Search Results (8,281)

Muscle–joint flexibility is defined as the ability of a muscle to stretch in a controlled manner, allowing a wide range of movement at the joints. While numerous methodologies exist for improving flexibility, few studies have investigated the role of athletes’ perceptual processes and awareness related to their own body and movement control during such training. In this pilot study, we explored how two different training protocols—static and dynamic stretching (control group, CON) and multi-joint resistance training (experimental group, EXP)—influence both flexibility and psychophysical awareness, understood as a multidimensional construct involving perceived flexibility improvements, self-assessed control over exercise execution, and cognitive-emotional responses such as engagement, motivation, and satisfaction during physical effort. The study involved 24 male amateur track-and-field athletes (mean age 23 ± 2.5 years), randomized into two equal groups. Over 12 weeks, both groups trained three times per week. Flexibility was assessed using the Sit and Reach Test at three time points (pre-, mid-, and post-intervention). A 2 × 3 mixed ANOVA revealed a significant group × time interaction (F = 20.17, p < 0.001), with the EXP group showing greater improvements than the CON group. In the EXP group, Sit and Reach scores increased from pre = 28.55 cm (SD = 4.91) to mid = 29.39 cm (SD = 4.67) and post = 29.48 cm (SD = 4.91), with a significant difference between pre and post (p = 0.01; d = 0.35). The CON group showed minimal changes, with scores of pre = 28.66 cm (SD = 4.92), mid = 28.76 cm (SD = 5.03), and post = 28.84 cm (SD = 5.10), and no significant difference between pre and post (p = 0.20; d = 0.04). Psychophysical awareness was assessed using a custom questionnaire structured on a 5-point Likert scale, with items addressing perception of flexibility, motor control, and exercise-related bodily sensations. The questionnaire showed excellent internal consistency (Cronbach’s α = 0.92). Within the EXP group, psychophysical awareness increased significantly (from 3.50 to 4.17; p = 0.01; d = 0.38), while no significant change occurred in the CON group (p = 0.16). Post-hoc power analysis confirmed small to moderate effect sizes within the EXP group, although between-group differences lacked sufficient statistical power. These results suggest that resistance training may improve flexibility and concurrently enhance athletes’ psychophysical self-awareness more effectively than traditional stretching. Such findings offer practical implications for coaches seeking to optimize flexibility training by integrating alternative methods that promote both physical and perceptual adaptations. Full article

The production of thermal energy in buildings using internal greenhouses makes it possible to obtain substantial gains in energy consumption and, at the same time, contribute to improving occupants’ thermal comfort (TC) levels. This article proposes a study on the producing and transporting of renewable thermal energy in a circular auditorium equipped with an enveloping semi-circular greenhouse. The numerical study is based on software that simulates the building geometry and the building thermal response (BTR) numerical model and assesses the TC level and indoor air quality (IAQ) provided to occupants in spaces ventilated by the proposed system. The building considered in this study is a circular auditorium constructed from three semi-circular auditoriums supplied with internal semi-circular greenhouses. Each of the semi-circular auditoriums faces south, northeast, and northwest, respectively. The semi-circular auditoriums are occupied by 80 people each: the one facing south throughout the day, while the one facing northeast is only occupied in the morning, and the one facing northwest is only occupied in the afternoon. The south-facing semi-circular greenhouse is used by itself to heat all three semi-circular auditoriums. The other two semi-circular greenhouses are only used to heat the interior space of the greenhouse. It was considered that the building is located in a Mediterranean-type climate and subject to the typical characteristics of clear winter days. The results allow us to verify that the proposed heating system, in which the heat provided to the occupied spaces is generated only in the semi-circular greenhouse facing south, can guarantee acceptable TC conditions for the occupants throughout the occupancy cycle. Full article

