Changes In Working Conditions Research Articles

METHODS OF TEACHING LITERATURE AS A SUBJECT

Teaching Russian literature as a foreign language to students has been an active area of research for the last several decades. There has been a constant increase in the number of students learning, due to the growing role of the Russian language in the academic environment and to the development of international relations in this area. The ultimate purpose of teaching Russian to international students is for them to speak English fluently (first of all, in a specific discipline), to express their thoughts, to understand their interlocutors, to reproduce and interpret orally and in writing the information obtained from print and audio sources. Learning Russian for professional purposes for non-philology university students is not only a means of mastering their future professions, but also a possibility to competently communicate in the professional environment and easily adapt to constantly changing working conditions. This study aims to show how to taught to non-philology students at the current stage of development of higher education, to specify the challenges and the teacher's tasks in this area and, finally, to discuss the development of communicative competence among students.

Low-cost sensor outlier detection framework for on-line monitoring of particle pollutants in multiple scenarios.

Monitoring the concentration of particle pollutants is very important for industrial production control and workers' health protection. Low-cost sensors are widely used to reduce deployment costs. The outliers in the observed data of pollutant concentration can be eliminated by outlier detection algorithms. However, it is difficult to meet the actual needs of changing working conditions or scene migration in factories by building a single algorithm for specific scenarios. It is a feasible scheme to identify the changing characteristics of data and adaptively adjust the outlier detection algorithm. From the point of view of data characteristics, we creatively match typical data types with high-performance algorithms. The framework proposed in this paper provides a general process including five basic tasks and uses a modular structure to complete the outlier detection target. The actual pollutant data of the workshops are used to evaluate the performance of our framework. At last, we compare eight different strategies under this framework and analyze the contribution of each step to outlier detection from the perspective of algorithm principle. The results show that low-cost sensors following the framework can meet the outlier detection requirements in the field of pollutant monitoring, thus greatly reducing the cost of algorithm selection and data adaptation.

DTCNNMI: A deep twin convolutional neural networks with multi-domain inputs for strongly noisy diesel engine misfire detection

Although machine learning-based intelligent detection methods have made many achievements for diesel engine misfire diagnosis, they suffer from a certain degree of performance degradation due to the strong environmental noise and the change of working conditions in the actual application scenarios. To tackle this issue, this study presents a deep twin convolutional neural networks with multi-domain inputs (DTCNNMI) for diesel engine misfire diagnosis under strong environmental noise and different working conditions. Vibration signals of engine cylinder heads at different speeds are collected experimentally and fed into input layers of the proposed model. It constructs three input layers to combine automatically extracted time-domain, time-frequency-domain and hand-craft time-domain statistical features, improving the model performance. Twin convolutional neural networks with large first-layer kernels are presented for extracting multi-domain information of vibration signals and resist influence of environmental noise and the change of operating conditions on the final diagnosis results. The effectiveness of the proposed approach is validated on the datasets collected experimentally and by making comparison with the existing representative algorithms. The results demonstrate that the accuracy and anti-noise performance of the proposed DTCNNMI outperforms the existing algorithms. On the constructed four datasets under different working conditions, the proposed method achieved at least 97.019% accuracy even when signal-to-noise ratio was −4 dB, which is much higher than those of the other methods. Moreover, the accuracy of the proposed DTCNNMI on datasets A, B, C and D is at least 61.177%, 33.334%, 17.646% and 29.961% higher than other methods, respectively.

A hierarchical optimal control strategy for continuous demand response of building HVAC systems to provide frequency regulation service to smart power grids

Ensuring the power balance and reliability of power grids is an increasing challenge due to the increasing involvement of intermittent renewable power generations. The use of existing heating, ventilation and air-conditioning (HVAC) systems in buildings has attracted increasing attention to implement continuous demand response in providing frequency regulation service, which can enhance instantaneous power balance and reliability of power grids without extra huge investment. However, the energy flexibility of buildings is not consistent, and the capacity available for frequency regulation service changes over time due to the changes of working conditions. In this study, a hierarchical optimal control strategy, consisting of a regulation bidding controller and a power use following controller, is proposed. It optimizes the power use baseline and regulation capacity, and controls HVAC systems to provide qualified frequency regulation service, considering the tradeoff between financial reward (regulation capacity) and thermal comfort while satisfying the operating constraints of HVAC systems. The proposed control strategy is validated on a simulation test platform. Results show that the strategy can maximize the use of regulation capacity provided by HVAC systems while ensuring the indoor environment control quality under a given guarantee rate.

