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Climate-Informed Water Allocation in Central Asia: Leveraging Decision Support System
No full textAs the impacts of climate change intensify, water resource conflicts are escalating globally, particularly in regions with uneven water distribution, such as Central Asia. Long-standing disputes over water allocation persist between Kyrgyzstan and Uzbekistan. This paper aims to examine the conflicts and challenges in water allocation between the two countries and explore the potential of Decision Support Systems (DSSs) as a viable solution. The paper begins by reviewing the historical evolution of water allocation in Central Asia, analyzing upstream–downstream disputes and notable cooperation efforts, with a focus on key water agreements. It then outlines the definitions, development, and classifications of DSSs in the context of water allocation and presents two illustrative case studies—the Tarim River Basin in Xinjiang, China, and the Nile River Basin in Africa. These cases demonstrate the applicability of DSSs in water-scarce regions with similar socio-ecological dynamics and complex multi-country, cross-sectoral water demands. Building on these insights, the paper analyzes the key challenges to implementing DSSs for transboundary water allocation in Central Asia, including limited data availability and sharing, insufficient technical capacity, chronic funding shortages, socio-political complexities, climate change impacts, and the inherent difficulty of modeling complex systems. In response, a set of targeted pragmatic recommendations is proposed. While acknowledging its limitations, the paper argues that establishing a structured, system-based decision-making framework—namely DSSs—can help stakeholders enhance climate-informed strategic planning and foster cooperation, ultimately contributing to more equitable and sustainable water resource allocation in the region
Pentamidine-Functionalized Polycaprolactone Nanofibers Produced by Solution Blow Spinning for Controlled Release in Cutaneous Leishmaniasis Treatment
No full textLeishmaniasis, a widespread, neglected infectious disease with limited effective treatments and increasing drug resistance, demands innovative therapeutic approaches. In this study, we report the fabrication of pentamidine (PTM)-loaded polycaprolactone (PCL) nanofibers using solution blow spinning (SBS) as a potential topical delivery system for cutaneous leishmaniasis (CL). Homogeneous PCL fiber mats were produced using a simple SBS set-up with a commercial airbrush after optimizing several working parameters. Drug release studies demonstrated sustained PTM release profile over time, which was mechanistically modeled by utilizing the complete nanofiber diameter distribution, obtained from SEM analysis of the blow-spun material. FTIR and XRD analyses were performed to investigate the drug–polymer interactions, revealing molecularly dispersed PTM at low-proportion drug/polymers and partial crystallinity at high loadings. The released PTM exhibited significant leishmanicidal activity against Leishmania major promastigotes. Biological investigations showed that SBS-formulated PTM treatment was consistent with the downregulation of parasite genes involved in cell division and DNA replication (cycA, cyc6, pcna, top2, mcm4) and upregulation of the drug response gene (prp1). The controlled delivery of PTM within SBS-fabricated PCL nanofibers provides an effective therapeutic approach to tackle CL and, through the incorporation of additional drugs, could be extended to address a broader range of cutaneous infections
A Holistic Framework for Sustainable Environmental Impact Assessment in Polymer Production: Systematic Review and Validation
No full textGlobal polymer production has rapidly escalated in response to the increasing global demand. These materials are highly regarded due to their superior strength-to-weight ratio, cost-effectiveness, and ease of manufacturing. This study presents a systematic review aimed at addressing the environmental challenges associated with polymer production. It also seeks to develop a Sustainable Conceptual Model for Environmental Impact Assessment (EIA), integrating key sustainability factors, which are often overlooked in existing frameworks. A systematic literature review was conducted following PRISMA guidelines, covering peer-reviewed studies published between 2015 and 2025 in the Scopus and Web of Science databases. Critical gaps in conventional EIA practices for polymer manufacturing were identified, forming the basis for the proposed integrated sustainability framework. The proposed model provides a structured methodology for assessing key sustainability dimensions across polymer production, enabling a more comprehensive evaluation of environmental impacts throughout the polymer production process. As validation for the model, a pilot study with 68 industry experts was analyzed through reliability testing, confirmatory factor analysis, and regression-based hypothesis testing. The results supported the proposed model. Industries can utilize the model to develop targeted sustainability strategies, minimize environmental footprints, and inform policymaking efforts aimed at improving the environmental performance of polymer manufacturing
Biostimulants Enhance the Growth and Nutritional Quality of Lettuce (Lactuca sativa L.)
