Search Results (81)

Accurate river discharge forecasting remains a critical challenge in hydrology, particularly in data-scarce mountainous regions where in situ observations are limited. This study investigated the potential of long short-term memory (LSTM) networks to improve discharge prediction by leveraging spatially distributed reanalysis data. Using the ERA5-Land dataset, we developed an LSTM model that integrates grid-based meteorological inputs and assesses their relative importance. We conducted experiments on two snow-dominated basins with contrasting physiographic characteristics, the Uba River basin in Kazakhstan and the Flathead River basin in the USA, to answer three research questions: (1) whether full-grid input outperforms reduced configurations and models trained on Caravan, (2) the impact of spatial resolution on accuracy and efficiency, and (3) the effect of partial spatial coverage on prediction reliability. Specifically, we compared the full-grid LSTM with a single-cell LSTM, a basin-average LSTM, a Caravan-trained LSTM, and coarser cell aggregations. The results demonstrate that the full-grid LSTM consistently yields the highest forecasting performance, achieving a median Nash–Sutcliffe efficiency of 0.905 for Uba and 0.93 for Middle Fork Flathead, while using coarser grids and random subsets reduces performance. Our findings highlight the critical importance of spatial input richness and provide a reproducible framework for grid selection in flood-prone basins lacking dense observation networks. Full article

Image super-resolution (SR) aims to reconstruct high-resolution (HR) images from low-resolution (LR) inputs. It plays a crucial role in applications such as medical imaging, surveillance, and remote sensing. However, due to the ill-posed nature of the task and the inherent limitations of imaging sensors, obtaining accurate HR images remains challenging. While numerous methods have been proposed, the traditional approaches suffer from oversmoothing and limited generalization; CNN-based models lack the ability to capture long-range dependencies; and Transformer-based solutions, although effective in modeling global context, are computationally intensive and prone to texture loss. To address these issues, we propose a hybrid CNN–Transformer architecture that cascades a pixel-wise self-attention non-local means module (PSNLM) and an adaptive dual-path multi-scale fusion block (ADMFB). The PSNLM is inspired by the non-local means (NLM) algorithm. We use weighted patches to estimate the similarity between pixels centered at each patch while limiting the search region and constructing a communication mechanism across ranges. The ADMFB enhances texture reconstruction by adaptively aggregating multi-scale features through dual attention paths. The experimental results demonstrate that our method achieves superior performance on multiple benchmarks. For instance, in challenging ×4 super-resolution, our method outperforms the second-best method by 0.0201 regarding the Structural Similarity Index (SSIM) on the BSD100 dataset. On the texture-rich Urban100 dataset, our method achieves a 26.56 dB Peak Signal-to-Noise Ratio (PSNR) and 0.8133 SSIM. Full article

The concrete industry increasingly seeks sustainable alternatives to conventional materials to reduce the environmental impact while maintaining structural performance. This study evaluates the use of locally sourced rhyolite as a coarse aggregate combined with recycled supplementary cementitious materials (SCMs) to address the sustainability and durability. Due to its high silica content, rhyolite is prone to the alkali–silica reaction (ASR), which may affect concrete durability. Concrete mixtures incorporating rhyolite with silica fume (SF), Class F fly ash (FA), and slag cement (SC) were tested for compressive strength, porosity, density, absorption, mortar bar expansion, electrical resistivity, and rapid chloride permeability. All rhyolite-based mixtures—regardless of SCM incorporation—achieved higher 90-day compressive strengths than the conventional control mixture, with 10% SF reaching the highest value. Additionally, each recycled SCM effectively reduced ASR-induced expansion, with 20% FA showing the most significant reduction and superior durability, including the greatest decrease in chloride permeability and the highest electrical resistivity, indicating enhanced corrosion resistance. These results confirm that rhyolite aggregates, when combined with SCMs, can improve durability and reduce ASR. Therefore, rhyolite shows potential for use in structural concrete under standard exposure conditions. This strategy supports circular economy goals by incorporating regional and recycled materials to develop concrete with improved durability characteristics. Full article

