Method of Measuring Torque-Speed Characteristics of Fractional Horsepower Motors

In determining experimentally the speed-torque characteristics of fractional horsepower servomotor, accurate measurement of small torques in necessary. The paper here describes the accurate measurement of small torques of the order of 50 gm-cm or even less. Principal of eddy-current damping is used on a thin metallic disc rotating in the air-gap of an electromagnet and the reaction torque due to eddy-currents in the metallic disc is balanced with the standard weights placed in a scale-pan

Few-Cycle High Energy Mid-Infrared Pulse From Ho:YLF Laser

Over the past decade, development of high-energy ultrafast laser sources has led to important breakthroughs in attoscience and strong-field physicsstudy in atoms and molecules. Coherent pulse synthesis of few-cycle high-energy laser pulse is a promising tool to generate isolated attosecond pulses via high harmonics generation (HHG). An effective way to extend the HHG cut-off energy to higher values is making use of long mid-infrared (MIR) driver wavelength, as the ponderomotive potential scales quadratically with wavelength. If properly scaled in energy to multi-mJ level and few-cycle duration, such pulses provide a direct path to intriguing attoscience experiments in gases and solids, which even permit the realization of bright coherent table-top HHG sources in the water-window and keV X-ray region. However, the generation of high-intensity long-wavelengthMIR pulses has always remained challenging, in particular starting from high-energy picosecond 2-μm laser driver, that is suitable for further energy scaling of the MIR pulses to multi-mJ energies by utilizing optical parametric amplifiers (OPAs). In this thesis, a front-end source for such MIR OPA is presented. In particular, a novel and robust strong-field few-cycle 2-μm laser driver directly from picosecond Ho:YLF laser and utilizing Kagome fiber based compression is presented. We achieved: a 70-fold compression of 140-μJ, 3.3-ps pulses from Ho:YLF amplifier to 48 fs with 11 μJ energy. The work presented in this thesis demonstrates a straightforward path towards generation of few-cycle MIR pulses and we believe that in the future the ultrafast community will benefit from this enabling technology. The results are summarized in mainly four parts: The first part is focused on the development of a 2-μm, high-energy laser source as the front-end. Comparison of available technology in general and promising gain media at MIR wavelength are discussed. Starting from the basics of an OPA, the design criteria, constraints on the pump & seed source and proper phase-matching conditions requirement for efficient amplification are discussed. In particular, starting from the challenge of developing a Ho:YLF oscillator, pulse amplification and the problem of gain narrowing are addressed. In the second part, various nonlinear compression schemes are discussed in general and specifically, inhibited-coupling Kagome fiber based compression is discussed. The experimental results for the generation of few-cycle, μJ-level 2-μm laser pulses in a two-stage compression scheme are then presented. In the third part, the seed pulse generation for the MIR OPA by utilizing supercontinuum (SC) are presented. The theoretical background of SC generation and the constraints on the pulse duration are discussed. Finally, in the last part, the results obtained are summarized in conclusion and the outlook in presented. The front-end source developed here can be used to generate few-cycle MIR pulses by employing nonoxide based nonlinear crystals. Moreover, as both the pump and seed pulses are derived from the same laser source, it offers the possibility of generating a passively carrier-envelope phase (CEP) stable idler

Sodium alginate microspheres for extending drug release: formulation and in vitro evaluation

In the present study, spherical microspheres of theophylline (TP) using sodium alginate as the hydrophilic carrier were prepared to prolong the release. The shape, surface and size characteristics were determined by scanning electron microscopy. The microspheres were found to be discreet and spherical in shape and had a smoother surface. The mean diameter of seven alginate microspheres formulations were between 7.6 ± 0.52 and 22.35 ±0.31 μm. It was observed that mean particle size of the microspheres increased with an increase in the concentration of polymer. The entrapment efficiency was found to be in the range of 70–93%. Optimized alginate microspheres were found to possess good sphericity, size and adequate entrapment efficiency. The in vitro release studies were carried out in pH progression media (pH 1.2, 2.5, 4.5, 7 and 7.4 solutions). Results indicated that percent drug release decreased with an increased alginate concentration. TP-loaded Alginate microspheres showed extended in vitro drug release thus use of microspheres potentially offers sustained release profile along with improved delivery of TP.Keywords: Extended drug delivery; Sodium alginate; Microspheres; Bronchial asthm

Exploring the Baseline: What Michigan Residents Know About Michigan State University Extension

Michigan’s Cooperative Extension Service, now called Michigan State University (MSU) Extension, has a long history of serving the state’s residents, especially in agricultural and rural program areas. Today’s Extension works to “help people help themselves” through programs aimed at meeting the needs of urban, suburban, and rural residents. But what do the state’s residents know about the programs offered through this organization? This paper looks at awareness surveys related to Cooperative Extension systems and examines the results of the MSU Extension Market Assessment Survey, a statewide telephone survey conducted by the MSU Institute for Public Policy and Social Research to explore what Michigan residents know about MSU Extension and its main programming areas. Responses were analyzed according to respondents’ ages, education levels, racial and ethnic backgrounds, region of the state and type of community of residence. Analysis showed more than half of Michigan residents were aware of MSU Extension, with wider awareness among older, white and rural residents. However, awareness of MSU Extension programs did not follow this trend. This study will provide information for Extension administrators, educators, and communicators in planning future programming and marketing efforts

Fast Yet Effective Machine Unlearning

Unlearning the data observed during the training of a machine learning (ML) model is an important task that can play a pivotal role in fortifying the privacy and security of ML-based applications. This paper raises the following questions: (i) can we unlearn a single or multiple classes of data from an ML model without looking at the full training data even once? (ii) can we make the process of unlearning fast and scalable to large datasets, and generalize it to different deep networks? We introduce a novel machine unlearning framework with error-maximizing noise generation and impair-repair based weight manipulation that offers an efficient solution to the above questions. An error-maximizing noise matrix is learned for the class to be unlearned using the original model. The noise matrix is used to manipulate the model weights to unlearn the targeted class of data. We introduce impair and repair steps for a controlled manipulation of the network weights. In the impair step, the noise matrix along with a very high learning rate is used to induce sharp unlearning in the model. Thereafter, the repair step is used to regain the overall performance. With very few update steps, we show excellent unlearning while substantially retaining the overall model accuracy. Unlearning multiple classes requires a similar number of update steps as for the single class, making our approach scalable to large problems. Our method is quite efficient in comparison to the existing methods, works for multi-class unlearning, doesn't put any constraints on the original optimization mechanism or network design, and works well in both small and large-scale vision tasks. This work is an important step towards fast and easy implementation of unlearning in deep networks. We will make the source code publicly available