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Acupuncture has been shown as an effective traditional Chinese medicine treatment method, especially for pain relief. Recently, laser acupuncture is becoming increasingly popular, thanks to its noninvasive and painless nature and effectiveness in treating diseases, proven by many studies (for example, some previous studies showed that low-power laser stimulation is able to increase the power of alpha rhythms and theta waves). In our prior work, we developed a novel laser acupuncture model that emulates lifting-and-thrusting operation commonly used in traditional needle acupuncture and showed its benefit in improving cardiac output and peripheral circulation. By extending our previous studies, in this work, we perform extensive experiments to understand the effect of such a system on electrodermal activity (EDA) of acupoints, pulse characteristics, and brainwave, to further verify its efficacy. In particular, we found that laser stimulation could cause significant changes in EDA of acupoints, pulse amplitude, pulse-rate-variability (PRV), and acupoint conductance, as a function of laser power and stimulation time. In addition, laser acupuncture with the lifting-and-thrusting operation has more significant effect on increasing the power of alpha and theta frequency bands as compared to laser acupuncture without the lifting-and-thrusting operation. Finally, given sufficient stimulation time (e.g., > 20 min), the performance of a low-powered laser acupuncture with the lifting-and-thrusting operation could be comparable to that of traditional needle acupuncture. [ABSTRACT FROM AUTHOR]

Aim: To understand the prevalence of depressive symptoms among foreign caregivers and the associated factors. Design: A cross‐sectional study. Methods: Data from 178 Indonesian foreign caregivers, selected based on convenience and snowball sampling in Taiwan, were collected between July 2019 and February 2020 using questionnaires. Stepwise multiple linear regression was used to identify the factors associated with depressive symptoms. Results: Approximately 30.3% of the foreign caregivers displayed depressive symptoms. The symptoms were more prevalent among the participants who were younger; had more social support; shared a bed with others; and experienced higher work‐related stress, more loneliness and physical discomfort. The findings suggest that nurses or nurse practitioners visiting patients at home should not only deliver care for them but also show concern for the psychological well‐being of the foreign caregivers of these patients. Moreover, interventions should be developed to alleviate or prevent the emergence of depressive symptoms among foreign caregivers. [ABSTRACT FROM AUTHOR]

Beam-based wireless power transfer and Fog/edge computing are promising dual technologies for realizing wireless powered Fog computing networks to support the upcoming B5G/6G IoT applications, which require latency-aware and intensive computing, with a limited energy supply. In such systems, IoT devices can either offload their computing tasks to the proximal Fog nodes or execute local computing with replenishing energy from the dedicated beamforming. However, effective integration of these techniques is still challenging, where two new issues arise: energy-aware task offloading and signal interferences from spillovers of wireless beamforming. In this paper, we observe that the beam-ripple phenomenon, which takes advantage of beamformer defects to transfer energy to IoT devices, is the key to jointly addressing these two issues. Different from traditional SWIPT technology, as in our approach the stream is not separately divided into data/energy streams, but target IoT devices can potentially harvest the whole stream. Inspired by this phenomenon, we treat the collaborative energy beamforming and edge computing design as a strongly $\mathcal {NP}$ -hard optimization problem. The proposed solution is an iterative algorithm to cascadingly integrate a polynomial-time $\left({1 - \frac {1}{e}}\right)$ -approximation algorithm, which achieves the theoretical upper bound in approximation ratio unless $\mathcal {P} = \mathcal {NP}$ , and an optimal dynamic programming algorithm. The numerical results show that the energy minimization goal among IoT devices can achieve, and the developed harvest-when-interfered protocol is practical in the wireless powered Fog computing networks. [ABSTRACT FROM AUTHOR]

Acupuncture plays an important role in traditional Chinese medicine (TCM) and is one kind of an inexpensive and effective treatment. However, some people might be reluctant to receive acupuncture treatment due to fear of pain. Laser acupuncture, thanks to its painless and infection-free advantages, has recently become an alternative choice to traditional acupuncture. The accuracy of acupuncture point positioning has a decisive influence on the quality of laser acupuncture. In this study, built on top of our prior work, we proposed a low-cost automated acupoint positioning system for laser acupuncture. By integrating several machine learning algorithms and computer vision techniques, we design and implement a robot-assisted laser acupuncture system on top of a smartphone. Our contributions include the following: (a) development of an effective acupoint estimation algorithm with a localization error less than 5 mm; (b) implementation of a smartphone-controlled automated laser acupuncture system with lift-thrust function, as a point-of-care device, that can be used by patients to relieve their symptoms at home. [ABSTRACT FROM AUTHOR]

