Search Results (7,605)

Watersheds provide fundamental hydrological ecosystem services for human well-being and the environment, such as water provisioning, hydrological cycle regulation, and erosion control; however, these services face increasing anthropogenic and climatic pressures. This study assessed individual and combined impacts on the hydrological functionality of the Piuray–Ccorimarca watershed (Cusco, Peru) using a calibrated Soil and Water Assessment Tool (SWAT) model, analyzing water yield, soil water storage, and sediment transport across 20 scenarios. An ensemble of 10 Coupled Model Intercomparison Project Phase 6 (CMIP6) models with bias correction was implemented, integrated with land transformation projections contemplating urban expansion associated with airport development and forest recovery through Payment for Ecosystem Services mechanisms. The results reveal climate change as the dominant driver, generating water yield increases and soil water content improvements primarily due to evapotranspiration decoupling that increases the runoff coefficient. In contrast, land use change produces substantially smaller hydrological effects but critically intensifies sediment yield. Spatial vulnerability analysis identified eight persistently critical sub-basins (20.5% of area) where soil water content emerged as the dominant limiting factor. These findings establish a clear management hierarchy prioritizing climate adaptation over land use interventions, with differentiated strategies required for critical zones demanding structural interventions versus non-critical areas amenable to flexible conservation approaches. Full article

This study evaluates how solution treatment and aging influence the deformation mechanisms, phase transformations and functional performance of NiTi alloys produced by laser powder bed fusion (LPBF). Tensile tests performed at room temperature (RT) and −20 °C (LT) were combined with Differential Scanning Calorimetry (DSC), X-ray Diffraction (XRD) and Transmission Electron Microscopy (TEM) analyses to correlate mechanical response with transformation thermodynamics and microstructural evolution. In the as-fabricated (AF) condition, deformation is governed by twinning and martensitic plasticity due to suppressed stress-induced martensite (SIM). Solution treatment (ST) restores reversible SIM at RT and preserves partial recoverability at LT as a result of microstructural homogenization and internal stress relief. Aging at 500 °C (A1h, A20h) promotes Ni₄Ti₃ precipitation, increasing transformation temperatures and stabilizing martensite, which leads to entirely irreversible deformation at both temperatures. These findings establish a clear functional continuum—ranging from recoverable (ST) to dissipative (AF) and fully irreversible (A20h) behavior—and provide a mechanistic framework for tailoring LPBF NiTi components for actuators, energy-storage and energy-dissipation applications. Full article

This study assessed the technical capacity and specific support needs of 28 small, medium, and community cassava processing centers across the Ruzizi Plain, Kinshasa, and Kongo Central Provinces of the Democratic Republic of Congo. A rapid appraisal methodology involving physical visits and direct interviews with proprietors and operators was conducted between March and May 2023. Data collection focused on product types, machinery, production capacity, operational status, challenges, and quality management. The study revealed significant technical and infrastructural deficiencies. Key challenges include reliance on inefficient sun-drying, inadequate infrastructure, lack of basic utilities, obsolete machinery, poor local capacity for machine repair, minimal adherence to Good Manufacturing Practices, and inadequate product quality testing, all leading to inconsistent product quality. The study highlights urgent need for investments in efficient drying facilities, equipment upgrades, and capacity building in quality control and business management. By differentiating technical assistance needs based on enterprise scale and product type, this study provides evidence-based recommendations essential for tailoring effective and sustainable intervention strategies to transform the DRC’s cassava processing sector and enhance food security. Full article

Understanding the intimate interactions between plants and their microbiota at the cellular level is essential for unlocking the full potential of plant holobionts in agricultural systems. Traditional bulk and microbial community-level sequencing approaches reveal broad community patterns but fail to resolve how distinct plant cell types interact with or regulate microbial colonization, as well as the diverse antagonistic and synergistic interactions and responses existing between various microbial populations. Recent advances in single-cell and spatial multi-omics have transformed our understanding of plant cell identities as well as gene regulatory programs and their dynamic regulation in response to environmental stresses and plant development. In this review, we highlight the single-cell discoveries that uncover the plant cell-type-specific microbial perception, immune activation, and symbiotic differentiation, particularly in roots, nodules, and leaves. We further discuss how integrating transcriptomic, epigenomic, and spatial data can reconstruct multilayered interaction networks that connect plant cell-type-specific regulatory states with microbial spatial niches and inter-kingdom signaling (e.g., ligand–receptor and metabolite exchange), providing a foundation for developing new strategies to engineer crop–microbiome interactions to support sustainable agriculture. We conclude by outlining key methodological challenges and future research priorities that point toward building a fully integrated cellular interactome of the plant holobiont. Full article

