Miracle-dz/UniFAR-FTUs huggingface dataset:
Developing an HMD-Based System for Assessing Football Players’ Bird’s-Eye View Transformation Capabilities

In football, if one wants to move the game forward tactically, one must quickly understand how and where the players are moving. It is hypothesised that the ability to convert first-person information into a bird’s-eye view is crucial for making quick and appropriate judgements. In particular, top players have shown great “depth perception”. Although a connection between the conversion of a first-person perspective to a bird’s-eye viewpoint and depth perception is plausible, the connection between these two skills has not yet been clarified. More studies are required to determine whether the transfer of a first-person perspective to a bird’s-eye perspective plays a role in the superior depth vision demonstrated by great players. Understanding this link might have implications for training and the improvement of athletes’ depth perceptions. The primary objective of this study is to design a system capable of systematically assessing first-person and bird’s-eye perspective conversion skills using a Head-mounted display (HMD) and to investigate the association between this ability and depth vision. We performed an experiment in which 12 experienced and 12 novice football players viewed randomly positioned player objects in a virtual football environment from a first-person viewpoint and then rebuilt the player objects position from the bird’s-eye perspective. The depth perception of an individual was also evaluated. Compared with novice players, experienced players were able to rebuild the placement of player objects with greater precision. There was no significant difference in depth perception between the two groups. In trials comprising 2-8 items, there were no significant differences between experienced and novice individuals in terms of the time necessary to locate objects precisely, regardless of expertise. However, in trials involving 10-16 items, the experienced group had a markedly decreased completion time. However, there was no link between the results and depth perception. These results imply that the capacity to convert first-person perspective to bird’s-eye view can be tested. However, the ability to convert a first-person perspective to a bird’s-eye view and depth perception may represent separate skills. This suggests that those who are adept at transforming a first-person viewpoint into a bird’s-eye view may not necessarily have great depth perception, and vice versa. Further studies are required to fully understand the links among these cognitive skills. Other factors, such as age, sex, and experience, may also influence cognitive skills. Future research should explore these variables to gain a more comprehensive understanding of the relationship between viewpoint transformation and depth perception.
EFFECTIVENESS OF MODERN WOUND TREATMENT USING WANDING TECHNIQUES MOIST WOUND HEALING IN HEALING DIABETIC ULCERS IN CLINIC RUMAT JAKARTA

Diabetes Mellitus often becomes a serious health problem in various developing and developed countries in the world. Most people with diabetes mellitus are at risk of developing diabetic ulcers. If not treated quickly it can develop into an infection. The newest wound healing methods currently have an important role in treating diabetic ulcers, one of which is Modern Dressing with Moist Wound Healing. The aim of this research is to identify the effectiveness of modern wound care using moist wound healing dressing techniques in healing diabetic ulcers at the Rumat clinic in Jakarta. The research design used in this study used a quasi-experimental design with a cross-sectional method approach. The population in this study were all patients who underwent wound care at the Rumat Jakarta clinic. The sampling used was accidental sampling, which took cases or respondents who happened to be present or available in a place according to the research context, namely 30 respondents. Based on the results of the Paired T-Test and Independent T-Test, the p-value was obtained (0.000 < α 0.05), this proves that there is the effectiveness of modern wound care using the Moist Wound Healing dressing technique in healing diabetic ulcers in home clinics in Jakarta. Meanwhile, in the control group, after carrying out bivariate analysis using statistical tests, a significance value of p value α = 0.080 (p > 0.05) was obtained. Thus, it can be said that there is no effectiveness of modern wound care using moist wound healing dressing techniques in healing diabetic ulcers at the Rumat Clinic in Jakarta. It is hoped that the Jakarta Rumat Clinic can improve the quality and quality of modern wound care services using moist wound healing dressing techniques for diabetic ulcer sufferers.
Circulating Immune Proteins: Improving the Diagnosis and Clinical Outcome in Advanced Non-Small Cell Lung Cancer

