Values Of Variables Research Articles

PERBEDAAN KECEPATAN BERJALAN PADA PENGGUNA TRANSTIBIAL PROSTHESIS TIPE SACH FOOT DAN SINGLE AXIS FOOT

Background: Amputation can be defined as the act of separating part of the body, part or all of an extremity. Currently, lower limb amputations account for 85-90% of all amputations and transtibial amputation is the most frequently performed type of amputation surgery. One of the problems with transtibial amputation is hyperextension and weakness of the ligaments in the knee. Knee hyperextension is a condition that occurs when the knee joint is extended (straightening the knee) excessively, thus putting pressure on the structure of the knee and the back of the knee joint. Ligament weakness in the knee is weakness in the anterior cruciate ligament, posterior cruciate ligament, medial collateral ligament and lateral collateral ligament. Choosing the right prosthesis components can overcome this. One component that plays an important role is the foot component. Methods: This type of research is observational analytic with a cross-sectional design. The research subjects were 17 transtibial prosthesis users. Data analysis in this study used Spearman Rank Test. Results: Based on the Spearman Rank results, the significance result was 0.653 for the type of foot with hyperextension, while for the type of foot with ligament weakness, the significance result was 0.434. Both variables had variable values > 0.05, meaning there was no correlation between foot type and hyperextension and ligament weakness in transtibial prosthesis users. Conclusion: There is no correlation type of foot to hyperextension and ligament weakness. In future research, it is hoped that research can be carried out with a larger number of samples and a more varied type of foot.

Analysis of Factors Influencing Farmers’ Decisions in Using Certified Seeds in Pemayung District Batang Hari Regency

This study was conducted with the objectives: 1). To identify factors that influence farmers' decisions in using certified seeds in Pemayung District. 2). To analyze factors that influence farmers' decisions in using certified seeds in Pemayung District. The study was carried out in the Batang Hari Regency's Pemayung District. Because the majority of rice growers here utilize certified seeds, this location was selected. After the discussion, a constant value (a) of -3.085 was determined. This indicates that the value of the seed usage decision is -3.085 if all the factors under study (age, income, experience, education level, and land area) have values equal to zero (0). The value of the age coefficient (b1) is 0.142. This indicates that, provided the values of the other variables remain constant, an increase of one unit in the age variable will result in a 0.142 unit drop in the seed usage decision's value. The coefficient value (b2) for education level is 0.518. The results of Nagelkerke R Square demonstrate the influence of the variables of age (X1), education (X2), farming experience (X3), land area (X4), and income (X5) on this variable. This indicates that a 42% explanation of the dependent variables can be attributed to the independent variables in the model. Other factors not included in this study, such as the activity of farmer groups, risk preferences, land ownership status, and others, account for the remaining 58% (100% - 58%). The Education variable, with a Wald value of 4.613 and a Significance value of 0.032, and the Business Experience variable are the variables with a Wald value larger than 3.841 and a significance level lower than 0.05. While other factors, such as income, land area, and age, have significance values larger than 0.050 and Wald values smaller than 3.841, they have no effect on farmers' decisions to use certified seeds.

Identification of amplitude-dependent aerodynamic damping from free vibration data using iterative unscented kalman filter

This study presents an innovative approach employing an iterative Unscented Kalman Filter (IUKF) for the identification of amplitude-dependent nonlinear aerodynamic damping using wind tunnel free vibration data. The wind-structure interaction system is represented as a single-degree-of-freedom system, with the amplitude-dependent aerodynamic damping modeled as a polynomial function of structural displacement and velocity. The augmented state variables, encompassing structural vibration frequency and polynomial coefficients for aerodynamic damping, are concurrently estimated from free vibration data using the UKF technique. To enhance the robustness of the identification results against variations in initial conditions, the UKF is applied iteratively by assigning the estimated polynomial coefficients as new initial values for the state variables. Validation of the IUKF-based method is performed through a numerical example featuring a typical bridge deck sectional model, as well as experimental data from two spring-suspended sectional models experiencing vertical vortex-induced vibration (VIV) and torsional post-flutter limit cycle oscillation. The feasibility of identifying amplitude-dependent aerodynamic damping for amplitude range not covered by the displacement signal is examined.