No full textBiostimulants have emerged as effective tools for enhancing both the productivity and quality of crops. In this study, we assessed the impact of the two commercial biostimulant products (Kiana Earth® and Kiana Climate®) on the growth, yield, and quality of lettuce (Lactuca sativa L.). Eight treatments were established, comprising six different biostimulant formulations, a normal control (no fertilizer applied), and a positive control (chemical fertilizer application). Biostimulant treatments significantly improved plant and stem diameters, fresh and dry biomass, and yield (p < 0.01). The best yields and morphological performance were obtained with samples receiving T6 (Kiana Climate® + 75:50:75 kg ha−1 N:P:K) and T7 (Kiana Earth® + 150:100:150 kg ha−1 N:P:K) applications, which comprised biostimulant–fertilizer combinations. Chlorophyll a, chlorophyll b, and total chlorophyll levels were significantly higher with than without biostimulant treatment, indicating that the biostimulants enhanced photosynthetic efficiency. Biochemical analyses further identified significant increases in vitamin C levels, total antioxidant capacity, total phenolic compounds, and flavonoid contents, especially with treatments T5 (Kiana Earth® + 75:50:75 kg ha−1 N:P:K)–T8 (Kiana Climate® + 150:100:150 kg ha−1 N:P:K). Nitrogen assimilation analysis showed that leaf NO3− levels were lower with the combined treatment than with chemical fertilizer alone, suggesting that the biostimulants improved nitrogen-use efficiency. Micronutrient (Fe, Zn, Cu, Mn, Na) and macronutrient (N, P, K, Ca, Mg, S) levels were significantly increased with biostimulant-enriched treatments, alongside a rise in soil organic matter. Biostimulants, especially when combined with mineral fertilization, significantly enhanced lettuce growth, yield, and nutritional quality, while also promoting soil fertility. These findings highlight the potential of biostimulants as valuable tools in conventional, regenerative, and organic agricultural practices, offering a sustainable approach to enhancing agricultural productivity while ensuring long-term soil fertility
The Effects and Scale of the Collapse of Regional Economies in Poland During the 2007–2009 Crisis and the COVID-19 Pandemic in the Aspect of Recent Energy Crisis Caused by the War in Ukraine
No full textThe purpose of this article is to assess the impact of three major crises—the global economic and financial crisis of 2007–2009, the COVID-19 pandemic (2020), and the energy crisis induced by the war in Ukraine (2022)—on the condition of regional economies in Poland, both before and after their occurrence. These events, which may be regarded as black swan phenomena, were examined using several indicators of regional economic performance: the Herfindahl–Hirschman Index (HHI), the dynamics of Gross Domestic Product (GDP), industrial output sold, construction and assembly output, and retail sales. The study is based on Statistics Poland GUS data ranging from the first quarter of 2004 to the fourth quarter of 2024. The findings indicate that some Polish voivodeships (Since 1 May 2004, Poland has been divided into regions (NUTS I) and voivodeships (NUTS II). In this division, the region is statistical in nature, but in the presented research we refer exclusively to voivodeships, and therefore we also use the terms “region” and “NUTS II” interchangeably in this work. The interchangeable use of these terms also stems from the practice in Polish literature) (regions) experienced increases in industrial concentration over the study period. In 2009, during the global financial crisis, seven regions (Dolnośląskie, Lubuskie, Małopolskie, Mazowieckie, Opolskie, Podkarpackie, and Pomorskie) exhibited a relatively high degree of industrial diversification. Seven others (Kujawsko-Pomorskie, Lubelskie, Łódzkie, Śląskie, Świętokrzyskie, Wielkopolskie, and Zachodniopomorskie) showed moderate concentration. The two eastern regions—Warmińsko-Mazurskie (HHI = 194) and Podlaskie (HHI = 315)—had the highest concentration of industrial production in that year. By 2024, the overall pattern remained consistent: seven voivodeships displayed high concentration, eight moderate concentration, and one (Podlaskie) exceptionally high concentration. The degree of cyclical convergence across regions was generally high throughout the examined period. However, notable differences emerged under crisis conditions. Synchronization was strongest during the COVID-19 pandemic, when abrupt and unexpected shocks produced relatively similar regional responses. In contrast, the financial crisis of 2007–2009 and the energy crisis of 2022 generated more heterogeneous regional effects, resulting in lower and more varied levels of convergence
