Design of a passive hydraulic simulator for abnormal muscle behavior replication

Spasticity and rigidity are two abnormal hypertonic muscle behaviors commonly observed in passive joint flexion and extension evaluation. Clinical evaluation for spasticity and rigidity is done through in-person assessment using qualitative scales. Due to the subjective nature of this evaluation method, diagnostic results produced from these clinical assessments can have poor reliability and inconsistency. Incorrect diagnosis and treatment often result in worsening of the abnormal muscle behaviors, reducing the quality of life and leading to an increased cost of healthcare. Several programmable, robotic simulators had been developed to improve the accuracy of clinical evaluation by providing clinician practical training opportunities; however none of these training devices are commercially available due to technical and manufacturing limitations. For this reason, a novel, purely mechanical, hydraulic-based simulator design was proposed as an alternative approach to abnormal muscle behavior simulation. The original goal of the project presented in this thesis was to address both spasticity and rigidity in the elbow joint during flexion; however due to time constraints, the initial prototype can only mimic spasticity. The hydraulic-based simulator utilized a novel damper design using viscous fluid in combination with creative flow channel configurations to replicate different levels of spasticity behaviors depicted on a qualitative scale. The simulator was capable of generating a wide range of speed-dependent force feedbacks without need for any computational controls. Preliminary results obtained from evaluating the simulator suggested the possibility of using this novel design in replicating the speed-dependent characteristics of spasticity. The framework and method implemented in the current simulator prototype could be further developed and expanded to replicate spasticity or other types of abnormal behaviors, such as rigidity, in various human joints (not limiting the design to just the elbow joint)

Loan Loss Provisioning of UK Commercial Banks Pre- and Post-Global Financial Crisis

This paper undertook a research on 29 UK commercial banks between the periods of 2006 to 2012 for observing the loan loss provisioning of the selected banks pre- and post- global financial crisis. There are two models were applied for this research, which are X-efficiency model and GMM model. We tested four hypotheses: 1) Do UK commercial banks conduct their provisioning relying on the business cycle? 2) Does income smoothing behaviour exist in the UK commercial banks? 3) Does capital management exist in the UK commercial banks? 4) Is Bank efficiency endogenous to loan loss provisioning? If yes / no, how does it correlate with loan loss provisioning? Our results showed no evidence for UK banks to conduct income smoothing and capital management through loan loss provisioning. However, we found a negative relationship between bank efficiency and loan loss provisioning and market concentration problem for the selected banks. The result suggests that the FCA should pay more attention to the loan portfolios of high market power banks

Loan Loss Provisioning of UK Commercial Banks Pre- and Post-Global Financial Crisis

This paper undertook a research on 29 UK commercial banks between the periods of 2006 to 2012 for observing the loan loss provisioning of the selected banks pre- and post- global financial crisis. There are two models were applied for this research, which are X-efficiency model and GMM model. We tested four hypotheses: 1) Do UK commercial banks conduct their provisioning relying on the business cycle? 2) Does income smoothing behaviour exist in the UK commercial banks? 3) Does capital management exist in the UK commercial banks? 4) Is Bank efficiency endogenous to loan loss provisioning? If yes / no, how does it correlate with loan loss provisioning? Our results showed no evidence for UK banks to conduct income smoothing and capital management through loan loss provisioning. However, we found a negative relationship between bank efficiency and loan loss provisioning and market concentration problem for the selected banks. The result suggests that the FCA should pay more attention to the loan portfolios of high market power banks

Improving tensor regression by optimal model averaging

Tensors have broad applications in neuroimaging, data mining, digital marketing, etc. CANDECOMP/PARAFAC (CP) tensor decomposition can effectively reduce the number of parameters to gain dimensionality-reduction and thus plays a key role in tensor regression. However, in CP decomposition, there is uncertainty which rank to use. In this article, we develop a model averaging method to handle this uncertainty by weighting the estimators from candidate tensor regression models with different ranks. When all candidate models are misspecified, we prove that the model averaging estimator is asymptotically optimal. When correct models are included in the candidate models, we prove the consistency of parameters and the convergence of the model averaging weight. Simulations and empirical studies illustrate that the proposed method has superiority over the competition methods and has promising applications

Two-Way Aerial Secure Communications via Distributed Collaborative Beamforming under Eavesdropper Collusion

Unmanned aerial vehicles (UAVs)-enabled aerial communication provides a flexible, reliable, and cost-effective solution for a range of wireless applications. However, due to the high line-of-sight (LoS) probability, aerial communications between UAVs are vulnerable to eavesdropping attacks, particularly when multiple eavesdroppers collude. In this work, we aim to introduce distributed collaborative beamforming (DCB) into UAV swarms and handle the eavesdropper collusion by controlling the corresponding signal distributions. Specifically, we consider a two-way DCB-enabled aerial communication between two UAV swarms and construct these swarms as two UAV virtual antenna arrays. Then, we minimize the two-way known secrecy capacity and the maximum sidelobe level to avoid information leakage from the known and unknown eavesdroppers, respectively. Simultaneously, we also minimize the energy consumption of UAVs for constructing virtual antenna arrays. Due to the conflicting relationships between secure performance and energy efficiency, we consider these objectives as a multi-objective optimization problem. Following this, we propose an enhanced multi-objective swarm intelligence algorithm via the characterized properties of the problem. Simulation results show that our proposed algorithm can obtain a set of informative solutions and outperform other state-of-the-art baseline algorithms. Experimental tests demonstrate that our method can be deployed in limited computing power platforms of UAVs and is beneficial for saving computational resources.Comment: This paper has been accepted by IEEE INFOCOM 202

