Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact of an exergame intervention on cognitive-motor functions and training experience in young team sports athletes: a non-randomized controlled trial

Introduction Team sports athletes need excellent perceptual-cognitive skills, particularly executive functions (EF) to strategically perform on the field. The transfer effect of cognitive training might be accomplished by the inclusion of cognitive stimuli into a physically active environment as these couplings are required in real game situations. A training approach that combines both components is exergaming. The primary objective of this study was to gain preliminary insights into the effects of exergaming on cognitive-motor functions in young team sports athletes. The secondary objective was to investigate participants' training experience and well-being over time. Methods Participants were assigned to the intervention or control group. In the intervention group, participants trained with the ExerCube—a mixed reality exergame. The training was planned for 10 weeks (two sessions per week à 25 min) but had to be shortened by 2 weeks due to COVID-19 restrictions. The control group had no additional training. Outcomes included a computer-based alertness test and a cognitive-motor test battery to assess different EF (flexibility, divided attention, and inhibition) via a FitLight Trainer setup. Results Twenty-four athletes [mean age (±SD) 15.0 ± 0.7 years], evenly split into the intervention group (N = 12; male N = 6; female N = 6; 14.7 ± 0.5 years) and the control group (N = 12; male N = 7; female N = 5; 15.3 ± 0.8 years), participated in the study. Participants in the intervention group performed on average 9.4 ± 3.3 training sessions over 8 weeks. Significant time x group interaction effects were evident for the cognitive-motor sub-tests flexibility (F = 12.176, p < 0.001, d = 1.488) and divided attention for auditive stimuli (F = 9.776, p = 0.002, d = 1.404) in favour of the intervention group. For the alertness test, a medium effect size (time x group interaction) was seen for the variability of the reaction time (F = 2.196, p = 0.138, d = 0.632) in favour of the intervention group. Training experience and well-being were consistently at medium to high levels. Conclusion The ExerCube training yielded positive effects on concentration, flexibility, and divided attention indicating that exergaming can be an innovative training approach for team sports athletes.
Distributed Aggregative Game for Multi-Agent Systems With Heterogeneous Integrator Dynamics

Distributed aggregative games have been intensely researched due to their wide application in various engineering scenarios. This brief aims to address the distributed aggregative game over the multi-agent system with heterogeneous high-order integrator dynamics and undirected, connected networks. In this game, the local objective function incorporates its local decision variable and an aggregative term that combines all agents’ decision variables. The aggregative term is estimated using the consensus method. Therefore, the Nash equilibrium-seeking strategy is created using the gradient descent flow and state feedback control technologies. In addition, it is proved that the Nash equilibrium is achieved with an exponential convergence rate through the use of the Lyapunov theory. Finally, the strategy’s utility is evaluated by two simulation examples.
Research on optical interconnection system employing deep learning assisted LDPC coding

In this paper, an intensity modulation direct detection interconnection system employing modeling-driven neural network (MDNN) assisted low-density parity-check code (LDPC) is proposed and experimentally demonstrated. The weights and biases are utilized to optimize the decoder parameters through model-driven deep learning LDPC decoding processing. Compared with traditional schemes that employ LDPC decoding with high computational complexity and redundancy, the proposed scheme has the advantages of a relatively lower complexity decoder and higher decoding gains. The results show that the MDNN signals can provide a 0.28-dB improvement in receiver power sensitivity and a 46% reduction in complexity in a 10-km IMDD optical four-pulse amplitude modulation interconnection system.
Effect of density and management of oil palm plantations on the avifauna of Southeastern Sierra Leone

Oil palm ( Elaeis guineensis ) is a tropical perennial plant that provides most of the vegetable oil traded internationally. Although native to Africa, oil palm is grown throughout the humid tropics, and the largest producers are now in Southeast Asia. In many regions of cultivation, oil palm has been identified as a leading cause of deforestation and biodiversity loss. In Sierra Leone, oil palm grows wild in secondary forest and fallow land, as well as in plantations; research into its impact on biodiversity is limited. The effects of natural (wild) and plantation oil palm on the avifauna of southeastern Sierra Leone was examined. Over a two-year period, point-count surveys of birds were conducted on six occasions during the wet and dry seasons. Four plots were established in each of the six land-use types: Primary Forest, secondary forest, farm-bush with few oil palms, farm-bush with many oil palms, small plantations and larger plantations. Results from the study suggest that small-scale oil palm plantations, even under traditional low-intensity management, have a reduced avifauna compared to farm-bush. The difference between secondary forest and farm-bush is small, suggesting that traditional management of oil palm as a “non-timber forest product” is less detrimental to biodiversity.
Explainable AI for Material Property Prediction Based on Energy Cloud: A Shapley-Driven Approach

