Variable Set Research Articles

비행 청소년의 사이버 문제 도박 수준과 사이버 도박 진입 동기 요인의 관계 분석

This study aims at examining the association between gambling motivation factors and cyber problem gambling level toward juvenile delinquents. With the recent development of IT devices, teenagers engage in cyber gambling through various motives such as advertising, friends, and monetary purposes. Accordingly, hierarchical regression analysis was used for juvenile delinquents under probation to analyze entry motivation factors that affect the level of cyber problem gambling. As a result of the analysis, the level of cyber problem gambling increased as juvenile delinquents had a stronger motivation to enter for financial purposes. In addition, through the setting of control variables, it was derived that the more parents tried to intervene strongly in the gambling behavior of juvenile delinquents, the higher the level of cyber problem gambling. Based on this, the motivation for entry affecting the level of cyber problem gambling was analyzed by dividing the group with high and low levels of intervention in parents' child gambling behavior. As a result, the higher the level of parental gambling intervention, the stronger the “friend experience” factor, and the stronger the “monetary purpose” factor in the group with low parental intervention, the higher the level of problem gambling. Besides, cyber problem gambling of juvenile delinquents requires an appropriate resolution of financial problems. It was also confirmed that support and response policies need to be classified by the level of gambling intervention according to the difference in parenting attitudes of parents. Further policy implications will be discussed.

A bi-variable precision rough set model and its application to attribute reduction

Significant indicators for measuring algorithms for attribute reduction include their fault tolerance. The majority of existing algorithms rely on the incorporation of fault tolerance at the upper and lower approximation operators. However, information granules partitioned by these algorithms are not inherently fault-tolerant. To enhance the fault tolerance of attribute reduction algorithms, this study introduces a new model based on variable precision rough sets, comprising of three parameters: the inclusion of fault tolerance in the comparison of attribute values between two objects, the partition of information granules, and the calculation of upper and lower approximation operators. As a result, the model tolerates errors, enhances the fault-tolerance ability of information granules, and improves its inclusivity. Using this new model, we design a novel algorithm that deletes both redundant and noise attributes to improve the classification accuracy. Through analysis of classification accuracy and robustness, we confirm that the novel algorithm's fault tolerance and robustness are maintained even after adding random Gaussian noise. The parameters included as part of the model are also found to be mostly unchanged after the addition of random Gaussian noise.

The Impact Of Social Media Marketing On Buying Behavior Of Ladies

Social media has impacted society in significant ways on the fiscal, political, and societal levels over the past ten years. Traditional media's impact is waning, but social media platforms are being adopted globally at an unprecedented rate, highlighting the extraordinary nature of the social media phenomenon. This study examines the effects of social media and influential personalities within social media on women’s buying behaviour, and the extent to which social media influences women’s buying behaviour. For this purpose, online survey forms were distributed to 226 women aged 18 to over 55 years. In addition to demographic information including age, education, income, marital status and social media types including Facebook, Instagram, Snapchat, WhatsApp, Twitter, and YouTube, we have six questions for the independent variable social media and five for the dependent variable setting female buying behaviour. Our data were analysed with the help of SPSS. The results show that part of women’s buying behaviour is determined by social media marketing. Other changes attributed to other factors that influence women’s buying behaviour.

Power Control Strategy for a Ferry’s DC Power System Using Supercapacitors

Integrated power systems are gaining popularity in the field of power systems and DC integrated power systems are considered promising for electric propulsion ships due to their simple grid topology, low fuel consumption, and easy access to new energy sources. However, the dynamic response characteristics of the power plant can be compromised when a variable speed generator is used in a DC power system, despite achieving energy savings. In this research, we investigate the power control strategy of a specific type of a ferry’s DC power plant. We establish a mathematical model and a Matlab/Simulink-based simulation model to analyze the performance of the proposed strategy. The research utilizes the fast charging and discharging advantages of supercapacitor storage devices to compensate for the dynamic impact delay of the power output when using the variable speed generator set. Additionally, an improved DC bus voltage droop control method that incorporates voltage compensation is proposed to mitigate problems related to large bus voltage fluctuations under sudden load change conditions, enabling better load distribution between different power sources. The simulation results confirm the effectiveness of the proposed strategy in optimizing the speed-seeking method of the variable speed diesel engine sets matching with the supercapacitor, and its positive impact on the dynamic performance of the propulsion system is demonstrated under variable load conditions resulting from ferry operations.