Immunotherapy has been proven a viable treatment option for non-small cell lung cancer (NSCLC) treatment in patients. However, some patients still do not benefit. Finding new predictive biomarkers for immunocheckpoint inhibitor (ICI) response will improve treatment management in the clinical routine. In this regard, liquid biopsy is a useful and noninvasive alternative to surgical biopsies. In the present study, we evaluated the potential diagnostic, prognostic, and predictive value of seven different soluble mediators involved in immunoregulation. Fifty-two plasma samples from advanced NSCLC treated in first-line with pembrolizumab at baseline (PRE) and at first response assessment (FR) were analyzed. In terms of diagnostic value, our results revealed that sFGL1, sGAL-3, and sGAL-1 allowed for optimal diagnostic efficacy for cancer patients. Additionally, the combination of sFGL1 and sGAL-3 significantly improved diagnostic accuracy. Regarding the predictive value to assess patients’ immune response, sCD276 levels at PRE were significantly higher in patients without tumor response (p = 0.035). Moreover, we observed that high levels of sMICB at PRE were associated with absence of clinical benefit (pembrolizumab treatment less than 6 months) (p = 0.049), and high levels of sMICB and sGAL-3 at FR are also related to a lack of clinical benefit (p = 0.027 and p = 0.03, respectively). Finally, in relation to prognosis significance, at PRE and FR, sMICB levels above the 75th percentile are related to poor progression-free survival (PFS) (p = 0.013 and p = 0.023, respectively) and overall survival (OS) (p = 0.001 and p = 0.011, respectively). An increase in sGAL3 levels at FR was associated with worse PFS (p = 0.037). Interestingly, high sGAL-3 at PRE was independently associated with PFS and OS with a hazard ratio (HR) of 2.45 (95% CI 1.14–5.25; p = 0.021) and 4.915 (95% CI 1.89–12.73; p = 0.001). In conclusion, plasma levels of sFGL1, sGAL-3, and sGAL-1 could serve as diagnostic indicators and sMICB, sCD276, and sGAL3 were linked to outcomes, suggesting their potential in assessing NSCLC under pembrolizumab treatment. Our results highlight the value of employing soluble immune biomarkers in advanced lung cancer patients treated with pembrolizumab at first-line.
OPTIMIZATION ALGORITHMS FOR PROJECTILE MOTION: MAXIMIZING RANGE AND DETERMINING OPTIMAL LAUNCH ANGLE

In this paper, we undertake an in-depth exploration of the optimization of parameters governing the trajectory of a projectile. Our primary objective is the determination of the optimal launch angle and initial velocity that yield the maximum achievable range for the projectile. To accomplish this, we leverage five distinct optimization methodologies, specifically the Nelder-Mead, Powell, L-BFGS-B, TNC, and SLSQP algorithms, in pursuit of our research goals. This paper offers a comprehensive analysis of the optimization procedures, shedding light on the impact of these diverse algorithms on the resultant outcomes. For each set of optimized parameters, the manuscript conducts extensive simulations of the projectile’s trajectory, presenting visual depictions of the paths traversed by the projectile. Additionally, our study incorporates comparative charts to emphasize the performance distinctions among various algorithms with respect to both maximum range and launch angle.
Political Partisanship and Belief in Misinformation: Operationalizing Theory of Planned Behavior to Predict Intentions to Quit Social Media

This study uses the theory of planned behavior to predict individuals’ intentions to quit social media. Attitude, subjective norms, and perceived behavioral control significantly predicted intentions to quit social media, accounting for 68 percent of variance among participants (N = 525) representing the US census data. Political partisanship and belief in misinformation also slightly increased the predictability of the TPB, suggesting that they can be used as moderators or antecedents of subjective norms in the future.
Deep Learning-Based Diagnosis of ASD Using Pre-Trained Convolutional Neural Network

Autism Spectrum Disorder (ASD) is a developmental condition affecting the brain, characterized by challenges in social interaction, communication abilities, and repetitive behaviors. Diagnosing ASD is complex due to its varied manifestations in behavior, onset, response to treatment, and coexisting conditions. To address this, our study introduces a methodological approach to diagnose ASD using structural magnetic resonance imaging (sMRI) data and deep learning algorithms. For our analysis, we obtained the necessary dataset from publicly accessible ABIDE-I and ABIDE-II databases. The sMRI data underwent pre-processing using a standardized pipeline, and we focused on the ±5 sagittal slices from the mid-sagittal plane. These slices were then utilized as input for the inception v3 deep learning model. The results of our experiment produced an average 10-fold cross-validation accuracy = 78.83%. Moreover, we achieved a sensitivity = 79.68%, specificity = 77.93%, precision = 79.47%, and f1-score = 79.44%. These metrics indicate the effectiveness of our proposed process pipeline in accurately classifying ASD-like neurodevelopmental disorders. In conclusion, our study provides a valuable methodology that may contribute to improved diagnostic classification of ASD and similar neurodevelopmental conditions using sMRI and advanced deep learning techniques.
Surviving and thriving: How changes in teaching modalities influenced student satisfaction before, during and after COVID-19