Utilizing waste heat in wastewater treatment plants for water desalination: Modeling and Multi-Objective optimization of a Multi-Effect desalination system using Decision Tree Regression and Pelican optimization algorithm

This paper examines the feasibility of using waste heat from wastewater treatment plants (WWTPs) for water desalination. A model was developed to utilize waste heat from the gensets at As Samra WWTP in Jordan, using real data and TRNSYS® software to calculate available waste heat. The desalination process was then modeled with ASPEN PLUS® software, focusing on multi-effect desalination (MED). Both series and parallel configurations for the MED system were compared. The study investigated the effects of system feeding flow rate, feeding pressure, and heat input on productivity, performance ratio, and recovery ratio. The study also introduces a novel optimization technique combining machine learning and modern optimization algorithms to maximize system productivity and performance. Initially, a decision tree regression (DTR) model is developed to establish relationships between key independent variables (flow rate, feed pressure, and heat input) and dependent variables (productivity, performance ratio, and recovery ratio). The Pelican Optimization Algorithm (POA) is then used to identify the optimal values of the independent variables for maximum productivity and performance. The results show that using a series configuration yields a system productivity of 3984.2 kg/hr, a performance ratio of 3.78, and a recovery ratio of 0.991 at a feed flow rate of 4000 kg/hr, feed pressure of 3 bars, and heat input of 719 kW. Optimal productivity (4421 kg/hr), performance ratio (3.81), and recovery ratio (0.851) are achieved at a feed flow rate of 5166 kg/hr, feed pressure of 3.2 bars, and heat input of 794 kW. The techno-economic assessment indicates a levelized cost of water of 1.63 USD/m3 for parallel configurations and 1.65 USD/m3 for series configurations, with a payback period of less than two years.

Response surface method-based efficient approach for the load and resistance factors calibration of breakwater foundation considering soil spatial variability

This study aims to propose an efficient approach to calibrate the load and resistance factors (LRFs) for the limit state design of breakwater foundations. The spatial variability of soil was considered by employing the Cholesky decomposition technique. To overcome the low computational efficiency of the Monte Carlo simulation (MCS), a response surface method (RSM) was utilized. Additionally, an efficient algorithm, namely the adaptive boundary-based particle filtering algorithm (ABB-PFA), was proposed with the use of RSM to automatically and quickly search optimal nominal values of random variables in the LRF calibration process. Noticeably, the optimal number of particles and values of the modification factor used in the ABB-PFA were observed to enhance the efficiency of the calibration process. The results showed that the computational efficiency of the calibration process is significantly increased by utilizing the response surface method and the proposed ABB-PFA. The efficiency increases the most if the number of particles is one and the value of the modification factor is 0.5 or 0.6. The influences of the random field element size on the efficiency of the calibration process were also investigated. By using the appropriate size, the efficiency is significantly increased while still obtaining a high accuracy of the LRFs calibration process.

Post-reflux swallow-induced peristaltic wave index and mean nocturnal baseline impedance predict proton pump inhibitor response in patients with reflux hypersensitivity.

Post-reflux swallow-induced peristaltic wave (PSPW) index and mean nocturnal baseline impedance (MNBI) have been shown to influence proton pump inhibitor (PPI) response in GERD patients. However, currently, little data concerning these variables in patients with reflux hypersensitivity (RH) are available. In this study, we aimed to evaluate, in RH patients, the prevalence of PPI responders and nonresponders and investigate the predictive value of impedance-pH variables, including PSPW and MNBI, on responses to PPI. A total of 108 RH patients who met ROME IV criteria were prospectively recruited from June 2018 to December 2022. The prevalence of PPI responders/nonresponders was calculated, and impedance-pH variables were compared between the response and nonresponse groups. Multiple logistic regression was used to investigate predictors for PPI response. Among 108 patients with RH, 60 patients (55.56%) were the PPI responders, and 48 (44.44%) were the nonresponders. Compared with the nonresponders, the PPI responders had a lower PSPW index (47.05±4.43 vs 51.33±3.50, P=0.004) and a decreased value of MNBI (1866.68±390.62 vs 2181.14±338.42, P=0.017). Multivariate logistic regression revealed that only the pathologic PSPW index (OR: 2.064) and MNBI (OR: 1.800) significantly influenced PPI response. Nearly half of RH patients were PPI nonresponders. Impedance-pH monitoring was more valuable than pH-only monitoring in associating PPI response to reflux in RH patients owing to the appraisal of the PSPW index and MNBI.

