Development and innovation in the fracture management of animals

In the early 1920's, veterinary orthopedic cases were just managed with the help of external coaptation and external fixation devices. In many places conventionally trained persons and veterinarians carried out this practice. Subsequently after a better understanding of fracture biomechanism many internal fixation techniques, have been adopted in animals, providing good success rate of the fracture repair in veterinary practice.After the formation of AO/ ASIF, fracture repair has branched out into many channels particularly in dogs ,cats ,horses and exotic pets. The availability of implants also facilitated the increasing demands from the animal owner to take these techniques. The topic is discussed from the history of fracture repair, the status of its use in animals, classification of fracture, biomechanism of fracture, factors responsible for bone healing fracture, enhancing factor for fracture healing and different fixation techniques (both external and internal)

In Situ parameter estimation of synchronous machines using genetic algorithm method

The paper presents an in situ parameter estimation method to determine the equivalent circuit parameters of the Synchronous Machines. The parameters of synchronous generator, both cylindrical rotor and salient pole rotor, are estimated based on the circuit model. Genetic algorithm based parameter estimation technique is adopted where only one set of in-situ measured load test data is used. Conventional methods viz., EMF, MMF, Potier triangle method uses rated voltage and rated current obtained from more than one operating condition to determine the parameters. However, Genetic Algorithm (GA) based method uses the working voltage and load current of a single operating point obtained from in-situ measured load test data to estimate the parameters. The test results of the GA based parameter estimation method are found to be closer to direct load test results and better than conventional methods

Benign intermuscular lipoma in a bitch

A case of intermuscular lipoma located between the external abdominal oblique and internal abdominal oblique muscles in a fourteen- year -old dog is described. Presenting signs, radiographic findings, surgical treatments and the follow-up treatment are discussed

Effects of biomaterials keratin-gelatin and basic fibroblast growth factor-gelatin composite film on wound healing in dogs

Eighteen clinically healthy dogs weighing 10-15 kg body weight were used in this study over a 20-day period. They were allocated randomly into 3 groups of 6 animals each. After the creation of 5 cm x 5cm open wound, Group I was control treated with Gentamycin ointment. Groups II and ill were treated with keratin-gelatin and basic fibroblast growth factor (bFGF)-gelatin composite film respectively. On application, the keratin-gelatin and bFGF-gelatin composite film were well accepted by the animals without any adverse reaction. On clinical examination, Group II showed bright beefy red color granulation tissue with angiogenesis when compared to Groups I and ill. On bacteriological examination, Staphylococcus aureus, Pseudomonas, Escherichia coli, Proteus and Klebsiella species were isolated from all the groups. Mean percentage of epithelialisation, wound contraction and total healing were significantly better in Group II (P<0.05). Keratin is a biocompatible protein which does not interfere with the body's normal immunologic response and therefore it can be used in extensive wounds and also in non healing chronic wounds which need a trigger to stimulate the normal healing process. In extensive wounds when there is lack of autologous tissue, biomaterials like keratin-gelatin may be beneficial and can be used

Approximately Truthful Forecasting Competitions

Forecasting competitions have emerged as a promising tool for identifying skilled forecasters, eliciting truthful beliefs, and improving decision-making across domains such as public health, fi- nance, and geopolitics. These competitions typically rely on proper scoring rules to reward accurate predictions, with the assumption that such rules incentivize honest reporting. However, when used in competitive settings where participants are ranked and rewarded based on relative performance, proper scoring rules alone fail to preserve truthfulness. Participants strategically misreport their beliefs to improve their chances of winning, compromising forecast accuracy. This thesis formally investigates the incentive properties of forecasting competitions and proposes new mechanisms that better align individual incentives with truthful reporting. We first establish fundamental lower bounds on the number of events required to identify high-quality forecasters. We then analyze the standard proper score mechanism, showing it can incentivize forecasters to extremize or hedge their reports depending on their beliefs about the quality of their competitors. Next, we study the Event Lotteries Forecast mechanism, which guarantees truthfulness but show it necessarily requires a large number of events to be effective, which is far from our previously established lower bound. Instead, we propose using FTRL as an alternative competition mechanism. We show that FTRL is approximately truthful: it incentives forecasters to report something very close to their beliefs. Furthermore, we show it is efficient and achieves the lower bound. We then show that FTRL is robust to correlation, a guarantee that no previous mechanism is able to give. In particular, we introduce the block correlation measure and show it measures the type of correlation we need to account for in the competition setting where standard correlation measures fail. We also derive a concentration bound for block-correlated random variables which may be of further interest. Finally, we show the guarantees of FTRL extend to an entirely different setting: online learning from strategic forecasters. Leveraging our approximate truthfulness results, we give the first no- regret guarantee for non-myopic strategic forecasters in this setting.</p