The rapid adoption of large language models (LLMs) in corporate and governmental systems has raised critical security concerns, particularly prompt injection attacks exploiting LLMs’ inability to differentiate control instructions from untrusted user inputs. This study systematically benchmarks neural network architectures for malicious prompt detection, emphasizing robustness against character-level adversarial perturbations—an aspect that remains comparatively underemphasized in the specific context of prompt-injection detection despite its established significance in general adversarial NLP. Using the Malicious Prompt Detection Dataset (MPDD) containing 39,234 labeled instances, eight architectures—Dense DNN, CNN, BiLSTM, BiGRU, Transformer, ResNet, and character-level variants of CNN and BiLSTM—were evaluated based on standard performance metrics (accuracy, F1-score, and AUC-ROC), adversarial robustness coefficients against spacing and homoglyph perturbations, and inference latency. Results indicate that the word-level 3_Word_BiLSTM achieved the highest performance on clean samples (accuracy = 0.9681, F1 = 0.9681), whereas the Transformer exhibited lower accuracy (0.9190) and significant vulnerability to spacing attacks (adversarial robustness ${\mathsf{\rho }}_{spacing}=0.61$). Conversely, the Character-level BiLSTM demonstrated superior resilience (${\mathsf{\rho }}_{spacing}=1.0$, ${\mathsf{\rho }}_{homoglyph}=0.98$), maintaining high accuracy (0.9599) and generalization on external datasets with only 2–4% performance decay. These findings highlight that character-level representations provide intrinsic robustness against obfuscation attacks, suggesting Char_BiLSTM as a reliable component in defense-in-depth strategies for LLM-integrated systems. Full article

Migraine and epilepsy are common neurological disorders that share overlapping symptoms, such as visual disturbances and altered consciousness, making accurate diagnosis challenging. Although their underlying mechanisms differ, both conditions involve recurrent irregular brain activity, and traditional EEG-based diagnosis relies heavily on clinical interpretation, which may be subjective and insufficient for clear differentiation. To address this challenge, this study introduces an automated EEG classification framework combining Dual Tree Complex Wavelet Transform (DTCWT) for feature extraction with a Random Subspace Ensemble Classifier for multi-class discrimination. EEG data recorded under photic and nonphotic stimulation were analyzed to capture both temporal and frequency characteristics. DTCWT proved effective in modeling the non-stationary nature of EEG signals and extracting condition-specific features, while the ensemble classifier improved generalization by training multiple models on diverse feature subsets. The proposed system achieved an average accuracy of 99.50%, along with strong F-measure, AUC, and Kappa scores. Notably, although previous studies suggest heightened EEG activity in migraine patients during flash stimulation, findings here indicate that flash stimulation alone does not reliably distinguish migraine from epilepsy. Overall, this research highlights the promise of advanced signal processing and machine learning techniques in enhancing diagnostic precision for complex neurological disorders. Full article

Probabilistic streamflow models play a pivotal role in quantifying hydrological uncertainty and form the backbone of modern risk management strategies for flood and drought forecasting, water allocation planning, and the design of resilient infrastructure. Unlike deterministic approaches that yield single-point estimates, these models provide a spectrum of possible outcomes, enabling a more realistic assessment of extreme events and supporting informed, sustainable water resource decisions. By explicitly accounting for natural variability and uncertainty, probabilistic models promote transparent, robust, and equitable risk evaluations, helping decision-makers balance economic costs, societal benefits, and environmental protection for long-term sustainability. In this study, we introduce the bounded half-logistic distribution (BHLD), a novel heavy-tailed probability model constructed using the T–Y method for distribution generation, where T denotes a transformer distribution and Y represents a baseline generator. Although the BHLD is conceptually related to the Pareto and log-logistic families, it offers several distinctive advantages for streamflow modeling, including a flexible hazard rate that can be unimodal or monotonically decreasing, a finite lower bound, and closed-form expressions for key risk measures such as Value at Risk (VaR) and Tail Value at Risk (TVaR). The proposed distribution is defined on a lower-bounded domain, allowing it to realistically capture physical constraints inherent in flood processes, while a log-logistic-based tail structure provides the flexibility needed to model extreme hydrological events. Moreover, the BHLD is analytically characterized through a governing differential equation and further examined via its characteristic function and the maximum entropy principle, ensuring stable and efficient parameter estimation. It integrates a half-logistic generator with a log-logistic baseline, yielding a power-law tail decay governed by the parameter $\beta$, which is particularly effective for representing extreme flows. Fundamental properties, including the hazard rate function, moments, and entropy measures, are derived in closed form, and model parameters are estimated using the maximum likelihood method. Applied to four real streamflow data sets, the BHLD demonstrates superior performance over nine competing distributions in goodness-of-fit analyses, with notable improvements in tail representation. The model facilitates accurate computation of hydrological risk metrics such as VaR, TVaR, and tail variance, uncovering pronounced temporal variations in flood risk and establishing the BHLD as a powerful and reliable tool for streamflow modeling under changing environmental conditions. Full article