This paper studies the adaptive practical tracking control (PTC) problem for a class of uncertain nonlinear systems (UNSs) with nontriangular structured uncertain terms and unknown parameters, where the boundary of nontriangular structured uncertain terms depends on all state variables. Based on the improved adaptive backstepping technique, the state feedback tracking controller and update laws are first constructed. Then, by seeking the linear relationship between the state vector and the error vector, and by utilizing the comparison principle, it is verified that the developed adaptive PTC scheme can ensure that all signals of the closed-loop system are bounded and the tracking error converges to a bounded region. Finally, two examples, including a numerical example and the dual-motor drive servo system, are provided to show the effectiveness of this control method. Full article

The potential damage to aviation products caused by vibration and shock during road transportation has long been overlooked, despite structural failure under dynamic loading emerging as a critical technical challenge affecting product reliability. For aviation components, both stress and vibration analysis are essential prerequisites prior to formal assembly. This study investigates a symmetric vertical tail, a common aviation structure, employing an innovative model group analysis method to characterize its dynamic stress and strain distributions under real transportation conditions. Experimental measurements of vibration acceleration and impact loads during transport served as input data for constructing a numerical model based on stress and vibration theory. The model elucidates the mechanical responses of the tail in both modal and vibrational states, enabling effectively evaluation of dynamic vibrations on the tail and its critical subcomponents during road transport. The findings provide actionable insights for optimizing aviation component packaging design, mitigating vibration-induced damage, and enhancing transportation safety. Full article

Various nature-based solutions (NbS)—such as constructed wetlands, drainage ditches, and vegetated buffer strips—have recently demonstrated strong potential for mitigating pollutant transport in open channels and river systems. Numerical modeling is a widely adopted and effective approach for assessing the performance of these interventions. This study presents the first development of a two-dimensional (2D) meshless advection–diffusion model based on an Eulerian smoothed particle hydrodynamics (SPH) framework, specifically designed to simulate passive pollutant transport in open channel flows. The proposed model marks a pioneering application of the ESPH technique to environmental pollutant transport problems. It couples the 2D depth-averaged shallow water equations with an advection–diffusion equation to represent both fluid motion and pollutant concentration dynamics. A uniform particle arrangement ensures that each fluid particle interacts symmetrically with eight neighboring particles for flux computation. To represent the pollutant transport process, the dispersion coefficient is defined as the sum of molecular and turbulent diffusion components. The turbulent diffusion coefficient is calculated using a prescribed turbulent Schmidt number and the eddy viscosity obtained from a Smagorinsky-type mixing-length turbulence model. Three analytical case studies, including one-dimensional transcritical open channel flow, 2D isotropic and anisotropic diffusion in still water, and advection–diffusion in a 2D uniform flow, are employed to verify the model’s accuracy and convergence. The model demonstrates first-order convergence, with relative root mean square errors (RRMSEs) of approximately 0.2% for water depth and velocity, and 0.1–0.5% for concentration. Additionally, the model is applied to a laboratory experiment involving 2D pollutant dispersion in a 90° junction channel. The simulated results show good agreement with measured velocity and concentration distributions. These findings indicate that the developed model is a reliable and effective tool for evaluating the performance of NbS in mitigating pollutant transport in open channels and river systems. Full article

Members of the genus Nemipterus are economically important fish species distributed in the tropical and subtropical Indo-West Pacific region. The majority of species in this genus inhabit waters with sandy–muddy substrates on the continental shelf, although different species are found at slightly varying water depths. In this study, we sequenced seven species within the genus Nemipterus after identifying the specimens using complementary morphological analysis and DNA barcoding. Each species yielded over 40,000,000 clean reads, totaling over 300,000,000 clean reads across the seven species. A total of 276,389 unigenes were obtained after de novo assembly and a total of 168,010 (60.79%) unigenes were annotated in the protein database. The comprehensive functional annotation based on the KOG, GO, and KEGG databases revealed that these unigenes are mainly associated with numerous physiological, metabolic, and molecular processes, and that the seven species exhibit similarity in these aspects. By constructing a phylogenetic tree and conducting divergence time analysis, we found that N. bathybius and N. virgatus diverged most recently, approximately during the Neogene Period (14.9 Mya). Compared with other species, N. bathybius and N. virgatus are distributed in deeper water layers. Therefore, we conducted selection pressure analysis using these two species as the foreground branches and identified several environmental-responsive genes. The results indicate that genes such as aqp1, arrdc3, ISP2, Hip, ndufa1, ndufa3, pcyt1a, ctsk, col6a2, casp2 exhibit faster evolutionary rates during long-term adaptation to deep-water environments. Specifically, these genes are considered to be associated with adaptation to aquatic osmoregulation, temperature fluctuations, and skeletal development. This comprehensive analysis provides valuable insights into the evolutionary biology and environmental adaptability of threadfin breams, contributing to the conservation and sustainable management of these species. Full article