Mobile Edge Computing (MEC) is a promising technique in the 5G Era to improve the Quality of Experience (QoE) for online video streaming due to its ability to reduce the backhaul transmission by caching certain content. However, it still takes effort to address the user association and video quality selection problem under the limited resource of MEC to fully support the low-latency demand for live video streaming. We found the optimization problem to be a non-linear integer programming, which is impossible to obtain a globally optimal solution under polynomial time. In this paper, we formulate the problem and derive the closed-form solution in the form of Lagrangian multipliers; the searching of the optimal variables is formulated as a Multi-Arm Bandit (MAB) and we propose a Deep Deterministic Policy Gradient (DDPG) based algorithm exploiting the supply-demand interpretation of the Lagrange dual problem. Simulation results show that our proposed approach achieves significant QoE improvement, especially in the low wireless resource and high user number scenario compared to other baselines. [ABSTRACT FROM AUTHOR]

We compared the performance of three widely used linear multivariate methods for autofluorescence spectroscopic tissues differentiation. Principal component analysis (PCA), partial least squares (PLS), and multivariate linear regression (MVLR) were compared for differentiating at normal, tubular adenoma/epithelial dysplasia and cancer in colorectal and oral tissues. The methods' performances were evaluated by cross-validation analysis. The group-averaged predictive diagnostic accuracies were 85% (PCA), 90% (PLS), and 89% (MVLR) for colorectal tissues; 89% (PCA), 90% (PLS), and 90% (MVLR) for oral tissues. This study found that both PLS and MVLR achieved higher diagnostic results than did PCA. Index Terms--Colorectal tissue, light-induced autofluorescence, multivariate linear regression, oral tissue, partial least squares, principal component analysis.

To meet the needs of many IoT applications with low-latency requirement, fog computing has been proposed for next-generation mobile networks to migrate the computing from the cloud to the edge of the network. In this paper, we study the fog computing service deployment problem, where the operator allocates and deploys the required computing and network resources on the edge of the network to accommodate the requests of various applications operated by the application service providers (ASPs). The operator negotiates with the ASPs to determine serving QoS of applications and how much to pay. A queuing-based latency performance model with bulk arrival is proposed for the problem to estimate the resources needed for the fog network to achieve the QoS requirements of applications. We then model and analyze the interactions between the operator and multiple ASPs as sequential one-to-many bargaining using Nash bargaining. Next, to find the optimal bargaining sequence, we propose an improved optimal algorithm, along with fast heuristic algorithms, to find the optimal sequence with low complexity. Through extensive simulations, we show that the fog service can benefit all parties, and the proposed optimal and heuristic algorithms can improve the OP's payoff by averages of 21.24 and 14.16 percent respectively. [ABSTRACT FROM AUTHOR]

Mobile Edge Computing (MEC) is a promising paradigm to ease the computation burden of Internet-of-Things (IoT) devices by leveraging computing capabilities at the network edge. With the yearning needs for resource provision from IoT devices, the queueing delay at the edge nodes not only poses a colossal impediment to achieving satisfactory quality of experience (QoE) for the IoT devices but also to the benefits of the edge nodes owing to escalating energy expenditure. Moreover, since the service providers may differ, computationally competent entities’ computing services should entail economic compensation for the incurred energy expenditure and the capital investment. Therefore, the workload allocation mechanism, where we consider flat-rate and dynamic pricing schemes in the multi-layer edge computing structure, is much-needed. We use Stackelberg game to capture the inherent hierarchy and interdependence between the second-layer edge node (SLEN) and first-layer edge nodes (FLENs). A truthful admission control mechanism grounded on the optimal workload allocation is designed for FLENs without violating end-to-end (E2E) latency requirements. We prove that a Stackelberg equilibrium with the E2E latency guarantee and truthfulness exists and can be reached through proposed algorithm. Simulation results confirm the effectiveness of our scheme and illustrate several insights. [ABSTRACT FROM AUTHOR]

