Search Results (555)

Coal mine methane (CMM) separation faces significant challenges due to high humidity and multicomponent conditions, under which the selective adsorption performance of CH₄ is substantially degraded compared with idealized laboratory scenarios. This review systematically analyzes the fundamental causes of this discrepancy by integrating water vapor occupation, competitive adsorption, and structural constraints into a unified framework. Water molecules preferentially occupy high-energy adsorption sites and reconstruct the interfacial energy landscape, while strongly adsorbing components such as CO₂ further suppress CH₄ uptake through competitive displacement. These coupled effects lead to a pronounced deviation between theoretical adsorption capacity and actual separation performance. To address this issue, this work proposes an evaluation paradigm centered on effective working capacity, which reflects the practically recoverable CH₄ under cyclic operation rather than equilibrium limits. The applicability of this framework is demonstrated through comparative analysis across different adsorbent systems, highlighting the critical roles of moisture resistance, structural stability, and competitive resilience. Finally, key material design strategies and process-level optimization approaches are discussed to enhance sustainable CH₄ separation under realistic conditions. This review provides a process-oriented perspective for bridging the gap between material performance and engineering application in CMM utilization. Full article

Existing research on the landscape patterns of urban parks and green spaces demonstrates a disproportionate focus across tiers within China’s urban hierarchy. Numerous studies have concentrated on economically developed first-tier cities, such as Beijing, Shanghai, and Guangzhou. In contrast, medium-to-large non-first-tier cities, especially provincial capitals and emerging cities within the first- and second tiers, have been relatively understudied, although they have received increasing attention in recent years. This bias extends regionally, with studies predominantly examining cities in the more developed central and eastern regions, while less-developed areas and lower-tier cities receive significantly less attention. This study tracks changes in park quantity, spatial concentration, patch structure and driver associations at three planning-related time points. Shenyang provides a distinct cold-region and old industrial city case, shaped by long winters, industrial renewal and outward urban growth. Furthermore, to inform park and green-space planning in Northeast China’s cold-climate cities, exemplified here by Shenyang, a major metropolis with a monsoon-influenced humid continental climate (Köppen Dwa), long cold winters, and relatively short warm summers, we document a shift in park distribution from the urban core to peripheral areas. Based on park vector layers reconstructed from planning documents, remote sensing interpretation and field verification, this study combined spatial analysis, landscape metric calculation and driver-association modeling. ArcGIS Pro was used to identify changes in distribution centers, directional extension and local clustering; FRAGSTATS 4.2 was used to calculate park landscape metrics; and SIMCA-P 14.1 was used to examine the statistical associations between selected landscape indicators and potential driving variables. The results show that the number and total area of parks in central Shenyang increased substantially from 2000 to 2024. Spatially, park distribution became less concentrated in the traditional inner city, while new clusters gradually appeared in peripheral districts and newly developed urban areas. The old urban core remained important, but its dominance weakened as park provision expanded outward. The landscape metric results further indicate that park expansion was accompanied by more irregular patch forms, stronger fragmentation and declining structural continuity. The driver association analysis suggests that climate conditions, population change, industrial restructuring, real estate investment, road construction and urban greening policies were related to different aspects of park landscape change. These associations should be interpreted as statistical relationships rather than direct causal effects. Overall, this study clarifies the spatial restructuring of park green spaces in a cold-region old industrial city and provides planning evidence for improving park connectivity, coordinating green space expansion with urban construction and supporting sustainable park system development in Northeast China. Full article

Today, the rapid progress in non-invasive light–matter interaction analysis is transforming the landscape of biomedical and life sciences driven by low-intensity light detection technologies. As the complexity of photonic applications continues to grow, the importance of single-photon detection techniques becomes pivotal. Among them, Time-Correlated Single-Photon Counting (TCSPC) has become the gold standard for precise, time-resolved reconstruction of rapid and faint optical signals. However, TCSPC has long been constrained by pile-up distortion, which worsens with increasing acquisition speed, typically limiting it to 5% of the excitation frequency. To overcome the operational constraints of conventional implementations, a novel TCSPC acquisition methodology has been introduced, independent of photodetector dead time, excitation intensity, and prior optical signal knowledge, still enabling distortion-free reconstruction of the measured light profiles. In this context, the development of single-photon detectors with short dead time and low timing jitter becomes crucial. This work presents a single-photon detection module based on a Silicon Photomultiplier, which delivers 750 ps FWHM output pulses with a 33.5 ps RMS IRF. Its performance is showcased through fluorescence measurements employing the constraint-free TCSPC methodology, achieving a photon count rate up to 166% of the excitation frequency with a minimal lifetime estimation error of just −1.46%. Full article

