Search results for: Pulmonary Embolism Severity Index (PESI) score

Authors: A. G. Margaryan

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Aims and Objectives: Assessment of health status and detection disorders of external respiration functions (ERF) during preventative medical examination among physicians of Armenia. Subjects and Methods: Overall, fifty-nine physicians (17 men and 42 women) were examined and spirometry was carried out. The average age of the physicians was 50 years old. The studies were conducted on the Micromedical MicroLab 3500 Spirometer. Results: 25.4% among 59 examined physicians are overweight; 22.0% of them suffer from obesity. Two physicians are currently smokers. About half of the examined physicians (50.8%) at the time of examination were diagnosed with some diseases and had different health-related problems (excluding the problems related to vision and hearing). FVC was 2.94±0.1, FEV1 – 2.64±0.1, PEF – 329.7±19.9, and FEV₁%/FVC – 89.7±1.3. Pathological changes of ERF are identified in 23 (39.0%) cases. 28.8% of physicians had first degree of restrictive disorders, 3.4% – first degree of combined obstructive/ restrictive disorders, 6.8% – second degree of combined obstructive/ restrictive disorders. Only three physicians with disorders of the ERF were diagnosed with chronic bronchitis and bronchial asthma. There were no statistically significant changes in ERF depending on the severity of obesity (P> 0.05). Conclusion: The study showed the prevalence of ERF among physicians, observing mainly mild and moderate changes in ERF parameters.

Keywords: Armenia, external respiration function, health status, physicians

Procedia PDF Downloads 197

Authors: Stephen McCarthy, Joanne Gray, Esther Carr, Gerard Danjoux, Paul Baker, Rhiannon Hackett

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Background: The Go Well Health (GWH) platform is a web-based programme that allows patients to access personalised care plans and resources, aimed at prehabilitation prior to surgery. The online digital platform delivers essential patient education and support for patients prior to undergoing total hip replacements (THR) and total knee replacements (TKR). This study evaluated the impact of an online digital platform (ODP) in terms of functional health outcomes, health related quality of life and hospital length of stay following surgery. Methods: A retrospective cohort study comparing a cohort of patients who used the online digital platform (ODP) to deliver patient education and support (PES) prior to undergoing THR and TKR surgery relative to a cohort of patients who did not access the ODP and received usual care. Routinely collected Patient Reported Outcome Measures (PROMs) data was obtained on 2,406 patients who underwent a knee replacement (n=1,160) or a hip replacement (n=1,246) between 2018 and 2019 in a single surgical centre in the United Kingdom. The Oxford Hip and Knee Score and the European Quality of Life Five-Dimensional tool (EQ5D-5L) was obtained both pre-and post-surgery (at 6 months) along with hospital LOS. Linear regression was used to compare the estimate the impact of GWH on both health outcomes and negative binomial regressions were used to impact on LOS. All analyses adjusted for age, sex, Charlson Comorbidity Score and either pre-operative Oxford Hip/Knee scores or pre-operative EQ-5D scores. Fractional polynomials were used to represent potential non-linear relationships between the factors included in the regression model. Findings: For patients who underwent a knee replacement, GWH had a statistically significant impact on Oxford Knee Scores and EQ5D-5L utility post-surgery (p=0.039 and p=0.002 respectively). GWH did not have a statistically significant impact on the hospital length of stay. For those patients who underwent a hip replacement, GWH had a statistically significant impact on Oxford Hip Scores and EQ5D-5L utility post (p=0.000 and p=0.009 respectively). GWH also had a statistically significant reduction in the hospital length of stay (p=0.000). Conclusion: Health Outcomes were higher for patients who used the GWH platform and underwent THR and TKR relative to those who received usual care prior to surgery. Patients who underwent a hip replacement and used GWH also had a reduced hospital LOS. These findings are important for health policy and or decision makers as they suggest that prehabilitation via an ODP can maximise health outcomes for patients following surgery whilst potentially making efficiency savings with reductions in LOS.

Keywords: digital prehabilitation, online digital platform, orthopaedics, surgery

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Authors: Pei Yi Lin

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Objective: The objective of this study is to use machine learning methods to build an early prediction classifier model for acute delirium to improve the quality of medical care for intensive care patients. Background: Delirium is a common acute and sudden disturbance of consciousness in critically ill patients. After the occurrence, it is easy to prolong the length of hospital stay and increase medical costs and mortality. In 2021, the incidence of delirium in the intensive care unit of internal medicine was as high as 59.78%, which indirectly prolonged the average length of hospital stay by 8.28 days, and the mortality rate is about 2.22% in the past three years. Therefore, it is expected to build a delirium prediction classifier through big data analysis and machine learning methods to detect delirium early. Method: This study is a retrospective study, using the artificial intelligence big data database to extract the characteristic factors related to delirium in intensive care unit patients and let the machine learn. The study included patients aged over 20 years old who were admitted to the intensive care unit between May 1, 2022, and December 31, 2022, excluding GCS assessment <4 points, admission to ICU for less than 24 hours, and CAM-ICU evaluation. The CAMICU delirium assessment results every 8 hours within 30 days of hospitalization are regarded as an event, and the cumulative data from ICU admission to the prediction time point are extracted to predict the possibility of delirium occurring in the next 8 hours, and collect a total of 63,754 research case data, extract 12 feature selections to train the model, including age, sex, average ICU stay hours, visual and auditory abnormalities, RASS assessment score, APACHE-II Score score, number of invasive catheters indwelling, restraint and sedative and hypnotic drugs. Through feature data cleaning, processing and KNN interpolation method supplementation, a total of 54595 research case events were extracted to provide machine learning model analysis, using the research events from May 01 to November 30, 2022, as the model training data, 80% of which is the training set for model training, and 20% for the internal verification of the verification set, and then from December 01 to December 2022 The CU research event on the 31st is an external verification set data, and finally the model inference and performance evaluation are performed, and then the model has trained again by adjusting the model parameters. Results: In this study, XG Boost, Random Forest, Logistic Regression, and Decision Tree were used to analyze and compare four machine learning models. The average accuracy rate of internal verification was highest in Random Forest (AUC=0.86), and the average accuracy rate of external verification was in Random Forest and XG Boost was the highest, AUC was 0.86, and the average accuracy of cross-validation was the highest in Random Forest (ACC=0.77). Conclusion: Clinically, medical staff usually conduct CAM-ICU assessments at the bedside of critically ill patients in clinical practice, but there is a lack of machine learning classification methods to assist ICU patients in real-time assessment, resulting in the inability to provide more objective and continuous monitoring data to assist Clinical staff can more accurately identify and predict the occurrence of delirium in patients. It is hoped that the development and construction of predictive models through machine learning can predict delirium early and immediately, make clinical decisions at the best time, and cooperate with PADIS delirium care measures to provide individualized non-drug interventional care measures to maintain patient safety, and then Improve the quality of care.