Pavement systems in wet-freeze regions are prone to cracking, rutting, and moisture damage, making it challenging to incorporate recycled materials into asphalt mixtures in a way that enhances sustainability while maintaining performance and constructability. This study investigates and demonstrates the combined benefits of using processed waste glass in a leveling course and high-content crumb rubber in a surface course, focusing on both laboratory and full-scale field assessments in a wet-freeze region of northern Michigan. A leveling course containing 10% waste glass aggregate and a surface course using 16% crumb rubber (by binder weight) modified asphalt were designed with low air voids (3.0–3.5%) to promote thicker asphalt binder films for improved crack resistance. Laboratory results demonstrated that the combination of a 10% glass aggregate leveling course and a 16% rubber-modified surface course significantly enhanced low-temperature fracture energy while maintaining robust rut resistance and moisture durability. Full-scale construction in northern Michigan corroborated these findings; field cores from rubber and glass sections surpassed performance thresholds for rutting, cracking, and noise reduction. This study demonstrates that integrating crumb rubber and waste glass into asphalt pavements offers both environmental and performance benefits. The approach presents a scalable solution for enhancing pavement durability in wet-freeze regions. Full article

Intrinsically disordered proteins (IDPs), such as tau, beta-amyloid (Aβ), and alpha-synuclein (αSyn), are prone to misfolding, resulting in pathological aggregation and propagation that drive neurodegenerative diseases, including Alzheimer’s disease (AD), frontotemporal dementia (FTD), and Parkinson’s disease (PD). Misfolded IDPs are prone to aggregate into oligomers and fibrils, exacerbating disease progression by disrupting cellular functions in the central nervous system, triggering neuroinflammation and neurodegeneration. Furthermore, aggregated IDPs exhibit prion-like behavior, acting as seeds that are released into the extracellular space, taken up by neighboring cells, and have a propagating pathology across different regions of the brain. Conventional inhibitors, such as small molecules, peptides, and antibodies, face challenges in stability and blood–brain barrier penetration, limiting their efficacy. In recent years, nanotechnology-based strategies, such as multifunctional nanoplatforms or nanoparticles, have emerged as promising tools to address these challenges. These nanoplatforms leverage tailored designs to prevent or remodel the aggregation of IDPs and reduce associated neurotoxicity. This review discusses recent advances in nanoplatforms designed to target tau, Aβ, and αSyn aggregation, with a focus on their roles in reducing neuroinflammation and neurodegeneration. We examine critical aspects of nanoplatform design, including the choice of material backbone and targeting moieties, which influence interactions with IDPs. We also highlight key mechanisms including the interaction between nanoplatforms and IDPs to inhibit their aggregation, redirect aggregation cascade towards nontoxic, off-pathway species, and disrupt fibrillar structures into soluble forms. We further outline future directions for enhancing IDP clearance, achieving spatiotemporal control, and improving cell-specific targeting. These nanomedicine strategies offer compelling paths forward for developing more effective and targeted therapies for neurodegenerative diseases. Full article

Proinsulin has three distinct regions: the well-folded A- and B-chains and the dynamic disordered C-peptide. The highly conserved B-chain is a hotspot for diabetes-associated mutations, including the severe loss-of-function R(B22)Q mutation linked to childhood-onset diabetes. Here, we explore R(B22)’s role in proinsulin stability using AlphaFold-predicted structures and metadynamics simulations to achieve enhanced sampling of the free energy landscape. Our results show that R(B22) stabilizes proinsulin by interacting with N86. Substituting R(B22) with E or Q disrupts this interaction, increasing conformational flexibility. The R(B22)Q variant exhibits a flattened free energy landscape, favoring unfolded states. Additional substitutions, including Gly, Ala, Lys, Tyr, Asp, and Phe, destabilize proinsulin to varying extents by weakening hydrogen bonding. Disrupting the R(B22)–N86 interaction broadly reduces inter-chain contacts, raising the risk of aggregation-prone states. Given the link between R(B22) mutations and diabetes, our study provides crucial molecular insights into proinsulin instability. These findings highlight the role of key inter-domain (A-Chain–B-chain, B-Chain–C-peptide, and A-Chain–C-peptide) interactions in maintaining protein structures and the implications this has for understanding disease-associated proinsulin variants. Full article

