Search Results (10,470)

The management and efficient use of water resources are crucial to the sustainable development of agriculture in arid regions. The Manas River Basin faces severe water shortages due to its arid climate and heavy reliance on irrigation water. Therefore, based on water footprint theory, this study comprehensively utilized the CROPWAT model, pathway analysis, and CMIP6 data to construct an integrated “assessment–driving–prediction” framework for crop water footprints, with the aim of revealing the evolution patterns and driving mechanisms of water footprints in river basins. The results showed that the cultivated area of crops in the Manas River Basin exhibited a nonlinear expansion trend from 1990 to 2020, with a total increase of 143.56% over the 30-year period. Among all crops, cotton occupied the largest cultivated area, accounting for 60.34% of the total. During the study period, the crop water footprint, crop blue water footprint, and crop green water footprint in the Manas River Basin showed overall upward trends, increasing by 1.07 × 10⁹ m³, 1.04 × 10⁹ m³, and 3.0 × 10⁷ m³, respectively. Total agricultural machinery power and per capita grain production are the main factors influencing changes in crop water footprint. Under future climate scenarios, the crop water footprint in the Manas River Basin is projected to follow the order SSP2-4.5 > SSP5-8.5 > SSP1-2.6. By 2100, the crop water footprint under the SSP2-4.5 scenario is expected to increase by 37.01% relative to 2020, posing substantial challenges to agricultural water resource management in the basin. In contrast, the crop water footprint under the SSP1-2.6 scenario remains relatively stable, indicating a more sustainable development pathway. Full article

This paper evaluates the extent to which European Union member states show alignment between strategic innovation capacity and sustainable development outcomes. To achieve this objective, indicators were collected from Eurostat for two dimensions: strategic capacity for innovation (public expenditure on research and development, human resources in science and technology, and the higher education graduation rate) and sustainable development outcomes (real GDP per capita, employment rate, risk of poverty or social exclusion, and greenhouse gas emissions). Going beyond traditional literature, we develop an analysis based on grey clustering using multiple scenarios to illustrate the complex, non-linear relationships and structural bottlenecks in member states. The stability of the classifications was further examined through threshold sensitivity testing across all scenarios and through 200,000 weight-perturbation simulations for an illustrative boundary case. The results reveal distinct performance typologies: a resilient group of “systemic leaders” (including Denmark, Sweden, and the Netherlands) demonstrating consistent excellence across all applied prioritization scenarios, and a stagnant core facing structural challenges regarding both innovation and sustainability (such as Romania and Hungary). The dynamic analysis covering 2021–2024 suggests that strong innovation-capacity indicators are not necessarily associated with equally strong sustainability-outcome indicators, while certain economies in Central and Eastern Europe show positive convergence trends. Supported by stability simulations conducted across multiple scenarios, the study highlights significant alignment gaps between innovation-capacity indicators and sustainability-outcome indicators across the European Union and offers public policy recommendations to stimulate sustainable cohesion and technology adoption. Full article

Clostridioides difficile infection (CDI) is a leading healthcare-associated infection worldwide, causing significant morbidity, mortality, healthcare burden, and costs. Clinical manifestations range from mild, self-limiting diarrhea to severe, life-threatening complications such as toxic megacolon and septic shock. Early identification of patients at high risk of severe disease is essential to guide clinical decision-making and optimize therapy. This narrative review summarizes recent epidemiological data, current trends, and known risk factors as clinical context for severity prediction and then examines the utility and limitations of biomarkers that may predict CDI severity, including inflammatory, hematological, fecal, renal, and immune-response biomarkers. While some markers are already used in guideline-based assessment or routine clinical practice (e.g., C-reactive protein, white blood cell count, serum creatinine), they have limited specificity. Other markers emerging from CDI research, including procalcitonin, interleukins, and presepsin, may provide complementary prognostic information. The key challenge is not simply to identify additional biomarkers but to determine which biomarkers are clinically useful, at which stage of CDI progression, and in which patients they add value beyond conventional severity criteria. Validated predictive models integrating combinations of these biomarkers with clinical and microbiological data are needed to support early risk stratification and therapeutic decision-making at the time of diagnosis. Full article