Grotta-Riparo di Uluzzo C (Uluzzo Bay, Apulia, southern Italy) preserves a long and complex stratigraphic sequence spanning from the Middle Palaeolithic to the Bronze Age, offering a valuable context for investigating depositional dynamics and human–environment interactions during the Late Pleistocene. Although recent multidisciplinary research has substantially advanced knowledge of the Uluzzian occupations, the Mousterian faunal record of the site has remained largely unexplored from zooarchaeological and taphonomic perspectives. This study examines the faunal assemblages from the Mousterian layers (E, F, and G), integrating material from historical excavations with those recovered during recent fieldwork. Zooarchaeological, taphonomic, and Zooarchaeology by Mass Spectrometry (ZooMS) analyses are combined to reconstruct local environmental conditions, evaluate the relative contribution of human and non-human agents to bone accumulation, and assess patterns of site use and deposit formation. The faunal spectrum indicates an ecologically heterogeneous landscape, consistent with a Mediterranean refugial setting during the Late Pleistocene. Taphonomic evidence points to complex and cumulative formation processes resulting from repeated, short-term human occupations interspersed with carnivore activity and natural depositional processes. The Mousterian deposits are therefore best interpreted as brief palimpsests rather than the result of continuous or intensive occupation. Placed within a regional framework, the Uluzzo C assemblages contribute to broader discussions on site formation processes and environmental variability in southern Italy and provide an important comparative baseline for the Middle to Upper Palaeolithic period. Full article

Combining landscape studies with historical studies and archeology is a scientific challenge: through the skills of historians, archeologists, agronomists, and computer scientists, an integrated and interdisciplinary methodology was adopted. The aim was to backtrack historical landscape changes and permanences in the rural landscape around Staffarda Abbey (Piedmont, Northwest Italy), a medieval monastery founded in the XII century on a surface of 1356 ha and its farms. Surveys, field observations, landscape observations, and historical and archival analyses (XII–XX century) were performed. Several document types, such as historical cartography, iconographies, cadastral maps, notes, descriptions, topographic maps, inventories, and photographs, were deeply analyzed. These documents referred to different historic periods (XII–XXI century) and provided qualitative and quantitative data. Using a fixed-wing drone, the aerial photographs were reworked and georeferenced. QGIS was used to perform diachronic analyses at the landscape level. The advanced land analysis methodologies were compared with the post-medieval cartographic data that were collected. The landscape dynamics and land-use changes were quantified over time, and routes, tree lines, hedgerows, and canals were recognized as qualifying elements. In this study, qualitative and quantitative data were collected, processed, systematized, and analyzed using a digital platform. Using different scales, readings, and interpretations, the landscape dynamics of a rural medieval site were reconstructed. Full article

The integration of culture and tourism has positioned literary heritage tourism as an important pathway for the sustainable development of cultural landscapes and urban regeneration. However, existing studies remain fragmented, lacking a systematic understanding of the spatial configuration, development processes, and the role of tourists in shaping these spaces. Addressing these gaps, this study adopts a tourist-perception perspective to examine seven types of literary heritage tourism space forms in Shanghai. Using online review data, TF–IDF and TextRank methods are applied to identify key space elements and their semantic relationships, enabling a data-driven analysis of spatial characteristics. The results identify three key dimensions, namely perceptual, conceptual, and experiential, which are further organised into 15 subcategories. A spatial analytical framework is developed to conceptualise the literary heritage tourism space as a process shaped by physical settings, symbolic interpretations, and experiential co-production. Furthermore, the findings suggest an interpretive framework of spatial reconstruction, in which tourist participation serves as a link between internal space elements and broader socio-cultural contexts. This study extends the application of spatial production theory from a perception-based perspective, combines computational text analysis with spatial interpretation, and offers practical implications for sustainable cultural landscape planning. Full article