Keywords: critically ill patients, machine learning methods, delirium prediction, classifier model

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Authors: T. Al-Alawi, N. Shorbaji, E. Rashaidi, M.Alidrisi

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Purpose/Objective(s): The main purpose of this study is to analyze the planning of SBRT treatments for localized prostate cancer with 6FFF and 10FFF energies to see if there is a dosimetric difference between the two energies and how we can increase the plan efficiency and reduce its complexity. Also, to introduce a planning method in our department to treat prostate cancer by utilizing high energy photons without increasing patient toxicity and fulfilled all dosimetric constraints for OAR (an organ at risk). Then toevaluate the target 95% coverage PTV95, V5%, V2%, V1%, low dose volume for OAR (V1Gy, V2Gy, V5Gy), monitor unit (beam-on time), and estimate the values of homogeneity index HI, conformity index CI a Gradient index GI for each treatment plan.Materials/Methods: Two treatment plans were generated for15 patients with localized prostate cancer retrospectively using the CT planning image acquired for radiotherapy purposes. Each plan contains two/three complete arcs with two/three different collimator angle sets. The maximum dose rate available is 1400MU/min for the energy 6FFF and 2400MU/min for 10FFF. So in case, we need to avoid changing the gantry speed during the rotation, we tend to use the third arc in the plan with 6FFF to accommodate the high dose per fraction. The clinical target volume (CTV) consists of the entire prostate for organ-confined disease. The planning target volume (PTV) involves a margin of 5 mm. A 3-mm margin is favored posteriorly. Organs at risk identified and contoured include the rectum, bladder, penile bulb, femoral heads, and small bowel. The prescription dose is to deliver 35Gyin five fractions to the PTV and apply constraints for organ at risk (OAR) derived from those reported in references. Results: In terms of CI=0.99, HI=0.7, and GI= 4.1, it was observed that they are all thesame for both energies 6FFF and 10FFF with no differences, but the total delivered MUs are much less for the 10FFF plans (2907 for 6FFF vs.2468 for 10FFF) and the total delivery time is 124Sc for 6FFF vs. 61Sc for 10FFF beams. There were no dosimetric differences between 6FFF and 10FFF in terms of PTV coverage and mean doses; the mean doses for the bladder, rectum, femoral heads, penile bulb, and small bowel were collected, and they were in favor of the 10FFF. Also, we got lower V1Gy, V2Gy, and V5Gy doses for all OAR with 10FFF plans. Integral dosesID in (Gy. L) were recorded for all OAR, and they were lower with the 10FFF plans. Conclusion: High energy 10FFF has lower treatment time and lower delivered MUs; also, 10FFF showed lower integral and meant doses to organs at risk. In this study, we suggest usinga 10FFF beam for SBRTprostate treatment, which has the advantage of lowering the treatment time and that lead to lessplan complexity with respect to 6FFF beams.

Keywords: FFF beam, SBRT prostate, VMAT, prostate cancer

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Authors: Aisha Khodijah Kholib Jati, Suharni Mohamad, Azlan Husin, Wan Suriana Wan Ab Rahman

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Introduction: Toxoplasmosis is caused by an obligate intracellular parasite, Toxoplasma gondii (T. gondii). It is commonly asymptomatic in normal individual, but it can be fatal to immunocompromised patients as it can lead to severe complications such as encephalitis, chorioetinitis and myocarditis. Objective: The aim of the study was to determine the seroprevalence of toxoplasmosis and its association with socio-demographic and behavioral characteristics among hemato-oncology patients in Hospital USM. Methods: In this cross-sectional study, 56 hemato-oncology patients were screened for immunoglobulin M (IgM) antibodies, immunoglobulin G (IgG) antibodies, and IgG avidity of T. gondii by using ELISA Kit (BioRad, USA). For anti-T. gondii IgG antibody, titer ≥ 9 IU/ml was considered as recent infection, while for IgM, ratio ≥ 1.00 was considered as reactive for the anti-T. gondii IgM antibodies. Low avidity index is considered as recent infection within 20 weeks while high avidity considered as past infection. T. gondii exposure, socio-demographic and behavioral characteristics was assessed by a questionnaire and interview. Results: A total of 28 (50.0%) hemato-oncology patients were seropositive for T. gondii antibodies. Out of that total, 27 (48.21%) patients were IgG+/IgM- and one patient (1.79%) was IgG+/IgM+ with high avidity index. Univariate analysis showed that age, gender, ethnicity, marital status, educational level, employment status, stem cell transplant, blood transfusion, close contact with cats, water supply, and consumption of undercooked meat were not significantly associated with Toxoplasma seropositivity rate. Discussion: The seropositivity rate of IgG anti-T. gondii was high among hemato-oncology patients in Hospital USM. With impaired immune system, these patients might have a severe consequence if the infection reactivated. Therefore, screening for anti-T. gondii may be considered in the future. Moreover, health programme towards healthy food and good hygiene practice need to be implemented.

Keywords: immunocompromised, seroprevalence, socio-demographic, toxoplasmosis

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Authors: Mohammad Safaeipour, Yousef Amer

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This study directs its focus on developing a conceptual model to offer a systematic and integrated method to weigh the related measures and evaluate a competence of hospitals and rank of the selected hospitals that involve and consider the stakeholders’ key performance indicators (KPI’s). The Analytical Hierarchy Process (AHP) approach will use to weigh the dimensions and related sub- components. The weights and performance scores will combine by using the Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) and rank the selected hospitals. The results of this study provide interesting insight into the necessity of process improvement implementation in which hospital that received the lowest ranking score.

Keywords: performance measurement system, PMS, hospitals, AHP, TOPSIS

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Authors: Pellecchia M. T., Savarese G., Carpinelli L., Calabrese M.

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Introduction: Parkinson's disease (PD) is characterized by a progressive loss of autonomy which undoubtedly has a significant impact on the quality of life of caregivers, and parents are the main informal caregivers. Caring for a person with PD is associated with an increased risk of psychiatric morbidity and persistent anxiety-depressive distress. The aim of the study is to investigate the burden on caregivers of patients with PD, through the use of multidimensional scales and to identify their personological and environmental determinants. Methods: The study has been approved by the Ethic Committee of the University of Salerno and informed consent for participation to the study was obtained from patients and their caregivers. The study was conducted at the Neurology Department of the A.O.U. "San Giovanni di Dio and Ruggi D’Aragona" of Salerno between September 2020 and May 2021. Materials: The questionnaires used were: a) Caregiver Burden Inventory - CBI a questionnaire of 24 items that allow identifying five sub-categories of burden (objective, psychological, physical, social, emotional); b) Depression Anxiety Stress Scales Short Version - DASS-21 questionnaire consisting of 21 items and valid in examining three distinct but interrelated areas (depression, anxiety and stress); c) Family Strain Questionnaire Short Form - FSQ-SF is a questionnaire of 30 items grouped in areas of increasing psychological risk (OK, R, SR, U); d) Zarit Caregiver Burden Inventory - ZBI, consisting of 22 items based on the analysis of two main factors: personal stress and pressure related to his role; e) Life Satisfaction, a single item that aims to evaluate the degree of life satisfaction in a global way using a 0-100 Likert scale. Findings: N ° 29 caregivers (M age = 55.14, SD = 9.859; 69% F) participated in the study. 20.6% of the sample had severe and severe burden (CBI score = M = 26.31; SD = 22.43) and 13.8% of participants had moderate to severe burden (ZBI). The FSQ-SF highlighted a minority of caregivers who need psychological support, in some cases urgent (Area SR and Area U). The DASS-21 results show a prevalence of stress-related symptoms (M = 10.90, SD = 10.712) compared to anxiety (M = 7.52, SD = 10.752) and depression (M = 8, SD = 10.876). There are significant correlations between some specific variables and mean test scores: retired caregivers report higher ZBI scores (p = 0.423) and lower Life Satisfaction levels (p = -0.460) than working caregivers; years of schooling show a negative linear correlation with the ZBI score (p = -0.491). The T-Test indicates that caregivers of patients with cognitive impairment are at greater risk than those of patients without cognitive impairment. Conclusions: It knows the factors that affect the burden the most would allow for early recognition of risky situations and caregivers who would need adequate support.

