Simplified Version Research Articles

Self Parking Car

Abstract: The use of vehicles has increased and results in creating parking issues. This research tackles the pressing need for improved urban parking efficiency and reduced human intervention through the exploration of self-parking cars. In this project, utilizing advanced sensor technologies and refined control strategies, has significantly enhanced accuracy, speed and adaptability in parking procedure, offering sensible solutions to urban challenges like traffic congestion and limited parking accessibility. In our self-parking car project, we use four motors to regulate movement, an Arduino processing unit to make decisions, and two ultrasonic sensors to identify obstacles. The motors allow for accurate vehicle movement, and the processing unit interprets sensor data and controls motor movements. Ultrasonic sensors identify obstacles, allowing the car to drive and park itself. This integration provides safe and efficient self-parking capability. While our work lays a solid foundation, ongoing research is advocated to fully unlock and integrate the transformative potential of self-parking cars into urban environments, addressing contemporary mobility issues. Sure, here's a simplified version. Continuously exploring and improving this technology is really important. It helps make sure that self-parking cars become a normal part of how we get around cities, which can change how we plan and move in the future.

Evaluating the psychometric properties of the simplified Chinese version of PROMIS-29 version 2.1 in patients with hematologic malignancies

The Patient-Reported Outcomes Measurement Information System 29-item Profile version 2.1 (PROMIS-29 V2.1) is a widely utilized self-reported instrument for assessing health outcomes from the patients’ perspectives. This study aimed to evaluate the psychometric properties of the PROMIS-29 V2.1 Chinese version among patients with hematological malignancy. Conducted as a cross-sectional, this research was approved by the Medical Ethical Committee of the Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College (registration number QTJC2022002-EC-1). We employed convenience sampling to enroll eligible patients with hematological malignancy from four tertiary hospitals in Tianjin, Shandong, Jiangsu, and Anhui province in China between June and August 2023. Participants were asked to complete a socio-demographic information questionnaire, the PROMIS-29 V2.1, and the Functional Assessment of Cancer Therapy-General (FACT-G). We assessed the reliability, ceiling and floor effects, structural, convergent discriminant and criterion validity of the PROMIS-29 V2.1. A total of 354 patients with a mean age of 46.93 years was included in the final analysis. The reliability of the PROMIS-29 V2.1 was affirmed, with Cronbach’s α for the domains ranging from 0.787 to 0.968. Except sleep disturbance, the other six domains had ceiling effects, which were seen on physical function (26.0%), anxiety (37.0%), depression (40.4%), fatigue (18.4%), social roles (18.9%) and pain interference (43.2%), respectively. Criterion validity was supported by significant correlations between the PROMIS-29 V2.1 and FACT-G scores, as determined by the Spearman correlation test (P < 0.001). Confirmatory factor analysis (CFA) indicated a good model fit, with indices of χ2/df (2.602), IFI (0.960), and RMSEA (0.067). The Average Variance Extracted (AVE) values for the seven dimensions of PROMIS-29 V2.1, ranging from 0.500 to 0.910, demonstrated satisfactory convergent validity. Discriminant validity was confirmed by ideal √AVE values. The Chinese version of the PROMIS-29 V2.1 profile has been validated as an effective instrument for assessing symptoms and functions in patients with hematological malignancy, underscoring its reliability and applicability in this specific patient group.

Reliability of the Walch Classification for Characterization of Primary Glenohumeral Arthritis: A Systematic Review.

The Walch classification has been widely accepted and further developed as a method to characterize glenohumeral arthritis. However, many studies have reported low and inconsistent measures of the reliability of the Walch classification. The purpose of this study was to review the literature on the reliability of the Walch classification and characterize how imaging modality and classification modifications affect reliability. A systematic review of publications that included reliability of the Walch classification reported through intraobserver and interobserver kappa values was conducted. A search in January 2021 and repeated in July 2023 used the terms ["Imaging" OR "radiography" OR "CT" OR "MRI"] AND ["Walch classification"] AND ["Glenoid arthritis" OR "Shoulder arthritis"]. All clinical studies from database inception to July 2023 that evaluated the Walch or modified Walch classification's intraobserver and/or interobserver reliability were included. Cadaveric studies and studies that involved subjects with previous arthroplasty, shoulder débridement, glenoid reaming, interposition arthroplasty, and latarjet or bankart procedure were excluded. Articles were categorized by imaging modality and classification modification. Thirteen articles met all inclusion criteria. Three involved the evaluation of plain radiographs, 10 used CT, two used three-dimensional (3D) CT, and four used magnetic resonance imaging. Nine studies involved the original Walch classification system, five involved a simplified version, and four involved the modified Walch. Six studies examined the reliability of raters of varying experience levels with none reporting consistent differences based on experience. Overall intraobserver reliability of the Walch classifications ranged from 0.34 to 0.92, and interobserver reliability ranged from 0.132 to 0.703. No consistent trends were observed in the effect of the imaging modalities or classification modifications on reliability. The reliability of the Walch classification remains inconsistent, despite modification and imaging advances. Consideration of the limitations of the classification system is important when using it for treatment or prognostic purposes.

