Search Results (234)

In this paper, a supply–demand–price network model incorporating a marginal feedback mechanism is proposed to characterize the evolution of market prices. Unlike classical supply–demand models, the marginal effect of excess demand, defined as the rate of change in excess demand, is explicitly introduced into the price adjustment process. As the coefficient of the marginal feedback term varies, the system exhibits rich and complex nonlinear dynamics. In particular, the model gives rise to a centrally symmetric double-wing chaotic attractor, as well as a pair of coexisting single-wing chaotic attractors. The transition routes among different dynamical regimes are systematically analyzed using phase portraits, bifurcation diagrams, and Lyapunov exponents. Furthermore, multistability phenomena are observed, including the coexistence of equilibrium points, limit cycles, and chaotic attractors. The corresponding basins of attraction are illustrated to reveal their intricate and interwoven structures. In addition, the emergence of endogenous chaos is investigated through both theoretical analysis and numerical simulations. Finally, the consistency between the model dynamics and real market data provides empirical evidence supporting the validity and applicability of the proposed framework. Full article

Mapping land cover, monitoring its changes, and simulating future alterations are essential tasks for sustainable land management. These processes enable accurate assessment of environmental impacts, support informed policymaking, and assist in the planning needed to mitigate risks related to urban expansion, deforestation, and climate change. This study proposes a GeoAI-based framework leveraging Multilayer Perceptron (MLP), a class of Artificial Neural Networks (ANNs), to predict land cover changes in the Aosta Valley region (NW Italy). The model uses Copernicus Earth Observation data, specifically Sentinel-1 and Sentinel-2 imagery, and is trained and validated on land cover maps derived from different time periods previously validated with ground truth data. The objective is to provide a predictive tool capable of simulating potential future landscape configurations, supporting proactive regional land use planning including regulatory constraints under the current land use plan. Model performance is evaluated using accuracy metrics. The land cover classification methodology follows established approaches in the scientific literature, adapted to the specific geomorphological characteristics of the Aosta Valley. To explore and visualize potential future land cover transitions, Sankey and chord diagrams are used in combination with zonal statistics and thematic plots. These provide detailed insights into the intensity, direction, and magnitude of landscape dynamics. Training data were stratified-sampled across the study area, covering a diverse set of land cover classes to ensure robustness and generalization of the MLP model. This GeoAI approach offers a scalable and replicable methodology for anticipating land cover dynamics, identifying vulnerable areas, and informing adaptive environmental management strategies at the regional scale, while simultaneously considering the latest urban planning regulations. Full article

This study focuses on enhancing wind speed prediction for wildfire spread simulation by proposing an integrated forecasting approach. The original wind speed series is first processed via variational mode decomposition (VMD), with its parameters [K, $\alpha$] optimized via particle swarm optimization (PSO). Every intrinsic mode function (IMF) resulting from this decomposition is predicted using a bidirectional long short-term memory model incorporating an attention mechanism (AM-BiLSTM), and the final wind series is reconstructed from these predictions. Model training and validation were conducted using data from controlled burning experiments in the Mao’er Mountain area of Heilongjiang Province, China. Predictive performance is evaluated through multiple statistical metrics, error distribution analysis, and Taylor diagrams. To assess practical utility, the predicted wind field is further applied in FARSITE to drive wildfire spread simulations. Results demonstrate that the PSO-VMD-AM-BiLSTM model provides reliable wind forecasts and contributes to improved fire spread prediction accuracy, indicating its potential for decision support in wildfire management. To achieve accurate forest fire spread prediction, we construct the MCNN model, which is based on early perception of understory wind fields using predicted wind speed data and adopts a multi-branch convolutional neural network architecture to extract fire spread features. FARSITE is employed to simulate forest fire spread in the Mao’er Mountain region, generating a dataset for model training and testing. After 50 training epochs, the loss value of the MCNN model converges, achieving optimal prediction performance when the combustion threshold is set to 0.7. Compared to models such as CNN, DCIGN, and DNN, MCNN shows improvements in evaluation metrics including precision, recall, Sørensen coefficient, and Kappa coefficient. To validate the model’s predictive performance in real fire scenarios, four field ignition experiments were conducted at the Liutiao Village test site: homogeneous fuel combustion, long fire line combustion, alternating fuel combustion, and multiple ignition source merging combustion. Comprehensive evaluation across the four experiments indicates that the model achieves precision, recall, Sørensen coefficient, and Kappa coefficient values of 0.940, 0.965, 0.953, and 0.940, respectively, with stable prediction errors below 6%. These results represent improvements over the comparative models DCIGN and DNN. The proposed MCNN model can adapt to forest fire spread prediction under different scenarios, offering a novel approach for accurate forest fire prediction and prevention. Full article

