Scientific Applications Research Articles

Overcoming Challenges in O-Nitration: Selective Alcohol Nitration Deploying N,6-Dinitrosaccharin and Lewis Acid Catalysis.

Nitrate esters hold pivotal roles in pharmaceuticals, energetic materials, and atmospheric processes, motivating the development of efficient synthesis routes. Here, we present a novel catalytic method for the synthesis of nitrates via the direct O-nitration of alcohols, addressing limitations of current traditional methods. Leveraging bench-stable and recoverable N,6-dinitrosaccharin reagent, our catalytic strategy employs magnesium triflate to achieve mild and selective O-nitration of alcohols, offering broad substrate scope and unprecedented large functional group tolerance (e.g. alkenes, alkynes, carbonyls). DFT mechanistic studies reveal a dual role of the magnesium catalyst in the activation of both the nitrating reagent and the alcohol substrate. They also unveil a barrierless proton transfer upon formation of a widely-accepted - yet elusive in solution - nitrooxonium ion intermediate. Overall, our work contributes to the development of mild, selective, and sustainable approaches to nitrates synthesis, with potential applications in drug discovery, materials science, and environmental chemistry.

Eligibility of Asian and European registry patients for phase III trials in heart failure with reduced ejection fraction.

Traditional approaches to designing clinical trials for heart failure (HF) have historically relied on expertise and past practices. However, the evolving landscape of healthcare, marked by the advent of novel data science applications and increased data availability, offers a compelling opportunity to transition towards a data-driven paradigm in trial design. This research aims to evaluate the scope and determinants of disparities between clinical trials and registries by leveraging natural language processing for the analysis of trial eligibility criteria. The findings contribute to the establishment of a robust design framework for guiding future HF trials. Interventional phase III trials registered for HF on ClinicalTrials.gov as of the end of 2021 were identified. Natural language processing was used to extract and structure the eligibility criteria for quantitative analysis. The most common criteria for HF with reduced ejection fraction (HFrEF) were applied to estimate patient eligibility as a proportion of registry patients in the ASIAN-HF (N=4868) and BIOSTAT-CHF registries (N=2545). Of the 375 phase III trials for HF, 163 HFrEF trials were identified. In these trials, the most frequently encountered inclusion criteria were New York Heart Association (NYHA) functional class (69%), worsening HF (23%), and natriuretic peptides (18%), whereas the most frequent comorbidity-based exclusion criteria were acute coronary syndrome (64%), renal disease (55%), and valvular heart disease (47%). On average, 20% of registry patients were eligible for HFrEF trials. Eligibility distributions did not differ (P=0.18) between Asian [median eligibility 0.20, interquartile range (IQR) 0.08-0.43] and European registry populations (median 0.17, IQR 0.06-0.39). With time, HFrEF trials became more restrictive, where patient eligibility declined from 0.40 in 1985-2005 to 0.19 in 2016-2022 (P=0.03). When frequency among trials is taken into consideration, the eligibility criteria that were most restrictive were prior myocardial infarction, NYHA class, age, and prior HF hospitalization. Based on 14 trial criteria, only one-fifth of registry patients were eligible for phase III HFrEF trials. Overall eligibility rates did not differ between the Asian and European patient cohorts.

Jaynes–Cummings model breaks down when the cavity geometry significantly reduces free-space emission

Strong coupling between a single resonator mode and a single quantum emitter is key to a plethora of experiments and applications in quantum science and technology and is commonly described by means of the Jaynes–Cummings model. Here, we show that the Jaynes–Cummings model only applies when the cavity does not significantly change the emitter’s decay rate into free-space. Most notably, the predictions made by the Jaynes–Cummings model become increasingly wrong when approaching the ideal emitter-resonator systems with no free-space decay channels. We present a Hamiltonian that provides, within the validity range of the rotating wave approximation, a correct theoretical description that applies to all regimes. As minimizing the coupling to free-space modes is paramount for many cavity-based applications, a correct description of strong light-matter interaction is therefore crucial for developing and optimizing quantum protocols.