Regression without regrets –initial data analysis is a prerequisite for multivariable regression

Statistical regression models are used for predicting outcomes based on the values of some predictor variables or for describing the association of an outcome with predictors. With a data set at hand, a regression model can be easily fit with standard software packages. This bears the risk that data analysts may rush to perform sophisticated analyses without sufficient knowledge of basic properties, associations in and errors of their data, leading to wrong interpretation and presentation of the modeling results that lacks clarity. Ignorance about special features of the data such as redundancies or particular distributions may even invalidate the chosen analysis strategy. Initial data analysis (IDA) is prerequisite to regression analyses as it provides knowledge about the data needed to confirm the appropriateness of or to refine a chosen model building strategy, to interpret the modeling results correctly, and to guide the presentation of modeling results. In order to facilitate reproducibility, IDA needs to be preplanned, an IDA plan should be included in the general statistical analysis plan of a research project, and results should be well documented. Biased statistical inference of the final regression model can be minimized if IDA abstains from evaluating associations of outcome and predictors, a key principle of IDA. We give advice on which aspects to consider in an IDA plan for data screening in the context of regression modeling to supplement the statistical analysis plan. We illustrate this IDA plan for data screening in an example of a typical diagnostic modeling project and give recommendations for data visualizations.

Drying the mystery: a novel electronic sensor to quantify soft-tissue desiccation and natural mummification for forensic taphonomy

This study investigates the desiccation process of soft-tissue in South Africa, analyzing its interaction with environmental parameters and its implications for estimating the post-mortem interval. Through the examination of four decomposing porcine bodies across two summer seasons and one winter season, the research quantifies desiccation patterns using custom-designed and constructed printed circuit boards to measure the moisture content of body tissue over time. Generalized additive models were used to determine the environmental forces driving desiccation. Tissue resistivity was tested against the environmental predictor variables to determine the amount of variation they account for, and predicted values of the region-specific tissue resistivity variables were measured for each decomposing body. Results reveal distinct desiccation trajectories between summer and winter, with summer conditions conducive to precocious natural mummification. Environmental factors, particularly temperature and solar radiation, emerge as significant drivers of desiccation. This study represents the first quantitative analysis of deep tissue desiccation internationally, but also the first quantitative assessment of desiccation and natural precocious mummification in the Western Cape, South Africa. The exploration of desiccation as a potential indicator for estimating PMI opens new avenues for research and the integration of innovative methodologies and technologies promises to revolutionize forensic taphonomy practices.

Developing mapping algorithms to predict EQ-5D health utility values from Bath Ankylosing Spondylitis Disease Activity Index and Bath Ankylosing Spondylitis Functional Index among patients with Ankylosing Spondylitis

BackgroundPreference-based measures of health-related quality of life (HRQoL), such as the EQ-5D or the SF-6D, are essential for health economic evaluation. However, they are rarely included in clinical trials of ankylosing spondylitis (AS). This study aims to develop mapping algorithms to predict EQ-5D-3L and EQ-5D-5L health utility scores from the Bath Ankylosing Disease Activity Index (BASDAI) and the Bath Ankylosing Spondylitis Functional Index (BASFI).MethodsPatients with AS were recruited from the largest tertiary hospital in Shandong province, China, between December 2019 and October 2020. Patients were selected by convenience sampling method according to the following criteria: (1) diagnosed with AS according to the New York criteria; (2) aged 18 years and above; and (3) without mental disorders; (4) able to understand the questionnaires; (5) without serious complications. There were 243 patients who completed the face-to-face questionnaire survey, and 5 cases with missing values in key variables were excluded. Ordinary least squares, censored least absolute deviations, Tobit, adjusted limited dependent variable mixture model and beta-mixture model (BM) in the direct approach and ordered logit and multinomial logit (Mlogit) model in the response approach were used to develop mapping algorithms. Mean absolute error, root mean square error, Spearman’s correlation coefficient and concordance correlation coefficient were used to access predictive performance.ResultsThe 238 patients with AS had a mean age of 35.19 (SD = 9.59) years, and the majority (74.47%) were male. The observed EQ-5D-3L and EQ-5D-5L health utility values were 0.88 (SD = 0.12) and 0.74 (SD = 0.27), respectively. The EQ-5D-5L had higher conceptual overlap with the BASDAI and BASFI than the EQ-5D-3L did. The Mlogit was the best-performing model for the EQ-5D-3L, and the BM showed better performance in predicting EQ-5D-5L than other direct and indirect mapping models did.ConclusionThis study demonstrates that the EQ-5D-5L, rather than EQ-5D-3L, should be selected as the target outcome measure of HRQoL in patients with AS in China, and the BM mapping algorithm could be used to predict EQ-5D-5L values from BASDAI and BASFI for health economic evaluation.