Decoding trust: A reinforcement learning perspective

Decoding trust: A reinforcement learning perspective

Leveraging regional mesh refinement to simulate future climate projections for California using the Simplified Convection-Permitting E3SM Atmosphere Model Version 0

Abstract. The spatial heterogeneity related to complex topography in California demands high-resolution (&lt; 5 km) modeling, but global convection-permitting climate models are computationally too expensive to run multi-decadal simulations. We developed a 3.25 km California climate modeling framework by leveraging regional mesh refinement (CARRM) using the U.S. Department of Energy (DOE)'s global Simple Cloud-Resolving E3SM Atmosphere Model (SCREAM) version 0. Four 5-year time periods (2015–2020, 2029–2034, 2044–2049, and 2094–2099) were simulated by nudging CARRM outside California to 1° coupled simulation of E3SMv1 under the Shared Socioeconomic Pathways (SSP)5-8.5 future scenario. The 3.25 km grid spacing adds considerable value to the prediction of the California climate changes, including more realistic high temperatures in the Central Valley and much improved spatial distributions of precipitation and snowpack in the Sierra Nevada and coastal stratocumulus. Under the SSP5-8.5 scenario, CARRM simulation predicts widespread warming of 6–10 °C over most of California, a 38 % increase in statewide average 30 d winter–spring precipitation, a near-complete loss of the alpine snowpack, and a sharp reduction in shortwave cloud radiative forcing associated with marine stratocumulus by the end of the 21st century. We note a climatological wet precipitation bias for the CARRM and discuss possible reasons. We conclude that SCREAM RRM is a technically feasible and scientifically valid tool for climate simulations in regions of interest, providing an excellent bridge to global convection-permitting simulations.

Exploring ZnO nanoparticles: UV–visible analysis and different size estimation methods

The study investigates several theoretical and empirical methods for determining the size of zinc oxide nanoparticles (ZnO NPs) and studies their characteristics by using solely UV–visible spectroscopy. The investigation begins with the green synthesis of three samples of ZnO NPs using three different reducing agents Aspergillus Niger fungal biomass (AN), Aloe barbadensis Mille (ABM) and Ocimum tenuiflorum (OT) leaves, respectively with Zinc Sulphate as a precursor involving precipitation method. The study analyses the UV–visible data of each sample to obtain optical bandgap, Urbach energy, refractive index, optical conductivity. and apply several methods or Models such as Effective Mass Approximation (EMA) or Brus Equation (BE), Hyperbolic Band Model (HBM), Meulenkamp's Expression (ME), Tight Binding Model (TBM) and a simplified version of EMA (SEMA) to estimate the size of ZnO NPs. The effectiveness of size-determining models was assessed using two sets of one-way analysis of variance (ANOVA) for each ZnO sample and models Followed by Tukey post-hoc analysis to identify the exact sample that is different from other samples.