This paper leverages analytics methods to investigate the impact of changes in teaching modalities shaped by the COVID-19 pandemic on undergraduate students’ satisfaction within a Spanish brick-and-mortar higher education institution. Unlike research that has focused on faculty- or programme-level data, this study offers a comprehensive institutional perspective by analysing large-scale data (N = 83,532) gathered from satisfaction surveys across all undergraduate courses in eight faculties from 2018 to 2021. The longitudinal analysis revealed significant changes (p < 0.05) in satisfaction indicators, particularly overall satisfaction and perceived workload. During the emergency remote teaching period, there was a significant decrease in satisfaction and high levels of variability across courses. However, a year after emergency remote teaching, with increased implementations of technology-supported online and mixed teaching modalities, satisfaction measures not only recovered but exceeded pre-COVID levels in the aforementioned indicators when the teaching modality was fully co-located. The variability of answers also reached historical lows, reflecting more uniform student experiences. These findings highlight the resilience of educators and the current higher education system and suggest a capacity to learn and improve from disruptive pedagogical changes. The study also provides insights into how data analytics can help monitor and inform the evolution of teaching practices. Implications for practice or policy Higher education institution administrators should improve the understanding of the effects derived from changes in their teaching and learning models, for example, in teaching modalities and related technology support. Student satisfaction data analytics offer useful indicators to study the impact of those effects. Higher education institutions should provide support for educators to ensure minimal deviations from expected averages of educational quality indicators regardless of the educators’ capacity to adapt to changes in the teaching models.
Risk of sarcopenia in older adults with type 2 diabetes mellitus: evaluation of the SARC-F and SARC-CalF as screening tools

Objective: To evaluate the diagnostic power of SARC-F and SARC-CalF as screening tools for sarcopenia risk in older adults with type 2 diabetes mellitus. Methodology: This cross-sectional study of 128 patients was conducted at the endocrinology outpatient clinic of a hospital in Recife, Brazil between July 2022 and February 2023. Sarcopenia was diagnosed according to original and updated European Consensus criteria for older adults. Muscle mass was assessed with electrical bioimpedance, muscle strength was assessed with a handgrip test, and physical performance was assessed with gait speed. Sarcopenia risk was assessed using the SARC-F and SARC-CalF instruments. The sensitivity, specificity, positive and negative predictive values, receiver operating characteristic curve, and area under the curve were analyzed to determine the best diagnostic performance. Results: According to the original and updated versions of the European Consensus criteria, the prevalence of sarcopenia was 25.00% and 10.90%, respectively. Sarcopenia risk was 17.20% according to the SARC-F and 23.40% according to the SARC-CalF. The sensitivity and specificity of the SARC-F ranged from 12.55% to 36.11% and 71.87% to 92.39%, respectively, while those of the SARC-CalF ranged from 47.22% to 85.71% and 82.46% to 88.89%, respectively. The area under the curve for the SARC-F and SARC-CalF varied between 0.51 and 0.71 and 0.67 and 0.86, respectively.Conclusions: The SARC-CalF had better diagnostic performance for all analyzed diagnostic criteria. Adding calf circumference to the SARC-F was an effective screening method for sarcopenia risk in the study population.
Frequency of heterotic hybrids in relation to general combining ability of parents in sweet corn

The success of developing prominent hybrids directly depends on the selection of parents with good combining ability which can transfer desirable genes with additive effects to their progeny. The data of 42 hybrids generated using 7 × 7 full diallel design; their seven parents along with three check hybrids were subjected to combining ability analysis from the experiment that was carried out during rainy season 2019. The analysis of variance showed significant general combining ability, and specific combining ability mean sum of squares for all the thirteen characters studied. It is obvious from the results that three lines (SC Sel 2, SC Sel 1 and SC Sel 3) showed high overall general combining ability status, suggesting these lines as good general combiners across traits. Eighteen hybrids had high overall specific combining ability status, while nearly 52% (22 hybrids), 57% (24 hybrids) and 47% (20 hybrids) of crosses showed high overall mid-parent, better-parent and standard heterosis. The unique superiority of crosses involving high overall general combiner parent in the crosses highlighted the importance of using such parents to realize high heterotic crosses. A non-linear relationship between high overall specific combining ability status and heterotic status of hybrids was noticed. The probability of obtaining a cross with high standard heterosis was more with employing parents with high general combining ability status.
Stock Market Simulation by Micro-Macro GAN

In recent years, research on the fusion of computer simulations with real data, as seen in data assimilation, has been active. In addition, with the development of machine learning, especially deep learning, computer simulations are increasingly using deep learning models in addition to physical models. In the field of finance, research has also been conducted to use machine learning models when simulating markets, for example, to emulate synthetic orders placed by virtual traders. However, it is fundamentally difficult to generate realistic macro dynamics such as the price from micro order dynamics. In this study, we propose a new market simulation method by machine learning model to generate macro dynamics from micro order dynamics. The market simulator built by the machine learning model, which is called Micro-Macro GAN, is trained by coupling two mechanisms. The first mechanism generates micro order dynamics and trains a generator of micro order dynamics to distinguish the real from the synthetic order data using a Wasserstein Generative Adversarial Network (WGAN), which is called the Micro GAN. The second mechanism generates macro dynamics from the micro order dynamics generated by the Micro GAN and trains a generator of macro dynamics to distinguish the real from the synthetic macro data using a Sig-W GAN, which is called the Macro GAN. The macro dynamics data is converted to a signature, and finally, the Sig- W metric is computed as a loss function. As a demonstration, training was performed on Toyota Motor Corporation taken from the Flex Full data provided by the Japan Exchange Group (JPX). The order dynamics data generated from the Micro GAN and the Micro-Macro GAN were compared to the real order dynamics data. The results showed that the Micro-Macro GAN results were more similar to the real dynamics data than the Micro GAN results.
Spatial-temporal joint constraints based tracking method for infrared UAV