Multi-attribute optimization of eco-manufacturing based criteria in CNC manufacturing systems using an evolutionary algorithm

Manufacturing technology has evolved over the years with the development of CNC manufacturing systems, flexible manufacturing, rapid prototyping, smart manufacturing etc Simultaneously, the development of new and exotic materials to match specific requirements has shaped new problems in manufacturing. The materials developed thus far require special tools, lubricating agents, and extra-care in machining without compromising the quality. Further the study of carbon emissions in manufacturing sector has also gained unusual spotlight in view of their deleterious effect on the ecological balance. The manufacturing systems have to be consequently designed and developed, such that they generate minimal quantity of emissions without forfeiting the prime objectives of quality and tool morphology. The present work is principally intended to analyse the effect of cutting parameters on the emission rate of greenhouse gases, tool wear and work-piece temperature concurrently. These studies are accomplished in both dry and wet conditions on computer numerical control machining system. The machining process involved plain facing of a Ti-6Al-4V hardened material. The experimental studies are realized using both single point and multi-point cutting tools and are supplemented with the application of Multi-Objective Genetic Algorithm (MOGA). The MOGA generated set of pareto-fronts for the four machining conditions were subjected to VIKOR, TOPSIS and LINMAP decision making approaches to arrive at the optimum values of decision variables. The optimum cutting parameters obtained in single point cutting tool machining in dry conditions are speed (873.2 rpm), feed (0.199 mm rev−1) and depth of cut (0.25 mm), while the corresponding values of responses are tool wear (67.19 μm), work-piece temperature (39.36 °C) and carbon emission (0.138 Kg-CO2). The equivalent values for multi-point cutting were determined as 899.8 rpm, 0.195 mm rev−1 and 0.25 mm, while the responses for these optimal conditions are 69.92 μm, 39.48 °C and 0.137 Kg-CO2 in that order.

Ferulic acid-g-tamarind gum/guar gum based in situ gel-forming powders as wound dressings

The current research endeavour aimed to synthesize ferulic acid grafted tamarind gum/guar gum (FA-g-TG/GG) based powders as wound dressings, which could form in situ gels upon contact with wound exudates. In this context, variable amounts of FA were initially grafted with TG via the Steglich esterification reaction protocol and the resulting conjugates were subsequently amalgamated with GG and lyophilized to produce dry powders (F-1 – –F-3) with average particle size within 5.10–5.54 μm and average angle of repose ∼30°. These powders were structurally characterized with 1H NMR, FTIR, DSC, TGA, XRD and SEM analyses. Pristine TG, FA-g-TG and FA-g-TG/GG powders (F-2) revealed their distinct morphological structures and variable negative zeta potential values (−11.06 mV–25.50 mV). Among various formulation (F-1–F-3), F-2 demonstrated an acceptable powder-to-gel conversion time (within 20 min), suitable water vapour transmission rates (WVTR, 2564.94 ± 32.47 g/m2/day) and excellent water retention abilities and swelling profiles (4559.00 ± 41.57 %) in wound fluid. The powders were cytocompatible and conferred antioxidant activities. The powders also displayed fibroblast cell proliferation, migration and adhesion properties, implying their wound-healing potentials. Thus, the developed in situ gel-forming powders could be employed as promising dressings for wound management.