Full-duplex techniques improve spectral efficiency in cellular networks by allowing a transceiver to simultaneously transmit and receive data with the same radio resource. To minimize self-interference and inter-node interference due to the deployment of full-duplex radio, users have to feed back their measurements of interference to allow the base station to effectively allocate resources and form transmission pairs. Considering that the interference measurements are private information, users may not report the true values and may try to gain their own benefits. To solve this problem, we propose a strategy-proof resource allocation mechanism for full-duplex cellular networks. The proposed scheme, which is based on auction and matching theories, guarantees that users report true values of their channel quality and utilities, and therefore guarantees the correctness of feedback information and the efficiency of resource allocation. Moreover, the proposed matching-based mechanism is theoretically proved to be stable and Pareto efficient. Simulation results confirm that, compared to a centralized scheme with non-truthful information from users, the proposed scheme can achieve an 11 $\%$ gain. [ABSTRACT FROM AUTHOR]

The live video streaming services have been suffered from the limited backhaul capacity of the cellular core network and occasional congestions due to the cloud-based architecture. Mobile Edge Computing (MEC) brings the services from the centralized cloud to nearby network edge to improve the Quality of Experience (QoE) of cloud services, such as live video streaming services. Nevertheless, the resource at edge devices is still limited and should be allocated economically efficiently. In this paper, we propose Edge Combinatorial Clock Auction (ECCA) and Combinatorial Clock Auction in Stream (CCAS), two auction frameworks to improve the QoE of live video streaming services in the Edge-enabled cellular system. The edge system is the auctioneer who decides the backhaul capacity and caching space allocation and streamers are the bidders who request for the backhaul capacity and caching space to improve the video quality their audiences can watch. There are two key subproblems: the caching space value evaluations and allocations. We show that both problems can be solved by the proposed dynamic programming algorithms. The truth-telling property is guaranteed in both ECCA and CCAS. The simulation results show that the overall system utility can be significantly improved through the proposed system. [ABSTRACT FROM AUTHOR]

Vehicular fog computing (VFC) is a promising approach to provide ultra-low-latency service to vehicles and end users by extending fog computing to the conventional vehicular networks. Parked vehicle assistance (PVA), as a critical technique in VFC, can be integrated with smart parking in order to exploit its full potentials. In this paper, we propose a smart VFC system, by combining both PVA and smart parking. A VFC-aware parking reservation auction is proposed to guide the on-the-move vehicles to the available parking places with less effort and meanwhile exploit the fog capability of parked vehicles to assist the delay-sensitive computing services by monetary rewards to compensate for their service cost. The proposed allocation rule maximizes the aggregate utility of the smart vehicles and the proposed payment rule guarantees incentive compatibility, individual rationality, and budget balance. We further provide an observation stage with dynamic offload pricing update to improve the offload efficiency and the profit of the fog system. The simulation results confirm the win–win performance enhancement to the fog node controller, the smart vehicles, and the parking places from the proposed design. [ABSTRACT FROM AUTHOR]

Presents information on a study which characterized the optical-beam-control dynamics of torsion-beam deformable-mirror-device (DMD) spatial light modulators, to determine properties important to their operation. Description of the DMD architecture; Use of the optical beam deflection to measure the mechanical response of individual DMD pixels; Measurement of the DMD resonant frequency.

With the large expected demand of wireless communication, Device-to-Device (D2D) communication has been proposed as a promising technology to enhance network performance. Nevertheless, the selfish nature of potential D2D users may impale the performance of D2D-enabled network. In this paper, we propose a D2D-enabled cellular network framework, which support a novel group D2D mode under overlay D2D communication. The group-based design is derived from the discussions of two common D2D modes, divided and shared D2D modes, regarded as special cases. The proposed framework provides a pricing-based dynamic Stackelberg game for optimal mode selection and spectrum partitioning. We propose the incentive compatible pricing strategy to provide proper incentive for these selfish potential D2D pairs to make optimal choices in mode selection. Our results show that the pricing and spectrum partition strategy effectively prevents selfish potential D2D users from harming the system performance while fully exploits the potential of D2D communication. [ABSTRACT FROM AUTHOR]

Deal selection on Groupon is a typical social learning and decision making process, where the quality of a deal is usually unknown to the customers. The customers must acquire this knowledge through social learning from other social medias such as reviews on Yelp. Additionally, the quality of a deal depends on both the state of the vendor and decisions of other customers on Groupon. How social learning and network externality affect the decisions of customers in deal selection on Groupon is our main interest. We develop a data-driven game-theoretic framework to understand the rational deal selection behaviors cross social medias. The sufficient condition of the Nash equilibrium is identified. A value-iteration algorithm is proposed to find the optimal deal selection strategy. We conduct a year-long experiment to trace the competitions among deals on Groupon and the corresponding Yelp ratings. We utilize the dataset to analyze the deal selection game with realistic settings. Finally, the performance of the proposed social learning framework is evaluated with real data. The results suggest that customers do make decisions in a rational way instead of following naive strategies, and there is still room to improve their decisions with assistance from the proposed framework. [ABSTRACT FROM PUBLISHER]

