Competitive Scores Research Articles

Internet Gaming Disorder and Competitiveness: A Cross-Sectional Study

Internet Gaming Disorder (IGD) and Competitiveness are growing challenges for adolescents. This cross-sectional study examines IGD and Competitiveness in adolescent boys (N = 220) and girls (N = 135) who play online games via a convenient sampling technique. The study also examines weekly game time. The results show that adolescent boys and girls differ in IGD and competitiveness scores with the evidence of the significance of the main effect for the assessment of IGD with F (1,353) = [9.569], p = .002 and Competitiveness with F (1,353) = [4.562], p = .033 respectively. Additionally, excessive internet gaming can increase IGD and competition in both male and female adolescents. It suggests that gaming for more than 20 hours per week may increase IGD symptoms and encourage Competitiveness in adolescents. The findings emphasize the importance of monitoring and regulating adolescents' gaming behaviours, especially excessive gaming. Parents, educators, and healthcare professionals can help identify adolescents at risk of IGD and provide appropriate support and therapies. Promoting appropriate gaming habits and preventing the detrimental effects of excessive gaming in adolescents is crucial.

Temporal Development GAN (TD-GAN): Crafting More Accurate Image Sequences of Biological Development

In this study, we propose a novel Temporal Development Generative Adversarial Network (TD-GAN) for the generation and analysis of videos, with a particular focus on biological and medical applications. Inspired by Progressive Growing GAN (PG-GAN) and Temporal GAN (T-GAN), our approach employs multiple discriminators to analyze generated videos at different resolutions and approaches. A new Temporal Discriminator (TD) that evaluates the developmental coherence of video content is introduced, ensuring that the generated image sequences follow a realistic order of stages. The proposed TD-GAN is evaluated on three datasets: Mold, Yeast, and Embryo, each with unique characteristics. Multiple evaluation metrics are used to comprehensively assess the generated videos, including the Fréchet Inception Distance (FID), Frechet Video Distance (FVD), class accuracy, order accuracy, and Mean Squared Error (MSE). Results indicate that TD-GAN significantly improves FVD scores, demonstrating its effectiveness in generating more coherent videos. It achieves competitive FID scores, particularly when selecting the appropriate number of classes for each dataset and resolution. Additionally, TD-GAN enhances class accuracy, order accuracy, and reduces MSE compared to the default model, demonstrating its ability to generate more realistic and coherent video sequences. Furthermore, our analysis of stage distribution in the generated videos shows that TD-GAN produces videos that closely match the real datasets, offering promising potential for generating and analyzing videos in different domains, including biology and medicine.

Competition Dynamics in Invertebrates Across Geographical Gradients

Studying competition dynamics of invertebrate species is essential to understanding species distribution, how species become invasive, and dealing with pests and pollinators. However, there is a gap in the literature when it comes to what geographical factors influence invertebrate competition. This study investigates competition dynamics of terrestrial invertebrates across various ecosystems and geographical areas, as well potential predictors for the patterns observed. I used food lures across sites in multiple countries, and recorded the number of visitors and level of consumption to attribute a competition score to each lure. Three different food types were used to account for dietary preferences (Fat, Protein, and Sugar). The results show that there is a significant variation of competition scores across sites. In addition, I identified a significant statistical interaction between sites and food types in relation to competition scores, which suggests that competition is different between the sites, but the pattern of variation differs per food type. A regression was conducted to see if Net Primary Productivity or the geological age of an ecosystem were predictors of the variation in competition scores. Geological age came out statistically significant for the scores in the Fat food type, suggesting that it may be an important driver for invertebrate competition. Idiosyncrasies of the sites are discussed with the aim to identify other factors that may affect competition levels and their patterns. This research contributes to the study of competition dynamics in terrestrial invertebrates and could inspire an approach to predicting interactions and effects of new or introduced species.