Background: Distinguishing genuine kinase–substrate motifs from background noise is a growing challenge, as mass spectrometry (MS)-based global phosphoproteomics identifies a rapidly expanding set of phosphorylation sites. One of the major limitations is selecting an appropriate background model that systematically controls both technical and biological sources of bias. Although using the entire proteome as a background in a FASTA format considers the overall amino acid composition, it is still prone to biases from protein abundance and the uneven distribution of sequence space (particularly around low-abundance proteins). By contrast, internal background methods can control experiment-specific detection biases, but they may not fully capture residue-specific compositions or general trends in phosphorylation. Methods: I develop a Dual-Background Enrichment (DBE) framework with a position-specific enrichment (PSE) strategy, which involves analyzing motif enrichment against two distinct background models: (1) A residue-heterogeneous internal background composed of phospho-motifs centered on the residue; e.g., phosphoserine (pS) motifs are tested relative to the pool of all detected phosphothreonine (pT) and phosphotyrosine (pY) motifs from the same experiment. (2) A FASTA background that includes all S, T, and Y residues in the UniProtKB proteome sequences. Results: Motifs are classified as high confidence if they meet statistical significance (q ≤ 0.05, fold enrichment > 1.5) against both background models. Conclusion: By applying the DBE strategy to a large-scale phosphoproteomics dataset, we distinguish motifs driven by amino acid composition (enriched in FASTA background only) from those reflecting kinase substrate specificity (enriched in both backgrounds). This dual-reference approach reduces false positives arising from sequence composition bias and enriches high-confidence candidate kinase recognition motifs. Full article

Artificial intelligence (AI) is increasingly shaping higher education, particularly in the development of academic writing and speaking skills. While AI tools offer immediate feedback and personalized learning opportunities, existing research often focuses on their effectiveness without fully addressing their pedagogical and ethical implications. This creates a need for a more critically informed understanding of how AI influences language learning. This study examines the role of artificial intelligence (AI) in enhancing both academic writing and speaking skills in higher education through a systematic review of recent empirical studies. Drawing on 109 studies published between 2022 and 2025, the review adopts PRISMA guidelines to identify trends in the use of these tools. The findings indicate significant benefits, including increased learner engagement, improved linguistic accuracy, and immediate individualized feedback. These benefits include lexical development, structural coherence, improved pronunciation, and increased learner confidence through iterative practices. However, the review also identifies critical challenges, including risks of overreliance, reduced learner autonomy, and concerns related to linguistic bias. To address these concerns, the study proposes the implementation of the Mediated AI-Pedagogy Cycle, which positions educators as mediating agents between AI affordances and learner development. The study contributes a pedagogically grounded framework for integrating AI into higher education language instruction. Full article

Sample preparation is frequently a time-consuming process and can be a major bottleneck in many analytical techniques that involve some form of modification to a sample so that it can be analyzed without interference or to increase its sensitivity. As part of the movement towards “green analytical chemistry”, the reduction in organic solvent usage and toxicity via alternative solvents compared to those traditionally used in analytical chemistry has gained increasing interest. Although ionic liquids were thought to have limitations, deep eutectic solvents (DESs) are being looked at as alternatives to traditional organic solvents in analytical chemistry because of their ability to produce a “tunable” set of physico-chemical properties that enable the selective and efficient extraction of a wide variety of analytes from a very diverse array of matrices. Although deep eutectic solvents have attracted increasing attention in analytical extraction applications, a systematic comparison of their performance across various extraction techniques is still lacking. This review fills this gap by offering a comprehensive and integrated evaluation of DES-based extraction approaches, emphasizing the interdependence between solvent characteristics, extraction efficiency, selectivity, and sustainability. The insights presented herein are intended to support the rational selection of appropriate DES-based extraction strategies for diverse analytical purposes. Moreover, these findings are expected to contribute to the advancement of greener, more efficient sample preparation methodologies within the field of green analytical chemistry. In this review article, we describe several analytical chemistry techniques that utilize DESs, such as dispersive liquid–liquid microextraction, solid-phase extraction, ultrasound-assisted extraction, etc., and explain the basic principles and mechanisms behind each technique. Additionally, comparative evaluations are provided to identify the relative advantages and disadvantages of the techniques mentioned above in terms of extraction efficiency and selectivity, and speculation regarding future trends and challenges in DES-based extraction systems will also be included. By integrating recent advances and comparative performance assessments, this review serves as a reference for researchers and industry practitioners, fostering innovation and promoting the wider adoption of sustainable extraction technologies. Full article

