Search Results (244)

Football performance results from the dynamic interaction between physical, tactical, technical, and psychological dimensions—each of which also influences player well-being, recovery, and readiness. However, integrated monitoring approaches remain scarce, particularly in youth and sub-elite contexts. This systematic review screened 341 records from PubMed, Scopus, and Web of Science, with 46 studies meeting the inclusion criteria (n = 1763 players; age range: 13.2–28.7 years). Physical external load was reported in 44 studies using GPS-derived metrics such as total distance and high-speed running, while internal load was examined in 36 studies through session-RPE (rate of perceived exertion × duration), heart rate zones, training impulse (TRIMP), and Player Load (PL). A total of 22 studies included well-being indicators capturing fatigue, sleep quality, stress levels, and muscle soreness, through tools such as the Hooper Index (HI), the Total Quality Recovery (TQR) scale, and various Likert-type or composite wellness scores. Tactical behaviours (n = 15) were derived from positional tracking systems, while technical performance (n = 7) was assessed using metrics like pass accuracy and expected goals, typically obtained from Wyscout^® or TRACAB^® (a multi-camera optical tracking system). Only five studies employed multivariate models to examine interactions between performance domains or to predict well-being outcomes. Most remained observational, relying on descriptive analyses and examining each domain in isolation. These findings reveal a fragmented approach to player monitoring and a lack of conceptual integration between physical, psychological, tactical, and technical indicators. Future research should prioritise multidimensional, standardised monitoring frameworks that combine contextual, psychophysiological, and performance data to improve applied decision-making and support player health, particularly in sub-elite and youth populations. Full article

With the rise in short-term intercultural mobility programs, enhancing students’ adaptive capacity through structured experiential learning has become a key concern. Using constructivist grounded theory, this study draws on extending the situated learning and embodied cognition theories as analytical frameworks to explore international students’ cross-cultural adaptation within the context of study tours. It develops a three-phase framework (pre-departure, on-site immersion, and post-tour reflection) to trace their transition from cultural distance to adaptation. The findings reveal that the process through which international students shift from cross-cultural distance to multidimensional adaptation can be further accelerated by environmental settings, situational behaviors, and short-term emotional responses within study tour contexts. Moreover, culture-led and nature-led environments evoke distinct patterns of participation and emotional responses, facilitating varying degrees and dimensions of adaptation across psychological, social, and cultural domains. The study extends situated and embodied learning perspectives by conceptualizing study tours as dynamic, context-sensitive learning sites. By considering adaptation processes context-dependent, this study deepens the understanding of how learning, emotion, and environment interact to shape intercultural development and offers practical insights for designing responsive, stage-sensitive study tour programs. Full article

We introduce the concept of the magnetic metapole—a theoretical extension of classical multipole theory involving a fractional j pole count (related to the harmonic degree n as j = 2ⁿ). Defined by a scalar potential with colatitudinal dependence and no radial variation, the metapole yields a magnetic field that decays as 1/r and is oriented along spherical surfaces. Unlike classical multipoles, the metapole cannot be described as a point source; rather, it corresponds to an extended or filamentary magnetic distribution as derived from Maxwell’s equations. We demonstrate that pairs of oppositely oriented metapoles (up/down) can, at large distances, produce magnetic fields resembling those of classical monopoles. A regularized formulation of the potential resolves singularities for the potential and the field. When applied in a bounded region, it yields finite field energy, enabling practical modeling applications. We propose that the metapole can serve as a conceptual and computational framework for representing large-scale magnetic field structures particularly where standard dipole-based models fall short. This construct may have utility in both geophysical and astrophysical contexts, and it provides a new tool for equivalent source modeling and magnetic field decomposition. Full article