MODERN RISKS OF EDUCATIONAL PROJECTS AND DEVELOPMENT OF MANAGEMENT OF HIGHER EDUCATION INSTITUTIONS ON THE BASIS OF THE BALANCE MODEL OF MANAGEMENT FUNCTIONS

The work is devoted to the study of changes in modern challenges and threats to the development of higher education in Ukraine and the construction of a model of balance of managerial functions based on the use of the methodology of I. Adizes. The current conditions of the pandemic and its impact on reforming approaches to the transformation of higher education management are analyzed from the standpoint of the project approach. It is established that the current state of work in the education system is accompanied by insufficient readiness of teachers, difficulties in technical equipment, low efficiency of production and training practices, increased individual work of teachers with students and low culture of distance learning. It is noted that the higher education system and some universities responded as quickly as possible to the force majeure situation and changed working conditions, introduced measures and practices aimed at reducing the spread of coronavirus infection and maintaining the quality of staff and student education. Areas for protection against the existing threat of a pandemic and further development of the higher education system are singled out, namely: measures of international cooperation, measures by the state and measures taken within the institutions of higher education themselves. The use of the Adizes code to build a balanced and effective management of a higher education institution that will help the organization to function effectively now and in the long run is considered. Managers must be proactive to be effective. Only integrated organic systems can be truly effective, in which there is interchangeability of the components of the overall structure, and the internal interdependence between them is carried out at the physical level.

Exploring dynamic relationships between employees’ personalities and psychosocial work factors

ABSTRACT The current study addresses the potentially dynamic relationship between employees’ personality and their working conditions. A six-year full-panel longitudinal study of employed individuals was used to test whether (I) task-related, (II) social and (III) organizational work factors contribute to change Big-Five personality traits over time and whether personalities change working conditions. Bivariate latent change score analyses were conducted on repeated-measures data (four waves) from 2356 Norwegian employees. The results showed that specific work factors pertaining to task-related and social characteristics (i.e. leadership) were associated with personality trait changes. Contrary to our expectations, none of the work factors predicted change in neuroticism and extraversion, and we offer several possible explanations for these findings. The results also showed that all personality traits may play an active role in shaping specific attributes of the work environment over time and thereby shed light on how employees’ working conditions emerge.

Evaluation of Risk Factors of Cardiovascular Diseases in Shimoga Population

Background: The morbidity and mortality rate is increasing due to cardiovascular disease around the globe. This may be due to drastic changes in lifestyle, food habits, work, stress, genetic factors, and environmental conditions. The present study is undertaken to evaluate the risk factors of cardiovascular disease (CVD) in Shimoga region. Objective: Evaluation of risk factors of CVD in patients who admitted for treatment in McGann Teaching District Hospital, Shimoga. Methods: A retrospective study was conducted in Mc Gann's Teaching District Hospital, Shimoga from October 2017 to March 2018. Data about sociodemographic parameters such as age, sex, height, weight, smoking, the status of diabetes mellitus, type of occupation, and diet, tobacco, and alcohol consumption were retrieved from the medical record section and statistical analysis was done using Statistical Package for the Social Sciences (SPSS). Results: Out of 262 CVD cases, 60% were male and 40% were female. Based on the body mass index (BMI), 1.9% were underweight, 40.1% were normal, 34.4% were overweight, and 23.7% were obese. Sedentary (64.9%) lifestyle was significantly higher when compared to moderate (2.3%) and heavy (32.8%). Diabetes, smoking, tobacco, and alcohol consumption were seen to be significant while food custom was not significant among the study population ( P < .05). Conclusion: In our study, a higher percentage of CVD patients falls under normal BMI when compared to other studies. This may be due to lifestyle, socioeconomic status, ethnicity, and family heredity. Further extensive studies should be carried out to find the main cause of CVD in Shimoga population.