Smooth least absolute deviation estimators for outlier-proof identification

The paper proposes to identify the parameters of linear dynamic models based on the original implementation of least absolute deviation estimators. It is known that the object estimation procedures synthesized in the sense of the least sum of absolute prediction errors are particularly resistant to occasional outliers and gaps in the analyzed system data series, while the classical least squares procedure unfortunately becomes of little use for reliably identifying systems in the presence of destructive measurement errors. Bearing in mind that the classic task of minimizing the quality functional of absolute deviations encounters fundamental analytical problems, it is proposed to use a dedicated iterative estimator for off-line evaluation of the parameters of the analyzed process. In addition, a simplified recursive version of the absolute deviation estimation procedure was developed, which allows for practical on-line tracking of the evolution of variable parameters of non-stationary systems. Importantly, a novel refinement of the discussed absolute deviation estimators was proposed to effectively overcome some inconvenient numerical effects. We also present an interesting comparison of the improved (by non-linear modification) iterative absolute-deviation estimator with the classical Gauss-Newton gradient algorithm, which leads to constructive conclusions. Finally, using computer simulations, the operation of the developed iterative and recursive estimators minimizing the absolute deviation is illustrated. The work ends with an indication of directions for further research.

Sustainability and impact of an intervention to improve initiation of tuberculosis preventive treatment: results from a follow-up study of the ACT4 randomized trial

In a cluster randomized trial (clinicaltrials.gov: NCT02810678) a flexible but comprehensive health system intervention significantly increased the number of household contacts (HHC) identified and started on tuberculosis preventive treatment (TPT). A follow-up study was conducted one year later to test the hypotheses that these effects were sustained, and were reproducible with a simplified intervention. We conducted a follow-up study from May 1, 2018 until April 30, 2019, as part of a multinational cluster randomized trial. Eight sites in 4 countries that had received the intervention in the original trial received no further intervention; eight other sites in the same countries that had not received the intervention (control sites in the original trial) now received a simplified version of the intervention. This consisted of repeated local evaluation of the Cascade of care for TB infection, and stakeholder decision making. The number of HHC identified and starting TPT were repeatedly measured at all 16 sites and expressed as rates per 100 newly diagnosed index TB patients. The sustained effect of the original intervention was estimated by comparing these rates after the intervention in the original trial with the last 6 months of the follow-up study. The reproducibility was estimated by comparing the pre-post intervention changes in rates at sites receiving the original intervention with the pre-post changes in rates at sites receiving the later, simplified intervention. With regard to the sustained impact of the original intervention, compared to the original post-intervention period, the number of HHC identified and treated per 100 newly diagnosed TB patients was 10 more (95% confidence interval: 84 fewer to 105 more), and 1 fewer (95% CI: 22 fewer to 20 more) respectively up to 14 months after the end of the original intervention. With regard to the reproducibility of the simplified intervention, at sites that had initially served as control sites, the number of HHC identified and treated per 100TB patients increased by 33 (95% CI:-32, 97), and 16 (-69, 100) from 3 months before, to up to 6 months after receiving a streamlined intervention, although differences were larger, and significant if the post-intervention results were compared to all pre-intervention periods. Up to one year after it ended, a health system intervention resulted in sustained increases in the number of HHC identified and starting TPT. A simplified version of the intervention was associated with non-significant increases in the identification and treatment of HHC. Inferences are limited by potential bias due to other temporal effects, and the small number of study sites. Funded by the Canadian Institutes of Health Research (Grant number 143350).

A novel imaging marker of cortical “cellularity” in multiple sclerosis patients

Pathological data showed focal inflammation and regions of diffuse neuronal loss in the cortex of people with multiple sclerosis (MS). In this work, we applied a novel model (“soma and neurite density imaging (SANDI)”) to multishell diffusion-weighted MRI data acquired in healthy subjects and people with multiple sclerosis (pwMS), in order to investigate inflammation and degeneration-related changes in the cortical tissue of pwMS. We aimed to (i) establish whether SANDI is applicable in vivo clinical data; (ii) investigate inflammatory and degenerative changes using SANDI soma fraction (fsoma)—a marker of cellularity—in both cortical lesions and in the normal-appearing-cortex and (iii) correlate SANDI fsoma with clinical and biological measures in pwMS. We applied a simplified version of SANDI to a clinical scanners. We then provided evidence that pwMS exhibited an overall decrease in cortical SANDI fsoma compared to healthy subjects, suggesting global degenerative processes compatible with neuronal loss. On the other hand, we have found that progressive pwMS showed a higher SANDI fsoma in the outer part of the cortex compared to relapsing–remitting pwMS, possibly supporting current pathological knowledge of increased innate inflammatory cells in these regions. A similar finding was obtained in subpial lesions in relapsing–remitting patients, reflecting existing pathological data in these lesion types. A significant correlation was found between SANDI fsoma and serum neurofilament light chain—a biomarker of inflammatory axonal damage—suggesting a relationship between SANDI soma fraction and inflammatory processes in pwMS again. Overall, our data show that SANDI fsoma is a promising biomarker to monitor changes in cellularity compatible with neurodegeneration and neuroinflammation in the cortex of MS patients.