Abnormal Diagnosis Method of Self-Powered Power Supply System Based on Improved GWO-SVM

In order to solve the problem of low abnormal diagnosis rate of self-powered power supply system, an improved grey wolf optimization-support vector machine (GWO-SVM) algorithm combined with maximal information coefficient (MIC) are proposed. First, the feature sets of 11 kinds of monitoring data are optimized and selected based on MIC for self-powered power supply system. By eliminating redundant variables and insensitive variables, feature variable sets with great influence on abnormal diagnosis are selected. Second, by upgrading the selection method of control parameter σ from linear to nonlinear, an improved GWO-SVM algorithm that can take into account both global and local search capabilities is proposed. Furthermore, the optimal feature set which has great influence on abnormal diagnosis is selected as the input of the proposed algorithm, and then the abnormal diagnosis method combining the improved GWO-SVM with MIC is constructed for self-powered power supply system. The specific algorithm flow and step are given. Finally, compared with other algorithm, the simulation experiments show that the GWO-SVM method has a higher accuracy and a higher recall rate for the abnormal diagnosis in the self-powered power supply system.

Research of features of oscillating process’ behavior in the nonlinear system of individual traction drive of an electrobus

BACKGROUND: When a vehicle is in motion, self-oscillations which properties are dependent on slip rate in a contact patch may occur in the area of tire interaction with ground surface. Oscillations frequency will vary in dependence with value of wheel slip relative to ground surface. Soft self-oscillations are excited by variable set of initial conditions at full slip in traction and driven wheel rolling modes as well as in mixed braking mode with partial slip. Hard mode of self-oscillations occurs at full wheel slip in braking mode. These processes have a negative impact on the processes in electric drive and mechanical drivetrain reducing their efficiency and may cause damage of components. Oscillations in the system are excited by interaction forces of an elastic tire with ground surface featuring vertical oscillations due to elastic behavior of its interaction with road unevenness. AIMS: Research of features of oscillating process behavior in the nonlinear system of individual traction drive of an electrobus. METHODS: Simulation of self-oscillation excitation processes in the area of contact interaction of a wheel and road was carried out in the MATLAB/Simulink software package. RESULTS: The article features the results of simulation and experimental studies of self-oscillation excitation processes of the KAMAZ 6282 electrobus moving on asphalt-concrete surface. It was found that vertical wheel displacement when moving through unevenness lead to oscillating behavior of vertical reaction forces in contact patches and, as a consequence, to oscillating behavior of longitudinal reaction forces, torque and rotation velocity of the shaft of the traction electric motor of the individual drive. It was defined that tire oscillation frequency is 67 Hz that coincides with electric motor shaft rotation oscillation frequency and this value is the same for both experiment and simulation. CONCLUSIONS: Practical value of the study lies in ability of using the study results at development of self-oscillation processes exclusion algorithms as a part of vehicle control system.

Autism Spectrum Disorder: general aspects and concerns about Brazilian politics

Autism Spectrum Disorder (ASD) encompasses a highly variable set of neurodevelopmental disorders with early onset and non-degenerative pattern. According to the World Health Organization's International Classification of Diseases (11th Revision), ICD-11, ASD is characterized as “persistent deficits in the ability to initiate and to sustain reciprocal social interaction and social communication, and by a range of restricted, repetitive, and inflexible patterns of behaviour, interests or activities that are clearly atypical or excessive for the individual's age and sociocultural context.”

