Recent advances in the role of rehabilitative therapies for Parkinson’s disease: A literature review

Regardless of medical therapies and surgical interventions for Parkinson’s disease, patients develop progressive disability. The role of therapies is to maximize functional ability and minimize secondary complications through movement rehabilitation within a context of education and support for the whole person. The overall aim is to optimize independence, safety and wellbeing, thereby enhancing quality of life. Trials have shown that physiotherapy has short-term benefits in Parkinson’s disease. However, which physiotherapy intervention are most effective remains unclear. This article provides a guidance framework rather than a ’recipe’ for treatment. This review shows that a wide range of rehabilitative therapy interventions to treat Parkinson’s disease have been tested. There is a need for more specific trials with improved treatment strategies to underpin the most appropriate choice of therapy intervention and the outcomes measured. According to research in the literature, this review is of particular importance because it discusses many rehabilitation therapies for patients with Parkinson\u27s disease in a single paper, for the first time. The aim of this review article is to evaluate the effectiveness of one therapy intervention compared with a second approach in patients with Parkinson’s disease

Investigation of vibration’s effect on driver in optimal motion cueing algorithm

The increased sensation error between the surroundings and the driver is a major problem in driving simulators, resulting in unrealistic motion cues. Intelligent control schemes have to be developed to provide realistic motion cues to the driver. The driver’s body model incorporates the effects of vibrations on the driver’s health, comfort, perception, and motion sickness, and most of the current research on motion cueing has not considered these factors. This article proposes a novel optimal motion cueing algorithm that utilizes the driver’s body model in conjunction with the driver’s perception model to minimize the sensation error. Moreover, this article employs H1 control in place of the linear quadratic regulator to optimize the quadratic cost function of sensation error. As compared to state of the art, we achieve decreased sensation error in terms of small root-mean-square difference (70%, 61%, and 84% decrease in case of longitudinal acceleration, lateral acceleration, and yaw velocity, respectively) and improved coefficient of cross-correlation (3% and 1% increase in case of longitudinal and lateral acceleration, respectively)

Assessment of solar photovoltaic water pumping of WASA tube wells for irrigation in Quetta Valley aquifer.

This paper investigates the impact of tube wells on the discharge and water table of the Quetta Valley aquifer and conducts a financial analysis of the solar photovoltaic water pumping system (SPVWP) in comparison with a typical pumping system for the Water and Sanitation Agency of Quetta’s (WASA) tube wells. Quetta Valley is dependent on groundwater as surface resources are on decline and unpredictable. The population of this city has exponentially increased from 0.26 million in 1975 to 2.2 million in 2017 which has put a lot of pressure on the groundwater aquifer by installing more than 500 large capacity tube wells by WASA and Public Health Engineering (PHE) departments in addition to thousands of low‐capacity private tube wells. The unprece-dented running of these wells has resulted in drying of the historical Karez system, agricultural activities, and the sharp increase in power tariffs. There are 423 tube wells in operation installed by WASA in addition to PHE, Irrigation and Military Engineering Services (MES), which covers 60% of the city’s water demand. The results will be beneficial for organizations and positively impact the operation of these wells to meet public water demand. For the two zones, i.e., Zarghoon and Chiltan in Quetta Valley, recommendations are given for improved water management

Control of an anthropomorphic manipulator using LuGre friction model - Design and experimental validation

Automation technology has been extensively recognized as an emerging field in various industrial applications. Recent breakthrough in flexible automation is primarily due to deployment of robotic arms or manipulators. Autonomy in these manipulators is essentially linked with the advancements in non-linear control systems. The objective of this research is to propose a robust control algorithm for a five degree of freedom (DOF) robotic arm to achieve superior performance and reliability in the presence of friction. A friction compensation-based non-linear control has been proposed and realized for the robotic manipulator. The dynamic model of the robot has been derived by considering the dynamic friction model. The proposed three-state model is validated for all the joints of the manipulator. The integral sliding mode control (ISMC) methodology has been designed; the trajectories of system every time begin from the sliding surface and it eliminates the reaching phase with assistance of integral term in the sliding surface manifold. The designed control law has been first simulated in Matlab/Simulink environment to characterize the control performance in terms of tracking of various trajectories. The results confirm the effectiveness of the proposed control law with model-based friction compensation. The transient parameters like settling and peak time have improvement as well have better results with friction than without considering the friction. The proposed control law is then realized on an in-house developed autonomous articulated robotic rducational platform (AUTAREP) and NI myRIO hardware interfaced with LabVIEW. Experimental results also witnessed the trajectory tracking by the robotic platform

A multifunctional polymeric micelle for targeted delivery of paclitaxel by the inhibition of the P-glycoprotein transporters