Effect of Cow Dung and Urban Waste Compost in Reducing the Accumulation of Cadmium (Cd) and Lead (Pb) in Amaranth Grown in Contaminated Soil

Aims: The effect of cow dung and urban waste compost on the capacity of amaranth to absorb cadmium and lead was studied in contaminated soil. Study Design: Amaranth was cultivated in pot trials randomized blocks with five treatments and four replications for each treatment: control; contaminated soil; contaminated soil with cow dung; contaminated soil with compost and contaminated soil with cow dung and compost. Place and Duration of Study: The trial was carried out in laboratory conditions in the Research Institute for Applied Sciences and Technologies in Ouagadougou from march to April 2022. Methodology: Lead and cadmium concentrations in amaranth leaves and stems was determined using atomic emission spectrometry (MP-AES) after acid digestion. Results: The results showed that contaminated soil with 5 mg kg-1 of lead and 0.2 mg kg-1 of cadmium had no significant effect on amaranth growth. In the dried leaves, mean cadmium levels were 93.5 mg kg-1 in the absence of cow dung and 4.14 mg kg-1 in the presence of cow dung. Mean cadmium levels in dry stems were 64 mg kg-1 and 2.1 mg kg-1 respectively in the absence and presence of cow dung. Lead uptake did not vary significantly in the presence of amendments (0.44 mg kg-1) or in absence of amendments (0.75 mg kg-1) in the stems. The cow dung treatment was more effective than the compost treatment. However, our results showed that the two amendments reduced cadmium transfer by 90% and lead transfer by 70% to amaranth.

Sensitivity analysis of space-based water vapor differential absorption lidar at 823 nm

Measurements of water vapor are important for understanding the hydrological cycle, the thermodynamic structure of the lower troposphere, and broader atmospheric circulation. Subsequently, many scientific communities have emphasized a need for high-accuracy and spatial resolution profiles of water vapor within and above the planetary boundary layer (PBL). Advancements in lidar technologies at the NASA Langley Research Center are ongoing to enable the first space-based water vapor differential absorption lidar (DIAL) that can provide high-accuracy and vertical resolution retrievals of moisture in the PBL and through the mid-troposphere. The performance of this space-based DIAL is assessed here for sensitivity throughout the troposphere and globally with representative canonical cases of water vapor and aerosol loading. The specific humidity retrieval sensitivity to systematic and random errors is assessed, and measurement resolutions and capabilities are provided. We show that tunable operation along the side of the 823-nm absorption line allows for the optimization of the lower-tropospheric water vapor retrievals across different meteorological regimes and latitudes and provides the operational flexibility needed to dynamically optimize random errors for different scientific applications. The analysis presented here suggests that baseline and threshold systematic error requirements of <1.5% and <2.5%, respectively, are achievable. Random error is shown to dominate the retrieval, with errors on the order of 5% within the PBL being achievable with 300-m vertical 50-km horizontal resolutions over open ocean and on the order of 10%–15% over high-albedo surfaces. The flexibility of the DIAL method to trade retrieval precision for spatial resolution is shown, highlighting its strengths over passive techniques to tailor retrievals to different scientific applications. Combined, the total error budget demonstrated here indicates a high impact for space-based DIAL, with technologies being advanced for space missions within the next 5–10 years.

Tunable Radiation Patterns on Temperature-Dependent Materials

The utilization of optical antennas for active control of far-field radiation at the subwavelength scale is crucial in various scientific and technological applications. We propose a thermally tunable disk design of indium tin oxide (ITO) and aluminum gallium nitride (Al0.18Ga0.82As), enabling a switch between absorption and scattering. Furthermore, the control of far-field radiation pattern can be easily realized by combining ITO and Al0.18Ga0.82As to enhance or suppress emission. Our results demonstrate that hybrid structures can be dynamically tuned with temperature variations. In the proposed design, a frequency is achieved at the wavelength of 1240 nm. The thermal tunability of hybrid structures introduces new multifunctional possibilities for light manipulation, thereby enhancing the potential applications of new devices in the near-infrared range.

Tetrafluoroisopropylation of alkenes and alkynes enabled by photocatalytic consecutive difluoromethylation with CF2HSO2Na

Direct assembly of complex fluorinated motifs from simple fluorine sources is an attractive frontier of synthetic chemistry. Reported herein is an unconventional protocol for achieving tetrafluoroisopropylation by using commercially available CF2HSO2Na as a convenient source of the tetrafluoroisopropyl [(CF2H)2CH] group, which finds widespread applications in life science and material science. Visible-light-induced hydrotetrafluoroisopropylation of alkenes and carbotetrafluoroisopropylation of alkynes have been thus developed. Various structurally diverse α-tetrafluoroisopropyl carbonyls and cyclopentanones are selectively constructed under mild conditions. A photocatalytic triple difluoromethylation cascade, driven by consecutive reductive radical/polar crossover processes, leads to the direct assembly of a tetrafluoroisopropyl moiety from CF2HSO2Na. This C1-to-C3 fluoroalkylation protocol provides a practical strategy for the rapid construction of polyfluorinated compounds that are otherwise difficult to access, thus significantly enhancing the boundary of fluoroalkylation chemistry.