Sensorless Load Torque Estimation and Passivity Based Control of Buck Converter Fed DC Motor

Passivity based control of DC motor in sensorless configuration is proposed in this paper. Exact tracking error dynamics passive output feedback control is used for stabilizing the speed of Buck converter fed DC motor under various load torques such as constant type, fan type, propeller type, and unknown load torques. Under load conditions, sensorless online algebraic approach is proposed, and it is compared with sensorless reduced order observer approach. The former produces better response in estimating the load torque. Sensitivity analysis is also performed to select the appropriate control variables. Simulation and experimental results fully confirm the superiority of the proposed approach suggested in this paper

A Theoretical Approach in the Design of Single Frame 28 V DC and 270 V DC Dual Voltage Generator

Armored Fighting Vehicles (AFVs) generally operate with a 28 V DC electrical system. However, the demand for electrical power in AFVs has exceeded the capabilities of the existing 28V system. The additional load growth necessitates larger wire sizes, which adds extra weight and cost to the vehicle. Introducing a dual-bus architecture (28 V DC and 270 V DC) can lead to the efficient operation of the electrical system while meeting future demand. This&nbsp;paper presents the design of a Brushless Direct Current (BLDC) Dual Voltage Generator (DVG), which simultaneously outputs two voltages (28 V DC &amp; 270 V DC) from a single frame across a wide operational speed range. The&nbsp;design process includes a detailed description of the individual stages, accompanied by analytical parameters and software-generated results. The modeling and analysis of the generator were carried out using Motorsolve design&nbsp;software. The obtained results are presented and thoroughly discussed in this paper

Consistent Polyhedral Surrogates for Top-kk Classification and Variants

Top-$k$ classification is a generalization of multiclass classification used widely in information retrieval, image classification, and other extreme classification settings. Several hinge-like (piecewise-linear) surrogates have been proposed for the problem, yet all are either non-convex or inconsistent. For the proposed hinge-like surrogates that are convex (i.e., polyhedral), we apply the recent embedding framework of Finocchiaro et al. (2019; 2022) to determine the prediction problem for which the surrogate is consistent. These problems can all be interpreted as variants of top-$k$ classification, which may be better aligned with some applications. We leverage this analysis to derive constraints on the conditional label distributions under which these proposed surrogates become consistent for top-$k$. It has been further suggested that every convex hinge-like surrogate must be inconsistent for top-$k$. Yet, we use the same embedding framework to give the first consistent polyhedral surrogate for this problem

COVID-19 and thrombosis: searching for evidence

Early in the pandemic, COVID-19-related increases in rates of venous and arterial thromboembolism were seen. Many observational studies suggested a benefit of prophylactic anticoagulation for hospitalized patients using various dosing strategies. Randomized trials were initiated to compare the efficacy of these different options in acutely ill and critically ill inpatients as the concept of immune-mediated inflammatory microthrombosis emerged. We present a case-based review of how we approach thromboembolic prophylaxis in COVID-19 and briefly discuss the epidemiology, the pathophysiology, and the rare occurrence of vaccine-induced thrombotic thrombocytopenia

Forecasting Competitions with Correlated Events

Beginning with Witkowski et al. [2022], recent work on forecasting competitions has addressed incentive problems with the common winner-take-all mechanism. Frongillo et al. [2021] propose a competition mechanism based on follow-the-regularized-leader (FTRL), an online learning framework. They show that their mechanism selects an $\epsilon$-optimal forecaster with high probability using only $O(\log(n)/\epsilon^2)$ events. These works, together with all prior work on this problem thus far, assume that events are independent. We initiate the study of forecasting competitions for correlated events. To quantify correlation, we introduce a notion of block correlation, which allows each event to be strongly correlated with up to $b$ others. We show that under distributions with this correlation, the FTRL mechanism retains its $\epsilon$-optimal guarantee using $O(b^2 \log(n)/\epsilon^2)$ events. Our proof involves a novel concentration bound for correlated random variables which may be of broader interest