Search Results (2,777)

The intensification of agricultural land use (A-LUI) is a central driver of global environmental change, affecting soil health, water quality, biodiversity, and greenhouse gas balances. Monitoring A-LUI remains challenging because it is shaped by multiple management practices, ecological processes, and spatio-temporal dynamics. This review provides a comprehensive synthesis of existing definitions and standards of A-LUI at national and international levels (FAO, OECD, World Bank, EUROSTAT) and evaluates in situ methods alongside the rapidly expanding potential of remote sensing (RS). We introduce a novel RS-based taxonomy of A-LUI indicators, structured into five complementary categories: trait, genesis, structural, taxonomic, and functional indicators. Numerous examples illustrate how traits and management practices can be translated into RS proxies and linked to intensity signals, while highlighting key challenges such as sensor limitations, cultivar variability, and confounding environmental factors. We further propose an integrative framework that connects management practices, plant and soil traits, RS observables, validation needs, and policy relevance. Emerging technologies—such as hyperspectral imaging, solar-induced fluorescence, radar, artificial intelligence, and semantic data integration—are discussed as promising pathways to advance the monitoring of A-LUI across scales. By compiling and structuring RS-derived indicators, this review establishes a conceptual and methodological foundation for transparent, standardised, and globally comparable assessments of agricultural land use intensity, thereby supporting both scientific progress and evidence-based agricultural policy. Full article

This work proposes a photonics-aided W-band integrated sensing and communication (ISAC) system using photonics-aided frequency multiplication to suppress phase noise. Conventional dual-laser architectures suffer from phase noise accumulation, degrading both communication reliability and sensing resolution. To address this, we integrate photonics-aided frequency multiplication with orthogonal frequency-division multiplexing (OFDM), enabling a unified signal structure that simultaneously encodes communication data and radar waveforms without redundant resource allocation. Theoretical analysis reveals phase noise cancellation through coherent beating of symmetrically filtered sidebands in the photodetector (PD). Results demonstrate concurrent delivery of probability shaping (PS)-256QAM OFDM signals with a symbol error rate below 4.2 × 10⁻² and radar sensing with a 13.6 dB peak-to-sidelobe ratio (PSLR). Under a 1 MHz laser linewidth, the system achieves a 3.2 dB PSLR improvement over conventional methods, validating its potential for high-performance ISAC in beyond-5G networks. Full article

This paper reconceptualizes intellectual safety in mathematics spaces by centering the voices and lived experiences of BIPOC students. The marginalization of BIPOC students is compounded by structural racism, historical exclusion, and deficit narratives that continue to shape academic environments, especially in mathematics contexts. While definitions of intellectual safety reflect white, Eurocentric norms, we argue that for BIPOC students, intellectually safe environments must be anti-racist, culturally responsive, and rooted in belonging. We started with existing definitions of intellectual safety and incorporated a more critical approach to sense of belonging. Through ethnographic research design we gathered student interviews and daily journal entries from a 12-day anti-racist mathematics summer institute for secondary students. Analysis revealed that while existing attributes captured much of the scholar’s joy, cultural affirmation, and belonging, new themes, like pride/confidence, clarity/transparency, and being listened to, emerged directly from how students experienced intellectual safety in practice. This led to a refinement of our initial conceptualization. This study provides insight into how intellectual safety manifests in a space intentionally designed to support BIPOC youth in exploring mathematics in agentic and culturally sustaining ways. Full article

The representation of visual hierarchy remains a central concern in contemporary design research and practice. Current design guidelines advocate establishing hierarchical relationships through dimensions such as size or distance, yet concrete and effective integration strategies remain elusive. This study, grounded in Gestalt principles, investigates the impact of design layouts on hierarchical perception under the interaction of proximity and similarity. This experiment systematically controls the size (1:1, 1:3/4 and 1:1/2, respectively) and distance (namely 1/4 R, 1/2 R, 3/4 R, 1 R, 5/4 R and 3/2 R) of two circular objects, employing a 3 × 6 two-way ANOVA. The results indicate that the interaction between size and distance can exert a significant influence on the perception of hierarchy. Among these factors, structural type emerges as the dominant element shaping hierarchical perception, with overlapping structures showing pronounced hierarchical suggestive characteristics that significantly outperform tangential and separated structures. Size can partially regulate this effect. When dimensions are equal, tangential and separated structures show no significant difference in perceived hierarchy. However, with the aid of size disparity, the hierarchical sense of separated structures can be markedly enhanced, yielding an effect stronger than that produced by shared edges. Full article