This study investigates the roles of wang (忘, forgetting) and buwang (不忘, non-forgetting) in early Confucian texts, emphasizing their interdependence within a yin–yang framework. Rather than signifying total erasure, for the purposes of the mnemic process, wang functions as a selective and creative mode, aiding cognitive refinement by withdrawing content considered secondary or extraneous. Primarily through close textual analysis of the Analects (Lunyu 論語), Mengzi (孟子), and Xunzi (荀子), this study shows how wang and buwang operate relationally, avoiding polar extremes in favor of a dynamic equilibrium. Conceptually, I argue that these terms are more accurately aligned with suspended and attentive modes of awareness, respectively. Tracing their pattern across these sources reveals the affirmative role of wang in optimizing memory, distancing the Confucian tradition from the notion of oblivion and offering a foundational perspective for future explorations of early Chinese memory-related discussions. Full article

Effectively managing immigrant workforces presents a significant contemporary challenge for organizations operating in a globalized world. Current management practices often fall short, failing to adequately address the complex interplay of social issues, cultural and linguistic distances, and the valuable human capital immigrants possess. This paper proposes a theoretically developed conceptual model for immigrant management, synthesized from a comprehensive review of systems theory, migration studies, and organizational governance literature. The model advances systems theory by operationalizing its core tenets—interdependence, feedback loops, and holistic perspective—into a practical governance framework for the specific domain of immigrant workforce integration, demonstrating the theory’s applicability to complex socio-organizational challenges. It outlines six interdependent subsystems—from needs assessment to end-of-work transitions. While conceptual, this paper lays a robust foundation for future empirical research by providing testable propositions regarding the efficacy of its subsystems and their impact on integration outcomes. It calls for empirical validation of the proposed relationships and the model’s overall effectiveness in diverse organizational contexts. By adopting this structured yet adaptable framework, organizations can move towards more agile governance practices in human resource management, allowing for iterative adjustments and fostering more resilient and sustainable immigrant integration. This approach directly contributes to addressing immigrant integration issues by offering a holistic, actionable framework that moves beyond piecemeal solutions, thereby enhancing organizational capability and promoting positive societal impact. Full article

The majority of people living in the U.S. report that spirituality is an important part of their life that is influential to their well-being. However, individuals vary widely in how they conceptualize spirituality, which often stems from and overlaps with their conceptualizations of God or so-called “God image”. Examination of people’s experiences of spirituality and conceptualization of God can enable psychologists to improve their understanding of individuals’ core personal experiences. This study evaluates both spirituality and God image, as well as the relationship between these two constructs, using qualitative hermeneutic analysis of interviews with 63 adults (51% women, 49% men, aged 18–75) from a midwestern U.S. university town. Substantial overlap characterized participants’ descriptions of spirituality and God. Participants tended to either view spirituality in relational terms or as an abstract concept, with the latter conceptualization being distanced from personal experience. Religious concepts and personal experiences were integral to most participants’ descriptions of both spirituality and God. The results emphasize the importance of addressing contextual worldviews about spirituality, inclusive of personal experiences. Full article

The resettlement of development-induced migrants is a complex socioeconomic and cultural process. The levels of place identity among migrants may profoundly affect their long-term stability and sustainable development in host communities. For long-distance displaced migrants, their social relations undergo drastic changes, and the extent of social-relations reconstruction determines their place identity in the resettlement area. Previous studies mainly concentrated on migrant compensation schemes, housing, and land allocation, and livelihood restoration. However, insufficient attention has been paid to the mechanism by which social-relations reconstruction shapes place identity. Drawing on sample survey data collected during 2022–2023 from migrants displaced from the Three Gorges Dam (TGD) in China to various other provinces, this study conceptualizes place identity in three dimensions: group identity, permanent settlement intention, and expectations for children. A structural equation model (SEM) was employed to investigate how social relations, categorized as geographical, home-tied, and carried-over relations, mediate the influence of multiple factors on place identity. The findings are that: (1) among development-induced migrants, social relations exerted significant positive effects on place identity, along with personal characteristics, socioeconomic characteristics, land, and housing factors. Moreover, family features, socioeconomic characteristics, and land and housing factors all had a significantly positive impact on social relations; (2) social relations acted as a full mediator between family features and place identity, and a partial mediator between socioeconomic characteristics/land and housing factors and place identity; (3) among geographical, home-tied, and carried-over social relations, geographical social relations had the largest effect on place identity, followed by carried-over relations. However, continuous dependence on home-tied social relations negatively affected the migrants’ development of place identity in new resettlement areas. This study elucidates the role of social relations in the socioeconomic and cultural reconstruction during migrant resettlement, offering insight for improving resettlement policies and promoting sustainable community integration. Full article