The misuse of UAVs has spurred the development of Anti-UAV technology. Infrared detector-based UAV tracking technology has become a research hotspot in the field of the Anti-UAV technology, but still faces the problem of tracking failure caused by background interference. To improve the accuracy and stability of infrared UAV tracking in the complex environments, a spatial-temporal joint constraints based infrared UAV tracking algorithm is proposed. First, a feature pyramid-based Siamese backbone is constructed to enhance the capability of feature extraction for infrared UAVs through cross-scale feature fusion. Next, a region proposal network based on spatio-temporal joint constraints is proposed. Under the constraints of template appearance features and target motion information, the location probability distribution of the infrared UAV is predicted in the entire image, and the prior anchor box is guided to focus on the candidate regions, realizing a soft adaptive search region selection mechanism. By focusing the search area, the anti-background interference capability of the local search strategy and the recapture capability of global search strategy are fused, which effectively mitigates the negative sample interference brought by global search and further enhances the discriminability of target features. Finally, the proposed algorithm is evaluated on the Anti-UAV dataset, achieving precision, success rate, and average precision of 89.5%, 64.9%, and 65.6%, respectively, with a tracking speed of 18.5 FPS. Compared with other advanced tracking algorithms, the proposed algorithm obtains better tracking performance and superior tracking performance in complex scenarios such as fast motion, thermal crossover and distractors interference.
Key Factors Influencing the Building of Arctic Shipping Routes

Arctic shipping routes attract more and more attention because of the increasing possibility to build commercial shipping routes which connect East Asia and Western Europe. To build profitable commercial shipping routes, shipping companies should study many issues about the Arctic carefully since it is a new and unfamiliar frontier. To find out key factors influencing the building of Arctic shipping routes, the authors applied the fuzzy Analytic Hierarchy Process (AHP) model in this study. Based on the AHP experts’ questionnaires, we applied the fuzzy AHP model to analyse the opinions of respondents. This article presents the results of an empirical survey conducted among shipping companies and academic researchers with ship captain experience to explore their attitudes toward the building of Arctic shipping routes. The findings showed that: ‘Safety and risk’ is the most important aspect affecting the building of Arctic shipping routes and in order of relative importance, the top six critical assessment factors are ‘navigation safety and risk analysis,’ ‘governance and cooperation,’ ‘navigation information,’ ‘cargo sources,’ ‘cost,’ and ‘navigator ability,’ respectively.
Austrian farmers perception of new weeds

Abstract The composition of weed floras in Central European fields has shifted creating a novel management issue: new weeds, that is, species that are currently spreading and increasing in impact. In their role as primary decision makers on the ground, farmers' perception of these new weeds plays a pivotal role in collecting information on their occurrence and control. We conducted an online survey to determine if Austrian farmers recognized 15 selected new weed taxa (12 species and 3 genera) from their farm. The 181 surveyed farmers also estimated the required management effort for these species and elicited their current management practices. Additional questions were posed to understand farmers' general perception of changes in the weed flora. We used a generalized linear mixed model to estimate differences in management effort and identify new weeds that merit monitoring and management programs. Two weed genera (Fallopia spp. and Panicum spp.) showed significantly higher than average management effort. The most commonly used management measures were manual removal, herbicide use and crop rotation. A majority of farmers reported changes in the weed flora; over two thirds reported new species and over one third reported new weeds that were difficult to control. In summary, our results suggest that respondents were aware of the challenges posed by new weeds but required more information on management and prevention strategies.
Graptolite distribution and lithofacies; some case histories

-Graptolites have been recognized for some time as exhibiting provincialism, particularly during the Early Ordovician. On the other hand, the popular image of graptolites is that they were surface, or near-surface, dwellers and their distributions were entirely unrelated to lithofacies. This study, on the basis of an examination of the distribution of graptolite faunas from particular zones from the Ordovician to early Devonian of the Canadian Cordillera and China, concludes that different lithofacies in the same sedimentary basin, may contain strikingly different faunas. Graptolite compositions vary from differences in abundances of some species, to total exclusion of distinctive genera or species in different lithotopes. The reasons for the differences are unknown, but appear to be related, indirectly at least, to water depth differences.