Research on Grading and Evolution of International Trade Development Quality of City Clusters Based on Logit-ISM Modeling

Abstract In order to further accelerate the realization of high-quality development of international trade and enhance the discourse power in international trade activities, this paper explores the grading and evolution of the quality of international trade development of city clusters. After analyzing the DEA static model and DEA-Malmquist dynamic evaluation model, combined with the entropy value method, the entropy value-DEA model is constructed to measure the comprehensive evaluation of the quality of international trade development of the city cluster, and the results are comprehensively analyzed. In addition, combined with the Logit-ISM model to analyze the factors affecting the development quality of international trade of city cluster and explore the grading and evolution of the development quality of international trade of city cluster from two perspectives of global statistics and global spatial trend. The results show that the fluctuation of the trade structure score is smaller than the trade scale, with an average growth rate of 0.165, and the trade competitiveness score basically stays in a state of growth except for 2008 and 2009, with an average annual growth rate of 0.972. The urban agglomerations in the eastern region account for a total of 0.38 of the city agglomerations in the city agglomerations synthesis, and the number of prefectural-level cities included is 106, which accounts for a total of 0.52 of the number of prefectural-level cities included in the city agglomerations. The quality of international trade development in city clusters in the eastern and northeastern regions stands out as the most significant. Based on the research in this paper, we advocate for the balanced development of the quality rating and evolution of each city cluster in international trade development.

Culture as a Hiring Criterion: Systemic Discrimination in a Procedurally Fair Hiring Process

Criteria used in hiring workers often do not reflect the skills required on the job. By comparing trainee performance for newly hired workers conditional on competitive civil service examination scores for hiring French public sector workers, we test whether women and men with the same civil service examination score exhibit similar performance in a job-related trainee programme. Both the civil service examination and trainee scores contain anonymous and non-anonymous components that we observe separately. We find that by the end of the trainee programme (first year of employment), women are outperforming men on both anonymous written and non-anonymous oral evaluations, a finding that holds both conditionally and unconditionally for the civil service examination results. According to further analysis, however, it is the anonymously graded “essay on common culture” civil service examination that, unlike the other CSE components, disadvantages women in this particular context.

Maximizing choreography and performance in artistic swimming team free routines: the role of hybrid figures

Hybrid figures serve as an important component of artistic swimming, however, no prior research has investigated objective indicators for predicting total scores in competition. This study aimed to identify significance of hybrid figure (HF) elements in predicting total scores in artistic swimming competitions and compare these variables between the Beijing team trials and international official competitions. Eight HF variables were measured in the videos from the international artistic swimming competitions, with calibration and measurement of the variables leg height index and leg angle deviation being performed by means of Kinovea. Multiple linear regression was conducted to predict the total scores based on these variables investigated for the international teams, which showed 5 significant predictors including movement frequency, leg height index, leg angle deviation, mean pattern duration, and rotation frequency. Wilcoxon signed-rank test was used to determine the differences in the variables between the Beijing team and the international teams (top 5), which showed significant difference on movement frequency, leg angle deviation, leg height index and total score. When designing hybrid figures choreography of team free routine in artistic swimming, coaches should prioritize appropriate movement frequency, pattern changes and rotations over excessively long durations. Overly complex HF choreography may lead to a decline on performance in artistic swimming competition.

Evaluation of sports tourism competitiveness of urban agglomerations in Guangdong-Hong Kong-Macao Greater Bay Area

In the era of experience economy, sports tourism has been emerged as a new tourism form and consumption hot spot. Evaluation of the competitiveness of sports tourism is helpful to accurately grasp the key competitive factors of sports tourism development and enhance regional sports tourism resource development, market expansion and product upgrading. This study adopted entropy-weight TOPSIS model to construct the index system of sports tourism competitiveness of urban agglomerations and investigated the sports tourism competitiveness of 11 cities in Guangdong-Hong Kong-Macao Greater Bay Area from 2016 to 2020. The obtained results showed that sports tourism development in the Greater Bay Area was unbalanced and obviously different. Guangzhou, Shenzhen and Hong Kong are competitive, Macao is average competitive and Dongguan, Jiangmen, Zhuhai, Foshan, Zhongshan, Huizhou and Zhaoqing are less competitive. Based on evolution trend, sports tourism competitiveness in Guangzhou has always been at the forefront of the Greater Bay Area, while those of Hong Kong and Macao have presented a declining trend. Also, sports tourism competitiveness in inland cities has been continuously enhanced and the development focus of sports tourism in Guangdong-Hong Kong-Macao Greater Bay Area has gradually shifted to mainland. The 11 cities of the Greater Bay Area were classified as strong, average and weak areas on the basis of their sports tourism competitiveness scores. Finally, in terms of the overall situation of Guangdong-Hong Kong-Macao Greater Bay Area and specific conditions of various cities, countermeasures and suggestions have been provided for sports tourism resource development, sports tourism service level, government functional departments, event brand building, enterprise integration, etc.