Joint probability analysis and mapping of typhoon-induced wind, wave, and surge hazards along southeast China

The southeastern coastal region of China frequently faces typhoon attacks, posing significant threats to its ever-growing infrastructure. Quantifying multiple typhoon-induced hazards in terms of wind, wave, and surge would provide valuable data to support structural design, risk assessments, and disaster mitigation. This study simulated the evolution of wave and surge driven by typhoon wind fields using the SWAN + ADCIRC coupling model, generating extreme samples of wind speed, significant wave height, and surge height. Trivariate joint probability distributions were established at six typical coastal stations (Hong Kong, Xiamen, Fuzhou, Wenzhou, Zhoushan, and Shanghai) based on optimal copula functions. Using the environmental contour and conditional return period methods, appropriate combinations of design environmental loads were analyzed for a specific return period. The results revealed two reasonable combination types for the 100-year design period. One involves the combination of the 100-year dominant variable value computed using a univariate probability distribution, with two 20-year secondary variable values calculated using the second conditional return period. The second combination selects a variable group corresponding to the maximum value of the dominant variable on the environmental surface. These methods and findings were further extended to the entire computational area to develop hazard maps covering the Southeast Sea area of China. The hazard maps show that the design values of significant wave height increase with distance from the coast. In contrast, the spatial distribution of surge presents the opposite pattern. This study provides a novel perspective on structural design values subjected to multiple disasters and offers a reference for offshore engineering construction in the sea area of southeast China.

Application of a Novel Dissolution Medium with Lipids for In Vitro Simulation of the Postprandial Gastric Content.

Food can change various physiological parameters along the gastrointestinal tract, potentially impacting postprandial drug absorption. It is thus important to consider different in vivo conditions during in vitro studies. Therefore, a novel dissolution medium simulating variable postprandial pH values and lipid concentrations was developed and used in this study. Additionally, by establishing and validating a suitable analytical method, the effects of these parameters on the dissolution of a model drug, cinnarizine, and on its distribution between the lipid and aqueous phases of the medium were studied. Both parameters, pH value and lipid concentration, were shown to influence cinnarizine behavior in the in vitro dissolution studies. The amount of dissolved drug decreased with increasing pH due to cinnarizine's decreasing solubility. At pH values 5 and 7, the higher concentration of lipids in the medium increased drug dissolution, and most of the dissolved drug was distributed in the lipid phase. In all media with a lower pH of 3, dissolution was fast and complete, with a significant amount of drug distributed in the lipid phase. These results are in accordance with the in vivo observed positive food effect on cinnarizine bioavailability described in the literature. The developed medium, with its ability to easily adjust the pH level and lipid concentration, thus offers a promising tool for assessing the effect of co-ingested food on the dissolution kinetics of poorly soluble drugs.

Multivariable Mendelian randomization with incomplete measurements on the exposure variables in the Hispanic Community Health Study/Study of Latinos

Multivariable Mendelian randomization allows simultaneous estimation of direct causal effects of multiple exposure variables on an outcome. When the exposure variables of interest are quantitative omic features, obtaining complete data can be economically and technically challenging: the measurement cost is high, and the measurement devices may have inherent detection limits. In this paper, we propose a valid and efficient method to handle unmeasured and undetectable values of the exposure variables in a one-sample multivariable Mendelian randomization analysis with individual-level data. We estimate the direct causal effects with maximum likelihood estimation and develop an expectation-maximization algorithm to compute the estimators. We show the advantages of the proposed method through simulation studies and provide an application to the Hispanic Community Health Study/Study of Latinos, which has a large amount of unmeasured exposure data.

Prognostic Value of Magnetic Resonance Imaging Variables Combined with Neutrophil-to-Lymphocyte Ratio in Patients with Cervical Traumatic Spinal Cord Injury