Uncertainty quantification in cloud models requires simultaneous characterization of fuzziness (via Entropy, En) and randomness (via Hyper-entropy, He), yet existing similarity measures often neglect the stochastic dispersion governed by He. To address this gap, we propose HECM-Plus, an algorithm integrating Expectation (Ex), En, and He to holistically model geometric and probabilistic uncertainties in cloud models. By deriving He-adjusted standard deviations through reverse cloud transformations, HECM-Plus reformulates the Hellinger distance to resolve conflicts in multi-expert evaluations where subjective ambiguity and stochastic randomness coexist. Experimental validation demonstrates three key advances: (1) Fuzziness–Randomness discrimination: HECM-Plus achieves balanced conceptual differentiation (δC₁/C₄ = 1.76, δC₂ = 1.66, δC₃ = 1.58) with linear complexity outperforming PDCM and HCCM by 10.3% and 17.2% in differentiation scores while resolving He-induced biases in HECM/ECM (C₁–C₄ similarity: 0.94 vs. 0.99) critical for stochastic dispersion modeling; (2) Robustness in time-series classification: It reduces the mean error by 6.8% (0.190 vs. 0.204, *p* < 0.05) with lower standard deviation (0.035 vs. 0.047) on UCI datasets, validating noise immunity; (3) Design evaluation application: By reclassifying controversial cases (e.g., reclassified from a “good” design (80.3/100 average) to “moderate” via cloud model using HECM-Plus), it resolves multi-expert disagreements in scoring systems. The main contribution of this work is the proposal of HECM-Plus, which resolves the limitation of HECM in neglecting He, thereby further enhancing the precision of normal cloud similarity measurements. The algorithm provides a practical tool for uncertainty-aware decision-making in multi-expert systems, particularly in multi-criteria design evaluation under conflicting standards. Future work will extend to dynamic expert weight adaptation and higher-order cloud interactions. Full article

Background/Objectives: This article explores the use of Ancient Greek as a prompt language in DALL·E 3, an Artificial Intelligence software for image generation. The research investigates three dimensions of Artificial Intelligence’s ability: (a) the sense and visualization of the concept of distance, (b) the mixing of representational as well as mythic contents, and (c) the visualization of emotions. More specifically, the research not only investigates AI’s potentialities in processing and representing Ancient Greek texts but also attempts to assess its interpretative boundaries. The key question is whether AI can faithfully represent the underlying conceptual and narrative structures of ancient literature or whether its representations are superficial and constrained by algorithmic procedures. Methods: This is a mixed-methods experimental research design examining whether a specified Artificial Intelligence software can sense, understand, and graphically represent linguistic and conceptual structures in the Ancient Greek language. Results: The study highlights Artificial Intelligence’s possibility in classical language education as well as digital humanities regarding linguistic complexity versus AI’s power in interpretation. More specifically, the research not only investigates AI’s potentialities in processing and representing Ancient Greek texts but also attempts to assess its interpretative boundaries. The key question is whether AI can faithfully represent the underlying conceptual and narrative structures of ancient literature or whether its representations are superficial and constrained by algorithmic procedures. The study highlights Artificial Intelligence’s possibility in classical language education as well as digital humanities regarding linguistic complexity versus AI’s power in interpretation. Conclusions: The research is a step toward a more extensive discussion on Artificial Intelligence in historical linguistics, digital pedagogy, as well as aesthetic representation by Artificial Intelligence environments. Full article