High-resolution imagery is essential for monitoring heterogeneous grassland ecosystems, yet the performance of generative adversarial network (GAN) super-resolution under varying landscape heterogeneity and operational application scenarios remains unclear. This study presents a landscape-aware evaluation of super-resolution methods in semi-arid savanna grasslands of the Edwards Plateau (Texas, USA) using paired multispectral imagery from PlanetScope (3 m) and unmanned aerial vehicle (UAV) platforms (0.03 m). Two GAN models, SRGAN and ESRGAN, were compared with a bicubic interpolation baseline. Image tiles were systematically stratified along ecologically relevant gradients of vegetation condition (NDVI quartiles), spatial structure (woody patch-based clusters), and textural complexity (GLCM entropy quartiles). Model performance was evaluated across three operational frameworks: intra-sensor downscaling, cross-sensor downscaling, and intra-to-cross generalization. Reconstruction fidelity was quantified using peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM), complemented by variability analysis to assess performance stability. Landscape heterogeneity strongly influenced downscaling outcomes. SRGAN performance declined in areas with dense vegetation, aggregated woody structure, and high-entropy textures, with large variability under cross-sensor and generalization scenarios. In contrast, ESRGAN demonstrated consistently robust performance across landscape gradients, whereas bicubic interpolation performed well only under intra-sensor conditions and drastically degraded under sensor transfer. These results demonstrate that vegetation condition, structural heterogeneity, and sensor-transfer scenarios jointly constrain super-resolution performance. Rather than serving as a model comparison exercise, this study emphasizes a landscape-aware framework for understanding how ecological heterogeneity and operational domain shifts jointly shape super-resolution behavior in grassland ecosystems, providing guidance for more reliable applications of deep learning-based remote sensing methods. Full article

High-liquid-limit clay exhibits pronounced water sensitivity due to the strong electrostatic repulsion and weak interparticle bonding within its microstructure, which often limits its direct engineering uses and complicates the reuse of excavated clayey soils generated during the construction of transportation infrastructure. In this study, inorganic salts (KCl, CaCl₂ and FeCl₃) and carboxyl-containing polymers (PAAS, HPMA and CMC) were screened to construct organic–inorganic composite stabilization systems. Based on the screening results, an organic–inorganic composite system composed of CaCl₂ and sodium polyacrylate (PAAS) was developed to regulate interfacial interactions and induce microstructural reconstruction in clay. The synergistic mechanisms governing particle aggregation and dispersion were systematically investigated through Atterberg limit tests, zeta potential measurements, DLVO theoretical calculations, particle size analysis, scanning electron microscopy (SEM) and immersion disintegration experiments, combined with multivariate statistical modeling. Among the tested salt–polymer formulations, a composite system with 2% CaCl₂ and 0.1% PAAS showed the most favorable overall performance, achieving an optimal balance between electrostatic compression and steric stabilization, leading to enhanced structural integrity and delayed water-induced disintegration. Ca²⁺ ions compress the diffuse double layer and promote particle flocculation, whereas adsorbed PAAS chains introduce steric hindrance and interfacial modification. Their synergistic interaction reconstructs the pore–aggregate framework and regulates the interparticle potential energy landscape. DLVO analysis indicates that the optimized system attains a moderate critical interaction distance (h_c = 7.31 nm) and primary minimum depth (DPM = −2.72 × 10⁻¹⁶ J), reflecting a balanced interfacial bonding state. Multivariate statistical analyses further reveal a dual control pathway, in which consistency primarily governs disintegration duration, with additional contributions from surface electrochemical properties, while surface properties, soil structure and consistency collectively influence disintegration initiation. These findings elucidate the interfacial regulation and structural evolution mechanisms in organic–inorganic composite systems and provide insights into the design of composite modifiers for water-sensitive particulate materials, particularly for the resource reuse of high-liquid-limit clay excavated during the construction of transportation infrastructure and related geotechnical engineering applications. Full article

The evolution and spatial dissemination of SARS-CoV-2 Omicron subvariants have been characterized by rapid lineage replacement and complex transmission dynamics influenced by regional connectivity. This study presents a comprehensive discrete phylogeographic analysis of 90,136 SARS-CoV-2 sequences collected in Poland from 2022 to 2024 to reconstruct the dispersal dynamics of major Omicron lineages, including BA.1, BA.2, BA.5, CH.1, XBB.1, and JN.1. Utilizing Bayesian statistical frameworks, we identified significant viral transitions between the 16 Polish voivodeships and established variant-specific dominance windows ranging from 2 to 4 months. Our findings reveal a highly dynamic epidemic landscape with shifting regional epicenters. The initial BA.1 wave was primarily driven by the Mazovian voivodeship, accounting for 36.1% of outward migration events. This pattern shifted dramatically with the rise in BA.2, which was centered in the industrial Silesian region in the south-west, a densely populated area with strong economic ties to neighboring countries, potentially reflecting a different introduction or transmission dynamic. Furthermore, the epidemic landscape continued to reconfigure during the BA.5 wave, marked by the emergence of new transmission hubs in eastern border regions such as Lublin. Subsequent lineages exhibited distinct geographic signatures: BA.5 spread broadly along the Baltic-central corridor, CH.1 was centered in the north-east, XBB.1 re-emerged in the west-central region of Greater Poland, and JN.1 was driven overwhelmingly by Lesser Poland. These transitions highlight that regional transmission hubs are transient and influenced by local factors such as population density, cross-border mobility, and socio-economic connectivity. This study underscores the critical value of dense genomic surveillance in identifying evolving dispersal routes to inform adaptive, region-specific public health interventions. Full article