We aimed to explore the prognostic significance of preoperative magnetic resonance imaging (MRI) variables and novel inflammatory indicators in predicting neurological recovery post-cervical traumatic spinal cord injury (TSCI) in the study. We enrolled a total of 244 patients diagnosed with acute cervical TSCI from two hospitals and evaluated the prognostic value of MRI variables (intramedullary hemorrhage, intramedullary lesion length (IMLL), maximum spinal cord compression (MSCC), and maximum canal compromise (MCC)) and novel inflammatory indicators (neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR), and systemic immune-inflammatory index (SII)) in patients with acute cervical TSCI. Among the 244 patients, 140 (57.38%) exhibited improved AIS grade conversion at 1-year follow-up. The results revealed intramedullary hemorrhage, IMLL, MCC, neutrophils, and NLR were significantly different compared with follow-up AIS grade. Furthermore, IMLL, MCC, WBCs, neutrophils, NLR, and LMR correlated with the follow-up AIS grade by Spearman’s correlation analysis. Multivariate analysis showed IMLL, intramedullary hemorrhage, NLR and admission AIS grade emerged as independent predictors of AIS grade conversion. The receiver operating characteristic curve (ROC) analysis showed that the novel model (combination of MRI variables, NLR and admission AIS grade) produced a larger area under the curve compared with using only intramedullary hemorrhage, IMLL, NLR or admission AIS grade individually. In conclusion, intramedullary hemorrhage and IMLL and NLR are predictors of AIS grade conversion after cervical TSCI. Therefore, we suggest the combination of MRI variables and NLR for the prognostic prediction of AIS grade conversion in patients with cervical TSCI.

Effective shape of ball-and-socket prosthesis in restoring range of thumb motion for total thumb carpometacarpal joint arthroplasty: three-dimensional motion analysis

PurposeTotal joint arthroplasty (TJA) has often been used to treat thumb carpometacarpal (CMC) osteoarthritis (OA). However, guidelines for the CMC prosthesis shape remain unclear. This study aimed to identify the effective shape of a ball-and-socket prosthesis in restoring the range of thumb motion after TJA.MethodsThe participants were 10 healthy young adult men (22–32 years; 26.8 ± 3.57 [mean ± SD]). CT scans were performed in eight static limb positions during abduction and flexion. We defined three design variables (offset R, height H, and neck rotation angle Φ) as the variables that determine the basic shape of the ball-and-socket prosthesis. The ideal values of these design variables were examined based on the results of a 3D motion analysis, which evaluated the change in the posture of the first metacarpal (r, h, and φ corresponding to R, H, and Φ, respectively) relative to the center of rotation (COR) during abduction and flexion. We also simulated the effect of these design variables on the range of thumb motion after TJA using 3D CAD.ResultsWe found that the values of r and h averaged over all limb positions were 6.92 ± 1.60 mm and 51.02 ± 1.67 mm, respectively, showing that these values remained constant regardless of limb position. In contrast, φ changed significantly. The simulation results indicated that Φ affected the range of thumb motion after TJA, and Φ = 0° relatively reproduced all limb positions compared to other values.ConclusionOur results suggested that the desirable values of R and H were the average of r and h over several limb positions and that Φ = 0° was effective in restoring the range of thumb motion after TJA. Our results will provide surgeons with new guidelines for selecting a prosthesis.

The Analysis of Work Fatigue and Proposed Improvement Using the Bourdon Wiersma Method and New Seven Tools

Background Employees of Public Relations work for 8 hours starting at 08.00-16.00, so it can lead to the work fatigue. Work fatigue is the condition of decreased efficiency and resilience of a person at work. Work fatigue can be caused by internal factors and external factors.The research that will be carried out is to analyze work fatigue and proposed improvements using the Bourdon Wiersma method and the New Seven Tools. Methods The method used to solve the problem is the Bourdon Wiersma method and New Seven Tools, using the Bourdon Wiersma Type Test GBT questionnaire. The respondents were 8 employees of the Public Relations division who work using computers. The aim of this research is to determine the level of speed, level of accuracy and level of consistency before and after activities, and work fatigue. Data analysis using several tools from New Seventools, namely Relationship Diagrams and Tree Diagrams. Results Based on research, the average fatigue score for speed before work was found that the average time speed for before work is 9.69 seconds with the value of speed interpretation (value 9, and WS = 14) including group B (Good), for after work was 10.05 seconds with a speed interpretation value (8.5, and WS = 13) including the CB class (Good Enough). The average accuracy for before work is 22.4 seconds with an interpretation accuracy value (value 5, and WS = -) including group R (Doubtful), for after work was 23 with an interpretation accuracy value (value 4.5, and WS = 7) included to group K (less). The average constant for before work was 11.58 with a constant interpretation value (value 5, and WS = -) including group R (Doubtful), for after work was 7.59 seconds with a constant interpretation value (value 5.5, and WS = 8) belongs to group R (Doubtful). Based on the Anova Test, the correlation value on the velocity variable was 0.937 with a very strong correlation level. The correlation value on the accuracy variable was 0.666 with a strong correlation level. The correlation value of the constant variable was 0.797 with a strong correlation level. Conclusions The main cause of fatigue in Public Relations employees is workload. The main cause of burnout in Public Relations employees is workload. The proposed improvement uses a Tree Diagram to reduce fatigue in Public Relations employees.