This study introduces a new method for modeling the nonlinear behavior of adhesively bonded composite steel–concrete T-beam systems. The model characterizes the interfacial behavior between the steel beam and the concrete slab using a strain compatibility approach within the framework of linear elasticity. It captures the nonlinear distribution of shear stresses over the entire depth of the composite section, making it applicable to various material combinations. The approach accounts for both continuous and discontinuous bonding conditions at the bonded steel–concrete interface. The analysis focuses on the top flange of the steel section, using a T-beam configuration commonly employed in bridge construction. This configuration stabilizes slab sliding, making the composite beam rigid, strong, and resistant to deformation. The numerical results demonstrate the advantages of the proposed solution over existing steel beam models and highlight key characteristics at the steel–concrete interface. The theoretical predictions are validated through comparison with existing analytical and experimental results, as well as finite element models, confirming the model’s accuracy and offering a deeper understanding of critical design parameters. The comparison shows excellent agreement between analytical predictions and finite element simulations, with discrepancies ranging from 1.7% to 4%. This research contributes to a better understanding of the mechanical behavior at the interface and supports the design of hybrid steel–concrete structures. Full article

In this paper, a linearized mixed finite volume element (MFVE) scheme is proposed to solve the nonlinear time fractional fourth-order reaction–diffusion models with the Riemann–Liouville time fractional derivative. By introducing an auxiliary variable $\sigma =\Delta u$, the original fourth-order model is reformulated into a lower-order coupled system. The first-order time derivative and the time fractional derivative are discretized by using the BDF2 formula and the weighted and shifted Grünwald difference (WSGD) formula, respectively. Then, a fully discrete MFVE scheme is constructed by using the primal and dual grids. The existence and uniqueness of a solution for the MFVE scheme are proven based on the matrix theories. The scheme’s unconditional stability is rigorously derived by using the Gronwall inequality in detail. Moreover, the optimal error estimates for u in the discrete $L^{\infty }\left(L^2(\Omega )\right)$ and $L^2\left(H^1(\Omega )\right)$ norms and for $\sigma$ in the discrete $L^2\left(L^2(\Omega )\right)$ norm are obtained. Finally, three numerical examples are given to confirm its feasibility and effectiveness. Full article

Risk zone delineation and mobility behavior control constitute critical measures in pandemic containment. Numerous studies utilize static demographic data or dynamic mobility data to calculate the high–risk zones present in cities; however, these studies fail to concurrently consider activity and mobility patterns of populations in both space and time, which results in many studies only being able to employ static geostatistical analytical methods, neglecting the transmission risks associated with human mobility. This study utilized the mobile phone signaling data of Shenzhen residents from 2019 to 2020 and developed a CP tensor decomposition algorithm to decompose the long-sequence spatiotemporal trajectory data to detect high risk zones in terms of detecting overlapped community structures. Tensor decomposition algorithms revealed community structures in 2020 and the overlapping regions among these communities. Based on the overlap in spatial distribution and the similarity in temporal rhythms of these communities, we identified regions with spatiotemporal co-location as high–risk zones. Furthermore, we calculated the degree of population mixing in these areas to indicate the level of risk. These areas could potentially lead to rapid virus spread across communities. The research findings address the shortcomings of currently used static geographic statistical methods in delineating risk zones, and emphasize the critical importance of integrating spatial and temporal dimensions within behavioral big data analytics. Future research should consider utilizing non-aggregated individual trajectories to construct tensors, enabling the inclusion of individual and environmental attributes. Full article

