Search Results (3,567)

Zooplankton are ubiquitous in aquatic ecosystems and play crucial roles in material cycling and energy flow. However, the mechanisms governing zooplankton community assembly, particularly habitat-specific differences, remain poorly understood. In this two-year study, we monitored zooplankton communities across reservoir and river habitats within the Chayuan watershed, a representative karst region in southwest China. Our findings revealed significant spatial divergence in water-quality variables (including water temperature, pH, total nitrogen, total phosphorus, permanganate index, dissolved oxygen, chlorophyll-a, and ammonia nitrogen) between habitats. Twenty-nine dominant zooplankton species were identified in reservoir and river communities, with only eight shared between the two habitats. The mechanisms underlying the corresponding zooplankton community structures showed distinct segregation between habitats, with deterministic processes predominating in reservoir communities (explaining 25.1% of the variation) and stochastic processes predominating in river communities (3.4% of the variation explained). Environmental drivers differed substantially between habitats: reservoir communities were primarily influenced by total nitrogen, dissolved oxygen, and chlorophyll-a concentrations, whereas river communities responded predominantly to ammonia nitrogen levels. This study provides novel insights into the divergent mechanisms governing zooplankton community assembly in lentic versus lotic systems within a shared karst watershed, offering theoretical foundations for ecosystem-specific management strategies in fragile karst environments. Future research should focus on key climatic variables (e.g., extreme precipitation) and hydrological dynamics (such as flow velocity and water residence time) to further elucidate the mechanisms behind zooplankton community assembly, providing deeper insights to facilitate effective ecosystem management in karst environments. Full article

Copper-doped diamond-like carbon films (Cu-DLC) are effective antibacterial materials and are fabricated using different techniques. By controlling the ratio of the graphite and diamond structures as well as the hydrogen bonds, the biocompatibility, chemical stability, wear resistance, and high hardness of Cu-DLC can be regulated. In this study, three types of Cu-DLC films were deposited on SUS304 substrates using Ar-sputtering with mixed targets comprising different C/Cu ratios. The films’ structures, surface, and antibacterial properties were investigated using electron probe microanalysis, Raman and X-ray photoelectron spectroscopy, atomic force microscopy, and ball-on-disk tests. The Cu concentration in the Cu-DLC films increased with an increase in its content in the target; however, no significant differences were observed in the Raman spectra. The surface composition, roughness, and dynamic friction coefficients were similar across all Cu-DLC films, which displayed smoothness and friction properties similar to those of standard DLC films without Cu. The antibacterial activity (R value) was evaluated as per ISO 22196. Although DLC films exhibited no antibacterial activity (R < 2), all the prepared Cu-DLC films displayed good antibacterial activity (R ≥ 2). The proposed deposition process facilitated Cu-DLC coating, thus promoting its use in the healthcare fields. Full article

This study explores spiritual resilience as a mechanism for sustaining cultural landscapes, focusing on the UNESCO World Heritage Site of the Studenica Monastery (Serbia). By analysing the monastery’s sacred network, which includes monasteries, hermitages, and churches, the study demonstrates how material heritage (architecture, art), intangible practices (monastic life, liturgy, traditional crafts), and the natural environment (UNESCO MaB Golija–Studenica Biosphere Reserve) form a cohesive system of resilience. The concept of spiritual resilience is examined as a dynamic process that links sacred architectural structures and enduring religious practices with authentic land use preserved over centuries. We have utilised a methodological framework combining historical mapping, GIS viewshed analysis in spatial planning, and multidisciplinary data synthesis (historical, architectural, artistic, ecological, ethnographic) with resilience indicators aligned with the UNESCO’s Cultural Landscape approach. The findings reveal that Studenica’s sacred network operates as a coupled socio-ecological system. Spiritual practices, including annual processions and land stewardship rituals, have been identified as key factors in enhancing biodiversity conservation while mitigating land-use conflicts. Historical mapping has been used to highlight the overlap between sacred sites and protected ecological zones, reflecting traditional stewardship practices. By reframing heritage as an adaptive process where spirituality serves as a conduit between tradition and innovation, the study proposes replicable strategies for UNESCO sites worldwide. The concept of sacred landscapes as resilience hubs is furthered by alignment with SDG 11 (Sustainable Cities and Communities). Full article