An Equivalence Between Fair Division and Wagering Mechanisms

We draw a surprising and direct mathematical equivalence between the class of fair division mechanisms, designed to allocate divisible goods without money, and the class of weakly budget-balanced wagering mechanisms, designed to elicit probabilities. Although this correspondence between fair division and wagering has applications in both settings, we focus on its implications for the design of incentive-compatible fair division mechanisms. In particular, we show that applying the correspondence to competitive scoring rules, a prominent class of wagering mechanisms based on proper scoring rules, yields the first incentive-compatible fair division mechanism that is both fair (proportional and envy-free) and responsive to agent preferences. Moreover, for two agents, we show that competitive scoring rules characterize the whole class of nonwasteful and incentive-compatible fair division mechanisms, subject to mild technical conditions. As one of several consequences, this allows us to resolve an open question about the best possible approximation to optimal utilitarian welfare that can be achieved by any incentive-compatible mechanism. Finally, because the equivalence greatly expands the set of known incentive-compatible fair division mechanisms, we conclude with an evaluation of this entire set, comparing the mechanisms’ axiomatic properties and examining their welfare performance in simulation. This paper was accepted by Yan Chen, behavioral economics and decision analysis. Supplemental Material: The data and online appendix are available at https://doi.org/10.1287/mnsc.2022.02615 .

The MEREC-AROMAN method for determining sustainable competitiveness levels: A case study for Turkey

Sustainable competitiveness represents a multifaceted phenomenon encompassing economic, environmental, and social dimensions. Macro-level competitiveness strategies are formulated based on the diverse capitals possessed by individual countries, thereby giving rise to variations in the sustainable competitiveness strategies of each country. This research introduces a novel hybrid method called the method based on the removal effects of criteria (MEREC)−alternative ranking order method accounting for two-step normalization (AROMAN) for determining sustainable competitiveness levels. This study aims to assess Turkey's sustainable competitiveness position vis-à-vis its border neighbors. Natural capital, resource efficiency and intensity, social capital, intellectual capital and innovation, economic sustainability, and governance efficiency are the Global Sustainable Competitiveness Index (GSCI) indicators. The GSCI indicators are employed as criteria for determining the sustainable competitiveness scores of countries. The findings show that the “resource efficiency and intensity” criterion has the highest level of significance. The sustainable competitiveness level of Turkey to its neighboring countries is elucidated based on the results. Recommendations are formulated for the development of strategies aimed at determining Turkey's position in the race for sustainable competitiveness. The introduced MEREC-AROMAN can be utilized to provide rules of thumb for other countries to improve their sustainable competitiveness. This research offers decision support for the formulation of countries' sustainable competitiveness strategies and policies, fostering awareness in the planning and establishment of regional collaborations among nations.

Construction of digital cultural tourism integration holiday system based on differential equation model

Abstract This paper first discusses the new mode of digital technology to boost the culture and tourism integration vacation industry and analyzes the specific application of digital technology in the culture and tourism integration vacation. Secondly, the index evaluation system is constructed with the goal of vacation destination competitiveness, and the gray time-lag prediction model is constructed by introducing the time-lag differential equation model combined with the gray correlation degree and the GM(1,1) model. Finally, an empirical analysis of competitiveness prediction was conducted to confirm the effectiveness of the gray time-lag prediction model. The results show that the tourism resource product has the highest comprehensive relevance, with a score of 0.9254, respectively. The use of the gray time lag prediction model can realize the prediction of the competitiveness score of the vacation destination and provide effective data support for the construction of a digital culture and tourism integration vacation system. This shows that the time-lag differential equation combined with the gray prediction model can be used to analyze the competitiveness score of the vacation destination, promote the deep integration of culture and tourism industry, and create a vacation system that better meets the expectations of tourists.

