Application of bacillus megaterium for subclinical mastitis in cows

The problem of treatment of subclinical mastitis often arises after calving, especially in cows of the first lactation. The paper presents the results of the therapeutic effect of Bacillus megaterium in subclinical mastitis in cows. The aim of the research. To determine the effect of Bacillus megaterium on the microflora of the gastrointestinal tract and colonies of microorganisms isolated from subclinical mastitis in cows. Materials and methods. The research was conducted in a dairy farm growing Holstein. Cows with subclinical mastitis of the experimental groups were given concentrated feed with the addition of Bacillus megaterium (1 × 109 CFU/g) at a dose of 15–35 g per animal. The control group of cows was given the usual diet for dairy cows. The experiment lasted 30 days. Results. The use of Bacillus megaterium (1 × 109 CFU/g) at a dose of 35 g per animal had a pronounced effect on the microflora of the gastrointestinal tract of cows. The amount of Lactobacillus sp. was 67 % higher and Bifidobacterium 58 % higher than the control. In addition, the level of opportunistic pathogens on Escherichia coli decreased by 45 %, Clostridium by 27 %, Enterobacteriaceae and Staphylococcus by 75 %, and Candida by 80 % compared to controls. The amount of microflora in milk decreased by 40.2 % and the number of somatic cells by 87.9 %. Conclusions. The therapeutic efficacy of Bacillus megaterium (1 × 109 CFU/g) at a dose of 35 g per animal for 30 days in subclinical mastitis has been proven. After treatment, the amount of microflora in cow's milk decreased by 40.2 % and the number of somatic cells by 87.9 %. A positive effect on the microflora of the gastrointestinal tract of cows, where the number of Lactobacillus sp. increased by 67 % and Bifidobacterium by 58 %. The level of opportunistic pathogenic microflora decreased by Escherichia coli – by 45 %, Clostridium – by 27 %, Enterobacteriaceae and Staphylococcus – by 75 %, Candida – by 80 %, compared to the control

Towards acoustic condition monitoring for detection and characterisation of laser induced breakdown in a gas turbine laser ignition system

Acoustic detection and characterisation of laser induced breakdown is an attractive proposition in laser ignition systems in which condition monitoring is necessary but where optical access for monitoring purposes is impractical. Presented is a signal processing method based on wavelet decomposition for the non-invasive detection of acoustic emissions resulting from laser induced breakdown in an atmospheric pressure combustion test rig, representative of a single combustion chamber in a sub 15 MW industrial gas turbine. The probability and consistency of laser induced breakdown is determined from the acoustic signal and used to characterize the operating conditions and identify abrupt and incipient or slowly developing faults

Roller element bearing acoustic fault detection using smartphone and consumer microphones

Roller element bearings are a common component and crucial to most rotating machinery; their failure makes up around half of the total machine failures, each with the potential to cause extreme damage, injury and downtime. Fault detection through condition monitoring is of significant importance. This paper demonstrates bearing fault detection using widely accessible consumer audio tools. Audio measurements from a smartphone and a standard USB microphone, and vibration measurements from an accelerometer are collected during tests on an electrical induction machine exhibiting a variety of mechanical bearing anomalies. A peak finding method along with use of trained Support Vector Machines (SVMs) classify the faults. It is shown that the classification rate from both the smartphone and the USB microphone was 95 and 100%, respectively, with the direct physically detected vibration results achieving only 75% classification accuracy. This work opens up the opportunity of using readily affordable and accessible acoustic diagnosis and prognosis for early mechanical anomalies on rotating machines

Педагогическое проектирование в управлении гражданским воспитанием студентов высшей школы

В статті розглядається педагогічне проектування як ефективна технологія модернізації управління громадянським вихованням студентів вузів. Пропонується програма діяльності щодо застосування проектних технологій в управлінській діяльності.The paper discusses pedagogic project management as an efficient technology of modernization of management of civil education of the students of higher education institutions and presents a program of activities based on use of project technologies in the managerial activities.Рассматривается педагогическое проектирование как эффективная технология модернизации управления гражданским воспитанием студентов высших учебных заведений. Предлагается программа деятельности по использованию проектных технологий в управленческой деятельности