Keywords: anxious-depressive axis, caregivers’ burden, Parkinson’ disease, psychological risks

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Authors: Elham Koohikerade, Silvio Jose Gumiere

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In this research, irrigation is highlighted as crucial for improving both the yield and quality of ‎potatoes due to their high sensitivity to soil moisture changes. The study presents a hybrid Long ‎Short-Term Memory (LSTM) model aimed at optimizing irrigation scheduling in potato fields in ‎Quebec City, Canada. This model integrates model-based and satellite-derived datasets to simulate ‎soil moisture content, addressing the limitations of field data. Developed under the guidance of the ‎Food and Agriculture Organization (FAO), the simulation approach compensates for the lack of direct ‎soil sensor data, enhancing the LSTM model's predictions. The model was calibrated using indices ‎like Surface Soil Moisture (SSM), Normalized Vegetation Difference Index (NDVI), Enhanced ‎Vegetation Index (EVI), and Normalized Multi-band Drought Index (NMDI) to effectively forecast ‎soil moisture reductions. Understanding soil moisture and plant development is crucial for assessing ‎drought conditions and determining irrigation needs. This study validated the spectral characteristics ‎of vegetation and soil using ECMWF Reanalysis v5 (ERA5) and Moderate Resolution Imaging ‎Spectrometer (MODIS) data from 2019 to 2023, collected from agricultural areas in Dolbeau and ‎Peribonka, Quebec. Parameters such as surface volumetric soil moisture (0-7 cm), NDVI, EVI, and ‎NMDI were extracted from these images. A regional four-year dataset of soil and vegetation moisture ‎was developed using a machine learning approach combining model-based and satellite-based ‎datasets. The LSTM model predicts soil moisture dynamics hourly across different locations and ‎times, with its accuracy verified through cross-validation and comparison with existing soil moisture ‎datasets. The model effectively captures temporal dynamics, making it valuable for applications ‎requiring soil moisture monitoring over time, such as anomaly detection and memory analysis. By ‎identifying typical peak soil moisture values and observing distribution shapes, irrigation can be ‎scheduled to maintain soil moisture within Volumetric Soil Moisture (VSM) values of 0.25 to 0.30 ‎m²/m², avoiding under and over-watering. The strong correlations between parcels suggest that a ‎uniform irrigation strategy might be effective across multiple parcels, with adjustments based on ‎specific parcel characteristics and historical data trends. The application of the LSTM model to ‎predict soil moisture and vegetation indices yielded mixed results. While the model effectively ‎captures the central tendency and temporal dynamics of soil moisture, it struggles with accurately ‎predicting EVI, NDVI, and NMDI.‎

Keywords: irrigation scheduling, LSTM neural network, remotely sensed indices, soil and vegetation ‎monitoring

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Authors: Sakti Mandal

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Varying distribution of land surface temperature in an urbanized environment is a globally addressed phenomenon. Usually has been noticed that criticality of surface temperature increases from the periphery to the urban centre. As the centre experiences maximum severity of heat throughout the year, it also represents most critical zone in terms of thermal condition. In this present study, an attempt has been taken to propose a quantitative approach of thermal critical zonation (TCZ) on the basis of seasonal temperature variation. Here the zonation is done by calculating thermal critical value (TCV). From the Landsat 8 thermal digital data of summer and winter seasons for the year 2014, the land surface temperature maps and thermally critical zonation has been prepared, and corresponding dataset has been computed to conduct the overall study of that particular study area. It is shown that TCZ can be clearly identified and analyzed by the help of inter-seasonal temperature range. The results of this study can be utilized effectively in future urban development and planning projects as well as a framework for implementing rules and regulations by the authorities for a sustainable urban development through an environmentally affable approach.

Keywords: thermal critical values (TCV), thermally critical zonation (TCZ), land surface temperature (LST), Landsat 8, Kolkata Municipal Corporation (KMC)

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Authors: M. Salama, K. Haggag, H. El-Sayed

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An environmentally and ecologically acceptable method for eradication of wool fabrics based on microwave irradiation (MWI) was described. The process would be a suitable alternative for mothproofing of wool using toxic degradative chemical or biological methods. The effect of microwave irradiation and exposure time on the extent of eradication of wool fabrics from moth larvae was monitored. The inherent properties of the MW-irradiated wool fabrics; viz. tensile properties, alkali solubility, and yellowing index, were not adversely altered.

Keywords: microwave, wool, fabric, moth, eradication, resistance

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Authors: Aussara Panya, Nunghathai Sawasdee, Mutita Junking, Chatchawan Srisawat, Kiattawee Choowongkomon, Pa-Thai Yenchitsomanus

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Dengue virus (DENV) infection is a global public health problem with approximately 100 million infected cases a year. Presently, there is no approved vaccine or effective drug available; therefore, the development of anti-DENV drug is urgently needed. The clinical reports revealing the positive association between the disease severity and viral titer has been reported previously suggesting that the anti-DENV drug therapy can possibly ameliorate the disease severity. Although several anti-DENV agents showed inhibitory activities against DENV infection, to date none of them accomplishes clinical use in the patients. The surface envelope (E) protein of DENV is critical for the viral entry step, which includes attachment and membrane fusion; thus, the blocking of envelope protein is an attractive strategy for anti-DENV drug development. To search the safe anti-DENV agent, this study aimed to search for novel peptide inhibitors to counter DENV infection through the targeting of E protein using a structure-based in silico design. Two selected strategies has been used including to identify the peptide inhibitor which interfere the membrane fusion process whereby the hydrophobic pocket on the E protein was the target, the destabilization of virion structure organization through the disruption of the interaction between the envelope and membrane proteins, respectively. The molecular docking technique has been used in the first strategy to search for the peptide inhibitors that specifically bind to the hydrophobic pocket. The second strategy, the peptide inhibitor has been designed to mimic the ectodomain portion of membrane protein to disrupt the protein-protein interaction. The designed peptides were tested for the effects on cell viability to measure the toxic to peptide to the cells and their inhibitory assay to inhibit the DENV infection in Vero cells. Furthermore, their antiviral effects on viral replication, intracellular protein level and viral production have been observed by using the qPCR, cell-based flavivirus immunodetection and immunofluorescence assay. None of tested peptides showed the significant effect on cell viability. The small peptide inhibitors achieved from molecular docking, Glu-Phe (EF), effectively inhibited DENV infection in cell culture system. Its most potential effect was observed for DENV2 with a half maximal inhibition concentration (IC50) of 96 μM, but it partially inhibited other serotypes. Treatment of EF at 200 µM on infected cells also significantly reduced the viral genome and protein to 83.47% and 84.15%, respectively, corresponding to the reduction of infected cell numbers. An additional approach was carried out by using peptide mimicking membrane (M) protein, namely MLH40. Treatment of MLH40 caused the reduction of foci formation in four individual DENV serotype (DENV1-4) with IC50 of 24-31 μM. Further characterization suggested that the MLH40 specifically blocked viral attachment to host membrane, and treatment with 100 μM could diminish 80% of viral attachment. In summary, targeting the hydrophobic pocket and M-binding site on the E protein by using the peptide inhibitors could inhibit DENV infection. The results provide proof of-concept for the development of antiviral therapeutic peptide inhibitors to counter DENV infection through the use of a structure-based design targeting conserved viral protein.