With the increasing severity of climate change, Carbon Capture, Utilization, and Storage (CCUS) technology has become essential for reducing atmospheric CO₂. Marine carbon sequestration, which stores CO₂ in seabed geological structures, offers advantages such as large storage capacity and high stability. Cryogenic hoses are critical for the ship-to-ship transfer of liquid CO₂ from transportation vessels to offshore carbon sequestration platforms, but their design methods and mechanical analysis remain inadequately understood. This study reviews existing cryogenic hose designs, including reinforced corrugated hoses, vacuum-insulated hoses, and composite hoses, to assess their suitability for liquid CO₂ transfer. Based on CO₂’s physicochemical properties, a conceptual composite hose structure is proposed, featuring a double-spring-supported internal composite hose, thermal insulation layer, and outer sheath. Practical recommendations for material selection, corrosion prevention, and monitoring strategies are provided to improve flexibility, pressure resistance, and thermal insulation, enabling reliable long-distance tandem transfer. A mechanical analysis framework is developed to evaluate structural performance under conditions including mechanical loads, thermal stress, and dynamic responses. This manuscript includes an introduction to the background, the methodology for data collection, a review of existing designs, an analysis of CO₂ characteristics, the proposed design methods, the mechanical analysis framework, a discussion of challenges, and the conclusions. Full article

Conceptually, this paper aims to help reduce the communication blind spots originating from the design of millimeter-wave (mmW) beamforming by deploying radio units of an open radio access network (O-RAN) with free-space optics (FSOs) as the backhaul and the fiber-optic link as the fronthaul. At frequencies exceeding 24 GHz, the transmission reach of 5G/6G beamforming is limited to a few hundred meters, and the periphery area of the sector operational range of beamforming introduces a communication blind spot. Using FSOs as the backhaul and a fiber-optic link as the fronthaul, O-RAN empowers the radio unit to extend over greater distances to supplement the communication range that mmW beamforming cannot adequately cover. Notably, O-RAN is a prime example of next-generation wireless networks renowned for their adaptability and open architecture to enhance the cost-effectiveness of this integration. A 200 meter-long FSO link for backhaul and a fiber-optic link of up to 10 km for fronthaul were erected, thereby enabling the reach of communication services from urban centers to suburban and remote rural areas. Furthermore, in the context of beamforming, reinforcement learning (RL) was employed to optimize the error vector magnitude (EVM) by dynamically adjusting the beamforming phase based on the communication user’s location. In summary, the integration of RL-based mmW beamforming with the proposed O-RAN communication setup is operational. It lends scalability and cost-effectiveness to current and future communication infrastructures in urban, peri-urban, and rural areas. Full article

This opinion piece postulates that quantitative environmental research and public health spatial analysts unknowingly tolerate certain spatial statistical model specification errors, whose remedies constitute some of the urgent emerging trends and issues in this subfield (e.g., forecasting disease spreading). Within this context, this paper addresses misspecifications affiliated with omitted variable bias complications arising from ignoring, and hence abandoning, negative spatial autocorrelation latent in georeferenced disease data, and/or being ill-informed about reigning teledependencies (i.e., long-distance spatial correlations). As imperative academic challenges, it advances elegant and convincing arguments to do otherwise. Its two particular themes are positive–negative spatial autocorrelation mixtures, and hierarchical autocorrelation generated by hegemonic urban systems. Comprehensive interpretations and implementations of these two conjectures constitute future research directions. Important conceptualizations for treatments reported in this paper include confounding variables and Moran eigenvector spatial filtering. This paper’s fundamental implication is an advocacy for a prodigious paradigm shift, a marked change in the collective mindsets and applications of spatial epidemiologists when specifying spatial regression equations to describe either environmental health data, or a publicly transparent geographic diffusion of diseases. Full article

Woody debris (WD) is an important element in forest ecosystems. It provides critical habitats for plants, animals, and insects. It is also a source of fuel contributing to fire propagation and sometimes leads to catastrophic wildfires. WD inventory is usually conducted through field surveys using transects and sample plots. Light Detection and Ranging (LiDAR) point clouds are emerging as a valuable source for the development of comprehensive WD detection strategies. Results from previous LiDAR-based WD detection approaches are promising. However, there is no general strategy for handling point clouds acquired by different platforms with varying characteristics such as the pulse repetition rate and sensor-to-object distance in natural forests. This research proposes a general and adaptive morphological WD detection strategy that requires only a few intuitive thresholds, making it suitable for multi-platform LiDAR datasets in both plantation and natural forests. The conceptual basis of the strategy is that WD LiDAR points exhibit non-planar characteristics and a distinct intensity and comprise clusters that exceed a minimum size. The developed strategy was tested using leaf-off point clouds acquired by Geiger-mode airborne, uncrewed aerial vehicle (UAV), and backpack LiDAR systems. The results show that using the intensity data did not provide a noticeable improvement in the WD detection results. Quantitatively, the approach achieved an average recall of 0.83, indicating a low rate of omission errors. Datasets with a higher point density (i.e., from UAV and backpack LiDAR) showed better performance. As for the precision evaluation metric, it ranged from 0.40 to 0.85. The precision depends on commission errors introduced by bushes and undergrowth. Full article