Deforestation remains a crucial Anthropocene challenge, driving biodiversity loss, carbon emissions, and socio-ecological disruption. Despite extensive study, the long-term structure, thematic evolution, and collaborative patterns of deforestation research remain insufficiently synthesized. This bibliometric analysis examines 5091 publications from WoS and Scopus (1974–2025), using RStudio (version 4.5.2 (31 October 2025)), VOSViewer (version 1.6.20), and Excel to analyze publication trends, citation patterns, thematic clusters, and collaboration networks. Results show rapid growth after 2000, with citation peaks in 2010 and 2020. Major thematic clusters include deforestation, climate change, agriculture, governance, REDD+, and remote sensing. Environmental Research Letters is the most influential journal; Fearnside, P., is the leading author, and the UC system is a top institution. The USA and Brazil lead nationally, with the Amazon, Congo Basin, and Southeast Asia as primary geographic foci, reflecting persistent North–South collaboration dynamics. Limitations include reliance on English-language publications and title-only search criteria, which may underrepresent non-Anglophone research. Future research should expand to multiple languages, incorporate gray literature, and examine the policy impacts of deforestation-free supply chain regulations, such as the EUDR. This review underscores deforestation science as a growing, multidisciplinary field that requires the integration of social and ecological sciences, AI, and geospatial tools, alongside stronger research-policy linkages and enhanced capacity in forest-affected regions. Full article

Wildfires are an increasing environmental and civil protection challenge across Southern Europe under intensifying climate variability and extreme heat conditions. This study presents a reproducible, climate-informed framework for analysing seasonal fire weather variability across Italy using the Canadian Fire Weather Index (FWI) system. The work was conducted during a Short-Term Scientific Mission funded by the NERO COST Action CA22164 at the University of Milan in September 2025. A harmonised Seasonal FWI dataset for Italy covering 2007–2024 was developed using Copernicus Climate Data Store products, Climate Data Operators (CDO), and R-based statistical workflows. Seasonal FWI metrics were spatially aggregated across 35 Italian ecoregions to evaluate temporal variability and regional fire danger patterns. Results reveal pronounced interannual variability, with mean FWI values ranging from approximately 16 in 2020 to 24 in 2016. Although no statistically significant monotonic trend was detected during 2007–2024, seasonal FWI values consistently exceeded the historical 1970–2000 baseline (~10–15), indicating a shift towards a higher fire danger regime. Figures illustrate the analysed ecoregions, regional burned area trends, and relationships between mean seasonal FWI and burned area. The openly available dataset and reproducible workflow provide a foundation for future climate-informed fire danger assessment and civil protection decision-support systems. Full article

Natural circulation is a key passive heat removal mechanism in advanced reactor systems, including Molten Salt Reactors (MSRs). Owing to the high operating temperatures and material challenges associated with molten salts, surrogate fluids with Prandtl numbers comparable to those of molten salts have emerged as promising candidates for studying heat transfer phenomena in MSRs. The present study marks the first experimental and numerical investigation using Therminol-66 (Th-66) simulant oil as a surrogate fluid for molten salts in a natural circulation (NC) test loop setup at the University of Idaho Thermal-Hydraulics Laboratory. Experimental temperature measurements and energy-balance-based mass flow rate estimations were used to validate a three-dimensional computational fluid dynamics (CFD) model developed in ANSYS FLUENT. Two numerical configurations were considered: an adiabatic-wall model and a model incorporating distributed heat losses. The inclusion of heat losses significantly improved predictive accuracy, reducing the maximum relative error in heater outlet temperature to 16.7%. The largest deviation of 35.5% was observed at the heater inlet, primarily due to differences in power distribution and hydraulic resistance between the experimental system and the simplified numerical model. The CFD model systematically overpredicted the mass flow rate, mainly as a result of geometric simplifications (e.g., omission of flanges and minor loss elements) and the assumption that the total heater power was applied directly to the immersed heater rods. On the experimental side, distributed heat losses and indirect mass flow rate estimation introduced additional uncertainty. Nevertheless, the CFD model successfully captured the overall thermal and hydraulic trends across all operating conditions. The validated simulations further provided detailed insight into local and global temperature and velocity distributions within the heater and cooler sections. The results highlight the importance of accurately representing thermal losses and hydraulic resistance to achieve reliable prediction of natural circulation behavior in surrogate MSR systems. Full article