Development of a Mission-Tailored Tail-Sitter MAV

Vertical takeoff and landing (VTOL) vehicles are among the most versatile UAVs, appropriate for various missions. Given that there are still open challenges regarding the VTOL design, this paper presents the full development and test cycle of a tail-sitter. IMAV 2022 competition rules were used to define the mission. A multidisciplinary design and optimization strategy was defined with the goal of maximizing competition score considering design, manufacturing, and competition constraints. The resulting vehicle was designed to fly at 18[Formula: see text]m/s while carrying 200[Formula: see text]g of payload with a total weight of approximately 720[Formula: see text]g. It flew for roughly 13 min at IMAV2022, helping its team to achieve 1st place at the “Package delivery challenge”. Further flight tests revealed the ultimate endurance performance as 18[Formula: see text]min.

Undue Burden the Medical School Application Process Places on Low-Income Latinos

Undue Burden the Medical School Application Process Places on Low-Income Latinos

Development of "Aria," a Compact, Quiet Personal Electric Helicopter

This paper describes the development and flight testing of a personal air vehicle by team Harmony for the GoFly Prize competition. The competition emphasized an open-air ight experience, and the nal aircraft was scored by size (≤ 8.5 ft), noise (<87 dBA), and speed (>30 kt). The highest scoring team would win a $1 million grand prize. Team Harmony endeavored to develop an aircraft which would maximize the competition score. A counterrotating coaxial electric helicopter con guration was chosen to maximize rotor area and reduce disk loading for efficiency and acoustic benefits. The rotors were designed through a parametric study using an in-house aerodynamics code, an in-house acoustics code, and CREATE TM -AV Helios CFD. A quiet electric power train and custom 11 kWh battery were developed. Flight control was implemented with dual, independent, electronically coupled swashplates. A one-third-scale prototype aircraft was first developed and flighttested. Then a full-scale, 520 lb (235.4 kg) prototype with an 8.45 ft(2.58 m) rotor diameter was developed and flight-tested. During hovering, the measured sound pressure levels at 50 ft(15.24 m) were 73 dBA. The full-scale vehicle crashed before its speed capabilities could be tested, but the aircraft would have likely scored well, achieving the goal of the project. This work encompassed several design trade-offs, especially between aerodynamic and acoustic performance in the rotor and blade design, and between acoustics and endurance in the power source selection, and in availability and flight-worthiness of off-the-shelf hardware for personal flight.

KisanQRS: A deep learning-based automated query-response system for agricultural decision-making

Delivering prompt information and guidance to farmers is critical in agricultural decision-making. Farmers’ helpline centres are heavily reliant on the expertise and availability of call centre agents, leading to inconsistent quality and delayed responses. To this end, this article presents Kisan Query Response System (KisanQRS), a Deep Learning-based robust query-response framework for the agriculture sector. KisanQRS integrates semantic and lexical similarities of farmers’ queries and employs a rapid threshold-based clustering method. Before clustering, KisanQRS employs data cleaning by performing steps such as the removal of special characters and extra white spaces. It also removes crop names from the queries, which helps in putting similar queries to the same clusters irrespective of the crop. Subsequently, semantic and lexical similarities of queries are computed, and a final similarity matrix is generated based on the weighted sum of these two similarities. Next, a clustering algorithm is applied to organize the queries into clusters according to their similarity. A query mapping module is trained on these cluster labels as the target. For this query mapping module, LSTM is found to be the optimal method. Next, the answer retrieval method clusters candidate answers for a crop, ranks these answer clusters based on the number of answers in a cluster, and selects the leader of each cluster, finally providing the top-K answers. The dataset used in our analysis consists of a subset of 34 million call logs from the Kisan Call Centre (KCC), operated under the Government of India. We evaluated the performance of the query mapping module on the data of five major states of India with 3,00,000 samples and the quantifiable outcomes demonstrate that KisanQRS significantly outperforms traditional techniques by achieving 96.58% top F1-score for a state. The answer retrieval module is evaluated on 10,000 samples and achieves a competitive NDCG score of 96.20%. KisanQRS could be useful in enabling farmers to make informed decisions about their farming practices by providing quick and pertinent responses to their queries.