The postagrogenic transformation of landscapes is one of the key problems leading to a decrease in soil fertility in the territory of Northwest Russia. In order to assess the degree of land degradation, field studies of soils from fallow lands in the Leningrad Region were carried out. Different evolutionary trends of ontogenesis of soils with types of soil parent materials were revealed. At morphological and micromorphological levels, degradation processes of old-arable horizons were noted, including secondary podzolization and decreasing Ap horizon thickness. Using a CHN analyzer, the stock levels of soil organic carbon and nitrogen of the studied chronoseries were estimated. The data obtained show that the carbon stocks of old-arable soils are lower than the benchmark ones due to the weak development of the Oi horizon. Carbon dynamics varied substantially by parent materials: soils on silt–clay materials showed a low 7.1% carbon decrease, while soils on sandy and bottom sediments increased by 139% and 163%, respectively, in old-arable horizons by the accumulation of coarse forms of carbon. For nitrogen, it was revealed that the highest stocks are observed in old-ploughed soils, which is due to the input of a large amount of plant residues from small-leaved forests. The content of biogenic elements in the soil showed separate evolutionary direction depending on parent materials: soils on silt–clay materials showed 7.6% phosphorus depletion and 15% potassium loss over 15–30 years, while soils on sandy materials demonstrated 18% phosphorus loss and 114% potassium increase during 30–86 years of fallow state. On the contrary, the content of nitrate and ammonium forms of nitrogen was higher than in the benchmark zonal soils, with nitrate nitrogen increasing by 150 times on sandy parent materials and ammonium nitrogen increasing by 102% in soils formed on bottom sediments over 35–70 years, which is due to the transformation of grass and forest plant residues. The duration of transformation and regradation of soils of fallow land depends on geogenic and bioclimatic conditions that determine the direction and speed of changes. Full article

The unified hardening model for clays and sands (CSUH) can adequately represent the stress–strain characteristics of various soil types. However, being an incremental elastoplastic constitutive model, the CSUH model requires extensive iterative computations during parameter identification, resulting in significant computational time. To improve computational efficiency, this study derives the elastoplastic compliance matrix and stress–strain incremental relationships under different stress paths, eliminating the repeated solving of equations typically required during iterative processes. Furthermore, a dynamic step size iterative method is proposed based on the changing slope characteristics of the stress–strain curves. This method divides the total axial strain into two segments: in the initial segment (approximately the first 30% of total strain), where the curve slope is steep, smaller step sizes with arithmetic progression distribution are employed, while in the latter segment (approximately the remaining 70%), characterized by a gentle curve slope, larger and uniformly distributed step sizes are adopted. Comparative analyses between the proposed dynamic step size method and the traditional constant-step iterative method demonstrate that, under the premise of ensuring calculation accuracy, the dynamic step size method significantly reduces the iteration steps from 3000 to 50, thus decreasing the computational time by approximately 47 times. Finally, the proposed method is applied to parameter identification of Fujinomori clay, calcareous sand, and Changhe dam rockfill materials using the CSUH model. The predictions closely match experimental results, confirming the CSUH model’s capability in accurately describing the mechanical behaviors of different soils under various stress paths. The dynamic step size iterative approach developed in this study also provides valuable insights for enhancing computational efficiency and parameter identification of other elastoplastic constitutive models. Full article

This study is dedicated to the design of multiple redox-active oligonuclear manganese complexes supported with a bis(tetradentate) ligand (TPDP = 1,3-bis(bis(2-pyridinylmethyl)amino)-2-propanol) for high-contrast electrochromism based on the reversible redox process between Mn(II) (colorless) and Mn(III) (dark brown). Pentanuclear Mn₅ complex 1 (colorless) was synthesized via a one-pot reaction of Mn²⁺ and TPDP, while tetranuclear Mn₄ complex 2 (brown) was obtained through aerial oxidation of complex 1. Mn₅ complex 1 features a central MnCl₆ unit connected to two Mn₂(μ-TPDP) fragments through μ₃-Cl⁻ and μ-Cl⁻, whereas Mn₄ complex 2 adopts a symmetric tetranuclear structure with two mixed-valence Mn₂^II,III(μ-TPDP)(μ-Cl) fragments that are further linked by μ-oxo. Electrochemical studies revealed multi-step reversible redox properties for both complexes, attributed to Mn^II/Mn^III processes with significant electronic coupling (ΔE_1/2 = 0.27–0.37 V) between Mn centers. Spectroelectrochemical analysis revealed dynamic optical modulation through the tunable d-d transition and ligand-to-metal charge transfer (LMCT) state through reversible multiple redox processes based on Mn(II) ⇆ Mn(III) interconversion. The fabricated electrochromic device (ECD) exhibited reversible and high optical contrast between the colored state (dark brown) and the bleaching state (colorless). The results highlight the potential of polynuclear manganese complexes as high-contrast electrochromic materials for next-generation smart windows and adaptive optical technologies. Full article