Keywords: dengue virus, dengue virus infection, drug design, peptide inhibitor

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Authors: Angela U. Makolo

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Protein-coding and Non-coding regions determine the biology of a sequenced transcriptome. Research advances have shown that Non-coding regions are important in disease progression and clinical diagnosis. Existing bioinformatics tools have been targeted towards Protein-coding regions alone. Therefore, there are challenges associated with gaining biological insights from transcriptome sequence data. These tools are also limited to computationally intensive sequence alignment, which is inadequate and less accurate to identify both Protein-coding and Non-coding regions. Alignment-free techniques can overcome the limitation of identifying both regions. Therefore, this study was designed to develop an efficient sequence alignment-free model for identifying both Protein-coding and Non-coding regions in sequenced transcriptomes. Feature grouping and randomization procedures were applied to the input transcriptomes (37,503 data points). Successive iterations were carried out to compute the gradient vector that converged the developed Protein-coding and Non-coding Region Identifier (PNRI) model to the approximate coefficient vector. The logistic regression algorithm was used with a sigmoid activation function. A parameter vector was estimated for every sample in 37,503 data points in a bid to reduce the generalization error and cost. Maximum Likelihood Estimation (MLE) was used for parameter estimation by taking the log-likelihood of six features and combining them into a summation function. Dynamic thresholding was used to classify the Protein-coding and Non-coding regions, and the Receiver Operating Characteristic (ROC) curve was determined. The generalization performance of PNRI was determined in terms of F1 score, accuracy, sensitivity, and specificity. The average generalization performance of PNRI was determined using a benchmark of multi-species organisms. The generalization error for identifying Protein-coding and Non-coding regions decreased from 0.514 to 0.508 and to 0.378, respectively, after three iterations. The cost (difference between the predicted and the actual outcome) also decreased from 1.446 to 0.842 and to 0.718, respectively, for the first, second and third iterations. The iterations terminated at the 390th epoch, having an error of 0.036 and a cost of 0.316. The computed elements of the parameter vector that maximized the objective function were 0.043, 0.519, 0.715, 0.878, 1.157, and 2.575. The PNRI gave an ROC of 0.97, indicating an improved predictive ability. The PNRI identified both Protein-coding and Non-coding regions with an F1 score of 0.970, accuracy (0.969), sensitivity (0.966), and specificity of 0.973. Using 13 non-human multi-species model organisms, the average generalization performance of the traditional method was 74.4%, while that of the developed model was 85.2%, thereby making the developed model better in the identification of Protein-coding and Non-coding regions in transcriptomes. The developed Protein-coding and Non-coding region identifier model efficiently identified the Protein-coding and Non-coding transcriptomic regions. It could be used in genome annotation and in the analysis of transcriptomes.

Keywords: sequence alignment-free model, dynamic thresholding classification, input randomization, genome annotation

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Authors: Saori Fujimoto, Hiromi Kawasaki, Mari Murakami, Yoko Ueno

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Background: Coronavirus disease (COVID-19) has been spreading throughout the world. In Japan, many nursing universities have conducted online clinical practices to secure students’ learning opportunities. In the field of women’s health nursing, even after the pandemic ended, it will be worthwhile to utilize online practice in declining birthrate and reducing the burden of mothers. This study examined the learning effects of conducting online visits for mothers with infants during the COVID-19 pandemic by nursing students to enhance the students’ ability to carry out the online practice even in ordinary times effectively. Methods: Students were divided into groups of three, and information on the mothers was assessed, and the visits were planned. After role-play was conducted by the students and teachers, an online visit was conducted. The analysis target was the self-evaluation score of nine students who conducted online visits in June 2020 and had consented to participate. The evaluation contents included three items for assessment, two items for planning, one item for ethical consideration, five items for nursing practice, and two items for evaluation. The self-evaluation score ranged from 4 (‘Can do with a little advice’) to 1 (‘Can’t do with a little advice’). A univariate statistical analysis was performed. This study was approved by the Ethical Committee for Epidemiology of Hiroshima University. Results: The items with the highest mean (standard deviation) scores were ‘advocates for the dignity and the rights of mothers’ (3.89 (0.31)) and ‘communication behavior needed to create a trusting relationship’ (3.89 (0.31)).’ Next were the ‘individual nursing practice tailored to mothers (3.78 (0.42))’ and ‘review own practice and work on own task (3.78 (0.42)).’ The mean (standard deviation) of the items by type were as follows: three assessment items, 3.26 (0.70), two planning items, 3.11 (0.49), one ethical consideration item, 3.89 (0.31), five nursing practice items, 3.56 (0.54), and two evaluation items, 3.67 (0.47). Conclusion: The highest self-evaluations were for ‘advocates for the dignity and the rights of mothers’ and ‘communication behavior needed to create a trusting relationship.’ These findings suggest that the students were able to form good relationships with the mothers by improving their ability to effectively communicate and by presenting a positive attitude, even when conducting health visits online. However, the self-evaluation scores for assessment and planning were lower than those of ethical consideration, nursing practice, and evaluation. This was most likely due to a lack of opportunities and time to gather information and the need to modify and add plans in a short amount of time during one online visit. It is necessary to further consider the methods used in conducting online visits from the following viewpoints: methods of gathering information and the ability to make changes through multiple visits.

Keywords: infants, learning effects, mothers, online visit practice

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Authors: Uyanga Sukhbaatar, Ahmed Lbath, Mendamar Majig

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The Orienteering Problem is the most known example to start modeling tourist trip design problem. In order to meet tourist’s interest and constraint the OP is becoming more and more complicate to solve. The Multi Constraint Team Orienteering Problem with Time Windows is the last extension of the OP which differentiates from other extensions by including more extra associated constraints. The goal of the MCTOPTW is maximizing tourist’s satisfaction score in same time not to violate any of these constraints. This paper presents a genetic algorithmic approach to tackle the MCTOPTW. The benchmark data from literature is tested by our algorithm and the performance results are compared.

Keywords: multi constraint team orienteering problem with time windows, genetic algorithm, tour planning system

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Authors: Xupin Zhang, Maria Caterina Bramati, Enrest Fokoue

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Understanding the career placement of graduates from the university is crucial for both the qualities of education and ultimate satisfaction of students. In this research, we adapt the capabilities of dynamic Bayesian networks to characterize and predict students’ job placement using data from various universities. We also provide elements of the estimation of the indicator (score) of the strength of the network. The research focuses on overall findings as well as specific student groups including international and STEM students and their insight on the career path and what changes need to be made. The derived Bayesian network has the potential to be used as a tool for simulating the career path for students and ultimately helps universities in both academic advising and career counseling.