Construct validity of the simplified Chinese version of the instrument ‘Picture My Participation’

Background Preliminary evidence of the content validity of the simplified Chinese version of ‘Picture My Participation’ (PMP-C; Simplified) items and reliability of the subscale attendance for the effectiveness of the use with children and youth in mainland China has been collected. However, evidence of construct validity for the instrument is not yet available. Aim To explore the construct validity of the attendance scale in PMP-C (Simplified). Methods A cross-sectional study using convenience sampling was conducted using PMP-C (Simplified) with a picture-supported interview for 290 children and youths aged 5–21 with and without ID in urban and rural areas of mainland China. Exploratory factor analysis (EFA) was performed using the principal component analysis (PCA) to analyse the resulting data. Results The EFA extracted five factors with eigenvalues greater than one and the cumulative contribution rate of factors accounted for 51.62% of the variance. All items had factor loadings above 0.50. The five subcomponents included: organised activities, social activities, taking care of others, family life activities and personal care and development activities. Conclusion The results of the factor analysis support the construct validity of the PMP-C (Simplified) attendance scale. It provides further psychometric evidence that PMP-C (Simplified) is a sound measure to assess participation for children and youths in mainland China.

Fitness promotion in college: the relationships among students' perceived physical literacy, knowledge, and physical fitness.

The purpose of this study was to examine the relationships among perceived physical literacy (PPL), knowledge of physical activity and fitness (PAF knowledge), and physical fitness. Undergraduates (N = 968, female = 414; Mage = 18.64) from a public university in central China completed a simplified Chinese version of the PPL instrument, an online test for PAF knowledge, and seven health-related physical fitness tests. The PPL includes three dimensions: (a) confidence and physical competence, (b) motivation, and (c) interaction with the environment. The fitness tests measured lung capacity, body mass index (BMI), and performances in 800 (female)/1000 (male) meters run, 50 meters dash (50 M), sit-up (female) / pull-up (male), standing long jump (SLJ), and sit-and-reach. PPL and its dimensions significantly predicted six fitness test performances both in male (β: -0.42 - 0.37; p < 0.01-0.05; R2: 0.01-0.13) and female (β: -0.59 - 0.49; p < 0.01-0.05; R2: 0.03-0.13) students. PAF knowledge (β: -0.17 - 0.18; p < 0.01-0.05; R2: 0.01-0.05) significantly predicted BMI (males) and performances in 50 M (females) and SLJ (females) tests. To support college students' fitness development and maintenance, tailored physical activity and fitness promotion programs are needed to strengthen students' PPL and PAF knowledge.

Measuring dispositional empathy in South African children.

Empathy is a key factor to examine in development, because of its predictive associations with both aggression and successful prosocial behaviour. However, established measures of empathy for Low-to-Middle Income Countries, including South Africa, are lacking. In children, parent-report measures are key. However, a local study examining empathy and aggression (Malcolm-Smith et al., 2015) found poor psychometric performance for a widely used parent-report measure of dispositional empathy, the Griffith Empathy Measure (GEM). We thus investigated which of two questionnaires measuring dispositional cognitive and affective empathy perform better in this context. We contrasted internal consistency reliability of a simplified version of the GEM (SGEM; n = 160) and a parent-report version of the Questionnaire of Cognitive and Affective Empathy (QCAE; n = 440) in a low-mid socio-economic status sample. Convergence between the measures and factor structure were also assessed. The parent-report version of the QCAE performed well as a measure of child dispositional cognitive and affective empathy, with good reliability (overall α = 0.90 vs. SGEM α = .63), and confirmatory factor analysis supporting the two-factor structure. The SGEM's reliability and failure to correlate with QCAE indicated poor psychometric performance. This is the first psychometric evaluation of the QCAE as a parent-report measure, and our results indicate that it should prove useful for future assessments of dispositional empathy in children across a variety of contexts.