Sources of sedimentary organic matter in Arctic fjords: Evidence from lipid molecular markers

The identification of the origin of organic matter in Arctic fjord systems is especially challenging due to complex and highly variable environmental settings, which are impacted by ongoing rapid climate changes. In this study we use a multiproxy approach based on various lipid biomarkers to investigate the provenance of organic matter deposited in surface sediments in the four high-Arctic fjords located in the western part of Spitsbergen (Hornsund, Adventfjorden, Kongsfjorden, and Raudfjorden). This work focuses on the analyses of several classes of lipid biomarkers such as n-alkanes, saturated cyclic hydrocarbons (hopanes, steranes), highly branched isoprenoid (IP25 – ice proxy), alkenones and polar steroids, including sterols and stanols. The findings of this research indicate the occurrence of sedimentary organic matter derived from multiple marine and land-based sources in the studied fjords. The analysis of biomarkers reveals pronounced differences between fjords located in southern and northern parts of western Spitsbergen with regard to contributions of sedimentary organic matter derived from biogenic sources (phytoplankton, animal-derived material as well as terrestrial vascular plants), relative abundances of specific phytoplankton genera (diatoms vs. dinoflagellates), and intensity of bio-advection (Emiliania huxleyi) related to inflows of warm Atlantic Water. The uncommon n-alkane profiles, especially in the short-chain range, reported for investigated sediments are likely to reflect probable contributions related to ancient organic matter and/or microbial sources/activities. To our knowledge, this is the first such comprehensive research on the origin of organic matter deposited in surface sediments from the western Spitsbergen fjords, based on lipid molecular biomarkers.

Emergency risk assessment of sudden water pollution in South-to-North Water Diversion Project in China based on driving force–pressure–state–impact–response (DPSIR) model and variable fuzzy set

Emergency risk assessment of sudden water pollution in South-to-North Water Diversion Project in China based on driving force–pressure–state–impact–response (DPSIR) model and variable fuzzy set

An adaptive optimization method toward batch-wise variable set point of outlet moisture content for the tobacco drying process

The tobacco drying process in the cigarette production has an important effect on the final product quality. Therefore, the intelligent control methods have been widely investigated to ensure the stability of tobacco’s outlet moisture content. The existing work mostly uses a relatively fixed set point of the outlet moisture content for different tobacco batches, which can lead to unforeseen product quality after several processes following the drying process and inaccessible amount of dehydration for the rotary dryer. Some tobacco moisture prediction methods have been studied recently while the relationship with the intelligent control methods remain largely unexplored. To deal with these issues, a novel method is proposed in this paper to identify the optimal set point of the drying outlet moisture content for each tobacco batch. An encoder-decoder model is first developed to forecast the post-drying moisture trajectory. Then, an adaptive filter with specially designed mechanisms and a confidence interval of the dehydration level are constructed to obtain the design constraints. Based on all above, a constrained optimization problem is formulated and solved by the genetic algorithm. Extensive experiments on 895 tobacco batches from a large cigarette factory are carried out, which involves both algorithmic evaluation and field test. It turns out that the proposed method achieves the superior performance and leads to an improvement of the product quality in real production.

Nanoparticle growth in thermally diffusive sublimation-condensation systems with low vapor pressure solids

Engineered, condensational growth of aerosol particles is a well-established technique incorporated into particle detection and sampling systems. It is typically accomplished through passage of particles through controlled temperature, supersaturated systems with moderate-to-high vapor pressure compounds, which are liquid at room temperature. However, there are instances where it is advantageous to intentionally grow particles with room temperature, low vapor pressure (<10−3 Pa) solid compounds, either as part of particle collection or ionization systems. Sublimation-condensation, though demonstrated previously, is much less examined, particularly for nanoparticle growth. In this study, we examine a thermally diffusive, laminar flow sublimation-condensation system, wherein solid organic powders (all ionization facilitating matrices) are sublimated at moderate temperature (70–130 °C) and subsequent cooling results in vapor supersaturation, promoting growth of particles. The extent of particle growth was characterized via tandem differential mobility analyzer (TDMA) measurements, with 30 nm–200 nm particles. The grown diameter distributions were monitored for three different low vapor pressure organic solids at variable sublimator temperature settings. We found optimized operating conditions enabled particle growth above diameters of 500 nm. Growth experiments with 7 nm protein (transferrin) particles using ferulic acid as the working solid additionally yielded grown particles in excess of 200 nm in diameter. However, we also found heterogeneous growth occurred simultaneously with homogeneous nucleation of the working solid, which could not be avoided if growth to diameters >500 nm was desired. Homogeneously nucleated particles were broadly distributed in size, and generally smaller than the test particles grown by condensation, but still with diameters in excess of 100 nm. While SEM images of grown particles revealed smooth surfaces not indicative of agglomeration with homogeneously nucleated particles, we cannot rule out the contribution to growth of homogeneously nucleated particle coagulation with sampled particles in the growth system. By comparing to numerical simulations, we use TDMA measurements to estimate effective vapor pressures for the tested sublimated solids, assuming growth occurred solely by vapor condensation. Simulations suggest that saturation ratios in excess of 102 are needed for nanoparticle growth.

