Research on labor consumption and rescuers’ physiological parameters progress in the case of practical training in a mobile training facility

An important factor in ensuring success in an intervention operation in dangerous environments lays in the training process, in conditions as close as possible to real intervention situations, as well as the manner of setting up rescue teams. In order to organize the intervention process as efficiently as possible, when setting up rescue teams we must take into account work consumption and physiological parameters achieved by members of the rescue teams during practical training carried out within the mobile training facility. In case of special incidents, the rescue intervention can take place in areas with small (limited) sections, spaces with high temperature, in low visibility conditions, which would make rescuers’ intervention even more difficult. Conditions mentioned above can be easily created within the rescuers’ mobile training facility, endowment of INSEMEX Petroșani, facility that through its equipment and facilities, allows monitoring of labour consumption and physiological parameters of rescuers throughout and in all training situations. The current paper presents training methods of intervention and rescue personnel within the mobile training facility and progress of work consumption / physiological parameters of rescuers throughout these practical exercises, with the aim of preparing intervention and rescue teams for situations close to real ones.

An Automatic Sleep Stage Classification Algorithm Using Improved Model Based Essence Features

The automatic sleep stage classification technique can facilitate the diagnosis of sleep disorders and release the medical expert from labor-consumption work. In this paper, novel improved model based essence features (IMBEFs) were proposed combining locality energy (LE) and dual state space models (DSSMs) for automatic sleep stage detection on single-channel electroencephalograph (EEG) signals. Firstly, each EEG epoch is decomposed into low-level sub-bands (LSBs) and high-level sub-bands (HSBs) by wavelet packet decomposition (WPD), separately. Then, the DSSMs are estimated by the LSBs and the LE calculation is carried out on HSBs. Thirdly, the IMBEFs extracted from the DSSM and LE are fed into the appropriate classifier for sleep stage classification. The performance of the proposed method was evaluated on three public sleep databases. The experimental results show that under the Rechtschaffen’s and Kale’s (R&K) standard, the sleep stage classification accuracies of six classes on the Sleep EDF database and the Dreams Subjects database are 92.04% and 78.92%, respectively. Under the American Academy of Sleep Medicine (AASM) standard, the classification accuracies of five classes in the Dreams Subjects database and the ISRUC database reached 79.90% and 81.65%. The proposed method can be used for reliable sleep stage classification with high accuracy compared with state-of-the-art methods.

Efficient Digital Practices of Supplemental Vocational Education

Increasing importance and expansion of spectrum of functions of continuing education system in the era of digitalization will facilitate steady vocational and personal development of teachers. Searching for new pedagogical designs of supplemental vocational education can be considered as a force of sustainable development of occupational quality of teachers. In this context, the main aim of this work is to present one of significant constructs of such design of the system of supplemental vocational education: educational e-course. The main research methods are theoretical analysis of psychological and pedagogical, sociological and philosophical sources, analysis of actual data. The obtained result is the structure of educational e-course developed with consideration for decease in teachers’ labor consumption with obligatory preservation of education quality. Expert appraisal of the presented digital construct is based on reviews by teachers of educational entities, where advantages of electronic approach to development of educational course are mentioned and overall efficiency of such education is demonstrated.

Cultivation cost-benefit analysis of some important medicinal plants in Serbia

Driven by frequent misinformation about the level of profitability of growing certain medicinal plants, in this paper we presented a cost-benefit analysis based on twenty years of experience in field production. The observed costs and profits for peppermint, chamomile, lemon balm, marshmallow, valerian and pot marigold are based on the average values of production elements within the current prices of labor, energy and raw materials. Fixed costs in this paper were deliberately neglected and the discussion was based on the assumption of the existence and availability of infrastructure. In the cost analysis, we divided them into four main groups, which had different shares in total costs such as labor (45-79 %), drying (5 - 37 %), material (9-16 %) and machinery use (4-13 %). Regarding the level of profitability of cultivation of the six observed medicinal plants valerian was the most profitable with an estimated profit of over 4000 e/ha. Next best earning plants were lemon balm and marshmallow with about 3500 e/ha, while the income from peppermint and chamomile was more than twice lower and it was around 1500 e/ha. The lowest profit was realized by cultivating marigold (about 600 e/ha) due to the high labor consumption on the flower picking operation. In terms of labor consumption marshmallow, pot marigold and valerian are the most demanding with 365, 285 and 150 working days per hectare, respectively. The general conclusion of this observation of the profitability of growing medicinal plants would be that the producer must be aware of the costs and scope of labor engagement which should be expected per unit area before embarking on the calculation of production.

Technologies for building e-catalogs on local history: Transformations in the digital environment

Significant transformations in the technologies of e-catalog building in the area of local history are discussed. The author examines the main changes. She focuses on the local lore and history e-catalogs of the RF libraries and their specific features. The document support of the catalog development is presented. Based on the element structure of the technological process (goal, objects, subjects, resources, processes, instruments, methods, regulation documents, and results), the transformations in the technology are revealed. The author concludes that these changes are due to the expanding objects covered by the catalogs (including the networked); complicating regulations for the objects (mandatory competences for machine-readable cataloguing); radical changes to the advantage of digital information resources; need for continuing upgrade of hardand software; exacerbating problems of linguistic support of machine-readable cataloguing; new regulations, etc. The author also points to significantly complicating technologies. The author suggests that the solution may lie in labor consumption and efficiency calculations for the processes of the e-catalog elements design and use.