Objective: Using a case study to discuss the effects of traditional Chinese medicine (TCM) treatments on Type 1 diabetes mellitus.Clinical Features: A 4-year-old girl with sudden polyuria and nocturia (fasting plasma glucose level: 270mg/dL) was diagnosed with type 1 diabetes. Although multiple daily insulin injections were applied, her plasma glucose levels were still unstable. Therefore, she received a complementary TCM therapy by taking modified Liu-Wei-Di-Huang-Wan (3.0g/day; three times daily) for 3 months. After the treatments, her plasma glucose levels appeared to be more stable. HbA1c 6.5% and insulin injections were reduced to only once a day.Conclusions: The results suggest that complementary TCM therapy has the ability to assist some patients with type 1 diabetes mellitus in controlling their plasma glucose levels. [ABSTRACT FROM AUTHOR]

In advanced technologies, on-chip-variation (OCV) has accounted for a large proportion of clock skew, which limits the performance of a circuit. To mitigate the OCV problem, a mesh structure has been widely used in high-performance designs. Unfortunately, clock mesh structure also causes large power consumption and large power-ground surge current. Therefore, recently, several approaches have been proposed to apply resonant clock to reduce power consumption. However, previous works often suffer from area overhead because of the need to insert large decoupling capacitors. In this paper, we propose a novel resonant clock mesh structure, called ping-pong mesh, to overcome these drawbacks. Our ping-pong mesh contains two submeshes, each of which plays the role of the decoupling capacitor of the other, and the clocks in two submeshes operate in completely opposite phases. Our ping-pong mesh has the following two advantages: 1) a ping-pong mesh does not need additional decoupling capacitors as in previous works and 2) a ping-pong mesh can reduce the power-ground surge current about half of previous works. Benchmark data consistently show that our ping-pong mesh does work well in practice. [ABSTRACT FROM AUTHOR]

Device-to-device (D2D) communication could improve the efficiency of resource utilization in cellular networks by allowing nearby devices to communicate directly with each other. Nevertheless, one main challenge in D2D communication is resource allocation. We observed that the transmission quality in D2D communications can be significantly improved through a proper resource exchange. Based on this observation, we propose a novel D2D resource-allocation framework for a Long-Term Evolution Advanced (LTE-Advanced) system. We theoretically prove that any arbitrary algorithm, either distributed or centralized, will converge in the proposed framework whenever all performed exchanges are beneficial. Based on the concept of beneficial exchange, we propose a trader-assisted resource exchange (T-REX) mechanism, which is an exchange-based mechanism that converges in polynomial time and achieves Pareto optimality as an efficient and flexible solution to the D2D resource-allocation problem. The Evolved NodeB (eNodeB) regulates the D2D resource allocation through designing the trader preference functions in the T-REX mechanism. By applying game-theoretic analysis to the D2D communication system, we prove that all rational D2D pairs will truthfully report their information when the trader preference functions are properly designed. Finally, our simulation results show that the proposed T-REX mechanism significantly mitigates the interference experienced by D2D devices in LTE-Advanced systems. [ABSTRACT FROM PUBLISHER]

Carrier aggregation is introduced in LTE-Advanced to aggregate multiple bands of spectrum into a virtual carrier. User equipment (UE) with carrier aggregation capability can increase peak data rates by transmitting through an aggregated virtual carrier that provides greater transmission bandwidth. Nevertheless, further study is needed to determine how carrier aggregation should best be implemented and configured to effectively address the range of UE carrier quality and their heterogeneous quality of service (QoS) requirements. In addition, most existing resource allocation methods rely on the assumption that UE always reports information truthfully, which may be unrealistic when UEs act rationally from a game-theory perspective. To address these concerns, we provide a strategy-proof auction approach to carrier aggregation design in an LTE-Advanced system. We first formulate the resource allocation problem in carrier aggregation as a non-linear optimization problem, which is proved to be NP-hard. We then propose a strategy-proof auction with a greedy resource allocation algorithm to 1) find an efficient carrier activation and resource allocation solution under the QoS requirements of UEs, and 2) guarantee that all rational UEs truthfully report their QoS requirements. Finally, we conduct extensive simulations to evaluate system performance for the proposed auction design. [ABSTRACT FROM AUTHOR]