Kinetically Controlled and Nonequilibrium Assembly of Block Copolymers in Solution.

Covalent polymers are versatile macromolecules that have found widespread use in society. Contemporary methods of polymerization have made it possible to construct sequence polymers, including block copolymers, with high precision. Such copolymers assemble in solution when the blocks have differing solubilities. This produces nano- and microparticles of various shapes and sizes. While it is straightforward to draw an analogy between such amphiphilic block copolymers and phospholipids, these two classes of molecules show quite different assembly characteristics. In particular, block copolymers often assemble under kinetic control, thus producing nonequilibrium structures. This leads to a rich variety of behaviors being observed in block copolymer assembly, such as pathway dependence (e.g., thermal history), nonergodicity and responsiveness. The dynamics of polymer assemblies can be readily controlled using changes in environmental conditions and/or integrating functional groups situated on polymers with external chemical reactions. This perspective highlights that kinetic control is both pervasive and a useful attribute in the mechanics of block copolymer assembly. Recent examples are highlighted in order to show that toggling between static and dynamic behavior can be used to generate, manipulate and dismantle nonequilibrium states. New methods to control the kinetics of block copolymer assembly will provide endless unanticipated applications in materials science, biomimicry and medicine.

Application of Artificial Intelligence in Rehabilitation Science: A Scientometric Investigation Utilizing Citespace

This study presents a scientometric analysis of the intersection between rehabilitation science and artificial intelligence (AI) technologies, using data from the Web of Science (WOS) database from 2002 to 2022. The analysis employed a comprehensive search query with key AI-related terms, focusing on a wide range of publications in rehabilitation science. Utilizing the Citespace tool, the study visualizes and quantifies the relationships between key terms, identifies research trends, and assesses the impact of AI technologies in rehabilitation science. Findings reveal a significant increase in AI-related research in this field, particularly from 2017 onwards, peaking in 2021. The United States has been a leading contributor, followed by countries like England, Australia, Germany, and Canada. Major institutional contributions come from Harvard University and the Pennsylvania Commonwealth System of Higher Education, among others. A keyword co-occurrence network constructed through Citespace identifies nine distinct hot topics and various research frontiers, highlighting evolving focus areas within the field. Burst analysis of keywords indicates a shift from performance and injury-related research to an increasing emphasis on AI and deep learning in recent years. The study also predicts the potential impact of papers, spotlighting works by Kunze KN and others as significantly influencing future research directions. Additionally, it examines the evolution of knowledge bases in AI-related rehabilitation science research, revealing a multidisciplinary core that includes neurology, rehabilitation, and ophthalmology, extending to complementary fields such as medicine and social sciences. This scientometric analysis provides a comprehensive overview of AI's application in rehabilitation science, offering insights into its evolution, impact, and emerging trends over the past two decades. The findings suggest strategic directions for future research, policy-making, and interdisciplinary collaboration in rehabilitation science and AI.

Ilmu Interdisipliner dan Multidisipliner dalam Manajemen Pendidikan Islam

Problems related to Islamic education management can not only be solved with one solution. However, with the application of interdisciplinary and multidisciplinary science, problem solutions become more diverse and comprehensive. This study aims to explain how the application and implications of interdisciplinary and multidisciplinary science in Islamic education curriculum management. The research uses a qualitative approach with the library research method. The results of the analysis show that the importance of an interdisciplinary and multidisciplinary approach in the study of the Islamic education management curriculum, especially in facing current national and global issues

Smartphone-Based Citizen Science Tool for Plant Disease and Insect Pest Detection Using Artificial Intelligence