Remote sensing (RS) spectral time series provide a substantial source of information for the regular and cost-efficient monitoring of the Earth’s surface. Important monitoring tasks include land use and land cover classification, change detection, forest monitoring and crop type identification, among others. To develop accurate solutions for RS-based applications, often supervised shallow/deep learning algorithms are used. However, such approaches usually require fixed-length inputs and large labeled datasets. Unfortunately, RS images acquired by optical sensors are frequently degraded by aerosol contamination, clouds and cloud shadows, resulting in missing observations and irregular observation patterns. To address these issues, efforts have been made to implement frameworks that generate meaningful representations from the irregularly sampled data streams and alleviate the deficiencies of the data sources and supervised algorithms. Here, we propose a conceptually and computationally simple representation learning (RL) approach based on autoencoders (AEs) to generate discriminative features for crop type classification. The proposed methodology includes a set of single-layer AEs with a very limited number of neurons, each one trained with the mono-temporal spectral features of a small set of samples belonging to a class, resulting in a model capable of processing very large areas in a short computational time. Importantly, the developed approach remains flexible with respect to the availability of clear temporal observations. The signal derived from the ensemble of AEs is the reconstruction difference vector between input samples and their corresponding estimations, which are averaged over all cloud-/shadow-free temporal observations of a pixel location. This averaged reconstruction difference vector is the base for the representations and the subsequent classification. Experimental results show that the proposed extremely light-weight architecture indeed generates separable features for competitive performances in crop type classification, as distance metrics scores achieved with the derived representations significantly outperform those obtained with the initial data. Conventional classification models were trained and tested with representations generated from a widely used Sentinel-2 multi-spectral multi-temporal dataset, BreizhCrops. Our method achieved $77.06\%$ overall accuracy, which is $\sim 6\%$ higher than that achieved using original Sentinel-2 data within conventional classifiers and even $\sim 4\%$ better than complex deep models such as OmnisCNN. Compared to extremely complex and time-consuming models such as Transformer and long short-term memory (LSTM), only a $3\%$ reduction in overall accuracy was noted. Our method uses only $6.8k$ parameters, i.e., $\sim 400x$ fewer than OmnicsCNN and $\sim 27x$ fewer than Transformer. The results prove that our method is competitive in terms of classification performance compared with state-of-the-art methods while substantially reducing the computational load. Full article

The use of mercury in gold mining, as well as the presence of uncontrolled and illegal releases to the environment, continues to create severe pollution and public health risks for over 14 million people worldwide, particularly in developing countries. This study presents a modeling framework to estimate the fate and transport of mercury on a national-scale river network, where a physical-based conceptual model is implemented. Using the model, mercury concentrations are estimated for every river segment in the network, serving as a tool for effective management and control nationwide, enabling establishing intervention priorities. To test the framework’s suitability, it was applied to Colombia’s river network, a country with documented mercury pollution issues from gold mining. Results revealed persistent concentrations above 0.001 µg/L in all major basins, and concentrations close to 7 µg/L in active mining areas. The release of nearly 300 mines led to the contamination of river distance between 50 and 285 km downstream, exceeding local drinking water standards. The model results were validated with mercury concentration data of available studies in the country, showing small deviations between modeled and measured concentrations (<0.95 µg/L), confirming the robustness and suitability of the proposed framework as a screening assessment for national-scale mercury transport and fate. Full article