Characterization of Newly Synthesized Nanobiomaterials for the Treatment of White Spot Lesions
No full textBackground: White spot lesions (WSLs) are characterized by enamel demineralization. Minimally invasive treatments using infiltrating resins, such as the commercially available Icon®, are recommended. The need for such treatments justifies ongoing research into developing materials that can address existing limitations regarding strength, durability, and biocompatibility. Objectives: This study aimed to synthesize and characterize four novel nanobiomaterials by evaluating their physicochemical properties and biocompatibility compared to the commercial material Icon®. Materials and methods: The recipes for the experimental nanobiomaterials NB3, NB6, NB3F, and NB6F contain varying proportions of TEGDMA, UDMA, HEMA, Bis-GMA, and HAF-BaF2 glass. Mechanical and physicochemical characteristics were evaluated, such as flexural strength, measured using the three-point test; water absorption and solubility; fluoride release; polymerization conversion; and residual monomers, assessed using High-Performance Liquid Chromatography (HPLC). In vitro cell viability was assessed via colorimetry using human dysplastic oral keratinocytes (DOKs). Results: NB6 and NB6F demonstrated the greatest polymerization potential. NB3 exhibited the lowest water absorption and solubility due to its hydrophobic nature. Additionally, the inclusion of UDMA enhanced the strength and elasticity of NB3 when compared to NB6. Among the samples with fluoride additives (NB3F and NB6F), the highest fluoride release on day 7 occurred with the material lacking UDMA. In contrast, the NB3F sample containing UDMA released the least amount of fluoride on the same day. In quantitative terms, NB3 and NB6F exhibited the lowest levels of residual monomers, whereas NB6 showed the highest levels. Both NB3 and NB6 were significantly better tolerated by the cells, showing higher cell viability compared to the commercial material Icon®. Conclusions: The materials’ mechanical and physicochemical properties varied with component proportions, enabling identification of a suitable formulation for targeted clinical applications. Biocompatibility tests showed that the experimental NB3 and NB6 were better tolerated than Icon®. Furthermore, the incorporation of filler particles improved the mechanical strength of the experimental nanobiomaterials
Artisanal Mining Contamination of Metal(Loid)s in Madre De Dios River Sediments (Amazon) and Ecological Risk Assessment
No full textThis study evaluated the geochemical contamination and ecological risk of metal(oid)s (As, Cd, Cr, Cu, Pb, Hg, and Zn) in sediments from four sites within a section of the Madre de Dios River, Peru—an area affected by artisanal alluvial gold mining and with limited prior research that considers its local geochemical complexity. Sediment samples were collected between 2013 and 2020, spanning seven river flood seasons and four low river flow seasons. Background values were estimated using ProUCL 5.2, considering local climatic and geological conditions. Environmental quality indices revealed that sediments in the studied river section were mainly contaminated and exhibited high ecological risk due to Hg, used in gold amalgamation, which showed peak values in 2013 and subsequently declined to moderate levels. Cd exhibited contamination and ecological risk until 2016, with non-detectable values thereafter, while As, Cu, Cr, Pb, and Zn showed low environmental alteration. Factor analysis and principal component analysis indicated a natural origin for Cu, Cr, Pb, and Zn, whereas Hg showed an anthropogenic source linked to mining. Elevated concentrations of Hg, Cr, and Zn during the river flood season highlight the influence of hydrological dynamics on contaminant mobilization within these sites of the river section
Photo–Hall Effect Characteristics of InAs/GaAs Quantum Dot Photoconductors with Sub-Bandgap Photoexcitation
