INTENSITY OF STRESS AND SYMPTOMS OF JOB EXHAUSTION AMONG PARAMEDICS IN POLAND

BACKGROUND: The aim of this article is to show how the features of job exhaustion depend of the amount of work-related stress. MATERIALS AND METHODS: This research included a randomly selected group of 456 paramedics, aged between 21 and 51, working at hospital emergency departments and in mobile emergency teams. The authors used the Maslach Burnout Inventory Form and Perceived Job Stress Questionnaire Form. RESULTS: The most important factors in uencing general amount of stress in a paramedic’s job include as follows: the sense of responsibility; the sense of insecurity impacted with the system of work; and the sense of psychic workload. However, less important factors in uencing stress include: unpleasant work conditions; a lack of support; a lack of control; a sense of threat. CONCLUSIONS: Paramedics are exposed to job exhaustion syndrome which causes a lessening of feeling safe in their work place. The risk factors in uencing job exhaustion syndrome include as follows: uncertainty of work system; sense of psychic workload caused by work; poor social contacts causing a lack of assistance from others; a lack of positive motivators in one’s job, such as various rewards

PERFORMANCE OF CHEST COMPRESSIONS WITH THE USE OF THE NEW MECHANICAL CHEST COMPRESSION MACHINE LIFELINE ARM: A RANDOMIZED CROSSOVER MANIKIN STUDY IN NOVICE PHYSICIANS

BACKGROUND: The Lifeline ARM (ARM; De btech, Guilford, USA) is a new mechanical chest compression device. The aim of the current study was to compare the quality of single rescuer cardiopulmonary resuscitation (CPR) with and without ARM device. METHODS: In this randomized crossover manikin trial forty-four novice physicians participated. Thirty minutes of training was allotted for manual CPR and then for the ARM. The following day, every participant performed a 2-min CPR single rescuer scenario, once with manual CPR and once with the ARM. The primary outcome measure of the study is effective compression; de ned as compressions performed with the correct of depth of 50–60 mm, complete decompressions, and the correct pressure point of CC. RESULTS: The ARM, compared with manual CPR, carried out more effective compressions (96 [interquartile range, IQR; 94–98] vs. 36 [IQR; 33–41]%, p < 0.001). The compressions preformed with the use of the ARM, furthermore, were with a correct CC rate (100 [IQR; 99–101] vs. 130 [IQR; 124–140] min-1; p<0.001) and a correct depth (97 [IQR; 96–98] vs. 37 [IQR; 31–39]%; p<0.001). The result of resuscitation with ARM was signi cantly better than manual CPR (p<0.05) for all of the analyzed chest compression parameters (percentage of CC too deep, percentage of CC too shallow, percentage of correct pressure points and percentage of correct pressure releases), as well as for the ventilation parameters (tidal volume, ventilation rate, minute-volume, gastric in ations). CONCLUSION: During this simulated trial, when CPR was performed by novice physicians, the ARM signi cantly improved the quality of CPR. Further clinical trials should provide motivation to con rm the potential bene ts of ARM use during CPR

Propuesta metodológica para la identificación de tierras marginales mediante productos derivados de teledetección y datos auxiliares