UAV-enabled Collaborative Beamforming via Multi-Agent Deep Reinforcement Learning

In this paper, we investigate an unmanned aerial vehicle (UAV)-assistant air-to-ground communication system, where multiple UAVs form a UAV-enabled virtual antenna array (UVAA) to communicate with remote base stations by utilizing collaborative beamforming. To improve the work efficiency of the UVAA, we formulate a UAV-enabled collaborative beamforming multi-objective optimization problem (UCBMOP) to simultaneously maximize the transmission rate of the UVAA and minimize the energy consumption of all UAVs by optimizing the positions and excitation current weights of all UAVs. This problem is challenging because these two optimization objectives conflict with each other, and they are non-concave to the optimization variables. Moreover, the system is dynamic, and the cooperation among UAVs is complex, making traditional methods take much time to compute the optimization solution for a single task. In addition, as the task changes, the previously obtained solution will become obsolete and invalid. To handle these issues, we leverage the multi-agent deep reinforcement learning (MADRL) to address the UCBMOP. Specifically, we use the heterogeneous-agent trust region policy optimization (HATRPO) as the basic framework, and then propose an improved HATRPO algorithm, namely HATRPO-UCB, where three techniques are introduced to enhance the performance. Simulation results demonstrate that the proposed algorithm can learn a better strategy compared with other methods. Moreover, extensive experiments also demonstrate the effectiveness of the proposed techniques.Comment: This paper has been submitted to IEEE Transactions on Mobile Computin

An Online Joint Optimization Approach for QoE Maximization in UAV-Enabled Mobile Edge Computing

Given flexible mobility, rapid deployment, and low cost, unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) shows great potential to compensate for the lack of terrestrial edge computing coverage. However, limited battery capacity, computing and spectrum resources also pose serious challenges for UAV-enabled MEC, which shorten the service time of UAVs and degrade the quality of experience (QoE) of user devices (UDs) {\color{b} without effective control approach}. In this work, we consider a UAV-enabled MEC scenario where a UAV serves as an aerial edge server to provide computing services for multiple ground UDs. Then, a joint task offloading, resource allocation, and UAV trajectory planning optimization problem (JTRTOP) is formulated to maximize the QoE of UDs under the UAV energy consumption constraint. To solve the JTRTOP that is proved to be a future-dependent and NP-hard problem, an online joint optimization approach (OJOA) is proposed. Specifically, the JTRTOP is first transformed into a per-slot real-time optimization problem (PROP) by using the Lyapunov optimization framework. Then, a two-stage optimization method based on game theory and convex optimization is proposed to solve the PROP. Simulation results validate that the proposed approach can achieve superior system performance compared to the other benchmark schemes

Acupuncture as adjunctive treatment for linezolid-induced peripheral neuropathy: a case series report

BackgroundThe treatment of multidrug-resistant tuberculosis (MDR-TB) and pre-extensively drug-resistant tuberculosis (pre-XDR-TB) remains challenging due to the limited availability of effective drugs. Linezolid has emerged as a promising therapeutic option for these cases. However, its long-term use can lead to complications such as peripheral and optic neuropathies. Acupuncture, a cornerstone of traditional Chinese medicine, has been shown to be effective in the treatment of peripheral neuropathy (PN). This study examines the potential benefits of acupuncture in the treatment of linezolid-induced peripheral neuropathy (LIPN).MethodsFour patients, aged 27 to 60 years, diagnosed with LIPN, underwent daily acupuncture treatments. The main endpoint was to assess the efficacy of acupuncture in reducing neuropathic pain associated with LIPN in patients. This was primarily measured using changes in the Short Form McGill Pain Questionnaire (SF-MPQ) scores before and after acupuncture treatment.ResultsThree of the patients experienced significant symptom remission, while one experienced marginal improvement. Treatments ranged from 7 to 18 sessions. Specifically, the first patient reported substantial relief with a score reduction from 33 to 13; the second patient observed minimal change; the third patient’s score decreased dramatically from 10 to 2 after eight sessions; the last patient had a score reduction from 21 to 12 after five sessions, but did not continue treatment for a second assessment.ConclusionAcupuncture is a promising therapeutic approach for LIPN. However, larger and more thorough studies are needed to determine its full potential

Joint Task Offloading and Resource Allocation in Aerial-Terrestrial UAV Networks with Edge and Fog Computing for Post-Disaster Rescue

Unmanned aerial vehicles (UAVs) play an increasingly important role in assisting fast-response post-disaster rescue due to their fast deployment, flexible mobility, and low cost. However, UAVs face the challenges of limited battery capacity and computing resources, which could shorten the expected flight endurance of UAVs and increase the rescue response delay during performing mission-critical tasks. To address this challenge, we first present a three-layer post-disaster rescue computing architecture by leveraging the aerial-terrestrial edge capabilities of mobile edge computing (MEC) and vehicle fog computing (VFC), which consists of a vehicle fog layer, a UAV client layer, and a UAV edge layer. Moreover, we formulate a joint task offloading and resource allocation optimization problem (JTRAOP) with the aim of maximizing the time-average system utility. Since the formulated JTRAOP is proved to be NP-hard, we propose an MEC-VFC-aided task offloading and resource allocation (MVTORA) approach, which consists of a game theoretic algorithm for task offloading decision, a convex optimization-based algorithm for MEC resource allocation, and an evolutionary computation-based hybrid algorithm for VFC resource allocation. Simulation results validate that the proposed approach can achieve superior system performance compared to the other benchmark schemes, especially under heavy system workloads.Comment: 18 pages, 6 figure