Cities face growing challenges from climate change, including rising temperatures, extreme rainfall, and intensifying urban heat islands, resulting in significant socio-cultural costs. Urban areas are increasingly vulnerable to food insecurity during disasters, yet the potential of urban agriculture (UA) to address this challenge remains underexplored. This study focuses on Wellington, New Zealand (NZ), a region highly prone to earthquakes, to evaluate the role of UA in enhancing post-disaster food security. The study calculates vegetable self-sufficiency by mapping potential productive land, estimating vegetable yields, and assessing post-disaster food demands across multiple scenarios. Potential productive land was quantified using a reproducible GIS-based method, considering three soil-based UA types: private yards, communal gardens, and urban farms. Due to Wellington’s mountainous topography, slopes and aspects were used to select four land scenarios. Three yield scenarios were estimated using aggregated data from previous studies and cross-checked with local UA and NZ conventional farming data. Food demands were based on NZ’s recommended vegetable intake and three targeted population scenarios: the entire population, displaced populations, and vulnerable populations. Results indicate that potential productive land is primarily evenly distributed in the eastern part within the city boundary, accounting for 0.3% to 1.5% of the total area. Vegetable self-sufficient rates for Wellington through UA range from 3% to 75%, with higher rates for displaced and vulnerable populations. These figures significantly exceed the current self-sufficiency rate estimated in the authors’ preliminary research, indicating Wellington’s considerable potential to enhance post-disaster food security through expanding UA and promoting related initiatives. However, realizing this potential will require stronger policy support, integrating UA with urban planning and disaster preparedness. Full article

Soil degradation and erosion pose significant threats to agricultural sustainability in fragile ecosystems, such as the Loess Plateau in northern China. This study examines the long-term impacts of fertilization regimes and land-use systems on soil health, focusing on soil aggregate stability, fertility, and crop productivity. Six treatment combinations were evaluated in our study, including three continuous alfalfa fields (AL-CK, AL-P, and AL-NPM) and three continuous wheat fields (WH-NPM, WH-NP, and WH-P), each representing a combination of land use and three fertilization treatments: (1) no fertilization (CK), (2) inorganic fertilization (120 kg ha⁻¹ N, 60 kg ha⁻¹ P-NP), and (3) a combination of organic and inorganic fertilization (75 t ha⁻¹ cow manure-NPM). Soil samples were collected from three depths (0–10 cm, 10–20 cm, and 20–30 cm) to assess physical and chemical properties. We evaluated the long-term effects of different fertilization treatments on soil stability, fertility, and crop yield to explore the interactions among soil’s physical and chemical properties under two land-use types and to assess the effectiveness of combined organic and inorganic fertilization strategies in improving soil health and mitigating erosion in vulnerable landscapes. The study revealed significant depth-specific variations with surface layers (0–10 cm) showing the greatest improvement under NPM treatments, particularly in continuous alfalfa fields, which exhibited higher soil fertility, improved soil structure, and crop yield. In contrast, continuous wheat fields with minimal fertilization demonstrated significantly lower soil quality and productivity. Using the combination of mineral fertilizers and organic amendments, such as cow manure, proved to be the most effective strategy for significantly enhancing nutrient availability and overall soil health. Partial Least Squares Modeling (PLS-M) and Mantel analysis highlighted the critical role of fertilization management in maintaining soil quality, boosting crop productivity, and mitigating erosion in high-risk areas. This study emphasizes the importance of integrated nutrient management for sustainable land use and soil conservation in erosion-prone regions. Full article