Acoustic Condition Monitoring & Fault Diagnostics for Industrial Systems

Condition monitoring and fault diagnostics for industrial systems is required for cost reduction, maintenance scheduling, and reducing system failures. Catastrophic failure usually causes significant damage and may cause injury or fatality, making early and accurate fault diagnostics of paramount importance. Existing diagnostics can be improved by augmenting or replacing with acoustic measurements, which have proven advantages over more traditional vibration measurements including, earlier detection of emerging faults, increased diagnostic accuracy, remote sensors and easier setup and operation. However, industry adoption of acoustics remains in relative infancy due to vested confidence and reliance on existing measurement and, perceived difficulties with noise contamination and diagnostic accuracy. Researched acoustic monitoring examples typically employ specialist surface-mount transducers, signal amplification, and complex feature extraction and machine learning algorithms, focusing on noise rejection and fault classification. Usually, techniques are fine-tuned to maximise diagnostic performance for the given problem. The majority investigate mechanical fault modes, particularly Roller Element Bearings (REBs), owing to the mechanical impacts producing detectable acoustic waves. The first contribution of this project is a suitability study into the use of low-cost consumer-grade acoustic sensors for fault diagnostics of six different REB health conditions, comparing against vibration measurements. Experimental results demonstrate superior acoustic performance throughout but particularly at lower rotational speed and axial load. Additionally, inaccuracies caused by dynamic operational parameters (speed in this case), are minimised by novel multi-Support Vector Machine training. The project then expands on existing work to encompass diagnostics for a previously unreported electrical fault mode present on a Brush-Less Direct Current motor drive system. Commonly studied electrical faults, such as a broken rotor bar or squirrel cage, result from mechanical component damage artificially seeded and not spontaneous. Here, electrical fault modes are differentiated as faults caused by issues with the power supply, control system or software (not requiring mechanical damage or triggering intervention). An example studied here is a transient current instability, generated by non-linear interaction of the motor electrical parameters, parasitic components and digital controller realisation. Experimental trials successfully demonstrate real-time feature extraction and further validate consumer-grade sensors for industrial system diagnostics. Moreover, this marks the first known diagnosis of an electrically-seeded fault mode as defined in this work. Finally, approaching an industry-ready diagnostic system, the newly released PYNQ-Z2 Field Programmable Gate Array is used to implement the first known instance of multiple feature extraction algorithms that operate concurrently in continuous real-time. A proposed deep-learning algorithm can analyse the features to determine the optimum feature extraction combination for ongoing continuous monitoring. The proposed black-box, all-in-one solution, is capable of accurate unsupervised diagnostics on almost any application, maintaining excellent diagnostic performance. This marks a major leap forward from fine-tuned feature extraction performed offline for artificially seeded mechanical defects to multiple real-time feature extraction demonstrated on a spontaneous electrical fault mode with a versatile and adaptable system that is low-cost, readily available, with simple setup and operation. The presented concept represents an industry-ready all-in-one acoustic diagnostic solution, that is hoped to increase adoption of acoustic methods, greatly improving diagnostics and minimising catastrophic failures

Evidence for a universal length scale of dynamic charge inhomogeneity in cuprate superconductors

Time-resolved optical experiments can give unique information on the characteristic length scales of dynamic charge inhomogeneity on femtosecond timescales. From data on the effective quasiparticle relaxation time r in La2-xSrxCuO4 and Nd2-xCexCuO4 we derive the temperature- and doping- dependence of the intrinsic phonon escape length le, which, under certain circumstances, can be shown to be a direct measure of charge inhomogeneity. Remarkably, a common feature of both p and n-type cuprates - which has important consequences for superconductivity - is that as T  Tc from above, the escape length approaches the zero-temperature superconducting coherence length, le  s(0). In close vicinity of Tc, le appears to follow the critical behaviour of the Ginsburg-Landau coherence length, GL(T). In the normal state le is found to be in excellent agreement with the mean free path lm obtained from the resistivity data. The data on le also agree well with the data on structural coherence lengths ls obtained from neutron scattering experiments, implying the existence of complex intrinsic textures on different length scales which may have a profound effect on the functional properties of these materials.Comment: To appear in Physical Review Letter

STATE SUPPORT OF SMALL AND MEDIUM ENTREPRENEURSHIP IN RUSSIA AT FEDERAL AND REGIONAL LEVELS

The current trends in the development of the small and medium-sized business-es in the Russian Federation are considered, and its current state is described. The main directions of state support are highlighted. Despite the relatively high contribution to the provision of employment, small business in terms of such indicators as trade, economic efficiency plays a minor role in modern economic processes in our country. It is noted that the state of small and medium enterprises in the Russian Federation is quite different in different regions, which depends on their specialization, characteristics, availability of jobs, unemployment rate, population structure and other factors. Appropriate, according to the authors, is the assessment of the state of small business in relation to a specific region, and even the city

Continuous Acoustic Monitoring of Electrical Machines; Processing Signals from USB Microphone & Mobile Smartphone Sensors Detecting DC Motor Controller Fault

Transient current instability is one of the most common faults evident in Pulse Width Modulation (PWM) controlled brushless DC motors. This paper explores the under-developed field of real-time acoustic diagnostics for electrically based faults using consumer grade sensors. Current instabilities produce an audible torque transient on the motor, easily detectable using consumer acoustic sensors; a USB microphone and smartphone in this case. Two time-frequency signal processing techniques, Wavelet Packet Transform (WPT) and Empirical Mode Decomposition (EMD), are used to isolate information pertaining to the fault and are assessed for computational performance. This gives four processed signals to search for instabilities using a peak finding technique. We then compare the performance of each method. With the USB microphone WPT signal correlating the best results (93%), a simplistic logarithmic predictive model is used to estimate the durations for the next experimental run, in real-time. The results prove that readily accessible and affordable consumer acoustic sensors can be used for real-time fault diagnostics with a high degree of accuracy