Vibration spectroscopy is routinely used in analytical chemistry for molecular speciation. Less common is its use in studying the dynamics of reaction and transport processes. A shortcoming of vibration spectroscopies is that they are not inherently specific to chemical elements. Progress in synchrotron radiation-based X-ray technology has developed nuclear resonance vibration spectroscopy (NRVS), which can be used to produce element-specific vibration spectra and partial vibrational density of states (PVDOS), provided the material under investigation contains a Mössbauer-active element. While the method has been recently used successfully for protein spectroscopy, fewer studies have been conducted for condensed matter. We have employed NRVS on the BaSnO₃ perovskite structure, which is a model compound for ceramic proton conductors in intermediate temperature fuel cells. Since we used ¹¹⁹Sn as a Mössbauer isotope, the derived experimental PVDOS is specific to the element Sn in BaSnO₃. We show how this phonon DOS is used as an experimental anchor for the interpretation of the DFT-calculated PVDOS of BaSnO₃. Full article

This study explores the concept of hybridity in architecture, shaped by cultural exchange, globalization, and evolving socio-political contexts. In this research, hybridity in architecture is defined as a dynamic process that emerges within boundary spaces, where physical elements interact with evolving cultural, social, and political forces, resulting in adaptable and multilayered architectural environments. Despite the significance of hybridity in architecture, existing research lacks a comprehensive and systematic framework for its analysis. To bridge this gap, the study develops a conceptual framework that integrates archival research, literature synthesis, and an architectural analysis. The methodology combines a qualitative analysis of historical documents and design drawings to identify eight key indicators of hybridity—form, typology, program, mixed-use, multi-layering, user mixing, border spaces, and control/resistance—and applies them to a case study of the University of Baghdad. These indicators embody the interaction between the static and kinetic aspects of hybridity. The Static Aspect refers to the tangible outcomes of hybridity—such as mixed forms and functions—that materialize in built structures. In contrast, the Kinetic Aspect reflects the intangible dimensions, including ongoing social and cultural dynamics and shifts in power relations, which continuously reshape these hybrid forms. Together, these aspects illustrate that hybridity is both a product and a process, where material expressions emerge from social negotiations and, in turn, influence future adaptations. The findings reveal that the hybrid architecture evolves through complex interactions among historical references, contemporary needs, and socio-political forces. By establishing a systematic methodology for analyzing hybridity, this study bridges theoretical discourse with practical applications, providing architects and researchers with a robust analytical tool to assess hybrid architectural spaces within culturally diverse contexts. It also reinforces the understanding of hybridity as a dynamic force—one that not only results in physical architectural expressions but also evolves through ongoing cultural, social, and political interactions. Full article

This study aims to improve the accuracy of fire source detection, the efficiency of path planning, and the precision of firefighting operations in drone swarms during fire emergencies. It proposes an intelligent firefighting technology for drone swarms based on multi-sensor integrated path planning. The technology integrates the You Only Look Once version 8 (YOLOv8) algorithm and its optimization strategies to enhance real-time fire source detection capabilities. Additionally, this study employs multi-sensor data fusion and swarm cooperative path-planning techniques to optimize the deployment of firefighting materials and flight paths, thereby improving firefighting efficiency and precision. First, a deformable convolution module is introduced into the backbone network of YOLOv8 to enable the detection network to flexibly adjust its receptive field when processing targets, thereby enhancing fire source detection accuracy. Second, an attention mechanism is incorporated into the neck portion of YOLOv8, which focuses on fire source feature regions, significantly reducing interference from background noise and further improving recognition accuracy in complex environments. Finally, a new High Intersection over Union (HIoU) loss function is proposed to address the challenge of computing localization and classification loss for targets. This function dynamically adjusts the weight of various loss components during training, achieving more precise fire source localization and classification. In terms of path planning, this study integrates data from visual sensors, infrared sensors, and LiDAR sensors and adopts the Information Acquisition Optimizer (IAO) and the Catch Fish Optimization Algorithm (CFOA) to plan paths and optimize coordinated flight for drone swarms. By dynamically adjusting path planning and deployment locations, the drone swarm can reach fire sources in the shortest possible time and carry out precise firefighting operations. Experimental results demonstrate that this study significantly improves fire source detection accuracy and firefighting efficiency by optimizing the YOLOv8 algorithm, path-planning algorithms, and cooperative flight strategies. The optimized YOLOv8 achieved a fire source detection accuracy of 94.6% for small fires, with a false detection rate reduced to 5.4%. The wind speed compensation strategy effectively mitigated the impact of wind on the accuracy of material deployment. This study not only enhances the firefighting efficiency of drone swarms but also enables rapid response in complex fire scenarios, offering broad application prospects, particularly for urban firefighting and forest fire disaster rescue. Full article