Alzheimer’s disease (AD) is a complex neurodegenerative disorder characterized by extracellular amyloid plaques, predominantly consisting of amyloid-β (Aβ) peptides. The oligomeric form of Aβ is acknowledged as the most neurotoxic, propelling the pathological progression of AD. Interestingly, besides Aβ, other proteins are co-localized within amyloid plaques. Peptide analogs corresponding to the “aggregation-prone” regions (APRs) of these proteins could exhibit high-affinity binding to Aβ and significant inhibitory potential against the Aβ oligomerization process. The peptide analogs of co-localized protease, Cathepsin B, may act as such potent inhibitors. In silico studies on the complexes of the oligomeric state of Aβ and Cathepsin B peptide analogs were performed utilizing molecular docking and molecular dynamics simulations, revealing that these analogs disrupt the β-sheet-rich core of Aβ oligomers, a critical structural feature of their stability. Of the four peptide analogs evaluated, two demonstrated considerable potential by effectively destabilizing oligomers while maintaining low self-aggregation propensity, i.e., a crucial consideration for therapeutic safety. These findings point out the potential of APR-derived peptide analogs from co-localized proteins as innovative agents against AD, paving the way for further exploration in peptide-based therapeutic development. Full article

Satellite-based precipitation products (SPPs) are essential for climate monitoring, especially in regions with sparse observational data. This study compares the performance of the latest version (V07B) and its predecessor (V06B) of the Integrated Multi-satellitE Retrievals for GPM (IMERG) across South America and the adjacent oceans. It focuses on evaluating their accuracy under different precipitation regimes in Brazil using 22 years of IMERG Final data (2000–2021), aggregated into seasonal totals (summer, autumn, winter, and spring). The observations used for the evaluation were organized into 0.1° × 0.1° grid points to match IMERG’s spatial resolution. The analysis was restricted to grid points containing at least one rain gauge, and in cases where multiple gauges were present within a grid point the average value was used. The evaluation metrics included the Root Mean Square Error (RMSE) and categorical indices. The results reveal that while both versions effectively capture major precipitation systems such as the mesoscale convective system (MCS), South Atlantic Convergence Zone (SACZ), and Intertropical Convergence Zone (ITCZ), significant discrepancies emerge in high-rainfall areas, particularly over oceans and tropical zones. Over the continent, however, these discrepancies are reduced due to the correction of observations in the final version of IMERG. A comprehensive analysis of the RMSE across Brazil, both as a whole and within the five analyzed regions, without differentiating precipitation classes, demonstrates that version V07B effectively reduces errors compared to version V06B. The analysis of statistical indices across Brazil’s five regions highlights distinct performance patterns between IMERG versions V06B and V07B, driven by regional and seasonal precipitation characteristics. V07B demonstrates a superior performance, particularly in regions with intense rainfall (R1, R2, and R5), showing a reduced RMSE and improved categorical indices. These advancements are linked to V07B’s reduced overestimation in cold-top cloud regions, although both versions consistently overestimate at rain/no-rain thresholds and for light rainfall. However, in regions prone to underestimation, such as the interior of the Northeastern region (R3) during winter, and the northeastern coast (R4) during winter and spring, V07B exacerbates these issues, highlighting challenges in accurately estimating precipitation from warm-top cloud systems. This study concludes that while V07B exhibits notable advancements, further enhancements are needed to improve accuracy in underperforming regions, specifically those influenced by warm-cloud precipitation systems. Full article