The scientific community has raised increasing apprehensions over the transparency and interpretability of machine learning models employed in various domains, particularly in the field of materials science. The intrinsic intricacy of these models frequently results in their characterization as “black boxes”, which poses a difficulty in emphasizing the significance of producing lucid and readily understandable model outputs. In addition, the assessment of model performance requires careful deliberation of several essential factors. The objective of this study is to utilize a deep learning framework called TabNet to predict lead zirconate titanate (PZT) ceramics’ dielectric constant property by employing their components and processes. By recognizing the crucial importance of predicting PZT properties, this research seeks to enhance the comprehension of the results generated by the model and gain insights into the association between the model and predictor variables using various input parameters. To achieve this, we undertake a thorough analysis with Shapley additive explanations (SHAP). In order to enhance the reliability of the prediction model, a variety of cross-validation procedures are utilized. The study demonstrates that the TabNet model significantly outperforms traditional machine learning models in predicting ceramic characteristics of PZT components, achieving a mean squared error (MSE) of 0.047 and a mean absolute error (MAE) of 0.042. Key contributing factors, such as d33, tangent loss, and chemical formula, are identified using SHAP plots, highlighting their importance in predictive analysis. Interestingly, process time is less effective in predicting the dielectric constant. This research holds considerable potential for advancing materials discovery and predictive systems in PZT ceramics, offering deep insights into the roles of various parameters.
Quantum Computation via Multiport Discretized Quantum Fourier Optical Processors

The light's image is the primary source of information carrier in nature. Indeed, a single photon's image possesses a vast information capacity that can be harnessed for quantum information processing. Our scheme for implementing quantum information processing on a discretized photon wavefront via universal multiport processors employs a class of quantum Fourier optical systems composed of spatial phase modulators and 4f-processors with phase-only pupils having a characteristic periodicity that reduces the number of optical resources quadratically as compared to other conventional path-encoding techniques. In particular, this article employs quantum Fourier optics to implement some key quantum logical gates that can be instrumental in optical quantum computations. For instance, we demonstrate the principle by implementing the single-qubit Hadamard and the two-qubit controlled-not gates via simulation and optimization techniques. Due to various advantages of the proposed scheme, including the large information capacity of the photon wavefront, a quadratically reduced number of optical resources compared with other conventional path-encoding techniques, and dynamic programmability, the proposed scheme has the potential to be an essential contribution to linear optical quantum computing and optical quantum signal processing.
Evaluation of groundwater occurrences in the precambrian basement complex of Ilorin metropolis, southwestern Nigeria Ocena virov podtalnice iz predkambrijske podlage območja deželne prestolnice Ilorina v jugozahodni Nigeriji

Surface water from the Asa and Agba dams, which hitherto supply water to Ilorin metropolis is inadequate, hence the need to supplement with water from boreholes. Forty two boreholes drilled into migmatites, granite gneiss and quartzite in Ilorin area were evaluated. Borehole data showed varied overburden thickness (1.00 m to 36.00 m). The static water level (SWL) contour map showed a radial groundwater flow pattern trending in the NE–SW and NW–SE directions, which is consistent with the structural trends in the area. Estimated yields of the boreholes ranged from 0.30 l/s to 2.75 l/s. Pumping/recovery test of four selected boreholes showed increase in productivity from granite gneiss (transmissivity, T = 11.42 m 3 /d;
Detection of plant reflectance anomalies in mining areas using imaging spectroscopy

Underground mining activities lead to subsidence and ground movements at the topographic surface. The present and future modifications of the topographic situation have a permanent influence of the technical infrastructure as well as to changes of hydrological and ecological parameters. To detect possible influences of the vegetation it is necessary to define parameters, which can describe measured variables and resulting influences related to the considered geo-system. This is the prerequisite to detect relevant ecological effects. Because of the large extent of the affected area and the small-parcelled landscape structure, only high resolution imaging spectroscopy permits an appropriate way to map different biotopes and to detect changes. Vegetation spectra usually appear similar because the main reflectance components are the same. This fact requires the adoption of a sophisticated strategy to classify different vegetation types and to describe them in terms of vitality. Therefore a set of different parameters derived from the reflectance signatures of different vegetation types were analysed. This approach includes well known parameters like the description of the red edge and the derivation of reflectance features using continuum removed spectra at the green reflectance peak near 0,57 µm. In addition derivative analyses, originally developed for chemical spectrometry applications, were adapted. These calculations led to different parameters which were analysed in particular to detect anomalies in different reflectance signatures. This approach allows the analysis of different locations influenced by mining activities and the related changes of the hydrological situation.
Tiered on-the-ground implementation projects for Gulf of Mexico water quality improvements