[EN] The concept of marginal land (ML) is dynamic and depends on various factors related to the environment, climate, scale, culture, and economic sector. The current methods for identifying ML are diverse, they employ multiple parameters and variables derived from land use and land cover, and mostly reflect specific management purposes. A methodological approach for the identification of marginal lands using remote sensing and ancillary data products and validated on samples from four European countries (i.e., Germany, Spain, Greece, and Poland) is presented in this paper. The methodology proposed combines land use and land cover data sets as excluding indicators (forest, croplands, protected areas, impervious areas, land-use change, water bodies, and permanent snow areas) and environmental constraints information as marginality indicators: (i) physical soil properties, in terms of slope gradient, erosion, soil depth, soil texture, percentage of coarse soil texture fragments, etc.; (ii) climatic factors e.g. aridity index; (iii) chemical soil properties, including soil pH, cation exchange capacity, contaminants, and toxicity, among others. This provides a common vision of marginality that integrates a multidisciplinary approach. To determine the ML, we first analyzed the excluding indicators used to delimit the areas with defined land use. Then, thresholds were determined for each marginality indicator through which the land productivity progressively decreases. Finally, the marginality indicator layers were combined in Google Earth Engine. The result was categorized into 3 levels of productivity of ML: high productivity, low productivity, and potentially unsuitable land. The results obtained indicate that the percentage of marginal land per country is 11.64% in Germany, 19.96% in Spain, 18.76% in Greece, and 7.18% in Poland. The overall accuracies obtained per country were 60.61% for Germany, 88.87% for Spain, 71.52% for Greece, and 90.97% for Poland.[ES] El concepto de tierra marginal (ML) es dinámico y depende de factores relacionados con el entorno, el clima, la escala, la cultura y la economía. los métodos actuales de identificación de ML son también diversos y están basados en múltiples parámetros y variables derivados del uso y cobertura del suelo reflejando, en su mayoría, fines de gestión específicos. En este artículo se presenta una propuesta metodológica para la identificación de tierras marginales mediante el uso de productos derivados de teledetección y datos auxiliares, validándose sobre muestras obtenidas en cuatro países europeos: Alemania, España, Grecia y Polonia. La metodología combina datos de usos y coberturas del suelo como indicadores excluyentes (bosque, tierras de cultivo, áreas protegidas, áreas impermeables, cambios de usos del suelo, cuerpos de agua y áreas de nieve permanente) e información ambiental como indicadores de marginalidad, esto es, (i) propiedades físicas del suelo como la pendiente, profundidad de suelo, erosión del suelo, textura, porcentaje de fragmentos de textura gruesa del suelo, etc.; (ii) factores climáticos como el índice de aridez; (iii) propiedades químicas del suelo como pH, capacidad de intercambio catiónico, contaminantes y toxicidad, entre otros, con el objetivo de abordar una visión común de la marginalidad que integre un enfoque multidisciplinar. Para obtener las coberturas de ML primero se analizaron los indicadores excluyentes para delimitar las áreas con un uso del suelo establecido. En segundo lugar, se determinaron los umbrales para cada indicador de marginalidad a través de los cuales el suelo se transforma, disminuyendo progresivamente su aprovechamiento productivo. Finalmente, la superposición de las capas de indicadores de marginalidad se llevó a cabo con la herramienta Google Earth Engine. El resultado final se categorizó en 3 niveles de ML con diferente productividad: alta, baja y tierras potencialmente inadecuadas. Los resultados obtenidos indican que el porcentaje de tierras marginales sobre la extensión total de cada país analizado es de 11,64% en Alemania, 19,96% en España, 18,76% en Grecia y 7,18% en Polonia. La precisión global obtenida por país fue del 60,61% para Alemania, del 88,87% para España, del 71,52% para Grecia y del 90,97% para Polonia.This research has been funded by the European Commission through the H2020-MSCA-RISE-2018 MAIL project (grant 823805) and by the Fondo de Garantía Juvenil en I+D+i from the Spanish Ministry of Labour and Social Economy.Torralba, J.; Ruiz, L.; Georgiadis, C.; Patias, P.; Gómez-Conejo, R.; Verde, N.; Tassapoulou, M.... (2021). Methodological proposal for the identification of marginal lands with remote sensing-derived products and ancillary data. En Proceedings 3rd Congress in Geomatics Engineering. Editorial Universitat Politècnica de València. 248-257. https://doi.org/10.4995/CiGeo2021.2021.12729OCS24825

Change Detection Algorithm for the Production of Land Cover Change Maps over the European Union Countries

Contemporary satellite Earth Observation systems provide growing amounts of very high spatial resolution data that can be used in various applications. An increasing number of sensors make it possible to monitor selected areas in great detail. However, in order to handle the volume of data, a high level of automation is required. The semi-automatic change detection methodology described in this paper was developed to annually update land cover maps prepared in the context of the Geoland2. The proposed algorithm was tailored to work with different very high spatial resolution images acquired over different European landscapes. The methodology is a fusion of various change detection methods ranging from: (1) layer arithmetic; (2) vegetation indices (NDVI) differentiating; (3) texture calculation; and methods based on (4) canonical correlation analysis (multivariate alteration detection (MAD)). User intervention during the production of the change map is limited to the selection of the input data, the size of initial segments and the threshold for texture classification (optionally). To achieve a high level of automation, statistical thresholds were applied in most of the processing steps. Tests showed an overall change recognition accuracy of 89%, and the change type classification methodology can accurately classify transitions between classes

A Novel Fractional-Order RothC Model

A new fractional q-order variation of the RothC model for the dynamics of soil organic carbon is introduced. A computational method based on the discretization of the analytic solution along with the finite-difference technique are suggested and the stability results for the latter are given. The accuracy of the scheme, in terms of the temporal step size h, is confirmed through numerical testing of a constructed analytic solution. The effectiveness of the proposed discrete method is compared with that of the classical discrete RothC model. Results from real-world experiments show that, by adjusting the fractional order q and the multiplier term ζ(t,q), a better match between simulated and actual data can be achieved compared to the traditional integer-order model

Multi-temporal phenological indices derived from time series Sentinel-1 images to country-wide crop classification

Crop classification is a crucial prerequisite for the collection of agricultural statistics, efficient crop management, biodiversity control, the design of agricultural policy, and food security. Crops are characterized by significant change during the growing season, and this information can be used to improve classification accuracy. However, capturing variation in vegetation cover requires a reliable source of valid data. Sentinel-1 radar images are a good candidate, as they supply information about Earth’s surface every six days, independent of weather and light conditions. In this paper, we present a method for crop classification based on radar polarimetry. We propose a set of multi-temporal indices derived from time series Sentinel-1 images that aim to characterize crop phenology. A big data, object-oriented classification technique is developed and tested on 16 crop types for the whole of Poland. Our analysis found that overall accuracy varied (regionally) from 86.36 to 89.13% in 2019, and from 85.95 to 89.81% in 2020. F1 scores for individual crops varied from 0.73 to 0.99, and the use of our multi-temporal phenological indices increased F1 scores by about 0.14 compared to calculations using only basic parameters. Results obtained for the whole country demonstrate the efficacy of the method and its resistance to environmental conditions