P-glycoprotein (P-gP) efflux-mediated multidrug resistance is a fundamental aspect of chemotherapeutic failure in oncology. The current study aims to deliver paclitaxel (PTX) specifically at the target site with improved in vivo efficacy of poorly permeable PTX against solid tumors. Multifunctional polymeric micelles as targeted delivery have been devised for loading and release of PTX. Mucoadhesion, permeation enhancement, oral pharmacokinetics, biodistribution, and toxicological studies were carried out to fully elucidate the therapeutic outcomes of the polymeric micelles. Ex vivo permeation studies indicated a 7.89-fold enhancement in the permeation of PTX with mucopermeating papain functionalized thiolated redox micelles (PT-R-Ms) compared to the pure PTX. Moreover, PT-R-Ms exhibited a higher percentage of apoptotic cells (42.9 & PLUSMN; 0.07%) compared to pure PTX. Biodistribution studies revealed that fluorotagged PT-RMs accumulated in excised tumors and organs. The higher fluorescence intensity indicated the mucopermeation of micelles across the intestine. The orally administered PT-R-Ms efficiently overcome intestinal barriers and inhibit the P-gP efflux pump, resulting in increased bioavailability of PTX (up to 8-fold) in comparison to pure PTX. The enhanced anti-tumor efficacy and reduced toxic effects are key aspects of efficient cancer therapy. This study demonstrates that the use of mucopermeating PT-R-Ms is an encouraging approach to overwhelm the permeation barrier in cancer treatment.(Comunidad de Madrid

IoT applications and challenges in smart cities and services

Abstract Internet of Things (IoT) is a revolutionary and novel platform where a smart network connects to the large number of electronic devices via internet through available communication systems for reliable and real time connectivity, sensing thus acquiring data from sensors, computing and actuating devices. A review of the current status of IoT features, architecture, communication infrastructure and applications is presented here. Number of IoT applications and challenges in the development of smart cities are covered about this innovative technological platform in this review along with communication protocols characteristics and applications. IoT aided smart city services, health applications, transport applications and smart grid improvements are also discussed. Many challenges and issues related to security, communication, applications and system architecture along with future directions of IoT technology are elaborated in detail

A Novel Prosumer-Based Energy Sharing and Management (PESM) Approach for Cooperative Demand Side Management (DSM) in Smart Grid

Increasing population and modern lifestyle have raised energy demands globally. Demand Side Management (DSM) is one important tool used to manage energy demands. It employs an advanced power infrastructure along with bi-directional information flow among utilities and users in order to achieve a balanced load curve and minimize demand-supply mismatch. Traditionally, this involves shifting the electricity demand from peak hours to other times of the day in an optimized manner. Multiple users equipped with renewable resources work in coordination with each other in order to achieve mutually beneficial energy management. This, in turn, has generated the concept of cooperative DSM. Such users, called prosumers, consume and produce energy using renewable resources (solar, wind etc.). Prosumers with surplus energy sell to the grid as well as to other consumers. In this paper, a novel Prosumer-based Energy Sharing and Management (PESM) scheme for cooperative DSM has been proposed. A simulation model has been developed for testing the proposed method. Different variations of the proposed methodology have been experimented with different criteria. The results show that the proposed energy sharing scheme achieves DSM purposes in a useful manner

Efficient optimization techniques for resource allocation in UAVs mission framework.

This paper considers the generic problem of a central authority selecting an appropriate subset of operators in order to perform a process (i.e. mission or task) in an optimized manner. The subset is selected from a given and usually large set of 'n' candidate operators, with each operator having a certain resource availability and capability. This general mission performance optimization problem is considered in terms of Unmanned Aerial Vehicles (UAVs) acting as firefighting operators in a fire extinguishing mission and from a deterministic and a stochastic algorithmic point of view. Thus the applicability and performance of certain computationally efficient stochastic multistage optimization schemes is examined and compared to that produced by corresponding deterministic schemes. The simulation results show acceptable accuracy as well as useful computational efficiency of the proposed schemes when applied to the time critical resource allocation optimization problem. Distinguishing features of this work include development of a comprehensive UAV firefighting mission framework, development of deterministic as well as stochastic resource allocation optimization techniques for the mission and development of time-efficient search schemes. The work presented here is also useful for other UAV applications such as health care, surveillance and security operations as well as for other areas involving resource allocation such as wireless communications and smart grid

Efficient optimization techniques for resource allocation in UAVs mission framework

This paper considers the generic problem of a central authority selecting an appropriate subset of operators in order to perform a process (i.e. mission or task) in an optimized manner. The subset is selected from a given and usually large set of ‘n’ candidate operators, with each operator having a certain resource availability and capability. This general mission performance optimization problem is considered in terms of Unmanned Aerial Vehicles (UAVs) acting as firefighting operators in a fire extinguishing mission and from a deterministic and a stochastic algorithmic point of view. Thus the applicability and performance of certain computationally efficient stochastic multistage optimization schemes is examined and compared to that produced by corresponding deterministic schemes. The simulation results show acceptable accuracy as well as useful computational efficiency of the proposed schemes when applied to the time critical resource allocation optimization problem. Distinguishing features of this work include development of a comprehensive UAV firefighting mission framework, development of deterministic as well as stochastic resource allocation optimization techniques for the mission and development of time-efficient search schemes. The work presented here is also useful for other UAV applications such as health care, surveillance and security operations as well as for other areas involving resource allocation such as wireless communications and smart grid