Multivariate nonlinear analysis of climatological variables and its effect on local temperature.

Atmospheric temperature describes the specific heat content of the air at particular places and times. In this sense, the presented work proposes a multivariable system that takes data sets of different climatological variables, with the aim of regulating the temperature level. The diversity in climatological variables significantly affects precipitation, humidity, wind speed and temperature. Thus, a study has been carried out on these variables in terms of nonlinear dynamics. The aim of the work is to obtain a better understanding of the dynamics of local climatological variables. On the other hand, due to the fact that the time series analyzed are small, the analysis becomes complex, at the moment of joining all the variables in conjunction, and processing them by means of multivariate statistical prediction methodologies. The results have shown the mean values in the different variables with which a temperature is maintained between and , which shows possible work with multi-objective optimization of the obtained model.

Prevalence and influencing factors of sleep disorders in medical students after the COVID-19 pandemic

BackgroundThe prevalence of sleep disorders among medical students was high during the COVID-19 pandemic. However, fewer studies have been conducted on sleep disorders among medical students after the COVID-19 pandemic. This study investigated the prevalence and factors influencing sleep disorders among Chinese medical students after COVID-19.MethodsA total of 1,194 Chinese medical students were included in this study from 9th to 12th July 2023. We used the Self-administered Chinese scale to collect the demographic characteristics. In addition, we used the Chinese versions of the Self-rating Depression Scale (SDS), the Self-rating Anxiety Scale (SAS), and the Pittsburgh Sleep Quality Index (PSQI) to assess subjects’ depression, anxiety, and sleep disorders, respectively. The chi-square test and binary logistic regression were used to identify factors influencing sleep disorders. The receiver operating characteristic (ROC) curve was utilized to assess the predictive value of relevant variables for sleep disorders.ResultsWe found the prevalence of sleep disorders among medical students after COVID-19 was 82.3%. According to logistic regression results, medical students with depression were 1.151 times more likely to have sleep disorders than those without depression (OR = 1.151, 95% CI 1.114 to 1.188). Doctoral students were 1.908 times more likely to have sleep disorders than graduate and undergraduate students (OR = 1.908, 95% CI 1.264 to 2.880).ConclusionThe prevalence of sleep disorders among medical students is high after COVID-19. In addition, high academic levels and depression are risk factors for sleep disorders. Therefore, medical colleges and administrators should pay more attention to sleep disorders in medical students after the COVID-19 pandemic. Regular assessment of sleep disorders and depression is essential.

Demographic regulation processes in Pinna nobilis population subunits: Implications for restocking

A mass mortality event (MME) affected the endemic bivalve Pinna nobilis across the Mediterranean Sea in autumn 2016. This event affected specimens of all sizes, depth ranges and habitat types, resulting in a drastic decline in the species population close to 100%. Indeed, the consequences of a potential recovery, and therefore the viability of the populations of the species are unknown. To evaluate the likelihood of success when designing the reintroduction of the species in optimal areas, knowledge of population dynamics is essential. Here we compare two population subunits of Pinna nobilis from two areas with different environmental conditions, located in Cabrera National Park (protected area with low exposure to hydrodynamic activity) and San Telmo (unprotected area exposed to hydrodynamic activity), both in the Balearic Islands. A total of 259 individuals were tagged and monitored annually between 2011 and 2014, prior to the MME. Matrix population models were used to reduce the complexity of the age-structured population dynamics and assessing the degree of elasticity of the population. We showed that the subunit of San Telmo showed highly variable values of demographic rates, higher stochasticity and higher sensitivity to environmental variability than the population subunit of Cabrera National Park. The study shows the importance of the buffering effect of a large population as there are many adult individuals of many size classes in the population subunit of Cabrera National Park, as its age structure helps to cope with highly variable reproductive success. Therefore, these population subunits are more stable regardless of depth and would be a more suitable site for future reintroduction programs.