Land use is a remarkable human-induced change that has redesigned the Earth’s surface since the beginning of civilization. Due to the combination of rugged terrain and low-income levels in rural areas, people in watershed regions often resort to overexploiting forests, agricultural land, and grasslands beyond their capacity. As a result of these spatio-temporal changes in land use, various soil properties undergo changes. This study aims to determine the changes in some physical (texture, bulk weight, particle density, total porosity), hydro-physical (water holding capacity, permeability, field capacity, wilting point), physico-chemical (organic matter, pH, electrical conductivity), and erodibility (dispersion ratio, colloid–moisture equivalent ratio, erosion ratio, clay ratio, aggregate stability and K-factor of Universal Soil Loss Equation-USLE) properties of soil depending on land use in the Deviskel Watershed in the city of Artvin in Türkiye. For this purpose, disturbed (composite) and undisturbed (cylinder) soil samples were taken from a 0 to 20 cm depth at 108 different points in the determined areas (36 from forests, 36 from agricultural areas, and 36 from grassland areas). It was determined that 15 of the 19 soil properties examined showed statistical differences depending on the change in land use. All the examined soil properties, except for clay content, particle density, dispersion ratio, and aggregate stability, were found to be statistically significantly affected by the change in land use, and the reasons behind these changes were discussed. The particle density had the lowest coefficient of variation value (15.26%) while electrical conductivity had the highest coefficient of variation value (91.25%). According to erosion tendencies, all watershed soils were found to be susceptible to erosion. The average aggregate stability was 88.52% in forest soils, 84.84% in agricultural soils, and 85.48% in grassland soils. The average USLE-K factor was determined to be 0.22 for forests, while it was determined to be 0.17 and 0.18 for agriculture and grassland areas, respectively. According to the USLE-K factor, 68.37% of the watershed was dominated by moderately erodible soils, while 31.63% consisted of highly erodible soils. Based on the colloid–moisture equivalent ratio, erosion ratio, and clay ratio, which are statistically different erodibility features, the grassland soils of the research area were found to be more susceptible to erosion than forest and agricultural soils. In terms of aggregate stability, which indicates resistance to water erosion, forest areas had higher values, while agricultural lands were more prone to erosion. Full article

The accurate recognition of a brain tumor (BT) is crucial for accurate diagnosis, intervention planning, and the evaluation of post-intervention outcomes. Conventional methods of manually identifying and delineating BTs are inefficient, prone to error, and time-consuming. Subjective methods for BT recognition are biased because of the diffuse and irregular nature of BTs, along with varying enhancement patterns and the coexistence of different tumor components. Hence, the development of an automated diagnostic system for BTs is vital for mitigating subjective bias and achieving speedy and effective BT segmentation. Recently developed deep learning (DL)-based methods have replaced subjective methods; however, these DL-based methods still have a low performance, showing room for improvement, and are limited to heterogeneous dataset analysis. Herein, we propose a DL-based parallel features aggregation network (PFA-Net) for the robust segmentation of three different regions in a BT scan, and we perform a heterogeneous dataset analysis to validate its generality. The parallel features aggregation (PFA) module exploits the local radiomic contextual spatial features of BTs at low, intermediate, and high levels for different types of tumors and aggregates them in a parallel fashion. To enhance the diagnostic capabilities of the proposed segmentation framework, we introduced the fractal dimension estimation into our system, seamlessly combined as an end-to-end task to gain insights into the complexity and irregularity of structures, thereby characterizing the intricate morphology of BTs. The proposed PFA-Net achieves the Dice scores (DSs) of 87.54%, 93.42%, and 91.02%, for the enhancing tumor region, whole tumor region, and tumor core region, respectively, with the multimodal brain tumor segmentation (BraTS)-2020 open database, surpassing the performance of existing state-of-the-art methods. Additionally, PFA-Net is validated with another open database of brain tumor progression and achieves a DS of 64.58% for heterogeneous dataset analysis, surpassing the performance of existing state-of-the-art methods. Full article