Background: Recent advances in spatial transcriptomic technologies enable in situ gene expression profiling while preserving spatial context. This capability is particularly important for studying the tumor microenvironment (TME), where diverse and admixed cell populations interact within highly organized spatial niches that influence tumor progression and therapeutic response. However, the limited resolution of early spatial transcriptomic platforms results in each spatial spot capturing transcripts from multiple cell types, making accurate spot deconvolution or annotation a critical yet challenging step in downstream data analysis. The level of complexity will be particularly prominent in heterogeneous samples like the tumor microenvironments where multiple cell types are highly admixed and reliable single-cell reference atlases may usually be unavailable. Methods: In this paper, we developed our method called STEA, which is a novel and accurate reference-independent enrichment-based annotation algorithm for major cell type. Unlike the existing approaches, STEA does not require single-cell RNA sequencing datasets as reference, offering both flexibility and computational efficiency in execution. Results: We performed comprehensive benchmarking using a variety of simulated datasets across different platforms and scenarios and demonstrated the superior accuracy of STEA. Apart from synthetic data, we also evaluated multiple real datasets to further exemplify its practical applicability on both oncology-related and oncology-unrelated data. More importantly, we could confidently demonstrate the high concordance between prediction of STEA and histological classification by experienced pathologist. Conclusion: Our STEA algorithm provides a practical reference-independent framework to complement the cutting-edge spatial transcriptomics in genomics studies, facilitating accurate downstream high-dimensional spatial characterization of cellular and molecular landscapes, reconstruction of tissue architecture as well as cell–cell communication in malignant and non-malignant scenarios. Taken together, our comprehensive evaluation demonstrates the robustness and reliability of STEA, highlighting its potential as a valuable tool for studying complex tissue organization, particularly within heterogeneous TME. Full article

The impact of oncological diseases extends far beyond the clinical patient, profoundly affecting the mental health of caregivers, family members, and volunteers who navigate complex emotional landscapes of grief, anxiety, and trauma. While the domain of digital health has seen a proliferation of serious games aimed at pediatric patient education and treatment adherence, the specific perspective of the “second-order patient”, the caregiver or survivor, remains significantly under-explored. The primary objective of this study is to systematically review the current state of interactive narratives in oncology, palliative care, and grief support, identifying research gaps to inform the broader design space of empathy-driven serious games. Following the PRISMA guidelines, 31 articles were selected from an initial query of 116 records. Interventions were categorized into Serious Games, Games, and Gamification. The analysis reveals a critical thematic transition: early interventions relied heavily on biological “battle” metaphors to empower patients, whereas the current literature advocates for “thanatosensitive” designs that foster empathy. However, a distinct research gap persists regarding narratives that explore post-loss meaning reconstruction and the hospital volunteer experience. Synthesizing these findings, this paper establishes an evidence-based theoretical framework demonstrating a significant opportunity for games that prioritize dialogue and emotional processing over traditional winning conditions. As a practical application of these findings, we also briefly outline the conceptualization of a prototype simulating a widower’s experience volunteering in a palliative ward, shifting the ludic focus from defeating a disease to navigating loss. Full article

In Ghana’s political landscape, actors from both ruling and opposition parties deploy a range of linguistic and rhetorical strategies in their pursuit of political power. Prominent among these is political labeling, a discursive practice used to construct favorable self-images while delegitimizing opponents through derogatory and face-threatening expressions. This study examines how political labeling functions as a strategic tool for identity construction and power negotiation in Ghana’s electoral landscape. Situated within the fields of political discourse and communication studies, the study demonstrates how labeling operates simultaneously as a rhetorical and framing device that reflects and reinforces underlying sociopolitical power dynamics. Drawing on empirical data from major Ghanaian news portals, the study adopts an integrated analytical framework combining Framing Theory and the Theory of Impoliteness. It analyzes public labeling directed at three prominent political figures across three election cycles (2016, 2020, and 2024). The findings show that politicians, activists, and their supporters strategically deploy labels to reconstruct rivals’ identities, inflict reputational damage, and provoke ridicule, thereby undermining their perceived competence and public credibility. Focusing on derogatory labels, we argue that political labeling serves primarily to generate emotional responses, shape public perception, and mobilize collective action, ultimately influencing the trajectory of national political discourse. By examining the interplay between language, identity construction, and power, this research offers a nuanced account of how political labeling shapes individual attitudes, group dynamics, and the broader political culture in Ghana. Full article