This paper represents the results of ten years of monitoring using satellite imagery and aerial reconnaissance, followed by in-depth analysis utilizing LiDAR data and geomagnetic prospection techniques of the first two Neolithic rondels detected in Croatia—Markušica Brošov salaš and Gorjani Topole. Through the exclusive use of satellite and aerial image analysis, we were able to accurately determine the general size, shape, and number of ditches present at the sites under investigation. The wealth of information obtained from these images was sufficient for us to confidently interpret these formations as Neolithic rondels—meeting all the criteria commonly used. The addition of LiDAR data and geomagnetic prospection further enhanced our understanding by revealing a range of additional features and peculiarities across both sites, including within all identified ditch systems. These advanced methods allowed us to uncover details that would otherwise remain invisible through surface observation alone. Our research demonstrates the remarkable power of publicly available satellite imagery as a primary tool for archeological site detection and preliminary interpretation. The results from Markušica and Gorjani emphasize the scientific necessity of combining complementary remote sensing and geophysical techniques to overcome individual methodological limitations, providing robust documentation and interpretation of prehistoric enclosures in highly transformed landscapes. This research contributes novel insights into Neolithic social landscapes, monumentality, and land use strategies in Croatia while offering a methodological model for archeological prospection applicable across Central and Southeastern Europe. Full article

Cyanobacterial blooms have been increasingly detected in estuaries and freshwater tidal rivers. To enhance detailed monitoring, an efficient approach to detecting algal blooms through remote sensing is needed to focus more detailed monitoring focused on cyanobacteria. In this study, we compared different remote sensing processing methods to determine an efficient approach to mapping chlorophyll-a. Using a subset of paired chlorophyll-a observations with Sentinel-2 imagery (2015–2022), with sites located in the Chesapeake Bay and Indian River selected along gradients of salinity, turbidity, and trophic status, we compared the combined performance of two different atmospheric processing methods (Acolite, Polymer) and a suite of empirical (band ratio, spectral shape indices) and machine learning algorithms for chlorophyll-a prediction. Acolite outperformed Polymer, resulting in 176 observation points, compared to 106 observation points from Polymer, and a greater range in chlorophyll-a values (0–74 μg/L from Acolite compared to 0–36 μg/L from Polymer), although Polymer showed more responsiveness at lower chlorophyll-a levels. Two algorithms performed best in predicting chlorophyll-a, as well as trophic state and HABs risk classes: the machine learning mixture density network (MDN) approach and the one band-ratio approach (Mishra). Full article

Electrochemical detection is widely used in environmental, health, and food analysis due to its portability, low cost, and high sensitivity. However, when analytes with similar redox potentials coexist, overlapping voltammetric signals often occur, which compromises detection accuracy and sensitivity. In this study, a simple second-derivative image sharpening (IS) algorithm is applied to the electrochemical detection of chlorophenol (CP) isomers with similar redox behaviors. Specifically, a graphene-modified electrode was employed for the electrochemical detection of two chlorophenol isomers: ortho-CP (o-CP) and meta-chlorophenol (m-CP) in the range from 1.0 to 10.0 μmol/L. After image-sharpening, the peak potential difference between o- and m-CP increased from 0.08 V to 0.12 V. The limits of detection (LOD) for o-CP and m-CP decreased from 0.6 to 0.9 μmol/L to 0.12 and 0.31 μmol/L, respectively. The corresponding sensitivities also improved from 0.92 to 1.35 A/(mol L⁻¹) to 4.11 and 3.71 A/(mol L⁻¹), respectively. Moreover, the sharpened voltammograms showed enhanced peak resolution, facilitating visual discrimination of the two isomers. These results demonstrate that image sharpening can significantly improve peak shape, peak separation, sensitivity, and detection limit in electrochemical analysis. The obtained algorithm is computationally efficient (<30 lines of C++ (Version 6.0)/OpenCV, executable in <1 ms on an ARM-M0 microcontroller) and easily adaptable to various programming environments, offering a promising approach for data processing in portable electrochemical sensing systems. Full article