Combination Strategies of Variables with Various Spatial Resolutions Derived from GF-2 Images for Mapping Forest Stock Volume

Spectral features (SFs) and texture features (TFs) extracted from optical remote sensing images can capture the structural composition and growth information of forests, and combining remote sensing variables with a few ground measurement samples is a common method for mapping forest stock volume (FSV). However, the accuracy of mapping FSV using optical images with a high spatial resolution (one meter or sub-meters) is often lower than medium resolutions (larger than 10 m) using the same types of features and approaches. To overcome the limitations of high spatial resolution images in mapping FSV, down-scaled images with spatial resolution ranging from 1 to 30 m were obtained by GF-2 image to interpret the relationships between spatial resolutions of features and the accuracy of mapping FSV, and combination strategies of variables with various spatial resolutions were proposed to improve the accuracy of mapping FSV. The results show that the spatial resolution of features significantly affects the performance of employed models in estimating FSV, the sensitivity between SFs and FSV gradually increases with the decreasing of spatial resolution, and the optimal spatial resolutions of two types of features (SFs and TFs) are not synchronized in mapping forest FSV. After using combination strategies of variables with various spatial resolutions, the accuracy of mapping FSV is significantly higher than those derived from variable sets with the same spatial resolutions. It is proved that TFs derived from GF-2 images have great potential to improve the accuracy of mapping FSV, and the contribution of features depends on the approaches of extracting and combination strategies.

Analysis of the effect of compound training for explosive power on improving the psychological resilience of football athletes

Abstract In view of the problem that in the training of football athletes, coaches pay attention to their football skills and physical quality and ignore their psychological quality training, which leads to poor psychological quality of the athletes, this paper studies the effect of compound training on improving the psychological resilience of football athletes in depth. On the one hand, this paper modeled human lower-limb motion chain. First of all, the explosive power of lower limbs of football athletes was evaluated, and the maximum value of the product was obtained in the power-velocity curve, which aims to further increase the velocity and power of movements. Then, the human multi-rigid body model of lower limbs was established, and the motion chain curve planning of zero-point moment and center of gravity was carried out, which transforms the motion modeling problem of human lower-limb motion chain into the basic variable set problem of human lower-limb motion state. Finally, through the basic variables planning of human lower-limb motion state, the accurate modeling of human lower-limb motion chain was obtained. On the other hand, this paper analyzed and summarized the theory that compound training is capable of improving psychological resilience of football athletes, sorted out and integrated the influencing factors of psychological resilience, and on this basis conducted research and put forward a new development direction of psychological resilience. The experimental results show that the PEM value of goal focus and emotion control is 0.82, and the PEM value of positive cognition is 0.73, indicating that athletes’ performance in positive cognition has been significantly improved. Therefore, it is concluded that psychological resilience can reduce athletes’ athletic burnout, stimulate athletes’ athletic motivation, and enable athletes to take a more positive and optimistic attitude toward training and competition, so as to achieve better athletic performance.

Developing and Validating Models to Predict Progression to Proliferative Diabetic Retinopathy.