Keywords: dynamic bayesian networks, indicator estimation, job placement, social networks

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Authors: Jing (Claire) LI

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More than two decades from 2000 to 2020 of economic reforms have brought China unprecedented economic growth. There is an urgent call of research towards corporate social responsibility (CSR) in the context of China because while China continues to develop into a global trading market, it suffers from various serious problems relating to CSR. This study analyses the factors affecting the adoption of CSR practices by Chinese listed companies. The author proposes a new framework of factors of CSR adoption. Following common organisational factors and external factors in the literature (including organisational support, company size, shareholder pressures, and government support), this study introduces two additional factors, dynamic capability and regional culture. A survey questionnaire was conducted on the CSR adoption of Chinese listed companies in Shen Zhen and Shang Hai index from December 2019 to March 2020. The survey was conducted to collect data on the factors that affect the adoption of CSR. After collection of data, this study performed factor analysis to reduce the number of measurement items to several main factors. This procedure is to confirm the proposed framework and ensure the significant factors. Through analysis, this study identifies four grouped factors as determinants of the CSR adoption. The first factor loading includes dynamic capability and organisational support. The study finds that they are positively related to the first factor, so the first factor mainly reflects the capabilities of companies, which is one component in internal factors. In the second factor, measurement items of stakeholder pressures mainly are from regulatory bodies, customer and supplier, employees and community, and shareholders. In sum, they are positively related to the second factor and they reflect stakeholder pressures, which is one component of external factors. The third factor reflects organisational characteristics. Variables include company size and cultural score. Among these variables, company size is negatively related to the third factor. The resulted factor loading of the third factor implies that organisational factor is an important determinant of CSR adoption. Cultural consistency, the variable in the fourth factor, is positively related to the factor. It represents the difference between perception of managers and actual culture of the organisations in terms of cultural dimensions, which is one component in internal factors. It implies that regional culture is an important factor of CSR adoption. Overall, the results are consistent with previous literature. This study is of significance from both theoretical and empirical perspectives. First, from the significance of theoretical perspective, this research combines stakeholder theory, dynamic capability view of a firm, and neo-institutional theory in CSR research. Based on association of these three theories, this study introduces two new factors (dynamic capability and regional culture) to have a better framework for CSR adoption. Second, this study contributes to empirical literature of CSR in the context of China. Extant Chinese companies lack recognition of the importance of CSR practices adoption. This study built a framework and may help companies to design resource allocation strategies and evaluate future CSR and management practices in an early stage.

Keywords: China, corporate social responsibility, CSR adoption, dynamic capability, regional culture

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Authors: Pinna Beatrice., Laini Alex, Negro Giovanni, Burgazzi Gemma, Viaroli Pierluigi, Vezza Paolo

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Habitat models rarely consider macroinvertebrates as ecological targets in rivers. Available approaches mainly focus on single macroinvertebrate species, not addressing the ecological needs and functionality of the entire community. This research aimed to provide an approach to model the habitat of the macroinvertebrate community. The approach is based on the recently developed Flow-T index, together with a Random Forest (RF) regression, which is employed to apply the Flow-T index at the meso-habitat scale. Using different datasets gathered from both field data collection and 2D hydrodynamic simulations, the model has been calibrated in the Trebbia river (2019 campaign), and then validated in the Trebbia, Taro, and Enza rivers (2020 campaign). The three rivers are characterized by a braiding morphology, gravel riverbeds, and summer low flows. The RF model selected 12 mesohabitat descriptors as important for the macroinvertebrate community. These descriptors belong to different frequency classes of water depth, flow velocity, substrate grain size, and connectivity to the main river channel. The cross-validation R² coefficient (R²𝒸ᵥ) of the training dataset is 0.71 for the Trebbia River (2019), whereas the R² coefficient for the validation datasets (Trebbia, Taro, and Enza Rivers 2020) is 0.63. The agreement between the simulated results and the experimental data shows sufficient accuracy and reliability. The outcomes of the study reveal that the model can identify the ecological response of the macroinvertebrate community to possible flow regime alterations and to possible river morphological modifications. Lastly, the proposed approach allows extending the MesoHABSIM methodology, widely used for the fish habitat assessment, to a different ecological target community. Further applications of the approach can be related to flow design in both perennial and non-perennial rivers, including river reaches in which fish fauna is absent.

Keywords: ecological flows, macroinvertebrate community, mesohabitat, river habitat modeling

Procedia PDF Downloads 89

Authors: Carine Enow-Ayor Tarkang, Victorine Neh Akenji, Dmitri Rouwet, Jodephine Njdma, Andrew Ako Ako, Franco Tassi, Jules Remy Ngoupayou Ndam

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Groundwater is the major water resource in the whole of Bafia used for drinking, domestic, poultry and agricultural purposes, and being an area of intense agriculture, there is a great necessity to do a quality assessment. Bafia is one of the main food suppliers in the Centre region of Cameroon, and so to meet their demands, the farmers make use of fertilizers and other agrochemicals to increase their yield. Less than 20% of the population in Bafia has access to piped-borne water due to the national shortage, according to the authors best knowledge very limited studies have been carried out in the area to increase awareness of the groundwater resources. The aim of this study was to assess heavy metal contamination levels in ground and surface waters and to evaluate the effects of agricultural inputs on water quality in the Bafia area. 57 water samples (including 31 wells, 20 boreholes, 4 rivers and 2 springs) were analyzed for their physicochemical parameters, while collected samples were filtered, acidified with HNO3 and analyzed by ICP-MS for their heavy metal content (Fe, Ti, Sr, Al, Mn). Results showed that most of the water samples are acidic to slightly neutral and moderately mineralized. Ti concentration was significantly high in the area (mean value 130µg/L), suggesting another Ti source besides the natural input from Titanium oxides. The high amounts of Mn and Al in some cases also pointed to additional input, probably from fertilizers that are used in the farmlands. Most of the water samples were found to be significantly contaminated with heavy metals exceeding the WHO allowable limits (Ti-94.7%, Al-19.3%, Mn-14%, Fe-5.2% and Sr-3.5% above limits), especially around farmlands and topographic low areas. The heavy metal concentration was evaluated using the heavy metal pollution index (HPI), heavy metal evaluation index (HEI) and degree of contamination (Cd), while the Ficklin diagram was used for the water based on changes in metal content and pH. The high mean values of HPI and Cd (741 and 5, respectively), which exceeded the critical limit, indicate that the water samples are highly contaminated, with intense pollution from Ti, Al and Mn. Based on the HPI and Cd, 93% and 35% of the samples, respectively, are unacceptable for drinking purposes. The lowest HPI value point also had the lowest EC (50 µS/cm), indicating lower mineralization and less anthropogenic influence. According to the Ficklin diagram, 89% of the samples fell within the near-neutral low-metal domain, while 9% fell in the near-neutral extreme-metal domain. Two significant factors were extracted from the PCA, explaining 70.6% of the total variance. The first factor revealed intense anthropogenic activity (especially from fertilizers), while the second factor revealed water-rock interactions. Agricultural activities thus have an impact on the heavy metal content of groundwater in the area; hence, much attention should be given to the affected areas in order to protect human health/life and thus sustainably manage this precious resource.