The current study examined how pre-service early childhood teachers, through invented notation activities, intra-act with the agency of music and the environment to achieve a process of becoming-music, grounded in Barad’s agential realism, presuming that it is necessary to consider the encounter with music itself rather than perceiving it as a ‘teaching subject’. The collected data included 54 sets of invented notations performed by 22 pre-service teachers, recorded videos of their performances driven by their interpretations, their journals, and observational records and notes by the researcher. Qualitative analysis was conducted based on the intra-actions between the pre-service teachers and music. In the invented notation activity, pre-service teachers initiated their engagement by encountering musical concepts and experimenting with diverse art materials as well as daily-life items. They generated sounds and created three-dimensional invented notations designed to guide the performance of the sounds. Furthermore, they deeply responded to the symbols within the invented notations, connecting their daily lives and music. They continued to realize the becoming-music through the ongoing generation of N-dimensional sounds. Pre-service teachers, through invented notation activities, experienced musical thinking not as an acquisition of pre-established knowledge and skills but rather through a direct encounter with music itself. This suggests that invented notation activities provide a sustainable learning environment by facilitating a dynamic entanglement with music. Furthermore, it indicates that post-humanism, which proposes a relational symbiosis between human and nonhuman entities, serves as a fundamental framework for education for sustainable development. Full article

The growing energy demands and increasing environmental concerns in industrial manufacturing necessitate innovative solutions to reduce fuel consumption and lower carbon emissions. This paper presents Sustain AI, a multi-modal deep learning framework that integrates Convolutional Neural Networks (CNNs) for defect detection, Recurrent Neural Networks (RNNs) for predictive energy consumption modeling, and Reinforcement Learning (RL) for dynamic energy optimization to enhance industrial sustainability. The framework employs IoT-based real-time monitoring and AI-driven supply chain optimization to optimize energy use. Experimental results demonstrate that Sustain AI achieves an 18.75% reduction in industrial energy consumption and a 20% decrease in CO₂ emissions through AI-driven processes and scheduling optimizations. Additionally, waste heat recovery efficiency improved by 25%, and smart HVAC systems reduced energy waste by 18%. The CNN-based defect detection model enhanced material efficiency by increasing defect identification accuracy by 42.8%, leading to lower material waste and improved production efficiency. The proposed framework also ensures economic feasibility, with a 17.2% reduction in operational costs. Sustain AI is scalable, adaptable, and fully compatible with Industry 4.0 requirements, making it a viable solution for sustainable industrial practices. Future extensions include enhancing adaptive decision-making with deep RL techniques and incorporating blockchain-based traceability for secure and transparent energy management. These findings indicate that AI-powered industrial ecosystems can achieve carbon neutrality and enhanced energy efficiency through intelligent optimization strategies. Full article

Additive Manufacturing (AM) has become a revolutionary technology in manufacturing, attracting considerable attention in industrial applications recently. It allows for intricate fabrication, reduces material waste, offers design flexibility, and has economic implications. Nonetheless, the residual stresses generated during the AM process and their effects on microstructural evolution and material properties continue to pose significant challenges hindering its advancement. This paper investigates the evolution of microstructures, focusing on texture and grain size as influenced by processing parameters. It examines how these factors affect the performance of multi-phase materials, specifically in terms of elastic modulus, Poisson’s ratio, and yield strength, leading to variations in residual stress through analytical simulation. The authors developed a thermal model that considers heat transfer boundaries and the geometry of the molten pool. They simulated grain size by considering the heating and cooling processes, including thermal stress, the Johnson-Mehl-Avrami-Kolmogorov (JMAK) model, and grain refinement. The texture was simulated using the Columnar-to-Equiaxed Transition (CET) model, thermal dynamics, and Bunge calculations. The self-consistency model determines the properties based on the established texture distribution. Finally, both microstructure-affected and non-affected residual stresses were modeled and compared. Two gaps between microstructure-affected residual stress and non-affected analytical models appear at the depths of 0.02 mm and 0.078 mm. The results indicate that controlling process parameters and optimizing microstructures can effectively reduce residual stresses, significantly enhancing the overall performance of AM components. Hence, this work provides a more accurate analytical residual stress model and lays the foundation for better control of residual stress in the AM industry. Full article