Solid-state lighting, especially light-emitting diodes (LEDs), is revolutionizing indoor lighting due to its energy efficiency, long lifespan, low heat output, and enhanced color rendering. LEDs can quickly adjust light intensity, enabling the development of visible light communication (VLC) technology. However, the modulation bandwidth of phosphor-converted white LEDs commonly used for illumination is limited, potentially affecting the speed of the VLC links. This paper presents a receiver-side equalization technique to overcome bandwidth limitations in VLC links due to LEDs. The proposed approach utilizes a novel transimpedance amplifier with an embedded T-network shunt-feedback equalizer (TIA-TE) to introduce adjustable high-frequency peaking in the TIA’s frequency response. By incorporating this peaking, the system’s bandwidth is extended without sacrificing important performance parameters like gain, noise, or power dissipation. The TIA-TE is followed by a main amplifier and a standalone continuous-time linear equalizer (CTLE) for further signal conditioning, while a 50 Ω buffer interfaces the receiver with measurement equipment. Post-layout simulations in a 0.35 µm CMOS process validate the approach. Using a 4 pF photodiode, the system bandwidth was initially limited by the LED’s 3 MHz modulation bandwidth. The proposed TIA-TE extends the bandwidth to 8.4 GHz without sacrificing the gain or power dissipation. The subsequent CTLE further extends the bandwidth to 14 MHz. The receiver front end achieves a mid-band transimpedance of 110 dBΩ and an input-referred noise current of 7.2 nA_rms, while dissipating 2.48 mW (excluding the 50 Ω buffer). Simulated 28 Mb/s NRZ eye diagrams demonstrate the feasibility of the proposed TIA-TE architecture for LED-limited VLC links. Full article