In recent years, the integration of smartphone technology with novel sensing technologies, Artificial Intelligence (AI), and Deep Learning (DL) algorithms has revolutionized crop pest and disease surveillance. Efficient and accurate diagnosis is crucial to mitigate substantial economic losses in agriculture caused by diseases and pests. An innovative Apple® and Android™ mobile application for citizen science has been developed, to enable real-time detection and identification of plant leaf diseases and pests, minimizing their impact on horticulture, viticulture, and olive cultivation. Leveraging DL algorithms, this application facilitates efficient data collection on crop pests and diseases, supporting crop yield protection and cost reduction in alignment with the Green Deal goal for 2030 by reducing pesticide use. The proposed citizen science tool involves all Farm to Fork stakeholders and farm citizens in minimizing damage to plant health by insect and fungal diseases. It utilizes comprehensive datasets, including images of various diseases and insects, within a robust Decision Support System (DSS) where DL models operate. The DSS connects directly with users, allowing them to upload crop pest data via the mobile application, providing data-driven support and information. The application stands out for its scalability and interoperability, enabling the continuous integration of new data to enhance its capabilities. It supports AI-based imaging analysis of quarantine pests, invasive alien species, and emerging and native pests, thereby aiding post-border surveillance programs. The mobile application, developed using a Python-based REST API, PostgreSQL, and Keycloak, has been field-tested, demonstrating its effectiveness in real-world agriculture scenarios, such as detecting Tuta absoluta (Meyrick) infestation in tomato cultivations. The outcomes of this study in T. absoluta detection serve as a showcase scenario for the proposed citizen science tool’s applicability and usability, demonstrating a 70.2% accuracy (mAP50) utilizing advanced DL models. Notably, during field testing, the model achieved detection confidence levels of up to 87%, enhancing pest management practices.

Artificial intelligence empowering public health education: prospects and challenges

Artificial Intelligence (AI) is revolutionizing public health education through its capacity for intricate analysis of large-scale health datasets and the tailored dissemination of health-related information and interventions. This article conducts a profound exploration into the integration of AI within public health, accentuating its scientific foundations, prospective progress, and practical application scenarios. It underscores the transformative potential of AI in crafting individualized educational programs, developing sophisticated behavioral models, and informing the creation of health policies. The manuscript strives to thoroughly evaluate the extant landscape of AI applications in public health, scrutinizing critical challenges such as the propensity for data bias and the imperative of safeguarding privacy. By dissecting these issues, the article contributes to the conversation on how AI can be harnessed responsibly and effectively, ensuring that its application in public health education is both ethically grounded and equitable. The paper’s significance is multifold: it aims to provide a blueprint for policy formulation, offer actionable insights for public health authorities, and catalyze the progression of health interventions toward increasingly sophisticated and precise approaches. Ultimately, this research anticipates fostering an environment where AI not only augments public health education but also does so with a steadfast commitment to the principles of justice and inclusivity, thereby elevating the standard and reach of health education initiatives globally.

Individual quantification of ozone and reactive nitrogen species in mixtures by broadband UV–visible absorption spectra deconvolution

Abstract Ozone (O3), nitrogen oxides (NOx), and reactive nitrogen species (RNS) play critical roles in atmospheric-pressure plasma applications. Although it is crucial to individually quantify these species to understand atmospheric-pressure plasmas and increase their effectiveness, the lack of reliable and cost-effective diagnostics makes this difficult for many researchers. To address this problem, we introduce a new deconvolution method of broadband ultraviolet–visible absorption spectra for the simultaneous measurement of eight species—O3, NO, NO2, NO3, N2O4, N2O5, HONO, and HNO3. Processing of broadband spectra enables deconvolution of similar cross-section profiles and measurement of high densities exceeding the instrumental limit. Novel correction processes enable accurate analysis despite incomplete cross-section data and utilize a priori chemical knowledge to ensure theoretically reasonable results. Two case studies test the efficacy of the method: NO2 and N2O4 equilibria, and reactive species produced by a surface dielectric barrier discharge. With an analysis time of 15–20 ms per spectrum, the measured densities agree well with other theoretical and experimental results, and detection limits on the order of ppmv were achieved with a short path length of 15 cm. This spectral analysis method will facilitate the real-time monitoring of O3, NOx, and RNS in many scientific research and industrial applications of atmospheric pressure plasmas.

Of study enthusiasts and homebirds: students’ everyday mobility and sustainability dilemmas in online higher education

PurposeOnline education enables location-independent learning, potentially providing university students with more flexible study programs and reducing traffic-related CO2 emissions. This paper aims to examine whether online education can contribute to university-related sustainable everyday mobility, with particular consideration given to aspects of social sustainability and potential rebound effects. Specifically, it explores sustainability dilemmas that arise from conflicting social and ecological effects.Design/methodology/approachDrawing on qualitative data from mobility diaries and extensive semistructured interviews (n = 26) collected at Osnabrück University of Applied Sciences in Germany, this study deploys thematic analysis and a typification approach to analyze and classify students’ daily practices related to studying, mobility and dwelling, which may be impacted by online education.FindingsThe study identifies six distinct student types with diverse practices in studying, mobility and dwelling. Comparisons between student types reveal stark differences regarding professional and social goals that students associate with their studies, influencing university-related mobility and residential choices. This leads to varying assessments of online education, with some students expecting benefits and others anticipating severe drawbacks.Practical implicationsThe typology developed in this paper can assist Higher Education Institutions (HEIs) in comparable contexts in understanding the distinct needs and motivations of students, thereby proactively identifying sustainability dilemmas associated with online education. By leveraging these findings, HEIs can effectively balance diverse interests and contribute meaningfully to sustainability.Originality/valueTo the best of the authors’ knowledge, this study is among the first to systematically investigate conflicts and rebound effects of online education in the context of sustainable mobility within HEIs.