The rapid growth of hiking tourism presents a critical challenge for balancing visitor safety with the sustainable management of ecologically fragile mountain environments. Traditional models developed in urban settings struggle to capture the highly non-linear, heterogeneous, and zero-inflated characteristics of wilderness trekking behavior. In order to quantify the nonlinear and threshold-based effects of environmental variables on hikers’ spatial decisions in unstructured wilderness and to identify distinct behavioral regimes for segmented management, this study introduces an explainable machine learning framework to reconstruct hikers’ spatial decision-making in a complex mountainous system in Inner Mongolia, China. Random Forest (RF), XGBoost, and LightGBM were compared in predicting trail density and the Euclidean distance to the nearest trail. Results show that transforming behavioral traces into continuous proximity surfaces dramatically improves model performance, with XGBoost achieving the highest predictive accuracy for Trail_Dist. By integrating the SHapley Additive exPlanations framework, this study moves beyond black-box prediction to reveal the nonlinear mechanisms driving hiker behavior. Key findings include: (1) Nighttime light range exhibits a U-shaped threshold effect as the primary anthropogenic attractor. (2) Elevation shows an exponential inhibitory trend above 1238 m. (3) Strong spatial coupling exists between elevation and slope, alongside a landscape compensation effect where high Normalized Difference Vegetation Index (NDVI) areas attract off-trail movements. This research provides a robust methodological pathway for predicting behavior in unstructured outdoor environments. It offers a scientific foundation for smart scenic area management, including optimized route planning, precise ecological protection zoning, and targeted emergency rescue preparedness. Full article

Socio-environmental conflicts in mining regions are often examined through political, economic, or social lenses, while the role of land-system transformation remains less integrated into quantitative analysis. This study examines the co-evolution of socio-environmental conflict and territorial change in Las Bambas (Apurímac, Peru) as a socio-territorial process. Annual conflict records from the Peruvian Ombudsman’s Office (2007–2024) were combined with annual land-cover data from MapBiomas. Yearly conflict influence zones were reconstructed from reported affected communities and geographic features using buffered spatial entities and concave hull polygons. Clustering methods (K-medoids, DBSCAN, and agglomerative hierarchical clustering) and FP-Growth association rule mining were applied to 23 unique conflicts consolidated from the original records and encoded with 10 root causes. The most intense conflict phases were accompanied by measurable landscape transformations, including the emergence of mining-related land cover from 2012 onward, sustained loss of high-Andean natural vegetation, expansion of agricultural mosaics, urban growth along the Apurímac–Cusco corridor, and hydrological alterations in wetlands and headwaters. Three conflict typologies were identified, with unfulfilled company commitments emerging as the most recurrent co-occurring grievance. The dynamic polygon approach offers a replicable framework for linking conflict records with land-system change in extractive regions. Full article

Background/Objectives: Magnetic nanoparticles have emerged as powerful tools for biomedical imaging, targeted drug delivery, and hyperthermia therapy. Magnetic particle imaging (MPI) is among the most promising technologies built around its properties: a radiation-free, quantitative tomographic modality that detects superparamagnetic iron oxide nanoparticles (SPIONs) directly against a biologically silent background. This review synthesizes MPI’s physical principles, nanoparticle design strategies, and preclinical applications within the broader landscape of magnetic material engineering for biomedical use. Methods: A systematic review was conducted covering MPI signal generation and image reconstruction, nanoparticle core synthesis and surface coating approaches, and preclinical applications, spanning cell tracking, oncological imaging, vascular perfusion, neuroimaging, and MPI-guided theranostics. Studies were selected to provide quantitative benchmarks and direct comparisons with competing modalities where available. Results: MPI delivers signal-to-background ratios above 1000:1, iron-mass linearity at R² ≥ 0.99, regardless of tissue depth, and acquisition rates up to 46 volumes per second. Tracer architecture—encompassing single-core particles, multicore nanoflowers, and stimuli-responsive cluster designs—is the primary determinant of sensitivity, environmental robustness, and theranostic capability. Preclinical results include detection of cell populations in the low thousands, earlier ischaemia identification than diffusion-weighted MRI, real-time drug release quantification, and spatially confined tumour hyperthermia. Three translational bottlenecks are identified: the absence of a clinically approved tracer with optimal relaxation dynamics, hardware performance losses when scaling to human-bore systems, and overestimation of passive tumour accumulation in murine models. Conclusions: MPI illustrates how progress in magnetic material design directly expands clinical imaging and theranostic possibilities. Successful translation will require indication-driven, interdisciplinary development that integrates materials science, scanner engineering, and regulatory strategy in parallel. Full article