Against the dual backdrop of the rural revitalization strategy and the pursuit of high-quality, balanced urban–rural education, optimizing rural campus spaces has emerged as an important lever for addressing educational resource disparities and improving pedagogical quality. However, conventional evaluation of campus space optimization faces two systemic dilemmas. First, top-down decision-making often neglects the authentic needs of diverse stakeholders and place-based knowledge, resulting in spatial interventions that lose regional distinctiveness. Second, routine public participation is constrained by geographical barriers, time costs, and sample-size limitations, which can amplify professional cognitive bias and impede comprehensive feedback formation. The compounded effect of these challenges contributes to a disconnect between spatial optimization outcomes and perceived needs, thereby constraining the distinctive development of rural educational spaces. To address these constraints, this study proposes a novel method that integrates regional spatial feature recognition with digital media-based public perception assessment. At the data collection and ethical governance level, the study strictly adheres to platform compliance and academic ethics. A total of 12,800 preliminary comments were scraped from major social media platforms (e.g., Douyin, Dianping, and Xiaohongshu) and processed through a three-stage screening workflow—keyword screening–rule-based filtering–manual verification—to yield 8616 valid records covering diverse public groups across China. All user-identifying information was fully anonymized to ensure lawful use and privacy protection. At the analytical modeling level, we develop a Transformer-based deep learning system that leverages multi-head attention mechanisms to capture implicit spatial-sentiment features and metaphorical expressions embedded in review texts. Evaluation on an independent test set indicates a classification accuracy of 89.2%, aligning with balanced and stable scoring performance. Robustness is further strengthened by introducing an equal-weight alternative strategy and conducting stability checks to indicate the consistency of model outputs across weighting assumptions. At the scenario interpretation level, we combine grounded-theory coding with semantic network analysis to establish a three-tier spatial analysis framework—macro (landscape pattern/hydro-topological patterns), meso (architectural interface), and micro (teaching scenes/pedagogical scenarios)—and incorporate an interpretive stakeholder typology (tourists, residents, parents, and professional groups) to systematically identify and quantify key features shaping public spatial perception. Findings show that, at the macro level, naturally integrated scenarios—such as “campus–farmland integration” and “mountain–water embeddedness”—exhibit high affective association, aligning with the “mountain-water-field-village” spatial sequence logic and suggesting broad public endorsement of ecological campus concepts, whereas vernacular settlement-pattern scenarios receive relatively low attention due to cognitive discontinuities. At the meso level, innovative corridor strategies (e.g., framed vistas and expanded corridor spaces) strengthen the building–nature interaction and suggest latent value in stimulating exploratory spatial experience. At the micro level, place-based practice-oriented teaching scenes (e.g., intangible cultural heritage handcraft and creative workshops) achieve higher scores, aligning with the compatibility of vernacular education’s “differential esthetics,” while urban convergence-oriented interdisciplinary curriculum scenes suggest an interpretive gap relative to public expectations. These results indicate an embedded relationship between public perception and regional spatial features, which is further shaped by a multi-actor governance process—characterized by “Government + Influencers + Field Study”—that mediates how rural educational spaces are produced, communicated, and interpreted in digital environments. The study’s innovative value lies in integrating sociological theories (e.g., embeddedness) with deep learning techniques to fill the regional and multi-actor perspective gap in rural campus POE and to promote a methodological shift from “experience-based induction” toward a “data-theory” dual-drive model. The findings provide inferential evidence for rural campus renewal and optimization; the methodological pipeline is transferable to small-scale rural primary schools with media exposure and salient regional ecological characteristics, and it offers a new pathway for incorporating digital media-driven public perception feedback into planning and design practice. The research methodology of this study consists of four sequential stages, which are implemented in a systematic and progressive manner: First, data collection was conducted: Python and the Octopus Collector were used to crawl online comment data related to Fuwen Township Central Primary School, strictly complying with the user agreements of the Douyin, Dianping, and Xiaohongshu platforms. Second, semantic preprocessing was performed: The evaluation content was segmented to generate word frequency statistics and semantic networks; qualitative analysis was conducted using Origin software, and quantitative translation was realized via Sankey diagrams. Third, spatial scene coding was carried out: Combined with a spatial characteristic identification system, a macro–meso–micro three-tier classification system for spatial scene characteristics was constructed to encode and quantitatively express the textual content. Finally, sentiment quantification and correlation analysis was implemented: A deep learning model based on the Transformer framework was employed to perform sentiment quantification scoring for each comment; Sankey diagrams were used to quantitatively correlate spatial scenes with sentiment tendencies, thereby exploring the public’s perceptual associations with the architectural spatial environment of rural campuses. Full article

To address the challenge of predicting runoff processes in the middle reaches of the Yangtze River under the influence of complex river–lake relationships and human disturbances, this paper proposes a coupled model based on the Sparrow Search Algorithm-optimized Long Short-Term Memory neural network (SSA-LSTM) for daily runoff forecasting at the Jiujiang Hydrological Station. The input data were preprocessed through feature selection and sequence decomposition. Subsequently, the Sparrow Search Algorithm (SSA) was utilized to perform automated of key hyperparameters of the Long Short-Term Memory (LSTM) model, thereby enhancing the model’s adaptability under complex hydrological conditions. Experimental results based on multi-station hydrological and meteorological data of the middle reaches of the Yangtze River from 2009 to 2016 show that the SSA-LSTM achieves a Nash–Sutcliffe Efficiency (NSE) of 0.98 during the testing period (2016). The Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) are reduced by 49.3% and 51.3%, respectively, compared to the standard LSTM. A comprehensive evaluation across different flow levels, utilizing Taylor diagrams and error distribution analysis, further confirms the model’s robustness. The model demonstrates robust performance across different flow regimes: compared to the standard LSTM model, SSA-LSTM improves the NSE from 0.45 to 0.88 in high-flow scenarios, exhibiting excellent capabilities in peak flow prediction and flood process characterization. In low-flow scenarios, the NSE is improved from −0.77 to 0.72, indicating more reliable prediction of baseflow mechanisms. The study demonstrates that SSA-LSTM can effectively capture hydrological nonlinear characteristics under strong river–lake backwater and human disturbances, providing a high-precision and high-efficiency data-driven method for runoff prediction in complex basins. Full article