Effects of a staff-led multicomponent physical activity intervention on preschooler's fundamental motor skills and physical fitness: The ACTNOW cluster-randomized controlled trial

BackgroundFundamental motor skills (FMS) and physical fitness (FIT) play important roles in child development and provide a foundation for lifelong participation in physical activity (PA). Unfortunately, many children have suboptimal levels of PA, FMS, and FIT. The Active Learning Norwegian Preschool(er)s (ACTNOW) study investigated the effects of a staff-led PA intervention on FMS, FIT, and PA in 3–5-year-old children.MethodsPreschools in Western Norway having ≥ six 3–4-year-old children were invited (n = 56). Of these, 46 agreed to participate and were cluster-randomized into an intervention (n = 23 preschools [381 children, 3.8 yrs., 55% boys]) or a control group (n = 23 [438, 3.7 yrs., 52% boys]). Intervention preschools participated in an 18-month PA intervention involving a 7-month staff professional development between 2019 and 2022, amounting to 50 h, including face-to-face seminars, webinars, and digital lectures. Primary outcomes in ACTNOW were cognition variables, whereas this study investigated effects on secondary outcomes. FMS was measured through 9 items covering locomotor, object control, and balance skills. FIT was assessed as motor fitness (4 × 10 shuttle-run test) and upper and lower muscular strength (handgrip and standing long jump). PA was measured with accelerometers (ActiGraph GT3X +). All measures took place at baseline, 7-, and 18-month follow-up. Effects were analysed using a repeated measures linear mixed model with child and preschool as random effects and with adjustment for baseline scores.ResultsParticipants in the intervention preschools showed positive, significant effects for object control skills at 7 months (standardized effect size (ES) = 0.17) and locomotor skills at 18 months (ES = 0.21) relative to controls. A negative effect was found for handgrip strength (ES = -0.16) at 7 months. No effects were found for balance skills, standing long jump, or motor fitness. During preschool hours, sedentary time decreased (ES = -0.18), and light (ES = 0.14) and moderate-to-vigorous PA (ES = 0.16) increased at 7 months, whereas light PA decreased at 18 months (ES = -0.15), for intervention vs control. No effects were found for other intensities or full day PA.ConclusionsThe ACTNOW intervention improved some FMS outcomes and increased PA short-term. Further research is needed to investigate how to improve effectiveness of staff-led PA interventions and achieve sustainable improvements in children’s PA, FMS, and FIT.Trial registrationClinicaltrials.gov, identifier NCT04048967, registered August 7, 2019.FundingACTNOW was supported by the Research Council of Norway (grant number 287903), the County Governor of Sogn og Fjordane, the Sparebanken Sogn og Fjordane Foundation, and the Western Norway University of Applied Sciences.

The use of the Isolation Forest model for anomaly detection in measurement data

The subject of the research is the Isolation Forest model, which is a powerful and efficient tool for detecting anomalies in measurement data and outliers, applicable in various fields where ensuring high accuracy and reliability of measurements is important. The goal of the study is to apply the Isolation Forest model to identify unusual or anomalous patterns that differ from typical patterns in the output data. This is achieved by isolating anomalous patterns from normal ones through the construction of multiple different decision trees. The task of the research is to detect outliers in data obtained during the preparation for international comparisons on the state primary standard for mass and volume flow rate of fluid, mass and volume of fluid flowing through a pipeline, by measuring with a сoriolis flowmeter. Data collected during metrological studies undergo processing by the model to detect anomalies. This model analyzes the data and identifies anomalous or outlier values that may indicate systematic or random measurement errors. It enables quick and efficient detection of even the smallest deviations in the data, helping to maintain high accuracy and reliability of measurement results. The main methods for detecting outliers in statistical analysis, which are distribution-independent, are the Grubbs' criterion, interquartile range distribution, and standard deviation. They are sensitive to sample size but are simple and understandable tools. However, the Isolation Forest model also has its limitations, particularly it can be resource-demanding for large datasets. Additionally, it is necessary to consider that using the model requires proper parameter tuning to achieve optimal results. The results of the research include assessment of the Isolation Forest model's effectiveness by comparing it with traditional outlier detection methods. Comparative analysis of the results of different approaches to the same task is an effective method for evaluating the model's performance. Conclusion. The article concludes with the perspective of further research development in this direction. The work will focus on further developing methods for detecting anomalies in measurement data and improving the accuracy and reliability of measurement results in various application fields, which can find broad applications in science and industry.