Fault diagnosis of fracturing truck based on ITD and TET

The power system is the core part of fracturing truck, and its running state will directly determine the operation efficiency of fracturing truck. Fracturing trucks often operate under heavy load and noisy conditions, so the power system is prone to faults and difficult to diagnose. In order to solve the difficult problem of fracturing vehicle power system diagnosis under noise and changing working conditions, one method based on intrinsic time-scale decomposition (ITD) and transient-extracting transform (TET) is proposed. Firstly, using the intrinsic time-scale decomposition to dealing the collected vibration signal of the fracturing vehicle power system, multiple related components have been obtained, and then perform transient-extracting transform to processing the components with larger correlations to the original signal. The local features of the signal are enhanced and the fault features are extracted to complete the fault diagnosis. The test results showing that the proposed fault diagnosis method can accurately identify the fault condition of the power system. the conclusions obtained can provide a certain reference for the research and development of the power system fault diagnosis method of fracturing truck.

(Digitale) Elternzusammenarbeit in Kindertageseinrichtungen w\xe4hrend der Corona-Pandemie. Digitalisierungsschub oder verpasste Chance?

ZusammenfassungDie Schließung von Kindertageseinrichtungen (Kita) als Maßnahme zur Eindämmung des Corona-Virus stellte frühpädagogische Fachkräfte kurzfristig vor veränderte Tätigkeitsbedingungen und Möglichkeiten, den weiterhin bestehenden Bildungsauftrag umzusetzen. Die Zusammenarbeit mit Eltern, mit und ohne digitale Medien, spielt in dieser Hinsicht eine wichtige Rolle. Der Beitrag untersucht a) wie häufig und in welcher Form Fachkräfte die Elternzusammenarbeit in der Corona-Schließzeit umsetzten, b) welche Einstellungen frühpädagogische Fachkräfte zu digital-gestützter und allgemeiner Elternzusammenarbeit in der Corona-Schließzeit hatten und c) welche Rolle die Qualifikation der Fachkräfte, ihre Einstellungen und ihre wahrgenommene Unterstützung im Hinblick für die digitale und nicht digitale Elternzusammenarbeit in der Corona-Schließzeit spielten. Datenbasis bildet eine bundesweite Onlinebefragung von 3513 Fachkräften in Kitas während der Corona-Schließzeit. Die Ergebnisse zeigen, dass die wahrgenommene eigene Rolle im Hinblick auf die Elternzusammenarbeit einen positiven Einfluss darauf hat, ob mit den Eltern Kontakt aufgenommen wurde. Ob dieser Kontakt über digitale Medien passiert, hängt unter anderem von den Einstellungen der Fachkräfte zu digitalen Medien, der erwarteten Reaktion der Eltern auf diese Form der Elternzusammenarbeit und von der technischen Unterstützung im Implementationsprozess digitaler Medien in der Elternzusammenarbeit ab. Die Ergebnisse werden in Bezug auf strukturelle Bedingungen von Einrichtungen und professionelle Kompetenzen frühpädagogischer Fachkräfte für eine breite Implementierung digitaler Medien in der frühpädagogischen Praxis diskutiert.

Gaussian Process Kernel Transfer Enabled Method for Electric Machines Intelligent Faults Detection With Limited Samples