Accurate soil salinity estimation under small-sample agricultural conditions continues to pose a formidable challenge, attributed to the scarcity of labeled data, inherent representational limitations of single-backbone neural networks, and the heightened complexity of subsurface salinity inversion. To mitigate these intertwined challenges, this study developed a UAV-enabled soil salinity estimation framework that integrated lightweight convolutional neural networks and staged feature optimization, leveraging both RGB and multispectral imagery. A feature selection framework integrating random forest recursive feature elimination (RF-RFE), the one-standard-error (One-SE) criterion, and variance inflation factor (VIF) analysis was employed to reduce 129 candidate variables to a unified 16-channel feature set, which served as the common input for estimating both surface and subsurface soil salinity. Three lightweight single-backbone (VGGNet, ResNet, and DenseNet) and dual-backbone feature-level fusion networks (DenseResNet, DenseVGGNet, and ResVGGNet) were constructed and systematically evaluated for their performance in estimating both surface and subsurface soil salinity. Among the single-backbone networks, ResNet yielded the highest overall statistical accuracy, while DenseNet exhibited superior performance in preserving estimation trends. For surface soil salinity estimation, ResVGGNet achieved the best performance among all evaluated models, with an R² of 0.820, RMSE of 0.626 g/kg, MAE of 0.409 g/kg, and RPD of 2.31 on the test dataset. SHAP analysis further highlighted the dominant role of vegetation and salinity-sensitive indices, together with selected spectral mean features, and revealed spatially complementary response patterns among major input channels. Collectively, the integration of lightweight multi-backbone feature-level fusion with streamlined feature optimization strategies effectively enhances the accuracy, robustness, and interpretability of UAV-enabled soil salinity estimation, particularly under the constraint of small agricultural sample sizes. Full article

Medical ultrasound imaging relies heavily on ultrasound transducers, whose properties directly determine transducer performance. Alternating current poling (ACP) serves as a domain engineering platform to enhance the dielectric and piezoelectric properties of ferroelectric single crystals, ceramics, and piezocomposites compared to conventional direct current poling (DCP). Although several reviews cover the microscopic mechanisms of ACP, researchers have not yet systematically analyzed these materials from the perspective of device applications. This mini-review focuses on the impact of ACP on transducer performance, analyzing the relationship between material properties and device performance in ultrasound imaging transducers. We systematically evaluate the optimization efficacy of ACP across different piezoelectric material forms and bridge the gaps between material parameters and device metrics such as bandwidth and sensitivity. Finally, this review discusses the engineering challenges, structural design synergies, and future trends of ACP-based transducers. Full article

This paper explores the importance of forgiveness, reconciliation, and universal fraternity in achieving world peace, with a particular focus on the thoughts of Pope Francis. It examines the ethical and political dimensions of forgiveness as a transformative practice rooted in justice, memory, and responsibility. The paper also considers the role of interreligious and intercultural dialogue, the challenges posed by contemporary global trends, and the importance of political and social structures grounded in human dignity and solidarity. Finally, it addresses the continuity of these ideas in the early statements of Pope Leo XIV, emphasizing peace, dialogue, and unity. Full article