Mountain cities with complex topographies have always been highly vulnerable areas to global environmental change, prone to geological hazards, climate change, and human activities. Exploring and analyzing the vulnerability of coupling systems in mountain cities is highly important for improving regional resilience and promoting sustainable regional development. Therefore, a comprehensive framework for assessing the spatial vulnerability of mountain cities is proposed. A vulnerability assessment index system is constructed using three functional systems, ecological protection, agricultural production, and urban construction. Subsequently, the BP neural network and the genetic algorithm (GA) are combined to establish a vulnerability assessment model, and geographically weighted regression (GWR) is introduced to analyze the spatial influence of one-dimensional systems on the coupling system. Linzhou, a typical mountain city at the boundary between China’s second- and third-step terrains, was selected as a case study to demonstrate the feasibility of the framework. The results showed that the vulnerability of the ecological protection system was highly aggregated in the east–central region, that of the agricultural production system was high in the west, and that of the urban construction system was low in the central region and high in the northwestern region. The coupling system vulnerability was characterized by multispatial distribution. The complex topography and geomorphology and the resulting natural hazards are the underlying causes of the vulnerability results. The impact of ecological and urban systems on the coupling system vulnerability is more prominent. The proposed framework can serve as a reference for vulnerability assessments of other similar mountain cities with stepped topographies to support the formulation of sustainable development strategies. Full article

The global antibiotic resistance crisis has drawn attention to the development of treatment methods less prone to inducing drug resistance, such as antimicrobial photodynamic therapy (aPDT). However, there is an increasing demand for new photosensitizers capable of efficiently absorbing in the near-infrared (NIR) region, enabling antibacterial treatment in deeper sites. Additionally, advanced strategies need to be developed to avert drug resistance stemming from prolonged exposure. Herein, we have designed a conjugated oligoelectrolyte, namely TTQAd, with a donor-acceptor-donor (D-A-D) backbone, enabling the generation of reactive oxygen species (ROS) under NIR light irradiation, and cationic adamantaneammonium groups on the side chains, enabling the host-guest interaction with curcubit[7]uril (CB7). Due to the amphiphilic nature of TTQAd, it could spontaneously form nanoassemblies in aqueous solution. Upon CB7 treatment, the positive charge of the cationic adamantaneammonium group was largely shielded by CB7, leading to a further aggregation of the nanoassemblies and a reduced antibacterial efficacy of TTQAd. Subsequent treatment with competitor guests enables the release of TTQAd and restores its antibacterial effect. The reversible supramolecular switch for regulating the antibacterial effect offers the potential for the controlled release of active photosensitizers, thereby showing promise in preventing the emergence of drug-resistant bacteria. Full article

This manuscript presents a comprehensive study on the sustainable optimization of asphalt mixtures tailored for regions prone to flooding. The research addresses the challenges associated with water damage to asphalt pavements by incorporating innovative additives. The study centers on incorporating recycled Low-Density Polyethylene (LDPE) and a tailored Carnauba–Soybean Oil Additive, advancing asphalt mixtures with a Control mix, LDPE (5%) + Control, and LDPE (5%) + 3% Oil + Control. A critical aspect of the research involves subjecting these mixtures to 30 wetting and drying cycles, simulating the conditions prevalent in tropical flood-prone areas. The incorporation of innovative additives in asphalt mixtures has demonstrated significant improvements across various performance parameters. Tensile Strength Ratio (TSR) tests revealed enhanced tensile strength, with the LDPE (5%) + 3% Oil-modified mixture exhibiting an impressive TSR of 85.7%. Dynamic Modulus tests highlighted improved rutting resistance, showcasing a remarkable increase to 214 MPa in the LDPE (5%) with a 3% Oil-modified mixture. The Semi-Circular Bending (SCB) test demonstrated increased fracture resistance and energy absorption, particularly in the LDPE (5%) with 3% Oil-modified mixture. Hamburg Wheel-Tracking (HWT) tests indicated enhanced moisture resistance and superior rutting resistance at 20,000 cycles for the same mixture. Cantabro tests underscored improved aggregate shatter resistance, with the LDPE (5%) + 3% Oil-modified mixture exhibiting the lowest weight loss rate at 9.820%. Field tests provided real-world insights, with the LDPE (5%) + 3% Oil mixture displaying superior stability, a 61% reduction in deflection, and a 256% improvement in surface modulus over the control mixture. This research lays the groundwork for advancing the development of sustainable, high-performance road pavement materials, marking a significant stride towards resilient infrastructure in flood-prone areas. Full article