No full textThe photoconductive properties of an InAs/GaAs quantum dot (QD) superlattice have been characterized using photo–Hall measurements under sub-bandgap illumination. The multi-stacked InAs/GaAs QD structure was grown using molecular beam epitaxy and photo–Hall effect measurements were performed under illumination using light-emitting diodes with three different emission wavelengths: 940 nm, 1300 nm, and 1550 nm. The results have shown that the sign reversal occurs in the Hall coefficient (RH) as the illumination wavelength changes: RH is negative at 940 nm and 1300 nm, and positive at 1550 nm. The photocurrent at 940 nm illumination is ascribed to the electron hole pair generation in QDs, whereas the photocurrent at 1550 nm is dominated by the hole current generated through the midgap states in the structure. A simplified rate equation model involving two-step photoexcitation through the midgap states has revealed that the dominant photocarriers and the Hall coefficient can change depending on the photoexcitation power. The steady-state photocurrent behavior including the observed sign reversal in the Hall coefficient has been interpreted by the proposed model
Effects of Lipopolysaccharides from Hafnia alvei PCM1200, Proteus penneri 12, and Proteus vulgaris 9/57 on Liposomal Membranes Composed of Natural Egg Yolk Lecithin (EYL) and Synthetic DPPC: An EPR Study and Computer Simulations
No full textThe aim of this study was to investigate the effects of three lipopolysaccharides (LPS), obtained from Hafnia alvei PCM 1200, Proteus penneri 12, and Proteus vulgaris 9/57, on the fluidity of liposomal lipid membranes. The experiments were performed on liposomes composed of egg yolk lecithin (EYL) in the liquid-crystalline phase and synthetic lecithin (DPPC) in the gel phase. The experimental results were compared with data obtained from a computational model of the membrane surface layer. Membrane fluidity was assessed using EPR spectroscopy with the spin probes TEMPO (surface layer; changes in the F parameter) and 16-DOXYL (hydrophobic core; changes in the τ parameter). In EYL liposomes, all LPS samples induced a reduction in surface-layer fluidity (decrease in the F/F0 ratio). In contrast, effects on the hydrophobic core (τ/τ0) were observed only at low dopant concentrations (<0.2%), above which membrane fluidity plateaued. In DPPC membranes, the response was more complex: local minima in F/F0 and maxima in τ/τ0 were detected, indicating transient alterations in membrane stiffening and plasticization that depended on the specific LPS applied. Computational simulations of the membrane surface further confirmed the greater susceptibility of low-mobility systems (corresponding to the gel phase) to dopant-induced perturbations. In the model, the best agreement with the EPR data was obtained when an effective dopant charge of q = 3 was assumed
The Fusion Mechanism and Prospective Application of Physics-Informed Machine Learning in Bridge Lifecycle Health Monitoring
No full textBridge health monitoring is crucial for ensuring the safety and durability of infrastructure. In traditional methods, physics-based models have high interpretability but are difficult to handle complex nonlinear problems, while purely data-driven machine learning methods are limited by data scarcity and physical inconsistency. Physics-informed machine learning, as an emerging “gray box” paradigm, effectively integrates the advantages of both by embedding physical laws (such as control equations) into machine learning models in the form of constraints, priors, or residuals. This article systematically elaborates on the core fusion mechanism of physics-informed machine learning (PIML) in bridge engineering, innovative applications throughout the entire lifecycle of design, construction, operation, and maintenance, as well as its unique data augmentation strategy. Research has shown that PIML can significantly improve the accuracy and robustness of damage identification, load inversion, and performance prediction, and is the core engine for constructing dynamic and predictive digital twin systems. Despite facing challenges in complex physical modeling, loss function balancing, and engineering interpretability, PIML represents a fundamental shift in bridge health monitoring towards intelligent and predictive maintenance by combining advanced strategies such as active learning and meta learning with IoT technology