This paper investigates the cultural landscapes established by nineteenth-century German immigrants in South Australia and the southern Riverina of New South Wales, with particular attention to settlement patterns, architectural traditions and toponymic transformation. German immigration to Australia, though numerically modest compared to the Americas, significantly shaped local communities, especially due to religious cohesion among Lutheran migrants. These settlers established distinct, enduring rural enclaves characterized by linguistic, religious and architectural continuity. The paper examines three manifestations of these cultural landscapes. A rich toponymic landscape was created by imposing on natural landscape features and newly founded settlements the names of the communities from which the German settlers originated. It discusses the erosion of German toponyms under wartime nationalist pressures, the subsequent partial reinstatement and the implications for cultural memory. The study traces the second manifestation of a cultural landscapes in the form of nucleated villages such as Hahndorf, Bethanien and Lobethal, which often followed the Hufendorf or Straßendorf layout, integrating Silesian land-use principles into the Australian context. Intensification of land use through housing subdivisions in two communities as well as agricultural intensification through broad acre farming has led to the fragmentation (town) and obliteration (rural) of the uniquely German form of land use. The final focus is the material expression of cultural identity through architecture, particularly the use of traditional Fachwerk (half-timbered) construction and adaptations such as pug-and-pine walling suited to local materials and climate. The paper examines domestic forms, including the distinctive black kitchen, and highlights how environmental and functional adaptation reshaped German building traditions in the antipodes. Despite a conservation movement and despite considerable documentation research in the late twentieth century, the paper shows that most German rural structures remain unlisted and vulnerable. Heritage neglect, rural depopulation, economic rationalization, lack of commercial relevance and local government policy have accelerated the decline of many of these vernacular buildings. The study concludes by problematizing the sustainability of conserving German Australian rural heritage in the face of regulatory, economic and demographic pressures. With its layering of intangible (toponymic), structural (buildings) and land use (cadastral) features, the examination of the cultural landscape established by nineteenth-century German immigrants adds to the body of literature on immigrant communities, settler colonialism and landscape research. Full article

High-precision positioning is the foundation for the functionality of various intelligent agents. In complex environments, such as urban canyons, relative pose estimation using cameras is a crucial step in high-precision positioning. To take advantage of the ability of an inertial measurement unit (IMU) to provide relatively accurate gravity prior information over a short period, we propose a minimal solution method for the relative pose estimation of a stereo camera system assisted by the IMU. We rigidly connect the IMU to the camera system and use it to obtain the rotation matrices in the roll and pitch directions for the entire system, thereby reducing the minimum number of corresponding points required for relative pose estimation. In contrast to classic pose-estimation algorithms, our method can also calculate the camera focal length, which greatly expands its applicability. We constructed a simulated dataset and used it to compare and analyze the numerical stability of the proposed method and the impact of different levels of noise on algorithm performance. We also collected real-scene data using a drone and validated the proposed algorithm. The results on real data reveal that our method exhibits smaller errors in calculating the relative pose of the camera system compared with two classic reference algorithms. It achieves higher precision and stability and can provide a comparatively accurate camera focal length. Full article

In this paper, we calibrate the luminosity relation of gamma−ray bursts (GRBs) by employing artificial neural networks (ANNs) to analyze the Pantheon+ sample of type Ia supernovae (SNe Ia) in a manner independent of cosmological assumptions. The A219 GRB dataset is used to calibrate the Amati relation ($E_{\mathrm{p}}$-$E_{\mathrm{iso}}$) at low redshift with the ANN framework, facilitating the construction of the Hubble diagram at higher redshifts. Cosmological models are constrained with GRBs at high redshift and the latest observational Hubble data (OHD) via the Markov chain Monte Carlo numerical approach. For the Chevallier−Polarski−Linder (CPL) model within a flat universe, we obtain ${\Omega }_{\mathrm{m}}={0.321}_{-0.069}^{+0.078}$, $h={0.654}_{-0.071}^{+0.053}$, $w_0={-1.02}_{-0.50}^{+0.67}$, and $w_a={-0.98}_{-0.58}^{+0.58}$ at the 1 −$\sigma$ confidence level, which indicates a preference for dark energy with potential redshift evolution ($w_a\ne 0$). These findings using ANNs align closely with those derived from GRBs calibrated using Gaussian processes (GPs). Full article