Continuous growth in prices for primary energy sources and environmental restrictions on pollutant emissions justify investments in industrial facilities to minimize specific energy consumption. In addition, oil-producing and refining enterprises were built in previous decades, when energy efficiency problems were not so urgent, so little attention was paid to the development and application of tools for improvement. In this regard, at present, the application and development of methods for increasing energy efficiency is certainly relevant, especially for oil processing and stabilization units (OPSUs) at fields, through which all oil produced in a country passes. Our goal is to achieve heat integration of OPSUs with a capacity of 4 million tons of processed raw materials per year. In this study, for the heat integration of the OPSU, pinch-analysis methods with the construction of grid diagrams are used for a retrofitting project for increasing the energy efficiency of the heat exchange network (HEN) of an OPSU. The heat and economic analysis of the synthesized HEN were performed using Pinch 2.02 software. This paper presents a retrofitting-based energy-efficiency project for the OPSU HEN. A method for evolving the synthesized HEN by breaking heat load paths is applied to increase the economic efficiency of the retrofit project. The stability of the OPSU operation in the optimal mode is shown with the observed change in the bank interest rate. The implementation of the synthesized HEN will reduce specific energy consumption by 77%, decreasing CO₂ emissions released into the atmosphere by 30 thousand tons per year. Full article

The unique natural environment and climate of tropical island regions present significant challenges to construction. Under these variable natural conditions and complex construction processes, identifying and analyzing potential risks that could lead to vulnerabilities in construction safety systems and clarifying their transmission pathways remains a pressing issue. To fill this research gap, a GV-IB model for vulnerability analysis of construction safety systems in tropical island building projects (CSSTIBPs) was established. This model constructs a vulnerability analysis index system for tropical island construction safety systems based on the Grey Relational Analysis (GRA) and Vulnerability Scoping Diagram (VSD), considering exposure, sensitivity, and adaptability. By combining the artificial fish swarm algorithm with the K2 algorithm and the EM algorithm, an Improved Bayesian Network (IBN) is constructed to analyze and infer the influencing factors and disaster chains of vulnerability in tropical island construction safety systems. The IBN can effectively overcome the dependence on node order and data gaps in traditional Bayesian Network construction methods. The effectiveness of the model is verified by analyzing Hainan Island, China. The research results show that (a) The IBN stability verification showed an Area Under ROC Curve (AUC) of 0.783 > 0.7, indicating high effectiveness in identifying vulnerability factors. (b) Within the vulnerability measurement nodes of the CSSTIBPs, the influence on the system decreases in the following order is exposure (0.41), sensitivity (0.31), and adaptability (0.03). (c) Emergency response time, safety training, hazard identification time, accident response time, and duration of severe weather are key factors affecting the vulnerability of CSSTIBPs. Full article

The increasing use of vinyl-coated metal (VCM) sheets in home appliances requires robust forming processes to prevent defects such as delamination and wrinkling, especially under elevated temperatures and humidity. This study presents a deep neural network (DNN)-based multi-objective optimization framework to determine optimal stamping parameters for VCM sheets. A delamination limit diagram (DLD) is experimentally established by combining limit dome height tests with immersion tests, defining the critical strain boundary under environmentally conditions. A finite element (FE) based dataset of four process variables was then used to train a DNN surrogate model with high predictive accuracy. Using the trained DNN model, Pareto-based optimization identifies nondominated solutions balancing delamination and wrinkling. The optimal condition was validated by FE simulation, confirming simultaneous suppression of both defects within the DLD. The proposed DNN–Pareto framework provides and efficient and reliable tool for defect prediction and optimization in VCM stamping, ensuring high surface quality and environmental durability. Full article

Automatic modulation recognition (AMR) plays a critical role in intelligent wireless communication systems, particularly under conditions with a low signal-to-noise ratio (SNR) and complex channel environments. To address these challenges, this paper proposes a three-branch fusion network that integrates complementary features from the time, frequency, and spatial domains to enhance classification performance. The model consists of three specialized branches: a multi-channel convolutional branch designed to extract discriminative local features from multiple signal representations; a bidirectional long short-term memory (BiLSTM) branch capable of capturing long-range temporal dependencies; and a vision transformer (ViT) branch that processes constellation diagrams to exploit global structural information. To effectively merge these heterogeneous features, a path attention module is introduced to dynamically adjust the contribution of each branch, thereby achieving optimal feature fusion and improved recognition accuracy. Extensive experiments on the two popular benchmarks, RML2016.10a and RML2018.01a, show that the proposed model consistently outperforms baseline approaches. These results confirm the effectiveness and robustness of the proposed approach and highlight its potential for deployment in next-generation intelligent modulation recognition systems operating in realistic wireless communication environments. Full article