To develop models for progression of nonproliferative diabetic retinopathy (NPDR) to proliferative diabetic retinopathy (PDR) and determine if incorporating updated information improves model performance. Retrospective cohort study. Electronic health record (EHR) data from a tertiary academic center, University of California San Francisco (UCSF), and a safety-net hospital, Zuckerberg San Francisco General (ZSFG) Hospital were used to identify patients with a diagnosis of NPDR, age ≥ 18 years, a diagnosis of type 1 or 2 diabetes mellitus, ≥ 6 months of ophthalmology follow-up, and no prior diagnosis of PDR before the index date (date of first NPDR diagnosis in the EHR). Four survival models were developed: Cox proportional hazards, Cox with backward selection, Cox with LASSO regression and Random Survival Forest. For each model, three variable sets were compared to determine the impact of including updated clinical information: Static0 (data up to the index date), Static6m (data updated 6 months after the index date), and Dynamic (data in Static0 plus data change during the 6-month period). The UCSF data were split into 80% training and 20% testing (internal validation). The ZSFG data were used for external validation. Model performance was evaluated by the Harrell's concordance index (C-Index). Time to PDR. The UCSF cohort included 1130 patients and 92 (8.1%) patients progressed to PDR. The ZSFG cohort included 687 patients and 30 (4.4%) patients progressed to PDR. All models performed similarly (C-indices ∼ 0.70) in internal validation. The random survival forest with Static6m set performed best in external validation (C-index 0.76). Insurance and age were selected or ranked as highly important by all models. Other key predictors were NPDR severity, diabetic neuropathy, number of strokes, mean Hemoglobin A1c, and number of hospital admissions. Our models for progression of NPDR to PDR achieved acceptable predictive performance and validated well in an external setting. Updating the baseline variables with new clinical information did not consistently improve the predictive performance. Proprietary or commercial disclosure may be found after the references.

RENAL9: REDIEM 2.0: Renal Extremity Device to Measure Impedance, Edema, Movement

Background: Renal failure due to chronic kidney disease (CKD) is a leading cause of global mortality. CKD disrupts fluid balance regulation, inducing comorbidities such as excessive swelling of the extremities (edema) and restless leg syndrome (RLS). Clinical limitations impede the ability to quantitatively track edema and RLS in variable settings. We report on the development of a wearable device capable of continuously measuring and analyzing: 1. leg edema, 2. water content, and 3. RLS-specific leg movements. Methods: An enhanced system (2.0) was designed through integration of flex sensors, accelerometry, electromyography (EMG) and impedance biosensors into a flexible stocking that transmits data to a smartphone application for graphical interpretation. Assessment of components was performed through simulations of RLS and edema. EMG and accelerometry were assessed for identification accuracy by applying sensors to a subject performing movements of set type/frequency to simulate RLS. Subject’s plantar reflex was induced to verify increased EMG response to sudden reflexive motion. Flex sensors were assessed on simulated edema via a measured-fill hydration system with compressed sponges, measuring resistance in relation to circumference during expansion. Results: RLS sensor trials were able to differentiate RLS movement with EMG using peak detection software (Figure A). This was verified using accelerometry, which identified leg movement at >95% recognition accuracy. Edema sensor trials were able to accurately measure linear change in resistance during circumference expansion with an R2 value of 0.949 (Figure B). Further assessment will be performed to confirm water content using impedance sensors. Conclusion: Preliminary data demonstrates measurement and recognition accuracy in all components tested. Correlations identified between circumference expansion and resistance change allow for accurate quantification of swelling via resistance measurements. Motion differentiation/identification allows for tracking of RLS movement incidence and frequency. Ability to perform condition progression analysis on an aggregation of this data over time indicates future utility of the integrated device in CKD treatment. Continued work is intended to demonstrate this viability and conclude with a complete and comprehensive system for use by clinicians and patients.Figure 1. A. Representative EMG demonstrating RLS motion. Peaks identified by detection software are associated with sudden or jerk-like motions. B. Detection of circumference change (edema) via change in resistance using sensor.

Model-Free Sequential Predictive Control for MMC With Variable Candidate Set

Model-Free Sequential Predictive Control for MMC With Variable Candidate Set

Land cover and climatic conditions as potential drivers of the raccoon (Procyon lotor) distribution in North America and Europe

The raccoon is listed among the invasive alien species of EU concern requiring management actions. Projections of its global distribution have been mainly based on climatic variables so far. In this study, we aim to address the impact of land cover (LC) on the raccoon distribution in North America and Europe. First, we identified the LC types in which the observation sites are predominantly located to derive preferred LC types. Second, we used an ecological niche modelling (ENM) approach to evaluate the predictive power of climatic and LC information on the current distribution patterns of raccoons in both ranges. Raccoons seem to be more often associated to forested areas and mixed landscapes, including cropland and urban areas, but underrepresented in vegetation-poor areas, with patterns largely coinciding in both ranges. In order to compare the predictive power of climate variables and land cover variables, we conducted principal component analyses of all variables in the respective variable sets (climate variables and land cover variables) and used all PC variables that together explain 90% of the total variance in the respective set as predictors. Land cover only models resulted in patchy patterns in the projected habitat suitabilities and showed a higher performance compared to the climate only models in both ranges. In Europe, the land cover habitat suitability seems to exceed the current observed occurrences, which could indicate a further spread potential of the raccoon in Europe. We conclude that information on land cover types are important drivers, which explain well the spatial patterns of the raccoon. Consideration of land cover could benefit efforts to control invasive carnivores and contribute to better management of biodiversity, but also human and animal health.