Digital transformation in water conservancy and urban water utilities demands perception systems that are accurate, fast, and energy-efficient and maintainable over long service lifecycles at the edge. We present HydroSNN, a neuromorphic computer-vision framework that couples an event-driven sensing pipeline with a spiking-transformer backbone to support monitoring of canals, reservoirs, treatment plants, and buried pipeline networks. By reducing always-on compute and unnecessary data movement, HydroSNN targets sustainability goals in smart water infrastructure: lower operational energy use, fewer site visits, and improved resilience under harsh illumination and weather. HydroSNN introduces three novel components: (i) spiking temporal tokenization (STT), which converts asynchronous events and optional frames into latency-aware spike tokens while preserving motion cues relevant to hydraulics; (ii) physics-guided spiking attention (PGSA), which injects lightweight mass-conservation/continuity constraints into attention weights via a differentiable regularizer to suppress physically implausible interactions; and (iii) cross-modal self-supervision (CM-SSL), which aligns RGB frames, event streams, and low-cost acoustic/vibration traces using masked prediction to reduce annotation requirements. We evaluate HydroSNN on public water-surface and event-vision benchmarks (MaSTr1325, SeaDronesSee, DSEC, MVSEC, DAVIS, and DDD20) and report accuracy, latency, and an operation-based energy proxy. HydroSNN improves mIoU/F1 over strong CNN/ViT baselines while reducing end-to-end latency and the estimated energy proxy in event-driven settings. These efficiency gains are practically relevant for off-grid or power-constrained deployments and support sustainable development by enabling continuous, low-power monitoring and timely anomaly response. These results demonstrate that event-driven spiking vision, augmented with simple physics guidance, offers a practical and efficient solution for resilient perception in smart water infrastructure. Full article

The intelligent surgical knife (iKnife), based on rapid evaporative ionization mass spectrometry (REIMS), represents a transformative advance in intraoperative tissue characterization. By integrating mass spectrometry with electrosurgical dissection, the iKnife enables real-time differentiation between cancerous and healthy tissues through molecular fingerprinting of the aerosol generated during cutting. This innovation significantly shortens operative time by eliminating delays associated with conventional histopathological analysis and enhances surgical precision by providing continuous feedback on tissue composition. Since its inception by Zoltán Takáts and colleagues, the iKnife has demonstrated remarkable diagnostic accuracy across multiple cancer types, including breast, ovarian, and colorectal malignancies, with reported sensitivities and specificities > 90% in selected tumour types. Beyond oncology, REIMS technology also shows promise for microbial identification and metabolomic profiling. This review provides a comprehensive overview of the iKnife’s development, underlying principles, clinical validation, and emerging applications, as well as its integration into surgical workflows and the challenges remaining for widespread clinical adoption. Future perspectives include miniaturization, AI-driven spectral interpretation, and expansion into robotic and image-guided surgery. Full article