Traditional Artificial Intelligence (AI) based fault detection approaches need a large amount of data for the model learning. However, in a real-world system, it is very difficult and expensive to obtain massive labeled fault data. In addition, the working conditions of a motor are usually variable, conventional fault diagnosis models with weak generalization ability can only be used for fault detection under constant working condition. The performance of traditional AI based approaches decreases when the working condition changes. To this end, a novel deep Gaussian process (GP) kernel transfer based few-shot learning method (RNGPT) is proposed in this paper for the fault detection of electric machines. First, a deep residual network (ResNet) is used to extract the features of the raw data. Then, the encoded latent feature vector is fed into the GP with kernel transfer ability to make the motor fault detection and classification. The proposed method uses much less data than the traditional AI based method to achieve fault diagnosis under variable working condition, and does not cause an overfitting problem. Experimental results of two case studies demonstrate that the proposed RNGPT model can accurately and effectively detect motor faults with limited labeled data under different working conditions. Experimental results of RNGPT with radial basis function (RBF) kernel model on simulation data present that the fault detection accuracy of the proposed method is about 16% higher than the conventional deep learning methods, 6% higher than other few-shot learning based methods in 5-shot and 4% higher in 1-shot. Finally, experimental on a real-world dataset, the RNGPT-RBF model still has the highest fault diagnosis accuracy in 5-shot (99.39 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$ \pm $</tex-math></inline-formula> 0.09%) and 1-shot (98.55 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$ \pm $</tex-math></inline-formula> 0.16%).

Psychological well-being and employment status during the COVID-19 pandemic

IntroductionSeveral restrictive measures have been implemented to reduced COVID- 19 impact with unknown consequences on people daily life.ObjectivesThe primary objective is to asses the psychosocial impact and employment status changes since lockdown COVID-19 measures in Chile.MethodsCross-sectional study was implemented using an anonymous and self-administered online questionnaire. Adult people were invited to participate through social networks between May to June 2020. The questionnaire included sociodemographic information, coping strategies, changes in income and working conditions and psychological distress (K10 Scale).Results 3102 participants over 18 years answered the questionnaire. 69.9% reported psychological distress mainly women (82.2%), members of the public health system (59%), dependent workers (39.8%), people who suffered income reduction (36.8%)., and those who changed their employment status (26.4%). Participants who presented income reduction were 1.83 times more likely to present psychological distress than those without changes (p &lt;0.001)ConclusionsPandemic crisis had impacted population health, especially in some specifics groups that could be targeted for future interventions.

Psychosocial effects of COVID-19 pandemic in Bolivia. Preliminary results

IntroductionThe global health crisis due to Coronavirus Disease 2019 (COVID-19) and related containment measures have led to changes in daily life and, therefore, social and psychological impacts on the population.ObjectivesTo explore the psychological and social impact of COVID-19 in the general population of Bolivia.MethodsCross-sectional study was implemented using an anonymous and self-administered online questionnaire. Adult people were invited to participate through social networks between May to June 2020. The questionnaire included sociodemographic information, coping strategies, changes in income and working conditions and psychological distress (K10 Scale).ResultsA total of 878 adults living in Bolivia answered the questionnaire. Most people considered COVID-19 as a quite/very serious health problem that affects the entire population, without distinction. 65% reported to accomplish lock down measure, however, one of the main reasons for non-compliance is the need to go out to work. Half of participants (50%) reduced worked hours and 18% modified their employment contract. However, 70% reduced household income. A considerable percentage (62%) reported psychological distress (46% with moderate or severe). It was higher in women, young people and among those with lower household income. In addition, social networks and watching series and movies were the main coping strategies reported.ConclusionsThe COVID-19 pandemic has a considerable impact on psychological and social level. The negative impact was greater in some population groups such as women, young people, and those with a lower socioeconomic level, which may further increase inequities.

Compressed Air Energy Storage Experimental Platform with off-grid Operation

The compressed air energy storage(CAES) experimental platform can play a role in research and training. Due to the large changes in experimental working conditions and many sudden abnormal situations, it is easy to disturb the safe operation of the power grid when it is integrated into the power grid. By constructing a three-layer compressed air energy storage experimental platform, equipped with a complete test and operation monitoring system, using multiple sets of high-power light bulbs as loads, the experimental platform can be operated off-grid. It has the advantages of simple structure, low cost, wide adaptable voltage range, easy observation of experimental results, etc., and has broad application value.