Development of a Length-Based Cell-State Framework Toward the Re-Creation of Large-Scale Dense Congestion Patterns

This paper presents the results and novel findings of generating a simplified version of the prevailing traffic features that existed during a major evacuation. Leveraging the underlying framework provided by the widely used cell transmission model, the desire is to reconstruct the unique characteristics of large congestion patterns that propagate under dense traffic states, where limited attempts at scaling this base model have occurred. The length-based cell-state framework presented can reproduce large spatiotemporal congestion patterns that exist, specifically from large-scale evacuations. To further simplify, the framework considers traffic state heuristics which are calibrated through oblique cumulative count and occupancy curves. As a result of this preprocessing technique, an artifact was found from the use of the cumulative curves under the lens of Newell’s two-phase traffic flow theory where three unique, separate queued regimes were identified within the fundamental diagrams. The methodology re-created a unique large-scale congestion pattern that existed during a past regional evacuation event, Hurricane Irma, the subject of this paper. To test this methodology, a large-scale congested period was analyzed, both with probe vehicle trajectory data and stationary radar detector data. Results demonstrate that traffic re-creation into state-based contours was able to be verified near a 90% level of confidence even at large spatiotemporal extents.

A stochastic framework for rainfall intensity–timescale–return period relationships. Part I: Theory and estimation strategies

This work presents a stochastic framework for the construction of rainfall intensity–timescale–return period relationships, which was applied in the recent regionalization of design rainfall curves over the Greek territory, described in a companion paper. The methodology outlined herein builds upon the Koutsoyiannis’ et al. 1998 framework, which has been recently revisited and upgraded, and incorporates two different versions: (a) a theoretically consistent stochastic model applicable for rainfall intensity over any scale of interest and (b) a simplified version valid over small scales, which makes parameter estimation easier. Special focus is given to the presentation of the simplified version, which suffices for most engineering tasks. Parameter estimation approaches are presented in detail including the K-moments framework that allows for reliable high-order moment estimation and handling of bias due to spatiotemporal dependence.

Using machine learning to develop a five-item short form of the children’s depression inventory

BackgroundMany adolescents experience depression that often goes undetected and untreated. Identifying children and adolescents at a high risk of depression in a timely manner is an urgent concern. While the Children’s Depression Inventory (CDI) is widely utilized in China, it lacks a localized revision or simplified version. With its 27 items requiring professional administration, the original CDI proves to be a time-consuming method for predicting children and adolescents with high depression risk. Hence, this study aimed to develop a shortened version of the CDI to predict high depression risk, thereby enhancing the efficiency of prediction and intervention.MethodsInitially, backward elimination is conducted to identify various version of the short-form scales (e.g., three-item and five-item versions). Subsequently, the performance of five machine learning (ML) algorithms on these versions is evaluated using the area under the ROC curve (AUC) to determine the best algorithm. The chosen algorithm is then utilized to model the short-form scales, facilitating the identification of the optimal short-form scale based on predefined evaluation metrics. Following this, evaluation metrics are computed for all potential decision thresholds of the optimal short-form scale, and the threshold value is determined. Finally, the reliability and validity of the optimal short-form scale are assessed using a new sample.ResultsThe study identified a five-item short-form CDI with a decision threshold of 4 as the most appropriate scale considering all assessment indicators. The scale had 81.48% fewer items than the original version, indicating good predictive performance (AUC = 0.81, Accuracy = 0.83, Recall = 0.76, Precision = 0.71). Based on the test of 315 middle school students, the results showed that the five-item CDI had good measurement indexes (Cronbach’s alpha = 0.72, criterion-related validity = 0.77).ConclusionsThis five-item short-form CDI is the first shortened and revised version of the CDI in China based on large local data samples.

An efficient polar cubic equation of state for predictive modeling of phase behavior and critical phenomena of mixtures

AbstractA polar cubic equation of state (EOS) is developed by incorporating the dipolar theory of Jog and Chapman into the Soave‐Redlich‐Kwong (SRK) EOS. We propose simplifying assumptions in the dipolar term of Jog and Chapman to reduce the double and triple sums in the theory to single sums. The simplified version of the dipolar theory can significantly improve computational speed and can be used with either Cubic EOS or SAFT‐based EOS. The proposed model, which we call Simplified Polar SRK (SP‐SRK), is parametrized in a similar fashion to classical cubic EOS to exactly reproduce , and will self‐consistently reduce to the base SRK EOS in the absence of polar interactions. Binary VLE data with a non‐polar reference hydrocarbon is used to extract the polarity of the respective functional group. The model shows superior performance in capturing the phase behavior of polar mixtures compared to the base SRK.