Energetic structural materials (ESMs) are widely studied due to their high energy density, which enhances their potential in various industrial and engineering applications, such as in energy absorption systems, safety devices, and structural components that need to withstand dynamic loading. A high-strength WMoZrNiFe energetic structural material was prepared, and its mechanical properties and ignition behavior under dynamic loading were studied. Using the split-Hopkinson pressure bar (SHPB) experimental device, samples with different initial tilt angles of 0°, 30°, and 45° were dynamically loaded. The influence of the sample tilt angle on the ignition threshold was analyzed. The dynamic mechanical properties, failure modes, and ignition threshold based on the energy absorption of the WMoZrNiFe energetic structural material during the dynamic loading process were obtained. The results show that the material has a strain rate effect in the range of 1000 s⁻¹~3000 s⁻¹. The yield strength of the sample with a tilt angle of 0° increased from 1468 MPa to 1837 MPa, that of the sample with a tilt angle of 30° increased from 982 MPa to 1053 MPa, and that of the sample with an inclination angle of 45° increased from 420 MPa to 812 MPa. Through EDS elemental analysis, the ignition reaction mechanism of the WMoZrNiFe energetic structural material under dynamic compression was obtained. The violent reaction of the material occurred after the material fractured, and the active elements reacted with oxygen in the air. Full article

Mechanical impacts on loose biological objects caused by technological equipment can result in both external and internal damage, compromising the quality, storage life, and reproductive capacity of biological materials. This study addresses the need for a reliable methodology to assess such damage. The research aims to develop a systematic approach for identifying damage parameters in biological objects. The methodology involves applying artificial loading to biological samples, determining destructive forces, conducting tomography, processing images, and evaluating damage extent. Experiments were performed using a standard material testing machine and a custom-built impact test bench with varying parameters such as static and dynamic characteristics, object orientation, and load magnitude. The microstructure of the sample, in the form of 2D cross-sections and 3D images, was obtained using X-ray computed tomography. Image processing, with the Monte Carlo method, allowed for the calculation of microdamage coefficients. The key result of this study is the identification of a relationship between the microdamage coefficient of corn seeds and external load parameters. These findings are critical for understanding the effects of mechanical impact on biological materials. Future research should focus on expanding the study to other biological objects and enhancing measurement techniques for more precise damage assessment. Full article

As a representative third-generation Al-Li alloy, 2A97 alloy has attracted significant attention for applications in aeronautics and astronautics, but its poor hot workability and complex thermal deformation behavior, which make for difficult optimization, significantly limit its widespread industrial utilization. In this study, the thermal deformation behavior of 2A97 Al-Li alloy was systematically investigated via thermal compression tests conducted over a temperature range of 260–460 °C and strain rates ranging from 0.001 s⁻¹ to 1 s⁻¹. The effects of deformation parameters on the alloy’s microstructural evolution were examined using electron backscatter diffraction (EBSD) and transmission electron microscopy (TEM). Based on the dynamic materials model, a constitutive equation was established by analyzing the stress–strain data under various thermal deformation conditions. Furthermore, a thermal processing map was compiled to analyze the effects of the temperature and strain rate on the power dissipation efficiency and flow instability factor. The thermal deformation mechanisms were identified through combined analysis of the thermal processing map and microstructural features. Results indicate that the fraction of low-angle grain boundaries increases with a rising lnZ value (Zener–Hollomon parameter) during the thermal compression process. Dynamic recrystallization is the main deformation mechanism of 2A97 Al-Li alloy in the stable region, whereas the alloy exhibits flow localization in the unstable region. According to the thermal processing map, the optimal hot working windows for the 2A97 Al-Li alloy were determined to be (1) 360–460 °C at strain rates of 0.05 s⁻¹–1 s⁻¹, and (2) 340–420 °C at strain rates of 0.001 s⁻¹–0.005 s⁻¹. These conditions offer favorable combinations of microstructure and deformation stability, providing critical guidance for the thermo-mechanical processing of 2A97 alloy. Full article