The change pattern of CSR ecological strategy of trees and seedlings during different succession stages in tropical lowland rainforests

Monitoring changes to growth-survival strategies is beneficial during plant growth and development to understand the dynamics of community succession. We measured key leaf traits and calculated competition, stress-tolerance, and ruderals ecological strategy scores for trees and seedlings in different successional stages in tropical lowland rain forests. We analyzed functional trait patterns and CSR strategies with plant growth and development through the different succession stages. We found that trees used strategies that were shifted from S/CS and CS strategies to CS/CSR strategies along the succession. However, seedlings maintained the use of the S/CSR strategy. Seedlings showed lower leaf dry matter content, higher specific leaf area, and their leaf area was relatively conservative. We also observed that the functional traits of seedlings and large trees showed basically consistent changes through each succession stage. Using the standard deviation of C-, S-, and R-scores, we found that the ecological strategy width of trees is smaller, while seedlings have a wider range of ecological strategies. Together, this information can be used to define plant succession changes with functional plant trait changes using different CSR ecological strategies in tropical rainforests that are a threated due to human activities. The research results provide scientific basis for understanding the strategic change of plant growth and predicting the direction of forest function succession, and also provide theoretical support for the management of tropical lowland rainforest in China.

Force plate testing is correlated with jumping performance in elite Nordic skiers

IntroductionSki jumping (SJ) and Nordic combined (NC) athletes are at higher risk for femoroacetabular impingement (FAI) compared to the general population. ObjectivesThe purpose of this study was to determine the relationship between force plate testing metrics, Functional Movement Screen (FMS) scores, and radiographic signs of FAI and SJ performance. MethodsElite SJ and NC athletes were prospectively recruited. FMS testing was performed to assess muscular imbalance, instability, and immobility. Vertical jump testing was performed using a commercially available force plate device. Ski jump score and distance score were measured on a single jump for each athlete. ResultsTwenty-one athletes (10 SJ, 11 NC) with competition scores were included. On FMS testing, most SJ (67%) and NC (73%) athletes demonstrated core instability. There were higher rates of hip instability and ankle stiffness among SJ athletes, although not statistically significant (P > .05). Ski jump distance score was significantly positively correlated with force plate vertical jump height, maximum acceleration, maximum velocity, concentric impulse, counter-movement depth, and flight time (P < .05). FMS testing results and radiographic FAI were not significantly associated with competition scores. ConclusionsOur results show significant positive correlations between competition scores and force plate jump testing performance as measured by jump height, acceleration, maximum velocity, counter-movement depth, and concentric impulse in elite SJ and NC athletes. Our findings suggest that ski jumpers may benefit from training that emphasizes stretch-shortening cycles and eccentric-to-concentric contraction transition, which may optimize their performance and promote joint stability.

Parkinsonian Tremor Detection with Compact Convolutional Transformer from Bispectrum Representation of tri-Axial Accelerometer Signals.

After the breakthroughs of Transformer networks in Natural Language Processing (NLP) tasks, they have led to exciting progress in visual tasks as well. Nonetheless, there has been a parallel growth in the number of parameters and the amount of training data, which led to the conclusion that Transformers are not suited for small datasets. This paper is the first to convey the feasibility of Compact Convolutional Transformers (CCT) for the prediction of Parkinsonian postural tremor based on the Bispectrum (BS) representation of IMU accelerometer time series. The dataset includes tri-axial accelerometer signals collected unobtrusively in-the-wild while subjects are on a phone call, and labelled by neurologists and signal processing experts. The BS is a noise-immune, higher-order representation that reflects a signal's deviation from Gaussianity and measures quadratic phase coupling. We performed comparative classification experiments using the CCT, pre-trained CNNs such as VGG-16 and ResNet-50, and the conventional Vision Transformer (ViT). Our model achieves competitive prediction accuracy and F1 score of 96% with only 1.016 M trainable parameters, compared to the ViT with 21.659 M trainable parameters, in a five-fold cross-validation scheme. Our model also outperforms pre-trained CNNs such as VGG-16 and ResNet-50. Furthermore, we show that the performance gains are maintained when training on a larger dataset of BS images. Our effort here is motivated by the hypothesis that data-efficient transformers outperform transfer learning using pre-trained CNNs, paving the way for promising deep learning architecture for small-scale, novel and noisy medical imaging datasets.Clinical relevance- Novel deep learning model for unobtrusive prediction of Parkinsonian Postural Tremor from Bispectrum image representation of tri-axial accelerometer signals collected in-the-wild.