Pelatihan Penyusunan Surat Melalui Mail Merge Bagi Perangkat Gampong Kecamatan Peureulak

This training aims to provide knowledge and skills to gampong officials in Peureulak District in using the mail merge technique to compose letters efficiently and effectively. Participants will learn the steps in creating a template document that includes design patterns, formats, and variable settings that will be used to combine data. Next, participants will learn how to integrate data from external sources such as Excel into Word documents using the mail merge feature. They will learn how to set data filtering criteria, edit and modify documents, and add dynamic elements such as tables, images, and graphs. Training will also cover common troubleshooting techniques related to mail merge, such as resolving data merge errors, fixing incorrect formatting, and optimizing processes to increase productivity. Participants will be involved in hands-on practical sessions where they will be given the opportunity to try out the techniques, they have learned for themselves. This will help them hone their skills and gain confidence in using mail merge in their daily work. This training can provide significant benefits for gampong officials in Peureulak District, enabling them to improve efficiency and accuracy in compiling letters. By using mail merge, the process of writing letters can be automated, saving time and reducing the risk of human error.

Kernel similarity-based multigranulation three-way decision approach to hypertension risk assessment with multi-source and multi-level structure data

Early intervention and prevention of chronic diseases is a fundamental way to reduce disease incidence. Due to the complex pathogenic factors of disease, the assessment of disease risk requires consideration of the influence of multiple factors. In clinical practice, disease risk assessment may face data with multi-source, multi-type and multiple indicators of examination items. Mining useful information and rules in this complex information is the foundation for further decisions. However, the existing knowledge representation methods cannot comprehensively and accurately describe these characteristic of data. Therefore, we introduce the concept of multiple hybrid attribute information systems (MHAISs). Then, we defined the binary relations over MHAIS by fusing different types of kernel functions. On this basis, we construct a variable precision multigranulation kernel rough set (VPMGKRS), and propose a multigranulation three-way decision method over MHAIS. In addition, considering the individual difference between decision objects and the diversity of data characteristic among sources, we calculate the loss functions under different granular with the help of conditional probabilities, and then obtain the thresholds for the three-way decisions. Finally, we use 712 clinical random samples to conduct a simulation analysis applying the proposed method to assess the risk of hypertension. The experiment result confirmed the applicability and validity of the model.

Generalized fuzzy neighborhood system-based multigranulation variable precision fuzzy rough sets with double TOPSIS method to MADM

Multigranulation fuzzy rough sets (MFRSs), variable precision fuzzy rough sets (VPFRSs) and their combinations with traditional decision-making methods are fundamental in the research of multi-attribute decision-making (MADM) problems. However, the existing theories and methods have two deficiencies: (1) many existing MFRSs are based on fuzzy relations or fuzzy coverings, which may not effectively solve some MADM problems; (2) VPFRSs usually do not meet the inclusion property, which hinders their further development in theory and application. Noted that both fuzzy relations and fuzzy coverings can generate fuzzy neighborhood systems in a natural way, so the fuzzy neighborhood system provides a broader multigranulation framework. Inspired by this, by using the generalized fuzzy neighborhood system, we propose three multigranulation variable precision fuzzy rough set models, namely optimistic, pessimistic and compromise, respectively. As expected, these models contain many existing models as special cases, thus broadening the framework of rough set. Additionally, we verify that these models fulfill many basic algebra properties, particularly the generalized inclusion properties, which overcome the limitation of VPFRSs. Finally, by combining the proposed fuzzy rough set models with the TOPSIS method, we develop a new method to MADM. A series of experiments show that our method has some obvious advantages.