Both the Gulf Hypoxia Action Plan for Reducing, Mitigating, and Controlling Hypoxia in the Northern Gulf of Mexico and Improving Water Quality in the Mississippi River Basin (USEPA 2008) and the GOMA Governors' Action Plan II for Healthy and Resilient Coasts (GOMA 2009) call for the development and implementation of nutrient reduction strategies to reduce excess nutrient loads to the Gulf of Mexico. However, it is not just the Gulf of Mexico waters that are impacted by excess nutrients. The US Environmental Protection Agency Wadeable Streams Assessment (USEPA 2006) and the National Lakes Assessment (USEPA 2010) indicated almost one-third of the nation's stream miles and 20% of the lakes contain high total phosphorus (P) and total nitrogen (N) concentrations. Over 6,000 waterbodies in the United States are impaired by nutrients (USEPA 2011). The US Environmental Protection Agency Science Advisory Board has called for a 45% reduction of both N and P loads from the Mississippi River Basin to achieve the goal of a 5,000 km2 (1,930 mi2) hypoxic zone in the Gulf of Mexico (USEPA 2007). The approach taken by several states, including Mississippi, involves reduction of excess nutrients to attain the designated uses of state waterbodies for cumulative benefits…
Zero-Voltage-Switching Analysis Model of the Triple-Active-Bridge Converter

This study aims to analyze the zero-voltage-switching (ZVS) region of a Triple-Active-Bridge (TAB) converter with five degrees of freedom. A TAB converter is an isolated converter derived from a dual-active-bridge (DAB) converter and composed of three full bridges (FBs) coupled to three winding transformers. To reduce the switching loss of the 12 active switches that compose 3 FBs, the ZVS operation is essential. However, owing to the numerous operation modes derived by five-phase shift ratios, ZVS analysis is complicated, particularly in the time domain. Therefore, this study presents the ZVS analysis model of the TAB converter based on the generalized harmonic approximation (GHA). Through the GHA of a TAB converter, the proposed model consists of unified formulas applicable to all operating ranges of the converter. Unified formulas consider all parameters, such as series inductance, port voltage, parasitic capacitance, transformer voltage, and turn ratio. In the proposed model, the ZVS area is confirmed using five-phase ratios with voltage modulation ratios as variables and verified using MATLAB and experiments.
The age‐wage‐productivity puzzle: Evidence from the careers of top earners

There is an inverted u‐shaped relationship between age and wages in most labor markets, but the effects of age on productivity are often unclear. We use panel data in a market of high earners, professional footballers (soccer players) in North America, to estimate age‐productivity and age‐wage profiles. We find stark differences; wages increase for several years after productivity has peaked, before dropping sharply at the end of a career. This poses the question: why are middle‐aged workers seemingly overpaid? We investigate a range of possible mechanisms that could be responsible, only finding evidence that tentatively supports a talent discovery theory.
A process to foster pathology-related effects of design primes – how orthopedic patients might benefit from design features that influence health behaviour intention

A growing body of literature mainly in the context of consumer research indicates that the formal-aesthetic and conceptual design of objects can influence users’ thoughts, emotions and even behavioural patterns. While there is strong evidence regarding these effects on actual purchasing decisions, evidence on the effect of aesthetic design features (e.g., haptics, colour) on health-related mental concepts and intentions for health behaviour change is scarce. Based on insights from material and conceptual priming, this article illustrates the research-driven and evidence-based design process of two design primes and comprises pre-tests and an experiment in two settings on the effect of design on health behaviour focusing i.a. on intention for health behaviour change. In an evidence-based and research-driven process, two lecterns were designed to work as primes, i.e., to have a positive vs. negative influence on several mental constructs (sense of control, sense of coherence, resiliency, self-efficacy) and health-related intention. The lecterns differed mainly in terms of aesthetic appearance (e.g., material, colour, proportion, steadiness). They were tested in (a.) a university setting with students (n = 83) and (b.) a clinical setting with orthopaedic rehabilitation patients (n = 38). Participants were asked to perform an unrelated task (evaluation of an unrelated product) while standing at and using the lecterns. Overall, t-tests and Mann–Whitney-U tests show no significant differences but differing tendencies in a mentioning task. When asked to name health-promoting activities, in the clinical setting, participants using the “positive” prime (i.e., the steady lectern, n = 13) mentioned more sport-related aspects on average and a higher portion of sport-related aspects of their answers than participants using the “negative” prime (n = 11). In the university setting (positive: n = 36; negative n = 38), no such differences emerged. This finding gives reason to believe that the prime might be specifically effective in the clinical setting as it relates to physical activity being the most relevant topic of the patients’ pathology.
Plant–plant interactions vary greatly along a flooding gradient in a dam-induced riparian habitat