StackDRL: Stacked Deep Reinforcement Learning for Fine-grained Visual Categorization

Fine-grained visual categorization (FGVC) is the discrimination of similar subcategories, whose main challenge is to localize the quite subtle visual distinctions between similar subcategories. There are two pivotal problems: discovering which region is discriminative and representative, and determining how many discriminative regions are necessary to achieve the best performance. Existing methods generally solve these two problems relying on the prior knowledge or experimental validation, which extremely restricts the usability and scalability of FGVC. To address the "which" and "how many" problems adaptively and intelligently, this paper proposes a stacked deep reinforcement learning approach (StackDRL). It adopts a two-stage learning architecture, which is driven by the semantic reward function. Two-stage learning localizes the object and its parts in sequence ("which"), and determines the number of discriminative regions adaptively ("how many"), which is quite appealing in FGVC. Semantic reward function drives StackDRL to fully learn the discriminative and conceptual visual information, via jointly combining the attention-based reward and category-based reward. Furthermore, unsupervised discriminative localization avoids the heavy labor consumption of labeling, and extremely strengthens the usability and scalability of our StackDRL approach. Comparing with ten state-of-the-art methods on CUB-200-2011 dataset, our StackDRL approach achieves the best categorization accuracy.

Interrelation model capital/output in the conditions of a new technological platform

Many experts relate rehabilitation of the world economy to forming a new technological platform. In this work we made an attempt to show the trends in the interrelation between capital and output in the conditions of forming new technological platform. In order to do that, we used a modified productive function with technical progress saving labor consumption. Even though this model bears a simulative character, it has all the required capabilities to calculate interrelations “capital/output” for long periods of time and can be a tool to investigate the economical dynamics with consideration of the new character of the economic progress.

Isolation of Extracellular Vesicles: General Methodologies and Latest Trends

Background. Extracellular vesicles (EVs) play an essential role in the communication between cells and transport of diagnostically significant molecules. A wide diversity of approaches utilizing different biochemical properties of EVs and a lack of accepted protocols make data interpretation very challenging. Scope of Review. This review consolidates the data on the classical and state-of-the-art methods for isolation of EVs, including exosomes, highlighting the advantages and disadvantages of each method. Various characteristics of individual methods, including isolation efficiency, EV yield, properties of isolated EVs, and labor consumption are compared. Major Conclusions. A mixed population of vesicles is obtained in most studies of EVs for all used isolation methods. The properties of an analyzed sample should be taken into account when planning an experiment aimed at studying and using these vesicles. The problem of adequate EVs isolation methods still remains; it might not be possible to develop a universal EV isolation method but the available protocols can be used towards solving particular types of problems. General Significance. With the wide use of EVs for diagnosis and therapy of various diseases the evaluation of existing methods for EV isolation is one of the key problems in modern biology and medicine.

CALCULATION OF THE LABOR CONSUMPTION RATE FOR SHUTTERING WORKS WHILST CONSIDERING UNCERTAINTIES

Calculating construction costs and times is one of the most important and demanding tasks in construction management and economics. To arrive at a realistic calculation base, valid data and information is constantly being sought for labor consumption rates, output rates, productivity, material consumption, volumes in stock, number of transport cycles, and cost and time parameters that must be estimated or calculated ex ante. Ultimately, final cost and time parameters must be determined on the basis of such considerations and calculations. Accurate figures must be stated or submitted at the end of any analysis. These depend on the complexity of the building and on the conditions prevailing at the actual work stages and rely on more or less uncertain input data. One possible solution to this issue is to consider ranges that can deliver final conclusions on determined values. To systematically consider ranges in input parameters, this paper concentrates on applying probabilistic calculation methods based on Monte Carlo simulations. Key outcomes of probabilistic calculations include histograms that are used to directly capture the chance/risk ratio relative to a specific (selected) parameter. This paper presents a practical example of calculating the labor consumption rate for shuttering works to highlight the significance of the chosen chance/risk ratio and to show how it can be integrated into the systematic decision-making process adopted by the parties involved in the project.

CALCULATION OF THE LABOR CONSUMPTION RATE FOR SHUTTERING WORKS WHILST CONSIDERING UNCERTAINTIES

This paper focuses on the systematic consideration of uncertainties in calculating labor consumption rates for shuttering works. The magnitude of labor consumption rates results from a production system assessment. This paper outlines the basics of labor consumption rates and important factors that influence their level. A specific value must be selected at the end of any calculation. In labor-intensive activities, labor consumption rates are highly significant for calculating construction time and cost. To make important decisions, it is essential to know the chance/risk ratio relative to key parameters. For any quotation, this ratio illustrates the chances or risks associated with certain works and the entire project. To select the chance/risk ratio, existing uncertainties must be integrated into the probabilistic calculation (using Monte Carlo simulations) by distribution functions. The probabilistic calculation of labor consumption rates therefore creates the basis for any conclusions regarding chances and risks associated with specific costs or prices. Appropriate precautions can be taken as part of systematically managing chances and risks. As a result, the process of preparing and making decisions is characterized by a more systematic and target-driven approach. Furthermore, decision reliability and transparency are improved. An example of calculating the labor consumption rate for shuttering works demonstrates the influence of the selected distribution functions on results. It also illustrates its subsequent effect on the chance/risk ratio of adopted decisions.