Traditional end-effector robots for arm rehabilitation are usually attached at the hand, primarily focusing on coordinated multi-joint training. Therapy at an individual joint level of the arm for ...severely impaired stroke survivors is not always possible with existing end-effector robots. The Arm Rehabilitation Robot (AREBO)—an end-effector robot—was designed to provide both single and multi-joint assisted training while retaining the advantages of traditional end-effector robots, such as ease of use, compactness and portability, and potential cost-effectiveness (compared to exoskeletons). This work presents the design, optimization, and characterization of AREBO for training single-joint movements of the arm. AREBO has three actuated and three unactuated degrees of freedom, allowing it to apply forces in any arbitrary direction at its endpoint and self-align to arbitrary orientations within its workspace. AREBO’s link lengths were optimized to maximize its workspace and manipulability. AREBO provides single-joint training in both unassisted and adaptive weight support modes using a human arm model to estimate the human arm’s kinematics and dynamics without using additional sensors. The characterization of the robot’s controller and the algorithm for estimating the human arm parameters were performed using a two degrees of freedom mechatronic model of the human shoulder joint. The results demonstrate that (a) the movements of the human arm can be estimated using a model of the human arm and robot’s kinematics, (b) AREBO has similar transparency to that of existing arm therapy robots in the literature, and (c) the adaptive weight support mode control can adapt to different levels of impairment in the arm. This work demonstrates how an appropriately designed end-effector robot can be used for single-joint training, which can be easily extended to multi-joint training. Future work will focus on the evaluation of the system on patients with any neurological condition requiring arm training.
•A new direct discrete model approximation based internal model controller design is proposed.•Proposed scheme is validated on the single-input single-output and the multi-input multi-output discrete ...dynamical models.•A robust study of proposed discrete approximation based internal model controller is demonstrated on supersonic jet engine.•Effectiveness of the proposed design methodology is illustrated through a single machine infinite bus power system.
In this paper, a new direct discrete approximation based internal model control design is proposed to the linear discrete dynamical systems. The approximation method is used to determine an accurate and stable reduced-order model for the considered original higher-order discrete-time system. The method involves an enhanced Differential Evolution algorithm to ascertain the stable denominator polynomial coefficients, and the preferable reduced numerator polynomial coefficients are evaluated by using the improved discrete multi-point Padé approximation approach. The method deploys on discrete step integral square error minimization between the original dynamical system and the approximated model, together with retaining their discrete impulse response energy values. The approximated model has been considered an internal (predictive) model and proceeds with an optimal internal model controller design to improve the discrete dynamical system behaviour according to the reference input/the set point. The controller's best performance is attained by tuning the single filter parameter ′λ′ by minimizing the integral square error between the reference input and the actual output of the dynamical system using the enhanced differential evolution algorithm. The acceptability and applicability of the proposed process reduction-based controller design have been validated on a single-input single-output supersonic jet engine inlet dynamical model. The controller robust study is conducted by inserting 10% disruption uncertainty in the system dynamical model poles and zeros. The method has also been extended to the discrete multi-input multi-output dynamical model of the single machine infinite bus power system to develop an optimal internal model control-based power system stabilizer. The simulation results showing better reference input tracking, comparison of performance indices, and also highlight the efficacy of the proposed controller design.
A detailed statistical analysis is performed using Electron Back Scatter Diffraction (EBSD) to establish the effect of microstructure on twin nucleation and growth in deformed commercial purity ...hexagonal close packed (HCP) titanium. Rolled titanium samples are compressed along rolling, transverse and normal directions to establish statistical correlations for {10–12}, {11–21}, and {11–22} twins. A recently developed automated EBSD-twinning analysis software is employed for the statistical analysis. The analysis provides the following key findings: (I) grain size and strain dependence is different for twin nucleation and growth; (II) twinning statistics can be generalized for the HCP metals magnesium, zirconium and titanium; and (III) complex microstructure, where grain shape and size distribution is heterogeneous, requires multi-point statistical correlations.
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We present a multiwavelength study of the W40 star-forming region using infrared (IR) observations in the UKIRT JHK bands, Spitzer Infrared Array Camera bands, and Herschel PACS bands, 2.12 mu m H ...sub(2) narrowband imaging, and radio continuum observations from GMRT (610 and 1280 MHz), in a field of view (FoV) of ~34' x 40'. Archival Spitzer observations in conjunction with near-IR observations are used to identify 1162 Class II/III and 40 Class I sources in the FoV. The nearest-neighbor stellar surface density analysis shows that the majority of these young stellar objects (YSOs) constitute the embedded cluster centered on the high-mass source IRS 1A South. Radio continuum analysis shows that this region has a blister morphology, with the radio peak coinciding with a protostellar source.
This paper proposes a new multiobjective hybrid particle swarm optimization (PSO)-krill herd (KH) Pareto-based optimization algorithm to optimize number and location of the measurement devices for ...accurate state estimation (SE) in smart distribution networks. Three objectives are considered to be minimized: 1) the total cost; 2) the average relative percentage error (APE) of bus voltage magnitude; and 3) APE of bus voltage angle. As the objective functions are conflicting with respect to each other, a multiobjective Pareto-based nondominated sorting hybrid PSO-KH optimization algorithm is proposed. In this approach, the random variation in loads and the metrological error of the measurement devices are also taken into account. The proposed algorithm minimizes the cost and enhances the accuracy of the distribution state estimator for better monitoring and control of the system. Furthermore, the impacts of distributed generation on SE performance are also investigated. The feasibility of the proposed algorithm is demonstrated on IEEE 69-bus system and practical Indian 85-bus radial distribution network. The results obtained are compared with conventional KH algorithm and PSO, with well-known multiobjective nondominated sorting genetic algorithm and also with an existing technique based on dynamic programming method for validation.