The rise of digital platforms has transformed heritage interpretation from a single official narrative to multi-stakeholder participation. This study investigates how such platforms mediate the formation of a sense of place at the Kulangsu World Heritage Site (WHS). Data were collected from official narrative texts and user-generated content (UGC) on Dianping and Ctrip, and analyzed using high-frequency word statistics and semantic network analysis. The results reveal a clear divergence between official narratives, which emphasize Outstanding Universal Value (OUV), and tourist perceptions, which focus on visual landmarks and “check-in” practices shaped by the “digital gaze.” Moreover, the sense of place is shown to be a dynamic process, co-constructed through pre-visit expectations, on-site experiences, and post-visit reflections. The findings also highlight a transformation in tourists’ roles, shifting from passive cultural consumers to active participants in the co-construction of heritage values, with digital platforms serving as critical mediators. Theoretically, the study advances digital heritage scholarship by clarifying the mechanism of the digital gaze and the dynamic nature of sense of place. Practically, it underscores the importance of integrating official narratives with UGC to strengthen OUV communication, foster broader public engagement, and support the sustainable development of WHSs. Full article

The increasing frequency and intensity of wildfires pose severe risks to ecosystems, infrastructure, and human safety. In wildland–urban interface (WUI) areas, nearby vegetation strongly influences building ignition risk through flame contact and radiant heat exposure. However, limited research has leveraged Unmanned Aerial Systems (UAS) remote sensing (RS) to capture species-specific vegetation geometry and predict thermal responses during ignition events This study proposes a two-stage framework integrating UAS-based multispectral (MS) imagery, LiDAR data, and Fire Dynamics Simulator (FDS) modeling to estimate the maximum temperature (T) and heat flux (HF) of outdoor vegetation, focusing on Syzygium smithii (Lilly Pilly). The study data was collected at a plant nursery at Queensland, Australia. A total of 72 commercially available outdoor vegetation samples were classified into 11 classes based on pixel counts. In the first stage, ensemble learning and watershed segmentation were employed to segment target vegetation patches. Vegetation UAS-LiDAR point cloud delineation was performed using Raycloudtools, then projected onto a 2D raster to generate instance ID maps. The delineated point clouds associated with the target vegetation were filtered using georeferenced vegetation patches. In the second stage, cone-shaped synthetic models of Lilly Pilly were simulated in FDS, and the resulting data from the sensor grid placed near the vegetation in the simulation environment were used to train an XGBoost model to predict T and HF based on vegetation height (H) and crown diameter (D). The point cloud delineation successfully extracted all Lilly Pilly vegetation within the test region. The thermal response prediction model demonstrated high accuracy, achieving an RMSE of 0.0547 °C and R² of 0.9971 for T, and an RMSE of 0.1372 kW/m² with an R² of 0.9933 for HF. This study demonstrates the framework’s feasibility using a single vegetation species under controlled ignition simulation conditions and establishes a scalable foundation for extending its applicability to diverse vegetation types and environmental conditions. Full article

Integrated sensing and communication (ISAC) is a transformative technology for sixth-generation (6G) wireless networks. In this paper, we investigate end-to-end constellation mapping and demapping in ISAC systems, leveraging OFDM-based waveforms and an adaptive DNN architecture for pulse-based transmission. Specifically, we propose an end-to-end autoencoder framework that optimizes the constellation through adaptive symbol distribution shaping via deep learning, enhancing communication reliability with symbol mapping and boosting sensing capabilities with an improved peak-to-sidelobe ratio (PSLR). The autoencoder consists of an autoencoder mapper (AE-Mapper) and an autoencoder demapper (AE-Demapper), jointly trained using a composite loss function to optimize constellation points and achieve flexible performance balance in communication and sensing. Simulation results demonstrate that the proposed DNN-based end-to-end design achieves dynamic balance between PSLR of the autocorrelation function (ACF) and bit error rate (BER). Full article

Rapid urbanization in Southeast Asia has created a paradox between severe environmental degradation and the often-muted concern of urban residents. The objective of this study is to explain this disconnect by comparatively analyzing the structural factors that shape environmental perceptions among the urban middle class in Jakarta and Phnom Penh. Drawing on survey data from over 2000 households, the study reveals two distinct narratives. In Jakarta, the middle class reports a surprisingly low frequency of environmental problems, suggesting a “perception gap” driven by physical and social insulation from the city’s harshest realities. Conversely, in Phnom Penh, residents report higher concern, but their widespread silence when asked for solutions points not to apathy but to a sense of powerlessness within a top-down governance system. We argue that apparent environmental indifference is not a uniform phenomenon but a product of distinct structural forces. The paper’s contribution is to illustrate how urban fragmentation, class-based insulation, and perceived political agency shape the relationship between environmental reality and citizen concern. Full article