Human Fatigue Evaluation During Smart Working Sessions

AbstractIn the entire world, 2020 will be remembered as a pandemic year, in which COVID‐19 virus has forced to lockdown entire cities and countries. In this context, the concept of work is radically changed, especially for production companies that are forced to temporarily close their production plants. On the other hand, other professional figures, such as engineers, analysts, call center workers, school and university professors, etc., are experimenting a different way of working, the so‐called “smart working”, namely a type of work free of time and place constraints. This paper aims at investigating how the change of working conditions affects the perceived fatigue by workers. In particular, an experimental study is conducted on some smart workers by means of the PERCLOS (PERcentage of eyelid CLOSure) method, that allows to evaluate sleepiness by recording workers faces during the job and analyzing their mouths and eyes.

A novel strategy of continuous process transition and wide range throughput fluctuating ethylene column

For the wide range change process of working conditions in the chemical industry process, conventional control is difficult to meet the requirements of smooth control. Instead, the process transition control strategy represented by dynamic optimization is adopted to achieve the smooth change of working conditions. For complex processes, the application limit of dynamic optimization is that the indirect method is difficult to solve and the direct method cannot obtain continuous solutions. The analytical method based on frequency domain is proposed to avoid this situation. The system dynamic model is described by the transfer function to obtain the continuous process transition control strategy. Furthermore, the simplification of the control strategy is discussed by numerical analysis. The availability of the control strategy proposed in this paper is verified by the simulation study of an ethylene column with throughput fluctuating.

Performance Degradation Assessment of Rotary Machinery Based on a Multiscale Tsallis Permutation Entropy Method

Methods based on vibration analysis are currently regarded as the most conclusive means for fault diagnosis and health prognostics in rotary machinery. However, changing working conditions mean that the vibration signals originating from rotary machinery exhibit different levels of complexity. This complexity leads to increased difficulty in constructing health indicators (HIs). In this paper, we propose a multiscale Tsallis permutation entropy (MTPE) to construct the HIs of rotary machinery under different working conditions. MTPE values are a function of an entropy index and scale, which have the universality for handling the complexity of a permutated time series. The health condition of the rotary machinery was effectively represented by the MTPEs in conditional monitoring; the initial point of the unhealthy stage was found using the 3 σ interval. This was set as the alarm threshold according to the varying HI trend. Once this was established, dividing the stages into two-stage health stages (HS) was straightforward. Using a rolling bearing, a run-to-failure experiment was conducted and results suggested that the proposed method effectively assessed the status of the rotary machinery. Taken together, this study provided a novel complexity measure based on a methodology for constructing the HIs of rotary machinery and enriches conditional monitoring theory.

Weighted domain adaptation networks for machinery fault diagnosis

Intelligent fault diagnosis of machines has received much attention in this big data era. Most reported models are constructed under the assumption that the training and testing data are from the same distribution. However, data distribution will shift due to working condition changes, posing challenges on the performance of intelligent models. This study considers the case that out of many known working conditions with labeled historical data, the model is to be used under another unlabelled target working condition. A multiple source domain adaptation method is proposed to learn fault-discriminative but working-condition-invariant features from raw vibration signals. Different known working conditions will be assigned with different weights, on the basis of their distributional similarities to the target working condition. Two case studies are carried out to validate the effectiveness of the proposed method, respectively on rotating speed changes and load level changes.

Improved phase space warping method for degradation tracking of rotating machinery under variable working conditions