Automatic fracture characterization in CT images of rocks using an ensemble deep learning approach

The presence of fractures in a rock mass can have a substantial influence on its mechanical and hydraulic properties. For many years, computed tomography (CT) scan has been effectively utilized to investigate the internal structures of rock. However, quantitative characterization of fracture based on CT data remains a challenging endeavor due to the inevitable blurry appearance of fractures in CT images, complexity in fracture patterns and the heterogeneity of host rock. In this study, a deep learning-based method is presented for automatically and accurately segmenting rock fractures in CT images, with special consideration for directional ambiguous fractures embedded in heterogeneous backgrounds. Our method involves two stages: (1) fracture detection in the form of minimal bounding boxes using the Faster R-CNN deep learning algorithm, and (2) fracture segmentation inside the detected bounding boxes using the U-Net deep learning algorithm. The detection stage aims to establish spatial constraints for the segmentation process, thereby significantly minimizing undesirable noise and artifacts existing in the background from consideration and consequently improving the segmentation result. In addition, we also develop a system that can automatically extract fracture properties including orientation, length, aperture, and wall roughness of fractures based on detection and segmentation results from our method. Experiments on CT images of fractured coarse-grained granite, fine-grained sandstone, and shale core samples were carried out in an attempt to confirm the applicability of our approach. The comparison between fracture segmentation results and the manually-annotated ground truths shows that our approach can achieve a competitive dice score of up to 0.942 and also outperform other state-of-the-art deep learning approaches including Mask R-CNN and U-Net alone.

Understanding transfer learning for chest radiograph clinical report generation with modified transformer architectures

The image captioning task is increasingly prevalent in artificial intelligence applications for medicine. One important application is clinical report generation from chest radiographs. The clinical writing of unstructured reports is time consuming and error-prone. An automated system would improve standardization, error reduction, time consumption, and medical accessibility. In this paper we demonstrate the importance of domain specific pre-training and propose a modified transformer architecture for the medical image captioning task. To accomplish this, we train a series of modified transformers to generate clinical reports from chest radiograph image input. These modified transformers include: a meshed-memory augmented transformer architecture with visual extractor using ImageNet pre-trained weights, a meshed-memory augmented transformer architecture with visual extractor using CheXpert pre-trained weights, and a meshed-memory augmented transformer whose encoder is passed the concatenated embeddings using both ImageNet pre-trained weights and CheXpert pre-trained weights. We use BLEU(1-4), ROUGE-L, CIDEr, and the clinical CheXbert F1 scores to validate our models and demonstrate competitive scores with state of the art models. We provide evidence that ImageNet pre-training is ill-suited for the medical image captioning task, especially for less frequent conditions (e.g.: enlarged cardiomediastinum, lung lesion, pneumothorax). Furthermore, we demonstrate that the double feature model improves performance for specific medical conditions (edema, consolidation, pneumothorax, support devices) and overall CheXbert F1 score, and should be further developed in future work. Such a double feature model, including both ImageNet pre-training as well as domain specific pre-training, could be used in a wide range of image captioning models in medicine.

Docking Studies and Molecular Dynamics Simulations of Potential Inhibitors from the Brown Seaweed Sargassum polycystum (Phaeophyceae) against PLpro of SARS-CoV-2

The COVID-19 disease is a major problem affecting human health all over the world. Consequently, researchers have been trying to find solutions to treat this pandemic-scale disease. Even if there are vaccines and approved drugs that could decrease the spread of this pandemic, multidisciplinary approaches are still needed to identify new small molecules as alternatives to combat COVID-19, especially those from nature. In this study, we employed computational approaches by screening 17 natural compounds from the tropical brown seaweed Sargassum polycystum known to have anti-viral properties that benefit human health. This study assessed some seaweed natural products that are bound to the PLpro of SARS-CoV-2. By employing pharmacophore and molecular docking, these natural compounds from S. polycystum showed remarkable scores for protein targets with competitive scores compared to X-ray crystallography ligands and well-known antiviral compounds. This study provides insightful information for advanced study and further in vitro examination and clinical investigation for drug development prospects of abundant yet underexploited tropical seaweeds.