Chronic dermatological conditions such as acne vulgaris, androgenetic alopecia (AGA), and alopecia areata (AA) affect hundreds of millions worldwide and contribute substantially to quality-of-life impairment. Despite the availability of systemic retinoids, anti-androgens, and JAK inhibitors, therapeutic responses remain heterogeneous and relapse is common, underscoring the need for biologically grounded stratification. Over the past decade, large genome-wide association studies and functional analyses have clarified disease-specific and cross-cutting mechanisms. In AA, multiple independent HLA class II signals and immune-regulatory loci such as BCL2L11 and LRRC32 establish antigen presentation and interferon-γ/JAK–STAT signalling as central drivers, consistent with clinical responses to JAK inhibition. AGA is driven by variation at the androgen receptor and 5-α-reductase genes alongside WNT/TGF-β regulators (WNT10A, LGR4, RSPO2, DKK2), explaining follicular miniaturisation and enabling polygenic risk prediction. Acne genetics highlight an immune–morphogenesis–lipid triad, with loci in TGFB2, WNT10A, LGR6, FASN, and FADS2 linking follicle repair, innate sensing, and sebocyte lipid metabolism. Barrier modulators such as FLG and OVOL1, first described in atopic dermatitis, further shape inflammatory thresholds across acne and related phenotypes. Together, these findings position genetics not as an abstract catalogue of risk alleles but as a map of tractable biological pathways. They provide the substrate for patient-stratified interventions ranging from JAK inhibitors in AA, to endocrine versus morphogenesis-targeted strategies in AGA, to lipid- and barrier-directed therapies in acne, while also informing cosmetic practices focused on barrier repair, sebaceous balance, and follicle health. Full article

Single-molecule detection (SMD) has reformed analytical science by enabling the direct observation of individual molecular events, thus overcoming the limitations of ensemble-averaged measurements. This review presents a comprehensive analysis of the principles, devices, and emerging materials that have shaped the current landscape of SMD. We explore a wide range of sensing mechanisms, including surface plasmon resonance, mechanochemical transduction, transistor-based sensing, optical microfiber platforms, fluorescence-based techniques, Raman scattering, and recognition tunneling, which offer distinct advantages in terms of label-free operation, ultrasensitivity, and real-time responsiveness. Each technique is critically examined through representative case studies, revealing how innovations in device architecture and signal amplification strategies have collectively pushed the detection limits into the femtomolar to attomolar range. Beyond the sensing principles, this review highlights the transformative role of advanced nanomaterials such as graphene, carbon nanotubes, quantum dots, MnO₂ nanosheets, upconversion nanocrystals, and magnetic nanoparticles. These materials enable new transduction pathways and augment the signal strength, specificity, and integration into compact and wearable biosensing platforms. We also detail the multifaceted applications of SMD across biomedical diagnostics, environmental monitoring, food safety, neuroscience, materials science, and quantum technologies, underscoring its relevance to global health, safety, and sustainability. Despite significant progress, the field faces several critical challenges, including signal reproducibility, biocompatibility, fabrication scalability, and data interpretation complexity. To address these barriers, we propose future research directions involving multimodal transduction, AI-assisted signal analytics, surface passivation techniques, and modular system design for field-deployable diagnostics. By providing a cross-disciplinary synthesis of device physics, materials science, and real-world applications, this review offers a comprehensive roadmap for the next generation of SMD technologies, poised to impact both fundamental research and translational healthcare. Full article

Low-carbon chemical fires pose significant hazards, and remote sensing of high-temperature gas emissions from these fires is a critical method for identifying and assessing their environmental impact. Analyzing the spectral characteristics of gases produced by low-carbon chemical pool fires and developing spectral radiation models can establish a foundation for remote pollution monitoring. However, such studies remain scarce. Using a custom-built high-temperature gas spectroscopy platform, this study extracts spectral features of gases emitted by low-carbon chemical pool fires. We investigate spectral interference mechanisms among combustion products and develop a high-precision spectral radiation model to support remote fire pollution monitoring. Experimental results reveal distinct spectral bands for key gases: CO₂ peaks near 2.7 μm and 4.35 μm, SO₂ at 4.05 μm, 7.5 μm, and 9.0 μm, NO at 5.5 μm, and NO₂ at 3.6 μm and 6.3 μm. The proposed spectral radiation model accurately simulates the position and shape of spectral peaks. For carbon disulfide and acetonitrile combustion products, the model achieves prediction accuracies of 83.4–96.9% and 79.2–95.3%, respectively. Full article