Health Indicators (HI) that can effectively track the degradation state and are insensitive to the change of working conditions are of great significance to the process of Prognostic and Health Management (PHM). Based on the rapidly changing one-dimensional vibration signal to reconstruct the phase space, the Phase Space Warping (PSW) method was used to quantify the small curvature of the phase space trajectory caused by the damage, and then the HI that can represent the evolution process of the slow-changing damage could be obtained with the curvature. However, there are still several problems with the PSW method that need to be solved urgently. Firstly, it does not provide clear optimization criteria and adaptive determination strategies for the two key parameters of phase space reconstruction, which are, time delay and embedded dimension. Secondly, the HI extracted based on the PSW is greatly affected by noise and lacks a proper feature fusion strategy. Aiming at these problems, the Improved Phase Space Warping (IPSW) method is proposed in this paper. The contribution of IPSW is as follows: (1) IPSW considers the degree of comprehensive mutual information between multi-dimensional components, and uses comprehensive mutual information entropy as an optimization index, and gives clear guidelines on how to select optimal parameters; (2) IPSW introduces the correlation analysis method to fuse the acquired multi-modal trend components and then obtains the HI that can characterize the damage degradation trend. The simulation and experimental signal verification results of bearing fault degradation under the condition of variable speed show that the HI extracted based on the IPSW method can reduce the influence of variable speed and track the bearing damage degradation trend effectively.

The influence of changes in work stressors and coping resources on sleep disturbances: evidence from the OHSPIW cohort study

We investigated whether changes in psychosocial work conditions affect the risk of sleep disturbances. Data pertaining to 2738 males and 1431 females were obtained from the Occupational Health Study of Petroleum Industry Workers (OHSPIW), a prospective cohort study of Chinese petroleum industry workers. The subjects were assessed with regard to work-related stressors, coping resources, and sleep disturbances at baseline (2012) and follow-up (2018). The variations in work stressors and coping resources, which were assessed using the Occupation Stress Inventory-Reviewed (OSI-R), were calculated. Sleep disturbances were evaluated with the self-reported Pittsburgh Sleep Quality Index (PSQI). Increased work stressors (OR = 1.57, 95% CI = 1.24-1.99) and decreased coping resources (OR = 2.03, 95% CI = 1.48-2.78) were correlated with the likelihood of sleep disturbances in male and female workers. The primary risk factors included high role overload, increased responsibility, enhanced physical environment stressors, reduced self-care, and reduced rational coping. A reduction in work stressors was a protective factor against sleep disturbances in females only (OR = 0.63, 95% CI = 0.45-0.88). Coping resources had a modifying effect on the relationship between increased work stressors and sleep disturbances, with increased coping resources being associated with a lower odds of increased works stressors on sleep disturbances (OR = 1.29, 95% CI = 1.01-1.66) than decreased coping resources (OR = 3.60, 95% CI = 1.10-11.81). Changes in work stressors and coping resources have a significant influence on the risk of sleep disturbances. Our findings highlight important precautionary strategies to abate adverse psychosocial working environments and to strengthen coping resources to prevent work-related sleep disturbances.

Design of acoustic Fresnel zone plate for contact stress measurement of aero-engine rotor mating surface

• PR-FZP made of non-metallic materials with the acoustic impedance closer to that of water all have a strong focusing ability. • Adding matching layers can improve the focus gain of PR-FZP to a certain extent. • Adding a reflecting layer can significantly improve the focus gain of PR-FZP. Accurate measurement of the contact stress on the mating surface of the core engine's multi-stage rotor is of great significance for improving the structural connection quality and comprehensive performance of aero-engines. The phase reversal Fresnel zone plate (PR-FZP) can realize the focusing of ultrasonic wave and its focusing ability has an important influence on the measurement accuracy of contact stress based on ultrasonic method. To improve the focusing ability of PR-FZP, this paper compares the focusing effect of PR-FZP made of different materials and designs PR-FZP with different structures based on the in-depth analysis of the focusing principle of PR-FZP. Numerical simulation results show that among the six selected materials, the single material PR-FZP made of polymethyl pentene (PMP) is always the highest with the change of working conditions. For PR-FZP with matching layer structure (M-PR-FZP), the focus gain is 0.21 dB higher than that of PR-FZP made of a single material. The focus gain of PR-FZP with reflecting layer structure (R-PR-FZP) can reach 24.84 dB, which is 6.01 dB and 1.45 dB higher than that of single metal (brass) Soret Fresnel zone plate (Soret FZP) and single non-metallic material (PMP) PR-FZP, respectively. This study has important guiding significance for improving the focusing ability of PR-FZP and realizing the accurate measurement of contact stress of aero-engine rotor mating surface.