Against the backdrop of China’s commitment to achieving carbon peaking by 2030 and carbon neutrality by 2060, inter-provincial carbon emissions form a complex interconnected spatial network—clarifying its operational mechanisms is crucial for optimizing regional carbon reduction strategies. Based on 2006–2021 data from 30 Chinese provinces, this study constructs the China Provincial Carbon Emission Spatial Correlation Network (CPCESCN) using a modified gravity model. Social Network Analysis (SNA) explores its structural characteristics, while motif and QAP correlation analyses identify endogenous structural and attribute variables. Innovatively integrating Exponential Random Graph Models (ERGM) and Stochastic Actor-Oriented Models (SAOM), it investigates the network’s static formation mechanisms and dynamic evolution drivers. Results show CPCESCN has a stable multi-threaded structure without isolated nodes, with Jiangsu, Guangdong, Shandong, Zhejiang, Henan, and Sichuan as high-centrality core nodes with high centrality. GDP, green technology innovation, urbanization rate, industrialization rate, energy consumption intensity, and environmental regulations significantly influence network dynamics, with reciprocal relationships as key endogenous drivers. While geographic proximity still facilitates network formation, its impact has weakened notably, and functional complementarity has become the dominant evolutionary driver—based on the findings, policy suggestions are proposed, including deepening inter-provincial functional cooperation, implementing differentiated carbon reduction policies, and optimizing multi-dimensional low-carbon transformation systems. Full article

Based on prior transcriptome data, we established a core gene interaction network for Korean pine somatic embryo maturation and screened 18 core genes. These genes showed distinct differential expression in early somatic embryogenesis. In particular, PkGLP1-2-6 (Pkor04G01180) and PkGLP-1-2-21 (Pkor04G01200) were highly correlated in the network and can be regarded as key genes mediating Korean pine somatic embryo maturation. A total of 92 members of the PkGLP gene family were identified in the Korean pine genome, which can be classified into 8 subfamilies based on evolutionary relationships. Both PkGLP1-2-6 and PkGLP1-2-21 were localized in the cell membrane and nucleus. By means of a stable genetic transformation system, transgenic Korean pine calli overexpressing PkGLP1-2-6 and PkGLP1-2-21 were successfully established. The results demonstrated that the overexpression of PkGLP1-2-6 and PkGLP1-2-21 could effectively promote somatic embryogenesis and enhance the yield of somatic embryos. In the presence of exogenous abscisic acid (ABA), the somatic embryo yield of the transgenic lines was significantly higher than that of the wild-type controls. Compared with the wild-type controls, the SOD activity in the cell lines overexpressing PkGLP1-2-6 and PkGLP1-2-21 was significantly increased, whereas the activities of POD and CAT were decreased, and the contents of H₂O₂ and superoxide anion (O₂⁻) were significantly reduced. These results indicate that PkGLP1-2-6 and PkGLP1-2-21 are actively involved in the reactive oxygen species (ROS) scavenging process during somatic embryogenesis of Korean pine. The overexpression of PkGLP1-2-6 and PkGLP1-2-21 contributes to enhancing the antioxidant capacity of cells, thereby increasing the yield of somatic embryos. Full article

Boron neutron capture therapy (BNCT) is experiencing a global resurgence driven by advances in boron pharmacology, accelerator-based neutron sources, and molecular imaging-guided theranostics. BNCT produces high linear energy transfer particles with micrometer-range energy deposition, enabling cell-selective irradiation confined to boron-enriched tumor cells in a geometrically targeted region by the neutron beam. This mechanism offers the potential for exceptionally high therapeutic ratios, provided two core requirements are met: sufficient differential tumor uptake of ¹⁰B and a neutron beam with appropriate energy and penetration. After early clinical attempts in the mid-20th century were hindered by inadequate boron agents and reactor-based neutron beams, recent technological breakthroughs have made BNCT clinically viable. The development of hospital-compatible accelerator neutron sources, next-generation boron delivery systems (such as receptor-targeted compounds and nanoparticles), advanced theranostic approaches (such as ¹⁸F-BPA positron emission tomography and boron-sensitive magnetic resonance imaging), and AI-driven biodistribution modeling now support personalized treatment planning and patient selection. These innovations have catalyzed modern clinical implementation, exemplified by Japan’s regulatory approval of BNCT for recurrent head and neck cancer and the rapid expansion of clinical programs across Asia, Europe, and South America. Building on these foundations, BNCT has transitioned from a predominantly academic experimental modality into an increasingly commercialized and industrially supported therapeutic platform. The emergence of dedicated BNCT companies, international collaborations between accelerator manufacturers and hospitals, and pharmaceutical development pipelines for next-generation boron carriers has accelerated clinical translation. Moreover, BNCT now occupies a unique position among radiation modalities due to its hybrid nature, namely combining the biological targeting of radiopharmaceutical therapy with the external-beam controllability of radiotherapy, thereby offering new therapeutic opportunities where competitive approaches fall short. Emerging evidence suggests therapeutic promise in glioblastoma, recurrent head and neck cancers, melanoma, meningioma, lung cancer, sarcomas, and other difficult-to-treat malignancies. Looking ahead, continued innovation in compact neutron source engineering, boron nanocarriers, multimodal theranostics, microdosimetry-guided treatment planning, and combination strategies with systemic therapies such as immunotherapy will be essential for optimizing outcomes. Together, these converging developments position BNCT as a biologically targeted and potentially transformative modality in the era of precision oncology. Full article