The present study reports the genetic damage and the concentrations of trace metals and total petroleum hydrocarbons prevailing in natural populations of an edible fish, Arius arius in different ...seasons along the coast of Goa, India as an indicator of the pollution status of coastal water. Fish were collected from a suspected polluted site and a reference site in the pre-monsoon, monsoon and post-monsoon seasons. Physico-chemical parameters as well as the concentrations of total petroleum hydrocarbons (TPH) and trace metals in the water and sediment as well as the tissues of fish collected from these sites were recorded. The genotoxicity status of the fish was assessed employing the micronucleus test and comet assay. A positive correlation (p<0.001) was observed between the tail DNA and micronuclei in all the fish collected. Multiple regression analysis revealed that tissue and environmental pollutant concentrations and genotoxicity were positively associated and higher in the tissues of the fish collected from the polluted site. Pollution indicators and genotoxicity tests, combined with other physiological or biochemical parameters represent an essential integrated approach for efficient monitoring of aquatic ecosystems in Goa.
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•Genotoxicity assessment in Arius arius at polluted and unpolluted sites in Goa.•Trace metals and TPHs exhibit bioaccumulation in A. arius.•Environmental and tissue levels of pollutants are positively associated.•Pollutant concentrations are positively correlated with genotoxicity.•A. arius is a suitable bioindicator species.
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•Stoke shifted PL emission was exhibited by Gu2PbBr4.•Free exciton emission in Gu2PbI4.•PbI4 octahedron extends 2D chain influenced the light emission of Gu2PbI4.•Pb2+ led 1D confined ...anionic chain cause the blue shifted PL emission of Gu2PbBr4.•Self trapped states, high degree of distortion in excited lattice induces redshift in PL emission.
1D, 2D behaving systems of Guanidinium lead halides (Gu2PbX4, X = Br−,I−) were synthesized by simple solution technique. The structural and vibrational properties of the compounds are studied using powder X-ray diffraction, Raman spectroscopy and Fourier transform infrared spectroscopy. Uv–Visible absorption studies revealed redshift of absorption edge on substituting bromide with iodide in Gu2PbX4. Photoluminescence (PL), PL excitation, Time resolved PL analysis indicates free exciton emission at 518 nm in Gu2PbI4 and self trapped exciton emission at 517 nm in Gu2PbBr4. The mechanism underlying this emission provides fruitful insights in realizing efficient phosphors for solid state lighting applications.
Osteoarthritis causes significant pain and disability with no approved disease-modifying drugs. We systematically reviewed the evidence from both pre-clinical and human studies for the potential ...disease-modifying effect of metformin in osteoarthritis.
Ovid Medline, Embase and CINAHL were searched between inception and June 2021 using MeSH terms and key words to identify studies examining the association between metformin use and outcome measures related to osteoarthritis. Two reviewers performed the risk of bias assessment and 3 reviewers extracted data independently. Qualitative evidence synthesis was performed. This systematic review is registered on PROSPERO (CRD42021261052 and CRD42021261060).
Fifteen (10 pre-clinical and 5 human) studies were included. Most studies (10 pre-clinical and 3 human) assessed the effect of metformin using knee osteoarthritis models. In pre-clinical studies, metformin was assessed for the effect on structural outcomes (n = 10); immunomodulation (n = 5); pain (n = 4); and molecular pathways of its effect in osteoarthritis (n = 7). For human studies, metformin was evaluated for the effect on structural progression (n = 3); pain (n = 1); and immunomodulation (n = 1). Overall, pre-clinical studies consistently showed metformin having a chondroprotective, immunomodulatory and analgesic effect in osteoarthritis, predominantly mediated by adenosine monophosphate-activated protein kinase activation. Evidence from human studies, although limited, was consistent with findings in pre-clinical studies.
We found consistent evidence across pre-clinical and human studies to support a favourable effect of metformin on chondroprotection, immunomodulation and pain reduction in knee osteoarthritis. Further high-quality clinical trials are needed to confirm these findings as metformin could be a novel therapeutic drug for the treatment of osteoarthritis.
Time series modeling is a way to predict future values by examining temporal data. The present study analyzes the monthly mean soil moisture data at various depths: surface, profile, and root soil ...moisture, spanning from 1981 to 2022. The analysis employs two distinct approaches: the statistical seasonal autoregressive integrated moving average (SARIMA) and a deep learning long short-term memory (LSTM). The models are trained on a data set, covering the period from 1981 to 2021, acquired from the agricultural site at Andhra Loyola College in Vijayawada, Andhra Pradesh, India. Subsequently, the data from 2021 to 2022 is reserved for testing purposes. The study provides comprehensive insights into the design of both SARIMA and LSTM models, along with an evaluation of their performance using established error metrics such as the model mean absolute percentage error (MAPE), mean absolute error (MAE), and root mean squared error (RMSE). In the context of surface soil moisture prediction, the LSTM model demonstrates superior performance compared to SARIMA. Specifically, LSTM achieves a notably lower MAPE of 0.0615 in contrast to SARIMA’s 0.1541, a reduced MAE of 0.0316 compared to 0.0871, and a diminished RMSE of 0.0412 as opposed to 0.1021. This pattern of enhanced accuracy persists across profile and root soil moisture predictions, further establishing LSTM’s supremacy in predictive capability across diverse soil moisture levels.
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