzle diameter.In particular,the effects of frequency and duty ratio on the number of droplets were studied by assuming microcrystalline wax as work material.A deposi-tion matrix was obtained by using a nozzle with a diameter of 200μm.The average diameter of the matrix was 80.17μm with a standard deviation of 2.65μm and the average spacing was 301.25μm with a standard deviation of 6.80μm.Four deposition states after droplet injection were considered and verified under different velocities of F60,F10,F3,and F0.We also provide an explanation for the existence of a series of inclined columns showing up during the deposition processs.

Quantifying the Impact of Transportation Network Companies on Urban Congestion in a Medium Sized City

In the recent years,TNCs(transportation network companies)and on-demand ridesharing services have grown rapidly.Given conflicting reports on TNC impacts,a need exists to study mode choice shifts in the presence of TNC services and their effects on urban congestion.Using Birmingham,AL(Alabama)as a case study,this paper showcases the feasibility of modeling TNC services using the MATSim(Multi-Agent Transport Simulation)platform,and evaluating the impact of such services on traffic operations.Data used for the study were gathered from Uber drivers and riders through surveys,as well as the US Census.The results indicate that when 200,400,and 800 TNC vehicles are added to the network,the VKT(vehicle kilometers traveled)increase by 22%,23.6%,and 23.2%,respectively,compared to the baseline scenario(no TNC service).Analysis of hourly average speeds,hourly average travel times,and hourly volumes along study corridors further indicate that TNC services increase traffic congestion,in particular,during the AM/PM peak periods.Moreover,the study shows that the optimal TNC fleet size for the Birmingham region is 400 to 500 active TNC vehicles per day.Such fleet size minimizes idle time and the number of TNC vehicles hovering,which have adverse impacts on TNC drivers,and the environment while ensuring TNC service availability and reasonable waiting times for TNC customers.

A Matching Algorithm with Reinforcement Learning and Decoupling Strategy for Order Dispatching in On-Demand Food Delivery

The on-demand food delivery(OFD)service has gained rapid development in the past decades but meanwhile encounters challenges for further improving operation quality.The order dispatching problem is one of the most concerning issues for the OFD platforms,which refer to dynamically dispatching a large number of orders to riders reasonably in very limited decision time.To solve such a challenging combinatorial optimization problem,an effective matching algorithm is proposed by fusing the reinforcement learning technique and the optimization method.First,to deal with the large-scale complexity,a decoupling method is designed by reducing the matching space between new orders and riders.Second,to overcome the high dynamism and satisfy the stringent requirements on decision time,a reinforcement learning based dispatching heuristic is presented.To be specific,a sequence-to-sequence neural network is constructed based on the problem characteristic to generate an order priority sequence.Besides,a training approach is specially designed to improve learning performance.Furthermore,a greedy heuristic is employed to effectively dispatch new orders according to the order priority sequence.On real-world datasets,numerical experiments are conducted to validate the effectiveness of the proposed algorithm.Statistical results show that the proposed algorithm can effectively solve the problem by improving delivery efficiency and maintaining customer satisfaction.

Reinforcement Learning-Based Dynamic Order Recommendation for On-Demand Food Delivery

On-demand food delivery(OFD)is gaining more and more popularity in modern society.As a kernel order assignment manner in OFD scenario,order recommendation directly influences the delivery efficiency of the platform and the delivery experience of riders.This paper addresses the dynamism of the order recommendation problem and proposes a reinforcement learning solution method.An actor-critic network based on long short term memory(LSTM)unit is designed to deal with the order-grabbing conflict between different riders.Besides,three rider sequencing rules are accordingly proposed to match different time steps of the LSTM unit with different riders.To test the performance of the proposed method,extensive experiments are conducted based on real data from Meituan delivery platform.The results demonstrate that the proposed reinforcement learning based order recommendation method can significantly increase the number of grabbed orders and reduce the number of order-grabbing conflicts,resulting in better delivery efficiency and experience for the platform and riders.