Background: Transcatheter aortic valve replacement (TAVR) has emerged as a pivotal minimally invasive interventional therapy for aortic valve disease and has seen increasingly widespread clinical adoption in recent years. Despite its overall safety, the adverse events and even deaths in the postoperative period still account for a certain percentage. Accurate identification of high-risk patients is therefore critical for optimizing preoperative decision making, guiding individualized treatment strategies and improving long-term outcomes. However, existing scoring systems and predictive models fail to fully leverage multimodal clinical data from patients, resulting in suboptimal predictive accuracy that falls short of the demands of precision medicine, indicating substantial room for improvement. Methods: In this study, a multimodal deep learning model named MULTINet (multimodal learning for TAVR risk network) was constructed using data from the MIMIC-IV (Medical Information Mart for Intensive Care) cohort. This model achieved unimodal and multimodal modeling through a dual-branch structure, and, by using an attention pooling fusion module, flexibly handled the input that contained missing modalities, to predict the 30-day all-cause mortality in TAVR patients. The area under the receiver operating characteristic curve (AUC), the area under the precision–recall curve (AUPR) and the recall rate were used for prediction evaluation. The calibration degree was evaluated by calibration diagrams and Brier scores, and its clinical practicability was assessed through decision curve analysis (DCA). And the integrated gradient method was used to identify key predictive features to enhance interpretability of the model. Results: In the postoperative 30-day all-cause mortality prediction task, the MULTINet method achieved an AUC value of 0.9153, AUPR value of 0.5708 and Recall value of 0.8051, which was significantly superior to the XGBoost method (AUC 0.8958, AUPR 0.4053 and Recall 0.7793) and the MedFuse method (AUC 0.5571, AUPR 0.2487 and Recall 0.3089). The MULTINet method demonstrated more robust and reliable probability estimation performance, with a Brier score of 0.0269, outperforming XGBoost (0.0343) and MedFuse (0.2496). It achieved a higher net benefit in decision analysis, reflecting its effectiveness in strategy optimization and actual decision-making benefits. The renal function, cardiac function and inflammation-related indicators contributed greatly in the prediction process. Conclusions: The multimodal deep learning model proposed in this study named MULTINet enables adaptive integration of multimodal clinical information for predicting all-cause mortality within 30 days post-TAVR, substantially improving both predictive accuracy and clinical applicability, providing robust support for clinical decision making and boosting TAVR management toward greater precision and intelligence. Full article

Artificial neural networks in drug discovery have shown remarkable potential in various areas, including molecular similarity assessment and virtual screening. This study presents a novel multimodal Siamese neural network architecture. The aim was to join molecular electrostatic potential (MEP) images with the texture features derived from reduced density gradient (RDG) diagrams for enhanced molecular similarity prediction. On one side, the proposed model is combined with a convolutional neural network (CNN) for processing MEP visual information. This data is added to the multilayer perceptron (MLP) that extracts texture features from gray-level co-occurrence matrices (GLCM) computed from RDG diagrams. Both representations converge through a multimodal projector into a shared embedding space, which was trained using triplet loss to learn similarity and dissimilarity patterns. Limitations associated with the use of purely structural descriptors were overcome by incorporating non-covalent interaction information through RDG profiles, which enables the identification of bioisosteric relationships needed for rational drug design. Three datasets were used to evaluate the performance of the developed model: tyrosine kinase inhibitors (TKIs) targeting the mutant T315I BCR-ABL receptor for the treatment of chronic myeloid leukemia, acetylcholinesterase inhibitors (AChEIs) for Alzheimer’s disease therapy, and heterodimeric AChEI candidates for cross-validation. The visual and texture features of the Siamese architecture help in the capture of molecular similarities based on electrostatic and non-covalent interaction profiles. Therefore, the developed protocol offers a suitable approach in computational drug discovery, being a promising framework for virtual screening, drug repositioning, and the identification of novel therapeutic candidates. Full article