Plant–plant interactions under extreme environmental stress are still controversial. The stress gradient hypothesis (SGH) proposes that facilitation prevails under extreme environmental stresses, while an alternative view states that facilitation collapses or even switches back to competition at the extreme end of stress gradients. However, how the relationship between plant–plant interaction and periodic extreme flooding stress varies and its underlying mechanism are still unclear in a dam-regulated riparian ecosystem. We established a controlled experiment using two dominant species pairs (Cynodon dactylon–Cyperus rotundus and C. dactylon–Xanthium sibiricum) in the water level fluctuating zone of the Three Gorges Dam to examine their growth responses to the periodic extreme flooding stress. The results showed that as flooding stress increased, the competitive effect of C. dactylon on X. sibiricum shifted to facilitation, whereas the effect of X. sibiricum on C. dactylon maintained a strong inhibition. The plant height of X. sibiricum was the most important driver of the interaction between X. sibiricum and C. dactylon along the flooding gradient. The net effect of C. dactylon on C. rotundus shifted from neutral to negative, and the inhibitory effect of C. rotundus on C. dactylon became stronger at the extreme end of flooding stress. The root biomass of the two species was the key trait regulating their interaction with increasing flooding stress. Overall, the SGH was partially supported along our periodic extreme flooding stress gradient. Aboveground resource (light) might be the dominant factor driving the response of the interaction between annual plants and perennial clonal plants to periodic flooding stress, whereas belowground resource (water and nutrients) was probably the dominant factor for perennial clonal plants. Our study will help to further understand the environmental responses of plant–plant relationships and their regulatory mechanism, and the succession of riparian plant communities under extreme environmental changes, providing a basic theoretical basis and data support for the ecological restoration and management of riparian wetland vegetation.
Improvement of processing time for stereo vision-based pedestrian detection

In this paper, we propose the method of processing time improving for stereo vision-based pedestrian detection. Despite improving the detection performance and processing time by limiting the obstacle area using the disparity map, error of disparity map itself and variations of pedestrian size and position make the detection performance worse. Thus, we try to improve the processing time, maintaining the detection performance. Our proposes method consists of two steps, namely optimal region extraction step from approximate obstacle area extracted using the disparity map and improved pyramid-based pedestrian detection step for recognizing each region. The pyramid-based pedestrian detection step also consists of several steps such as region normalization, scaling up or down, recognition with training model. The proposed method is verified through the experiments using ETH data base.
Proteome Network Analysis Identifies Potential Biomarkers for Brain Aging

Background: Alzheimer’s disease and related dementias (ADRD) involve biological processes that begin years to decades before onset of clinical symptoms. The plasma proteome can offer insight into brain aging and risk of incident dementia among cognitively healthy adults. Objective: To identify biomarkers and biological pathways associated with neuroimaging measures and incident dementia in two large community-based cohorts by applying a correlation-based network analysis to the plasma proteome. Methods: Weighted co-expression network analysis of 1,305 plasma proteins identified four modules of co-expressed proteins, which were related to MRI brain volumes and risk of incident dementia over a median 20-year follow-up in Framingham Heart Study (FHS) Offspring cohort participants (n = 1,861). Analyses were replicated in the Cardiovascular Health Study (CHS) (n = 2,117, mean 6-year follow-up). Results: Two proteomic modules, one related to protein clearance and synaptic maintenance (M2) and a second to inflammation (M4), were associated with total brain volume in FHS (M2: p = 0.014; M4: p = 4.2×10–5). These modules were not significantly associated with hippocampal volume, white matter hyperintensities, or incident all-cause or AD dementia. Associations with TCBV did not replicate in CHS, an older cohort with a greater burden of comorbidities. Conclusions: Proteome networks implicate an early role for biological pathways involving inflammation and synaptic function in preclinical brain atrophy, with implications for clinical dementia.
Indonesian EFL Students’ Perception of Online Learning as Expressed through Metaphors

Language learning can get two essential benefits from metaphor, as it is an effective tool for language learning and a useful tool for revealing abstract, obscure, and tacit ideas. One of the most problematic phenomena in education today is online learning (OL). Although all higher learning institutions have adopted it for almost three years, OL is still far from being effective. To get a better understanding of OL, this study aims at exploring EFL students’ experience and perception of OL employing metaphor analysis. Involving 69 students of a private university in Jakarta, Indonesia, data was collected online through Google Form in which the participants were asked to complete a sentence running “Online learning is like … because …. ” The gathered metaphors were then analyzed using the content analysis procedures. The results showed the most predominantly metaphors produced by the participants are positive (32 items), followed by negative metaphors (25 items), and neutral metaphors (2 items). Based on the discussions, some suggestions are recommended for further research and OL practices improvement.