Self-adaptive hydrogel for breast cancer therapy via accurate tumor elimination and on-demand adipose tissue regeneration

The irregular defects and residual tumor tissue after surgery are challenges for effective breast cancer treatment.Herein,a smart hydrogel with self-adaptable size and dual responsive cargos release was fabricated to treat breast cancer via accurate tumor elimination,on-demand adipose tissue regeneration and effective infection inhibition.The hydrogel consisted of thiol groups ended polyethylene glycol(SH-PEG-SH)and doxorubicin encapsulated mesoporous silica nanocarriers(DOX@MSNs)double crosslinked hyaluronic acid(HA)after loading of antibacterial peptides(AP)and adipose-derived stem cells(ADSCs).A pH-cleavable unsaturated amide bond was pre-introduced between MSNs and HA frame to perform the tumor-specific acidic environment dependent DOX@MSNs release,meanwhile an esterase degradable glyceryl dimethacrylate cap was grafted on MSNs,which contributed to the selective chemotherapy in tumor cells with over-expressed esterase.The bond cleavage between MSNs and HA would also cause the swelling of the hydrogel,which not only provide sufficient space for the growth of ADSCs,but allows the hydrogel to fully fill the irregular defects generated by surgery and residual tumor atrophy,resulting in the on-demand regeneration of adipose tissue.Moreover,the sustained release of AP could be simultaneously triggered along with the size change of hydrogel,which further avoided bacterial infection to promote tissue regeneration.

重视饲用玉米发展 促进农业结构调整

在 明 确 了玉 米 对 种 植业 、养殖 业 和 农 业可 持 续 发 展 的 重 要 作 用 基 础 上 ,提 出 了 发 展 饲 用 玉 米 ,满 足 畜牧业 快速 发 展对 玉米 的大 量 需求 。对饲 用 玉米 提出 的 背景 、概 念、类型 和营 养 评价 标准 进 行了 评述 ,针 对 目前 的研 究现状 ,认为 饲 用 玉 米的 研 究 应 重点 加 强 品 种改 良 、整体 营 养 调 控 技 术 、产 量 (子 粒 和 秸 秆 )和 品 质 同 步 提 高 的 生 理 生化机 制和 产 业化 示范 四个 方 面。

急性分离大鼠新皮层神经元K_(ATP)通道特性

为研究急性分离大鼠皮层神经元上ＡＴＰ敏感钾通道的特性，本实验采用了膜片钳技术之膜内面向外记录方法。ＡＴＰ敏感钾通道电导为２００ｐＳ左右，翻转电位为０ｍＶ，有外向整流现象；通道开放常呈簇状猝发样开放，平均开放时间和开放概率随去极化程度增大而增加，随超极化程度增大而减少；低浓度ＡＴＰ有促进通道开放作用，并可激活二级电流；高浓度ＡＴＰ抑制通道开放概率并随浓度增大而增强；ＡＴＰ浓度增高到１或２ｍｍｏｌ／Ｌ时，通道电流被阻断。０.１ｍｍｏｌ／Ｌ优降糖可立即抑制通道活动。急性分离的皮层神经元上ＡＴＰ敏感钾通道特性与培养神经元有差别；低浓度ＡＴＰ可激活ＡＴＰ敏感钾通道，防止神经细胞过度兴奋，从而起到保护脑缺血缺氧损伤的作用。

RSView32在污水厂监控系统中应用

RSView32是一种基于人机接口界面组件、用于监控自动化设备及过程的组态软件。本文重点介绍了RSView32软件的功能特点及其在长沙污水厂自动控制系统中的应用实现,还介绍了污水处理控制系统总体结构和功能。

采用OPC技术实现竖炉烧结DCS系统与VB软件的接口

对竖炉烧结DCS系统及OPC技术特点进行了介绍,并且结合实际的开发和应用,给出一种针对DCS系统使用VB的驱动程序的通用实现方法。