Keywords: Bafia, contamination, degree of contamination, groundwater, heavy metal pollution index

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Authors: Lidija R. Jevrić, Sanja O. Podunavac-Kuzmanović, Strahinja Z. Kovačević

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Quantitative structure-retention relationship (QSRR) analysis was used to predict the chromatographic behavior of s-triazine derivatives by using theoretical descriptors computed from the chemical structure. Fundamental basis of the reported investigation is to relate molecular topological descriptors with chromatographic behavior of s-triazine derivatives obtained by reversed-phase (RP) thin layer chromatography (TLC) on silica gel impregnated with paraffin oil and applied ethanol-water (φ = 0.5-0.8; v/v). Retention parameter (RM0) of 14 investigated s-triazine derivatives was used as dependent variable while simple connectivity index different orders were used as independent variables. The best QSRR model for predicting RM0 value was obtained with simple third order connectivity index (3χ) in the second-degree polynomial equation. Numerical values of the correlation coefficient (r=0.915), Fisher's value (F=28.34) and root mean square error (RMSE = 0.36) indicate that model is statistically significant. In order to test the predictive power of the QSRR model leave-one-out cross-validation technique has been applied. The parameters of the internal cross-validation analysis (r2CV=0.79, r2adj=0.81, PRESS=1.89) reflect the high predictive ability of the generated model and it confirms that can be used to predict RM0 value. Multivariate classification technique, hierarchical cluster analysis (HCA), has been applied in order to group molecules according to their molecular connectivity indices. HCA is a descriptive statistical method and it is the most frequently used for important area of data processing such is classification. The HCA performed on simple molecular connectivity indices obtained from the 2D structure of investigated s-triazine compounds resulted in two main clusters in which compounds molecules were grouped according to the number of atoms in the molecule. This is in agreement with the fact that these descriptors were calculated on the basis of the number of atoms in the molecule of the investigated s-triazine derivatives.

Keywords: s-triazines, QSRR, chemometrics, chromatography, molecular descriptors

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Authors: Ruth Hegarty, Noel Connaughton

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Sustainability reporting has become a priority for many global multinational companies. This is associated with ever-increasing expectations from key stakeholders for companies to be transparent about their strategies, activities and management with regard to sustainability issues. The Global Reporting Initiative (GRI) encourages reporters to only provide information on the issues that are really critical in order to achieve the organisation’s goals for sustainability and manage its impact on environment and society. A key challenge for most reporting organisations is how to identify relevant issues for sustainability reporting and prioritise those material issues in accordance with company and stakeholder needs. A recent study indicates that most of the largest companies listed on the world’s stock exchanges are failing to provide data on key sustainability indicators such as employee turnover, energy, greenhouse gas emissions (GHGs), injury rate, pay equity, waste and water. This paper takes an indepth look at the approaches used by a select number of international sustainability leader corporates to identify key sustainability issues. The research methodology involves performing a detailed analysis of the sustainability report content of up to 50 companies listed on the 2014 Dow Jones Sustainability Indices (DJSI). The most recent sustainability report content found on the GRI Sustainability Disclosure Database is then compared with 91 GRI Specific Standard Disclosures and a small number of GRI Standard Disclosures. Preliminary research indicates significant gaps in the information disclosed in corporate sustainability reports versus the indicator content specified in the GRI Content Index. The following outlines some of the key findings to date: Most companies made a partial disclosure with regard to the Economic indicators of climate change risks and infrastructure investments, but did not focus on the associated negative impacts. The top Environmental indicators disclosed were energy consumption and reductions, GHG emissions, water withdrawals, waste and compliance. The lowest rates of indicator disclosure included biodiversity, water discharge, mitigation of environmental impacts of products and services, transport, environmental investments, screening of new suppliers and supply chain impacts. The top Social indicators disclosed were new employee hires, rates of injury, freedom of association in operations, child labour and forced labour. Lesser disclosure rates were reported for employee training, composition of governance bodies and employees, political contributions, corruption and fines for non-compliance. The reporting on most other Social indicators was found to be poor. In addition, most companies give only a brief explanation on how material issues are defined, identified and ranked. Data on the identification of key stakeholders and the degree and nature of engagement for determining issues and their weightings is also lacking. Generally, little to no data is provided on the algorithms used to score an issue. Research indicates that most companies lack a rigorous and thorough methodology to systematically determine the material issues of sustainability reporting in accordance with company and stakeholder needs.

Keywords: identification of key stakeholders, material issues, sustainability reporting, transparency

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Authors: Min Pan, Che-Yi Yang

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Behaviors in sports have been one of the research focuses for long. The possible causes of these deviant behaviors should be deterred to diminish such behaviors. The studies on moral disengagement in sports gained great attention internationally since then however studies regarding such issue are rather scarce in Taiwan. Hence, the study adopted literature review approach to retrospect researches on moral disengagement in sport so forth, introduced current instruments available – moral disengagement in sports (six factors, 32 items), moral disengagement in sports –short (1 factor, 8 items), and two Chinese version scales. It has been proved that moral disengagement in sport would render antisocial behaviors in sport context. It is also found players in team contact sports (e.g. basketball, football, and hockey) have higher moral disengagement at play. Male athletes tend to have higher moral disengagement than their female counterparts. Athletes competing in higher level also show higher moral disengagement. The study also summarized that factors such as coaching styles, emotion, self-orientation, motivation, and personality traits may deter the severity of moral disengagement of athletes hence further spur the antisocial behaviors in sports. It is suggested that a measurement of moral disengagement adequate for Taiwanese athletes and effective strategies for improving the antisocial behaviors should be developed based on the knowledge of moral disengagement in sports.

Keywords: antisocial behavior, attribution of blame, moral disengagement in sports measurement, nonresponsibility

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Authors: Muheddin Hamza, Entisar Etter

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This study is a part of investigating the possibility of blending two crude oils obtained from Libyan oil fields, namely crude oil (A) and crude oil (B) with different ratios, prior to blending the crude oils have to be compatible in order to avoid phase out and precipitation of asphaltene from the bulk of crude. The physical properties of both crudes such as density, viscosity, pour point and sulphur content were measured according to (ASTM) method. To examine the stability of both crudes and their blends, the oil compatibility model using microscopic, colloidal instability index (CII) using SARA analysis and asphaltene stabilization test using Turbiscan tests were conducted in the Libyan Petroleum Institute laboratories. Compatibility tests were carried out with both crude oils, the insolubility number (IN), and the solubility blending number (SBN), for both crude oils and their blends were calculated. The criteria for compatibility of any blend is that the volume average solubility blending number (SBN) is greater than the insolubility number (IN) of any component in the blend, the results indicated that both crudes were compatible. To support the results of compatibility tests the SARA analysis was done for the fractional determination of (saturates, aromatics, resins and asphaltenes) content. From this result, the colloidal Instability index (CII) and resin to asphaltenes ratio (R/A) were calculated for crudes and their blends. The results show that crude oil (B) which has higher (R/A) and lower (CII) is more stable than crude oil (A) and as the ratio of crude (B) increases in the blend the (CII) and (R/A) were improved, and the blends becomes more stable. Asphaltene stabilization test was also conducted for the crudes and their blends using Turbiscan MA200 according to the standard test method ASTM D7061-04, the Turbiscan shows that the crude (B) is more stable than crude (A) which shows a fair tendency. The (CII) and (R/A) were compared with the solubility number (SBN) for each crude and the blends along with Turbiscan results. The solubility blending number (SBN) of the crudes and their blends show that the crudes are compatible, also by comparing (R/A) and (SBN) values of the blends, it can be seen that they are complements of each other. All the experimental results show that the blends of both crudes are more stability.