Assessment of Perceived Attractiveness, Usability, and Societal Impact of a Multimodal Robotic Assistant for Aging Patients With Memory Impairments

The aim of the present study is to present the results of the assessment of clinical application of the robotic assistant for patients suffering from mild cognitive impairments (MCI) and Alzheimer Disease (AD). The human-robot interaction (HRI) evaluation approach taken within the study is a novelty in the field of social robotics. The proposed assessment of the robotic functionalities are based on end-user perception of attractiveness, usability and potential societal impact of the device. The methods of evaluation applied consist of User Experience Questionnaire (UEQ), AttrakDiff and the societal impact inventory tailored for the project purposes. The prototype version of the Robotic Assistant for MCI patients at Home (RAMCIP) was tested in a semi-controlled environment at the Department of Neurology (Lublin, Poland). Eighteen elderly participants, 10 healthy and 8 MCI, performed everyday tasks and functions facilitated by RAMCIP. The tasks consisted of semi-structuralized scenarios like: medication intake, hazardous events prevention, and social interaction. No differences between the groups of subjects were observed in terms of perceived attractiveness, usability nor-societal impact of the device. The robotic assistant societal impact and attractiveness were highly assessed. The usability of the device was reported as neutral due to the short time of interaction

Modelling Multi-Year Carbon Fluxes in the Arctic Critical Zone (Spitzbergen, NO)

<p>Presentation given at the Svalbard Science Conference 2023 (SSC23) that took place in Oslo, Norway on October 31st-November 01st, 2023. </p><p>Vegetation and soil regulate the terrestrial carbon cycle and contribute to the atmospheric CO2 concentration and Earth climate. The Arctic soil plays a major role in this cycle as the extension of permafrost areas is around 25% of the land in the Northern hemisphere and it is estimated that permafrost stores 2-3 times the atmospheric carbon. In the Holocene, the tundra has acted as a carbon sink, but it is not clear if the fast Arctic climate change will turn it into a carbon source. Yet, data regarding Arctic carbon fluxes are scarce and modelling of their fate is affected by large uncertainties. </p><p>With the aim of investigating the tundra carbon fluxes dynamics on the high Arctic, CNR established the Bayelva Critical Zone Observatory at the Ny Ålesund research station in Svalbard since 2019, equipped with an Eddy Covariance tower and portable flux chambers for the measurement of Gross Primary Productivity (GPP) and of Ecosystem Respiration (ER) variability at the point scale, making it possible to build empirical models that correlate such variables to climate and environmental parameters such as temperature, irradiance, moisture and phenology. A first model, published in 2022, identified temperature, solar irradiance, soil moisture and green fractional cover as drivers. Further measurements done in 2021 and 2022 adding further sites in the Bayelva basin, allowed us to enlarge the scale of application of the model. A further step will be the use of the high-resolution satellite data of the VENmS mission (4 meters, 1 day revisit time) to extend the modelling of GPP over the entire Broegger peninsula, facilitating the spatial upscaling of measured fluxes,identifying the main variables to be used in general vegetation models and allowing future projections of carbon fluxes under different climate change scenarios in the high Arctic tundra.</p&gt

Challenges for Service Robots—Requirements of Elderly Adults with Cognitive Impairments

ObjectiveWe focused on identifying the requirements and needs of people suffering from Alzheimer disease and early dementia stages with relation to robotic assistants.MethodsBased on focus groups performed in two centers (Poland and Spain), we created surveys for medical staff, patients, and caregivers, including: functional requirements; human–robot interaction, the design of the robotic assistant and user acceptance aspects. Using Likert scale and analysis made on the basis of the frequency of survey responses, we identified users’ needs as high, medium, and low priority.ResultsWe gathered 264 completed surveys (100 from medical staff, 81 from caregivers, and 83 from potential users). Most of the respondents, almost at the same level in each of the three groups, accept robotic assistants and their support in everyday life. High level priority functional requirements were related to reacting in emergency situations (calling for help, detecting/removing obstacles) and to reminding about medication intake, about boiling water, turning off the gas and lights (almost 60% of answers). With reference to human–robot interaction, high priority was given to voice operated system and the capability of robotic assistants to reply to simple questions.ConclusionOur results help in achieving better understanding of the needs of patients with cognitive impairments during home tasks in everyday life. This way of conducting the research, with considerations for the interests of three stakeholder groups in two autonomic centers with proven experience regarding the needs of our patient groups, highlights the importance of obtained results