An overlay macrocell-femtocell system aims to increase the system capacity with a low-cost infrastructure. To construct such an infrastructure, we need to solve some existing problems. First, there is a tradeoff between femtocell coverage and overall system throughput, which we defined as the cell-breathing phenomenon. In light of this, we propose a femtocell downlink cell-breathing control framework to strike a balance between the coverage and data rate. Second, due to the selfish nature of mobile stations, the system information collected from them does not necessarily reflect the true status of the system. Thus, we design FEmtocell Virtual Election Rule (FEVER), a voting-based direct mechanism that only requires users to report their channel quality information to the femtocell base station. Not only is it proved to be truthful and has low implementation complexity, but it also strikes a balance between efficiency and fairness to meet the different needs. The simulation results verify the enhanced system performance under the FEVER mechanism. [ABSTRACT FROM AUTHOR]

With the increasing ubiquity and power of mobile devices as well as the prevalence of social systems, more activities in our daily life are being recorded, tracked, and shared, creating the notion of social media. Such abundant and still growing real-life data, known as big data, provide a tremendous research opportunity in many fields. To analyze, learn, and understand such user-generated data, machine learning has been an important tool, and various machine-learning algorithms have been developed. However, since the user-generated data are the outcome of users? decisions, actions, and socioeconomic interactions, which are highly dynamic, without considering users? local behaviors and interests, existing learning approaches tend to focus on optimizing a global objective function at the macroeconomic level, while totally ignoring users? local interactions at the microeconomic level. As such, there is a growing need to combine learning with strategic decision making, which are two traditionally distinct research disciplines, to be able to jointly consider both global phenomena and local effects to better understand, model, and analyze the newly arising issues in the emerging social media with user-generated data. In this article, we present an overview of the emerging notion of decision learning, i.e., learning with strategic decision making, which involves users? behaviors and interactions by combining learning with strategic decision making. We will discuss some examples from social media with real data to show how decision learning can be used to better analyze users? optimal decision from a user?s perspective, as well as design a mechanism from the system designer?s perspective to achieve a desirable outcome. [ABSTRACT FROM PUBLISHER]

Nonpoint source pollution from urban areas has been identified as a leading contributor to impaired water quality. Floating treatment wetlands (FTWs) are cultivated plants growing on floating mats in open water. FTWs can be used to remove pollutants from runoff, but data on their effectiveness is limited. We conducted a field study of FTWs in a nutrient enriched urban wet pond to investigate vegetation biomass and phosphorus (P) accumulation/distribution, sustainability under ice encasement stress (which is a concern in temperate regions), and to assess the use of the FTW by species. Planted perennial macrophytes successfully adapted to stresses of the low dissolved oxygen (DO) concentrations (minimum: 1.2 mg/L) in summer, ice encasement in winter, and relatively low nutrient concentrations in the water (median: 0.15 mg/L TP and 1.15 mg/L TN). Pickerelweed produced more biomass and demonstrated higher P removal performance (10.44 g dry weight/plant and 7.58 mg P/plant) than softstem bulrush (2.20 g dry weight/plant and 1.62 mg P/plant). Based on the observed seasonal changes in biomass and P, we recommend harvest of above-ground vegetation is conducted in June for maximum P removal or in September to prevent P release due to senescence. Submerged tissues of pickerelweed, softstem bulrush, and yellow iris ( Iris pseudacorus ) survived ice encasement and regrew in the second year. Additionally, plant diversity increased during the study period through recruitment of both native and exotic wetland plants. Systematic observation of wildlife activities indicated eight classes of organisms inhabiting, foraging, breeding, nursing, or resting in the FTWs. This study suggests above-ground plant harvest can enhance P removal, and that softstem bulrush, yellow iris, and pickerelweed can be sustained over winter on the FTW. Future study is recommended to investigate the feasibility of multiple vegetation harvest and document the possible habitat creation by the use of FTWs. [ABSTRACT FROM AUTHOR]