Informal food trading is a cornerstone of urban livelihoods and food security in Namibia, yet traders operate under fragile conditions marked by limited capital, policy exclusion, and exposure to shocks such as COVID-19. Despite this vulnerability, traders exhibit resilience through everyday forms of social innovation. This study investigates how adaptive pricing, customer credit, and digital communication and e-payment practices function as pathways of resilience among 470 informal food traders in Windhoek, using Structural Equation Modelling to assess gender-differentiated determinants and outcomes. The analysis reveals that women’s adoption of adaptive pricing and digital tools is driven primarily by education and startup capital, while men’s innovation practices are shaped by vendor type and access to financing. Social innovations mediate the effects of these structural factors on enterprise growth, demonstrating that innovation acts as a critical mechanism linking resources and resilience. The study concludes that enhancing informal traders’ resilience requires policies that strengthen human and financial capital, improve digital inclusion, and recognize gendered differences in access to opportunity. It recommends targeted support for women’s entrepreneurial training, affordable credit, and digital infrastructure to transform the informal food sector into a more equitable and sustainable component of Namibia’s urban economy. Full article

Background: Mammary ductal hyperplasia represents a spectrum of benign proliferative breast lesions, some of which pose elevated risks for malignant transformation into ductal carcinoma in situ and invasive breast cancer. This narrative review explores why only specific types, particularly those with atypia, exhibit higher progression potential, synthesizing epidemiologic, histopathologic, molecular, and environmental insights. Methods: We reviewed key literature from databases, including PubMed, focusing on classification, risk stratification, genetic/epigenetic mechanisms, tumor microenvironment dynamics, and modifiable factors influencing progression. Results: Benign breast lesions are categorized into non-proliferative, proliferative without atypia, and proliferative with atypia, such as atypical ductal hyperplasia and atypical lobular hyperplasia. Atypia represents a morphologic continuum toward low-grade ductal carcinoma in situ, driven by genetic alterations, epigenetic reprogramming, and changes in the tumor microenvironment, including stromal remodeling, immune infiltration, hypoxia-induced angiogenesis, and extracellular matrix degradation. Dietary factors, such as high-fat intake and obesity, exacerbate progression through inflammation, insulin resistance, and adipokine imbalance, while environmental toxins, including endocrine disruptors, pesticides, and ionizing radiation, amplify genomic instability. Conclusions: Understanding differential risks and mechanisms underscores the need for stratified surveillance, biomarker-driven interventions, and lifestyle modifications to mitigate progression. Future research should prioritize molecular profiling for personalized prevention in high-risk hyperplasia. Full article

The rapid digital transformation of higher education institutions (HEIs) has created new opportunities to promote sustainability-focused teaching, learning, and assessment. At the same time, traditional assessment methods often fail to accurately measure complex skills needed for sustainability, such as systems thinking, critical reflection, and real-world problem-solving. This study examines the integration of adaptive learning platforms with performance-based assessment (PBA) as an innovative way to support inclusive, sustainability-oriented learning in higher education. Based on principles of Education for Sustainable Development (ESD), Universal Design for Learning (UDL), and constructivist learning theory, the study investigates how adaptive learning technologies tailor instruction for diverse learners while PBAs offer genuine measures of sustainability skills. Using a mixed-methods approach, data were gathered from forty-eight undergraduate students enrolled in an inclusive education course that used an adaptive learning module and PBA tasks. Learning analytics, rubric-based performance scores, and student perception surveys were analyzed to explore effects on engagement, accessibility, and skill development. The results show that this combined method enhances student inclusion, supports differentiated learning pathways, boosts engagement in sustainability tasks, and yields more complete evidence of sustainability competencies than traditional assessments. The study provides a framework for HEIs aiming to align digital transformation initiatives with sustainability objectives. It emphasizes the potential of integrating adaptive learning and PBA to promote innovative, inclusive, and sustainability-focused assessment practices. Implications for policy, curriculum design, and future digital sustainability efforts are also discussed. Full article