Keywords: asphaltene, crude oil, compatibility, oil blends, resin, SARA

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Authors: Jia Wang, Wanting Zhang, Yutong Xv, Zihan Guo, Weiguang Ma

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Background: Before becoming qualified nurses, newly graduated nurses(NGNs) must experience a painful transition period, even transition shocks. Transition shocks are public health issues. To address the transition issue of NGNs, many programs or interventions have been developed and implemented. However, there are no studies to understand and assess the transition state of newly graduated nurses from work to life, from external abilities to internal emotions. Aims: Assess the transition status of newly graduated nurses in China. Identify the factors influencing the transition status of newly graduated nurses. Methods: The multi-center cross-sectional study design was adopted. From May 2022 to June 2023, 1261 newly graduated nurse in hospitals were surveyed online with the the Demographic Questionnaire and Transition Status Scale for Newly Graduated Nurses. SPSS 26.0 were used for data input and statistical analysis. Statistic description were adopted to evaluate the demographic characteristics and transition status of NGNs. Independent-samples T-test, Analysis of Variance and Multiple regression analysis was used to explore the influencing factors of transition status. Results: The total average score of Transition Status Scale for Newly Graduated Nurses was 4.00（SD = 0.61）. Among the various dimensions of Transition Status, the highest dimension was competence for nursing work, while the lowest dimension was balance between work and life. The results showed factors influencing the transition status of NGNs include taught by senior nurses, night shift status, internship department, attribute of working hospital, province of work and residence, educational background, reasons for choosing nursing, types of hospital, and monthly income. Conclusion: At present, the transition status score of new nurses in China is relatively high, and NGNs are more likely to agree with their own transition status, especially the dimension of competence for nursing work. However, they have a poor level of excess in terms of life-work balance. Nursing managers should reasonably arrange the working hours of NGNs, promote their work-life balance, increase the salary and reward mechanism of NGNs, arrange experienced nursing mentors to teach, optimize the level of hospitals, provide suitable positions for NGNs with different educational backgrounds, pay attention to the culture shock of NGNs from other provinces, etc. Optimize human resource management by intervening in these factors that affect the transition of new nurses and promote a better transition of new nurses.

Keywords: newly graduated nurse, transition, humanistic car, nursing management, nursing practice education

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Authors: Elia Favarelli, Enrico Testi, Andrea Giorgetti

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Autonomous structural health monitoring (SHM) of many structures and bridges became a topic of paramount importance for maintenance purposes and safety reasons. This paper proposes a set of machine learning (ML) tools to perform automatic feature selection and detection of anomalies in a bridge from vibrational data and compare different feature extraction schemes to increase the accuracy and reduce the amount of data collected. As a case study, the Z-24 bridge is considered because of the extensive database of accelerometric data in both standard and damaged conditions. The proposed framework starts from the first four fundamental frequencies extracted through operational modal analysis (OMA) and clustering, followed by density-based time-domain filtering (tracking). The fundamental frequencies extracted are then fed to a dimensionality reduction block implemented through two different approaches: feature selection (intelligent multiplexer) that tries to estimate the most reliable frequencies based on the evaluation of some statistical features (i.e., mean value, variance, kurtosis), and feature extraction (auto-associative neural network (ANN)) that combine the fundamental frequencies to extract new damage sensitive features in a low dimensional feature space. Finally, one class classifier (OCC) algorithms perform anomaly detection, trained with standard condition points, and tested with normal and anomaly ones. In particular, a new anomaly detector strategy is proposed, namely one class classifier neural network two (OCCNN2), which exploit the classification capability of standard classifiers in an anomaly detection problem, finding the standard class (the boundary of the features space in normal operating conditions) through a two-step approach: coarse and fine boundary estimation. The coarse estimation uses classics OCC techniques, while the fine estimation is performed through a feedforward neural network (NN) trained that exploits the boundaries estimated in the coarse step. The detection algorithms vare then compared with known methods based on principal component analysis (PCA), kernel principal component analysis (KPCA), and auto-associative neural network (ANN). In many cases, the proposed solution increases the performance with respect to the standard OCC algorithms in terms of F1 score and accuracy. In particular, by evaluating the correct features, the anomaly can be detected with accuracy and an F1 score greater than 96% with the proposed method.

Keywords: anomaly detection, frequencies selection, modal analysis, neural network, sensor network, structural health monitoring, vibration measurement

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Authors: Öznur Saribaş, Si̇bel Kahraman Çeti̇ntaş

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It is aimed to compare target volume and critical organ doses by using CyberKnife (CK) in accelerated partial breast irradiation (APBI) in patients with early stage breast cancer. Three different virtual plans were made for Iris, fixed and multi-leaf collimator (MLC) for 5 patients who received radiotherapy in the CyberKnife system. CyberKnife virtual plans were created, with 6 Gy per day totaling 30 Gy. Dosimetric parameters for the three collimators were analyzed according to the restrictions in the NSABP-39/RTOG 0413 protocol. The plans ensured critical organs were protected and GTV received 95 % of the prescribed dose. The prescribed dose was defined by the isodose curve of a minimum of 80. Homogeneity index (HI), conformity index (CI), treatment time (min), monitor unit (MU) and doses taken by critical organs were compared. As a result of the comparison of the plans, a significant difference was found for the duration of treatment, MU. However, no significant difference was found for HI, CI. V30 and V15 values of the ipsi-lateral breast were found in the lowest MLC. There was no significant difference between Dmax values for lung and heart. However, the mean MU and duration of treatment were found in the lowest MLC. As a result, the target volume received the desired dose in each collimator. The contralateral breast and contralateral lung doses were the lowest in the Iris. Fixed collimator was found to be more suitable for cardiac doses. But these values did not make a significant difference. The use of fixed collimators may cause difficulties in clinical applications due to the long treatment time. The choice of collimator in breast SBRT applications with CyberKnife may vary depending on tumor size, proximity to critical organs and tumor localization.

Keywords: APBI, CyberKnife, early stage breast cancer, radiotherapy.

Procedia PDF Downloads 114

Authors: Henry Pariaman, Iwa Garniwa, Isti Surjandari, Bambang Sugiarto

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Availability of a complex system of thermal power plant is strongly influenced by the reliability of spare parts and maintenance management policies. A reliability-centered maintenance (RCM) technique is an established method of analysis and is the main reference for maintenance planning. This method considers the consequences of failure in its implementation, but does not deal with further risk of down time that associated with failures, loss of production or high maintenance costs. Risk-based maintenance (RBM) technique provides support strategies to minimize the risks posed by the failure to obtain maintenance task considering cost effectiveness. Meanwhile, condition-based maintenance (CBM) focuses on monitoring the application of the conditions that allow the planning and scheduling of maintenance or other action should be taken to avoid the risk of failure prior to the time-based maintenance. Implementation of RCM, RBM, CBM alone or combined RCM and RBM or RCM and CBM is a maintenance technique used in thermal power plants. Implementation of these three techniques in an integrated maintenance will increase the availability of thermal power plants compared to the use of maintenance techniques individually or in combination of two techniques. This study uses the reliability, risks and conditions-based maintenance in an integrated manner to increase the availability of thermal power plants. The method generates MPI (Priority Maintenance Index) is RPN (Risk Priority Number) are multiplied by RI (Risk Index) and FDT (Failure Defense Task) which can generate the task of monitoring and assessment of conditions other than maintenance tasks. Both MPI and FDT obtained from development of functional tree, failure mode effects analysis, fault-tree analysis, and risk analysis (risk assessment and risk evaluation) were then used to develop and implement a plan and schedule maintenance, monitoring and assessment of the condition and ultimately perform availability analysis. The results of this study indicate that the reliability, risks and conditions-based maintenance methods, in an integrated manner can increase the availability of thermal power plants.