The high-frequency and rare events barriers to neural closures of atmospheric dynamics

Recent years have seen a surge in interest for leveraging neural networks to parameterize small-scale or fast processes in climate and turbulence models. In this short paper, we point out two fundamental issues in this endeavor. The first concerns the difficulties neural networks may experience in capturing rare events due to limitations in how data is sampled. The second arises from the inherent multiscale nature of these systems. They combine high-frequency components (like inertia-gravity waves) with slower, evolving processes (geostrophic motion). This multiscale nature creates a significant hurdle for neural network closures. To illustrate these challenges, we focus on the atmospheric 1980 Lorenz model, a simplified version of the Primitive Equations that drive climate models. This model serves as a compelling example because it captures the essence of these difficulties.

Chinese Version of the Short Geneva Emotional Music Scale: Cross-cultural Adaptation and Psychological Measurement

In this study we developed the Chinese Geneva Emotion Music Scale (GEMS-C) and a simplified version (S-GEMS-C) verified by Chinese native speakers. As the GEMS is a one-word-item scale without context, we performed an expert revision process after forward-backward translation. The results were optimized through structured interviews to obtain the final Chinese GEMS (GEMS-C). We reduced the number of GEMS items to form a short version (S-GEMS-C). The GEMS-C and the S-GEMS-C were shown to have high reliability, construct validity, sensitivity, concurrent validity, and discriminant validity. Additionally, this study found a high correlation between the GEMS-C, S-GEMS-C, and OMES-M, and the difference between familiarity with the music and the degree of emotional arousal.

The Benson Complex Figure Test: Normative Data for the Healthy Iranian Population

Purpose: Visual-related abilities such as visual memory and visuo-constructional skills are among the cognitive abilities with fundamental importance for normal cognitive function, and their impairment is manifested in many neurological and psychiatric disorders. The present study aimed to generate normative data for the Benson Complex Figure Test (BCFT), a well-known simplified version of the Rey-Osterrieth Complex Figure Test, in Iran and to assess the effect of demographic variables of age, gender, and education on its various measures.&#x0D; Materials and Methods: The present study was conducted in 2017-2018 as part of the Iranian Brain Imaging Database (IBID) project. The study sample consisted of 300 normal individuals in the age range of 20 to 70 years, with an equal number of participants and an equal proportion of genders in each age decade (#60). Independent and dependent variables, respectively, were age (classified by five decades including 20-30-year-olds, 31-40-year-olds, 41-50-year-olds, 51-60-year-olds, and 61-70-year-olds) and performance in the BCFT (defined in terms of 3 scores on a copy, recall, and recognition of the geometric figure and 2 scores on time of copy and recall).&#x0D; Results: The correlation matrix among the variables showed that age and education have a significant correlation with most of the BCFT scores, while gender only has a significant correlation with recognition score. Multivariate analysis of variance showed the effect of age, gender, and their interaction on scores, while education did not make a significant difference in the BCFT scores. Also, the t-test showed a significant difference between men and women in recall and recognition, so women and men showed better performance in recall and recognition, respectively.&#x0D; Conclusion: In summary, our results suggest that demographic variables of age, gender, and education affect visual memory and visuospatial abilities, and it is essential to generate normative data for research or clinical settings.

Characteristic Mode Solution of Complex-Coefficient Complex-Solution Differential Equations

Computation of complex-coefficient complex-solution differential equations is a problem that arises in various domains of science and engineering. This paper aims at applying the Theory of Characteristic Modes (TCM) approach along with the Method of Moments (MoM) in solving these problems with emphasis on procedures for higher differential equations. Several available methods, known in literatures, are available for solving the problem. The complexity of the available methods differs based on the accuracy of the solution. In this paper, the general method is first presented and then a simplified version of it is proposed to solve high order differential equations. Two examples are illustrated, a third and a fourth order complex-coefficients complex-solution differential equations, to show the simplicity of the proposed method. The proposed approach can be also introduced along with other methods to solve these special occurrences differential equations and other boundary value problems.