The aim of this study is to analyze the factors that influence consumer referents or reference points and their interaction during the decision-making process, along with the principles of prospect theory in the metaverse with market and retail examples. We conducted an integrative literature review. Consumers’ preference for reference points is determined and structured during the buying process, which can be affected by potential signals and biased decisions. To guide consumers’ shopping experiences and purchasing behavior in the most effective way, marketers and organizations must investigate the factors that influence consumer reference points beyond physical or tangible attributes. Businesses must be adaptable and adapt their strategies to changing consumer preferences based on reference points. Our findings can advance discussions about how reference points are being used in the market by using consumer decision-making claims in the discursive construction of the metaverse. By comprehending this, developers can create better experiences and assist users in navigating virtual risks. Our research aids us in better comprehending the influence of referents on consumer purchasing decisions in the marketing communications field. Numerous opportunities for academic research into consumer reference points have arisen, in which individuals as digital consumers are influenced by the same biases and heuristics that guide their behavior in reality. Full article

The art of food–drink pairing has always fascinated gourmets and cooking enthusiasts. While wine has long held pride of place on the table, natural mineral water plays a central role in this new concept. Through the Pyramid of Natural Mineral Waters, we aim to explore the relationships between the structure of water and food, flavors and aromas, revealing a world of previously unexplored nuances and tastes. This new approach is based on the analysis of the fixed residue of water, i.e., the amount of mineral salts dissolved in it. The fixed residue gives the water unique organoleptic characteristics, influencing the perception of flavors and sensations in the mouth. By analyzing the technical data sheet of mineral waters designed by us, it is possible to identify their main characteristics and combine them in a consistent way with various dishes, as proposed in the pyramid scheme. There are many possible combinations between natural mineral waters and foods, depending on numerous factors, including the type of water and the salts dissolved in it, the type of food, the cooking method, and the types of sauces and condiments present in the dish. To guide consumers in this fascinating universe, the figure of the water sommelier, or so-called hydro-sommelier, was born. As expert connoisseurs of natural mineral waters, they are able to recommend the ideal water for every occasion, maximizing the taste characteristics of the food served at the table. This study is completed with the construction of the Pyramid of Natural Mineral Waters, which relates the composition of water, specifically the salient characteristics related to dissolved minerals, with the respective food combinations recommended by us, in relation to the structure of both water and food. Full article

In this study, we introduce a novel interdisciplinary framework that applies concepts from statistical physics, specifically lattice-gas models, to the classical order lot-sizing problem in supply chain management. Traditional methods often rely on heuristic or deterministic approaches, which may fail to capture the inherently probabilistic and dynamic nature of decision-making across multiple periods. Drawing on structural parallels between inventory decisions and adsorption phenomena in physical systems, we constructed a mapping that represented order placements as particles on a lattice, governed by an energy function analogous to thermodynamic potentials. This formulation allowed us to employ analytical tools from statistical mechanics to identify optimal ordering strategies via the minimization of a free energy functional. Our approach not only sheds new light on the structural characteristics of optimal planning but also introduces the concept of configurational entropy as a measure of decision variability and robustness. Numerical simulations and analytical approximations demonstrate the efficacy of the lattice gas model in capturing key features of the problem and suggest promising avenues for extending the framework to more complex settings, including multi-item systems and time-varying demand. This work represents a significant step toward bridging physical sciences with supply chain optimization, offering a robust theoretical foundation for both future research and practical applications. Full article