This study theoretically explores the mechanism of action of Chicoric acid against influenza virus based on network pharmacology, molecular docking, and molecular dynamics simulation techniques, aiming to provide insights for the development of new veterinary drugs for influenza. Potential targets for influenza virus action were identified using the PharmMapper (i.e. Version 2017) server and disease databases including GeneCards and OMIM. The STRING online analysis platform and Cytoscape 3.9.1 software were employed to construct a protein–protein interaction (PPI) network of the target proteins, followed by topological analysis to screen for key targets. Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed on the intersecting targets using the DAVID database. A “drug–target–pathway” network diagram was constructed using Cytoscape 3.9.1 software. Molecular docking was carried out with AutoDock 1.5.6 and PyMOL 2.5 software to identify dominant binding targets, followed by molecular dynamics simulation analysis. The results of network analysis showed that there were 31 potential targets of Chicoric acid; the protein interaction network suggested that UBC, UBA52, RPS27A, HCK, and CDKN1B may be the core targets of Chicoric acid; 55 cell biological processes were obtained by GO enrichment analysis, and 15 related signaling pathways were obtained by KEGG pathway enrichment analysis; molecular docking showed that UBC and UBA52 had a good affinity to Chicoric acid and may be the dominant target of Chicoric acid exerting its effect. Chicoric acid may play a role in antiviral activity by acting on the dominant protein of UBC and UBA52, thus achieving an anti-influenza virus effect. Full article

Excavation is a common requirement in engineering construction within rock masses. While excavation volumes are generally limited in road slope projects, they may become substantial in large-scale operations such as deep open pit mines. The interaction between time and cost in excavation processes is strongly controlled by rock mass excavatability, which has been recognized as a key factor in project budgets. Since the 1970s, excavatability assessment has therefore attracted considerable research interest in rock mechanics. In this study, the excavatability cases previously plotted on the Geological Strength Index (GSI) versus Uniaxial Compressive Strength of the Rock Mass (σ_{c_rm}) diagram in the literature were improved by employing an Artificial Neural Network (ANN). The ANN approach was used to investigate the boundaries between digger, ripper, and hammer+blasting excavation classes within the available case zones defined by GSI–σ_{c_rm} data pairs. The prediction performance of the developed rock mass excavatability chart is highly acceptable, with correct classification rates of 91.1% for blasting+hammer and ripper classes, and 87.2% for the ripper class. Considering GSI and σ_{c_rm} as the main input parameters, the proposed ANN-oriented excavatability chart is highly acceptable for preliminary equipment selection during the design stage of surface rock mass excavations, including slope cases. Full article

Background: A sexually active lifestyle is generally associated with positive effects on physical condition and health. However, engaging in sexual activity prior to a sports competition could affect athletic performance. This systematic review examines the current literature on the impact of pre-exercise sexual activity on sports performance, with particular attention paid to its effects on physiological, hormonal, cognitive, and perceptual markers. Method: Based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we systematically reviewed original studies published within the last 25 years. Eligible studies were randomized or non-randomized controlled design and indexed on PubMed, Scopus, Dialnet, and Cochrane. Additional sources were consulted including a network diagram with Connected Papers^®. Two methodological quality scales, McMaster University Occupational Therapy Evidence-Based Practice Research Group and Physiotherapy Evidence Database (PEDro), were used. The study was registered in PROSPERO (#CRD42023426555). Results: A total of 244 records were identified through the search process, of which 7 studies met the inclusion criteria. The studies involved 117 (115 men) physically and sexually active subjects including 29 elite top athletes. When comparing the sexual activity condition/group (SexG) to abstinence (AbsG), significant (p < 0.05) decreases were observed in average speed and maximum strength. In contrast, non-significant trends towards improvement (p > 0.05) were observed in exercise capacity, reaction time, and muscular endurance. No significant changes (p > 0.05) were found in physiological and hormonal biomarkers and fatigue perception. However, perceived exertion was significantly higher (p < 0.05) in SexG compared to AbsG. Conclusions: Current evidence does not conclusively support the influence of pre-exercise sexual activity on sports performance, or physiological and hormonal biomarkers. However, it could contribute to increased perception of exercise intensity. Full article