ON MURRAY LAW FOR OPTIMAL BRANCHING RATIO

Tree-like branching networks are widespread in nature and have found wide applications in engineering, where Murray’s law is generally adopted to optimally design tree-like systems, but it may become invalid in some cases. Here we give an energy approach to the analysis of the law and re-find Li–Yu’s law for the optimal ratio of the square root of 2 with a suitable constraint. When the cross-section of each branch is considered as a fractal pattern, a modified Murray’s law is obtained, which includes the original Murray’s law for a Peano-like pore and Li–Yu’s law for cylindrical branches, furthermore a useful relationship between the diameter and length of each hierarchy is obtained, which is contrary to the tree-like fractal patterns, and the new hierarchy is named as “fractal Murray tree”, which also has many potential applications in science, engineering, social science and economics. This paper is intended to serve as a foundation for further research into the fractal Murray tree and its applications in various fields.

Proteo: a framework for the generation and evaluation of malleable MPI applications

AbstractApplying malleability to HPC systems can increase their productivity without degrading or even improving the performance of running applications. This paper presents Proteo, a configurable framework that allows to design benchmarks to study the effect of malleability on a system, and also incorporates malleability into a real application. Proteo consists of two modules: SAM allows to emulate the computational behavior of iterative scientific MPI applications, and MaM is able to reconfigure a job during execution, adjusting the number of processes, redistributing data, and resuming execution. An in-depth study of all the possibilities shows that Proteo is able to behave like a real malleable or non-malleable application in the range [0.85, 1.15]. Furthermore, the different methods defined in MaM for process management and data redistribution are analyzed, concluding that asynchronous malleability, where reconfiguration and application execution overlap, results in a 1.15$$\times$$ × speedup.

Identification of ECA rules forming MACA in periodic boundary condition

Cellular automaton (CA) is a computing model which is emerging rapidly. It is largely used in different types of scientific applications and simulations due to its ability to solve complex problems using simple rule(s). Cellular automata (CAs) are used in different types of applications like cryptography, VLSI systems, fault detection, etc. Typically, most of these applications utilize one-dimensional, 2-state, 3-neighborhood CAs. This paper explores the concept of Next State RMT Transition Diagram (NSRTD) for characterization of all the Elementary Cellular Automata (ECA) rules in periodic boundary condition leading to the identification of all ECA rules forming more than two fixed points (referred to as Single Length Cycle Multi-Attractor CA (MACA)) for an arbitrary CA length (n). For this, the 88 Wolfram classification rules and their equivalent rules have been utilized to reduce the search complexity by avoiding exhaustive searching on all the 256 ECA rules.

Reliability of the Croatian Version of Motivation Scale for Free Time

Aim: The Motivation scale for free time was developed to measure motivation for activity of population in their free time. The aim of this study was to determine test-retest reliability of Croatian version of Motivation scale for free time on students. Subjects and Methods: The sample was consisted of 97 students of Karlovac University of Applied Sciences 64 men and 33 women). Study was conducted on two occasions and there were 137 students in first measurement and 97 students in second. The response rate to the second measurement was 70,8 %. Statistical data analysis was performed with the software package SPSS 23.0. The normality of the distribution was checked by the Kolmogorov - Smirnov test to justify the use of parametric procedures. The asymmetry coefficient ranged from +/- 3, and the flattening coefficient from +/- 10, and the distribution is considered normal, so the use of parametric procedures is allowed. The validity was checked in the form of correlations of individual particles with the total result in both measurements. Results: The validity in both measurements proved to be statistically significant. In the first measurement, the correlation range of each particle with the total score on the scale ranged between 0.4 to 0.74, which was statistically significant at a risk level of less than 1 %. In the second measurement, the correlation range of each particle with the total score on the scale ranged between 0.31 to 0.73, which was statistically significant at a risk level of less than 1 %. Conclusion: The results showed satisfactory reliability of Croatian version of motivation scale for free time on students.