Heterogeneous multimedia content delivery over wireless networks is an important yet challenging issue. One of the challenges is maintaining the quality of service due to scarce resources in wireless communications and heavy loadings from heterogeneous demands. A promising solution is combining multicasting and scalable video coding (SVC) techniques via cross-layer design, which has been shown to effectively enhance the quality of multimedia content delivery service in the literature. Nevertheless, most existing works on SVC multicasting system focus on the static scenarios, where a snapshot of user demands is given and remains the same. In addition, the economic value of the SVC multicasting system, which is an important issue from the service provider's perspective, has seldom been explored. In this paper, we study a subscription-based SVC multicasting system with stochastic user arrival and heterogeneous user preferences. A stochastic framework based on the multidimensional Markov decision process (M-MDP) is proposed to study the negative network externality existing in the proposed system and theoretically evaluate the corresponding system efficiency. A game-theoretic analysis is conducted to understand the rational demands from heterogeneous users under different subscription pricing schemes. By transforming the original dynamic and complex M-MDP revenue optimization problem into a traditional average-reward MDP problem, we show that the optimal pricing strategy that maximizes the expected revenue of the service provider can be derived efficiently. Moreover, the overall user's valuation on the system, e.g., social welfare, is maximized under such an optimal pricing strategy. Finally, the efficiency of the proposed solutions is evaluated through simulations. [ABSTRACT FROM PUBLISHER]

Floating treatment wetlands (FTWs) consist of emergent macrophytes that are placed on a floating mat in a pond for water treatment and aesthetic purposes. FTWs may have unique advantages with respect to treating urban runoff within existing retention ponds for excess nutrients. However, research is lacking in providing guidance on performance of specific species for treating urban runoff, and on timing of harvest. Harvesting is needed to remove nutrients permanently from the retention pond. We investigated vegetation effects on FTWs on nitrogen (N) and phosphorus (P) removal performance and storage in above-ground FTW macrophyte tissues. The study evaluated pickerelweed (PW, Pontederia cordata L.) and softstem bulrush (SB, Schoenoplectus tabernaemontani ) over time in microcosms flushed with water obtained from a nearby urban retention pond in northern Virginia near Washington, DC. While the literature exhibits a wide range of experimental sizes, using the term mesocosm, we have chosen the term microcosm to reflect the small size of our vessel; and do not include effects of sediment. The experiment demonstrated PW outperformed SB for P and N removal. Based upon analysis of the accumulated nutrient removal over time, a harvest of the whole PW and SB plants in September or October is recommended. However, when harvesting only the aerial parts, we recommend harvesting above-ground PW tissues in July or August to maximize nutrient removal. This is because PW translocates most of its nutrients to below-ground storage organs in the fall, resulting in less nutrient mass in the above-ground tissue compared to the case in the summer (vegetative stage). Further research is suggested to investigate whether vegetation can be overly damaged from multiple harvests on an annual basis in temperate regions. [ABSTRACT FROM AUTHOR]

Users in a social network are usually confronted with decision making under uncertain network state. While there are some works in the social learning literature on how to construct belief on an uncertain network state, few study has been made on integrating learning with decision making for the scenario where users are uncertain about the network state and their decisions influence with each other. Moreover, the population in a social network can be dynamic since users may arrive at or leave the network at any time, which makes the problem even more challenging. In this paper, we propose a Dynamic Chinese Restaurant Game to study how a user in a dynamic social network learns the uncertain network state and make optimal decision by taking into account not only the immediate utility but also subsequent users' negative influence. We introduce a Bayesian learning based method for users to learn the network state, and propose a Multi-dimensional Markov Decision Process based approach for users to achieve the optimal decisions. Finally, we apply the Dynamic Chinese Restaurant Game to cognitive radio networks and demonstrate from simulations to verify the effectiveness and efficiency of the proposed scheme. [ABSTRACT FROM PUBLISHER]

Abstract: This paper demonstrates a simple and easy approach for the one-step synthesis of Fe3O4-chitosan composite particles with tadpole-like shape. The length and diameter of the particles were adjustable from 638.3μm to ca. 798μm (length), and from 290μm to 412μm (diameter) by varying the flow rate of the dispersed phase. Mitoxantrone was used as the model drug in the drug release study. The encapsulation rate of the drug was 71% for chitosan particles, and 69% for magnetic iron oxide-chitosan particles, respectively. The iron oxide-chitosan composite particles had a faster release rate (up to 41.6% at the third hour) than the chitosan particles (about 24.6%). These iron oxide-chitosan composite particles are potentially useful for biomedical applications, such as magnetic responsive drug carriers, magnetic resonance imaging (MRI) enhancers, in the future. [Copyright &y& Elsevier]