Object detection in UAV aerial imagery is challenged by dense small targets, large-scale variation, complex backgrounds, and strict onboard computation limits. To address these issues, this paper proposes SMMNet (Structured-diffusion Mamba Mixture Network), a lightweight plug-and-play detection framework evaluated with YOLO family detectors. SMMNet contains three modules. The Structured Diffusion Feature Extractor (SDFE) uses anisotropic diffusion to preserve boundary-sensitive features during downsampling. The Mamba-driven Receptive-field Context Aggregator (MRCA) performs multi-directional selective state-space scanning to capture long-range context with linear complexity. The Mask-guided Bayesian Box Refinement (MBBR) applies a MAP-inspired confidence-adaptive box update using MobileSAM mask evidence and ELBO-based false-positive filtering. Using YOLOv13-S as the main detector, SMMNet achieves 32.8% mAP@0.5:0.95 and 52.6% mAP@0.5 on VisDrone2019 at 87 FPS on an NVIDIA A800 GPU, improving the YOLOv13-S baseline by 3.6 and 4.5 points, respectively. The added modules reduce throughput compared with the detector-only baseline (168 FPS), but the resulting 87 FPS remains real-time and provides a favorable accuracy–latency trade-off. Three independent-seed runs further show a mean paired gain of 3.60 ± 0.10 mAP on VisDrone2019, 2.53 ± 0.12 mAP on DroneVehicle, and 2.77 ± 0.06 mAP on SeaDronesSee for the YOLOv13-S setting. Additional experiments on DroneVehicle and SeaDronesSee, together with cross-backbone evaluations on YOLOv5/v6/v7/v8/v10/v11/v13 across different UAV benchmarks, show aligned performance trends under matched settings. Edge deployment on an NVIDIA Jetson Orin NX reaches 30 FPS under TensorRT FP16 inference at 15 W TDP, indicating the suitability of SMMNet for resource-constrained UAV perception. Full article

With the increasing importance of green design in the business landscape, designers are compelled to shift towards eco-design practices. However, existing methodologies face challenges related to resource requirements, abstract concepts, and industry specificity. To address these challenges and stimulate innovation, this study proposes a green design methodology that integrates TRIZ concepts and is anchored in TRIZ evolutionary trends. The methodology includes function and attribute analysis, the introduction of green features, the identification of TRIZ trends through a two-stage process, and the use of a developed system to improve calculation efficiency. Detailed design solutions are generated by combining green features, TRIZ trends, and inventive principles. A case study validates the methodology, showcasing its value in promoting sustainable development. By leveraging the evolutionary potential of products and incorporating TRIZ, the methodology offers a promising approach to address sustainability challenges and drive innovation. This research serves as a starting point for a practical and efficient design methodology that utilizes TRIZ concepts and a computer-aided application tool. Future steps involve stress-testing the methodology and exploring its application in different domains. Full article

Background/Objectives: The rapid expansion of digital evidence in modern investigations has created pressing challenges for maintaining integrity, traceability, chain of custody, and meaningful analysis across heterogeneous forensic artefacts. Conventional evidence management approaches often fall short in scalability, transparency, and the ability to correlate diverse digital evidence. This study presents BlockFECS, a blockchain-based proof-of-concept system for metadata-driven evidence correlation in digital forensics. Methods: BlockFECS uses Hyperledger Fabric to support auditable and tamper-resistant evidence management while capturing structured forensic metadata, including timestamps, locations, device IDs, user IDs, and file hashes. An off-chain weighted correlation algorithm assigns similarity scores between evidence pairs and classifies relationships as Related, Supplementary, Duplicate, or Unrelated. The system was evaluated using a simulated smart city accident scenario and tested for correctness, transaction latency, throughput, scalability trends, and concurrency behaviour across four computing environments. Results: Within the controlled proof-of-concept dataset, the correlation algorithm achieved 1.00 precision and recall for clear Related and Duplicate evidence relationships and high precision (0.90) for Supplementary relationships, although recall in this category was lower due to incomplete or noisy metadata. Performance testing showed that Create, Transfer, and Delete operations completed with sub-second latency, while correlation throughput exceeded 60 comparisons per second across all tested environments. Conclusions: The findings demonstrate the feasibility of combining blockchain-backed evidence integrity with lightweight metadata-driven forensic intelligence. BlockFECS contributes a proof-of-concept model for automating metadata-based evidence analysis while preserving provenance integrity and auditability, highlighting a promising direction for trustworthy and intelligent digital forensic investigation support. Full article