Keywords: integrated maintenance techniques, availability, thermal power plant, MPI, FDT

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Authors: Cindy de Frias, Erum Whyne

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Objectives: The current study examined whether the link between stress and health-related quality of life was buffered by protective factors, namely mindfulness, in a sample of middle-aged and older adults. Method: In this cross-sectional study, 134 healthy, community-dwelling adults (aged 50–85 years) were recruited from Dallas, Texas. The participants were screened for depressive symptoms and severity (using the Patient Health Questionnaire [PHQ-9]). All participants completed measures of self-reported health status (i.e., SF-36v2: mental and physical health composites), life stress (using the Elder’s Life Stress Inventory [ELSI]), and trait mindfulness (i.e., Mindful Attention Awareness Scale). Results: Hierarchical regressions (covarying for age, gender, and education) showed that life stress was inversely related to physical and mental health. Mindfulness was positively related to mental health. The negative effect of life stress on mental health was weakened for those individuals with greater trait mindfulness. Discussion: The results suggest that mindfulness is a powerful, adaptive strategy that may protect middle-aged and older adults from the well-known harmful effects of stress on healthy aging.

Keywords: health, stress, mindfulness, aging

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Authors: Javad Sarvestan, Zdenek Svoboda

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Force-time (F-T) curvature characteristics are globally referenced as the main indicators of athletic jump performance. Nevertheless, to the best of authors’ knowledge, no investigation tried to deeply study the relationship between F-T curve variables and real-game jump performance among elite volleyball players. To this end, this study was designated to investigate the association between F-T curve variables, including movement timings, force, velocity, power, rate of force development (RFD), modified reactive strength index (RSImod), and net impulse with spike jump height during real-game circumstances. Twelve young elite volleyball players performed 3 countermovement jump (CMJ) and 3 spike jump in real-game circumstances with 1-minute rest intervals to prevent fatigue. Shapiro-Wilk statistical test illustrated the normality of data distribution, and Pearson’s product correlation test portrayed a significant correlation between CMJ height and peak RFD (0.85), average RFD (r=0.81), RSImod (r=0.88) and concentric net impulse (r=0.98), and also significant correlation between spike jump height and peak RFD (0.73), average RFD (r=0.80), RSImod (r=0.62) and concentric net impulse (r=0.71). Multiple regression analysis also reported that these factors have a strong contribution in predicting of CMJ (98%) and spike jump (77%) heights. Outcomes of this study confirm that the RFD, concentric net impulse, and RSImod values could precisely monitor and track the volleyball attackers’ explosive strength, muscular stretch-shortening cycle function efficiency, and ultimate spike jump height. To this effect, volleyball coaches and trainers are advised to have an in-depth focus on their athletes’ progression or the impacts of strength trainings by observing and chasing the F-T curve variables such as RFD, net impulse, and RSImod.

Keywords: net impulse, reactive strength index, rate of force development, stretch-shortening cycle

Procedia PDF Downloads 133

Authors: Eda Ozkara San

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Nurse educators have been charged by several nursing organizations and accrediting bodies to provide innovative and evidence-based educational experiences, both didactic and clinical, to help students to develop the knowledge, skills, and attitudes needed to provide culturally competent nursing care to patients. Clinical simulation, which offers the opportunity for students to practice nursing skills in a risk-free, controlled environment and helps develop self-efficacy (confidence) within the nursing role. As one simulation method, the standardized patients (SPs) simulation helps educators to teach nursing students variety of skills in nursing, medicine, and other health professions. It can be a helpful tool for nurse educators to enhance cultural competence of nursing students. An alarming gap exists within the literature concerning the effectiveness of SP strategy to enhance cultural competence development of diverse student groups, who must work with patients from various backgrounds. This grant-supported, longitudinal, one-group, pretest and post-test educational intervention study aimed to examine the effect of the Diverse Standardized Patient Simulation (DSPS) cultural competence education strategy on students’ (n = 53) transcultural self-efficacy (TSE). The researcher-developed multidimensional DSPS strategy involved careful integration of transcultural nursing skills guided by the Cultural Competence and Confidence (CCC) model. As a carefully orchestrated teaching and learning strategy by specifically utilizing the SP pedagogy, the DSPS also followed international guidelines and standards for the design, implementation, evaluation, and SP training; and had content validity review. The DSPS strategy involved two simulation scenarios targeting underrepresented patient populations (Muslim immigrant woman with limited English proficiency and Irish-Italian American gay man with his partner (Puerto Rican) to be utilized in a second-semester, nine-credit, 15-week medical-surgical nursing course at an urban public US university. Five doctorally prepared content experts reviewed the DSPS strategy for content validity. The item-level content validity index (I-CVI) score was calculated between .80-1.0 on the evaluation forms. Jeffreys’ Transcultural Self-Efficacy Tool (TSET) was administered as a pretest and post-test to assess students’ changes in cognitive, practical, and affective dimensions of TSE. Results gained from this study support that the DSPS cultural competence education strategy assisted students to develop cultural competence and caused statistically significant changes (increase) in students’ TSE perceptions. Results also supported that all students, regardless of their background, benefit (and require) well designed cultural competence education strategies. The multidimensional DSPS strategy is found to be an effective way to foster nursing students’ cultural competence development. Step-by-step description of the DSPS provides an easy adaptation of this strategy with different student populations and settings.

Keywords: cultural competence development, the cultural competence and confidence model, CCC model, educational intervention, transcultural self-efficacy, TSE, transcultural self-efficacy tool, TSET

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Authors: Abdolamir Gatezadeh, Rezvan K. A. Mohamamdi, Arash Jelodari

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It has been well established that personality traits and self-compassion are associated with psychological well-being. Thus, the current research aimed to investigate the underlying mechanisms in a collectivist culture. Method: One hundred and fifty college students were chosen and filled out Ryff's Psychological Well-Being Scale, the NEO Personality Inventory, and Neff's Self-Compassion Scale. Results: The results of correlation analysis showed that there were significant relationships between the personality traits (neuroticism, extraversion, agreeableness, and conscientiousness) and self-compassion (self-kindness, isolation, mindfulness, and the total score of self-compassion) with psychological well-being. The regression analysis showed that neuroticism, extraversion, and conscientiousness significantly predicted psychological well-being. Discussion and conclusion: The cultural implications and future orientations have been discussed.

Keywords: college students, personality traits, psychological well-being, self-compassion

Procedia PDF Downloads 211