Search Results (696)

This study examines the impact of physicochemical and geological factors on radon concentrations in groundwater throughout the Mexican Altiplano. Geological diversity, uranium deposits, seismic zones, and geothermal areas with high heat flow are all potential factors contributing to the presence of radon in groundwater. To move beyond local-scale assessments, this research employs spatial prediction methodologies that incorporate geological and geochemical variables recognized for their role in radon transport and geogenic potential. Certain properties of radon enable it to serve as an ideal tracer, viz., short half-life, inertness, and higher incidence in groundwater than surface water. Twenty-five variables were analyzed in samples from 135 water wells. Geostatistical techniques, including inverse distance weighted interpolation and kriging, were used in conjunction with multivariate statistical analyses. Salinity and geothermal heat flow are key indicators for determining groundwater origin, revealing a dynamic interplay between geothermal activity and hydrogeochemical evolution, where high temperatures do not necessarily correlate with increased solute concentrations. The occurrence of toxic trace elements such as Cd, Cr, and Pb is primarily governed by lithogenic sources and proximity to mineralized zones. Radon levels in groundwater are mainly influenced by geological and structural features, notably rhyolitic formations and deep hydrothermal systems. These findings underscore the importance of site-specific groundwater examination, combined with spatiotemporal models, to account for uranium–radium dynamics and flow paths, thereby enhancing radiological risk assessment. Full article

Urbanization and mixed-land-use development significantly impact water quality dynamics in watersheds, necessitating continuous monitoring and advanced analytical techniques for sustainable water management. This study employs continuous wavelet analysis to investigate the temporal variability and correlations of real-time water quality parameters in the Credit River watershed, Ontario, Canada. The Integrated Watershed Monitoring Program (IWMP), initiated by the Credit Valley Conservation (CVC) Authority, has facilitated long-term real-time water quality monitoring since 2010. Fundamental and exploratory statistical analyses were conducted to identify patterns, trends, and anomalies in key water quality parameters, including pH, specific conductivity, turbidity, dissolved oxygen (DO), chloride, water temperature ($T_{H_{2}O}^{°}$), air temperature ($T_{air}^{°}$), streamflow, and water level. Continuous wavelet transform and wavelet coherence techniques revealed significant temporal variations, with “1-day” periodicities for DO, pH, ($T_{H_{2}O}^{°}$), and ($T_{air}^{°}$) showing high power at a 95% confidence level against red noise, particularly from late spring to early fall, rather than throughout the entire year. These findings underscore the seasonal influence on water quality and highlight the need for adaptive watershed management strategies. The study demonstrates the potential of wavelet analysis in detecting temporal patterns and informing decision-making for sustainable water resource management in rapidly urbanizing mixed-land-use watersheds. Full article

This work details the development of a 10-stage solid-stage linear transformer driver (SSLTD) capable of producing 24 kV, 1 kA pulses with a rise-time of ∼10 ns utilizing SiC MOSFET switches. Throughout the development process, various design parameters were investigated for their influence on the LTD’s performance. Among these considerations was an evaluation of the behavior of several nanocrystalline magnetic core materials subject to high-voltage pulsed conditions, with an emphasis on minimizing energy losses. Another design parameter of interest lies in the physical layout of the LTD structure, particularly the diameter of the central stalk and the dielectric material, which together define the characteristics of the coaxial transmission line, as well as the overall height of each stage. The influence of each of these parameters was weighed to optimize the final design for fastest output pulse rise-time, highest efficiency, and cleanest output pulse waveform profile across varying load resistance. This work also introduces a pulsed reset technique, where repetition-rated burst testing was used to find the maximum operational frequency of the LTD without driving the magnetic cores into saturation. Full article

Conventional fish population sampling methods such as electrofishing and netting, pose risks to fish and are often restricted to small, shallow headwater streams—especially where endangered species may be present. Additionally, non-capture surveying (e.g., snorkelling) can disturb fish and make observation more challenging. This study evaluates the effectiveness and reproducibility of remote underwater video (RUV) surveys in a shallow (<0.5 m deep), freshwater stream. Additionally, fish response to disturbances (e.g., shadows, noise, surface disruptions) were characterized. Fish abundance was estimated by maxN (maximum number of individuals observed in a single frame) and used multiple cameras placed in the same habitat (pool). Findings indicated a high consistency in maxN when fish numbers were low (<5 individuals), with increasing variability at higher numbers (>15 individuals). This suggests that single camera setups can reliably detect minimum abundance. Fish responses to four disturbances (e.g., shadows, noise, surface disruptions, mink) were noted throughout. Typically, these responses were short-lived, with fish returning to pre-disturbance maxN values within minutes, with the most significant response to researcher-induced disturbance occurring immediately after RUV deployment. Overall, RUVs proved effective for passive, non-capture fish monitoring in shallow, sensitive habitats, producing replicable data with minimal impact caused by researcher disturbance. This technique can be added to our toolboxes for studying small-bodied fishes in challenging environments. Full article

The current study examined the impression management gap between sustainability and management reports, which in this study serves as a proxy for greenwashing in sustainability reporting. The study sample comprised 192 reports from 24 companies, covering the period 2020–2023. Impression management gaps were estimated across four dimensions—tone, analytical thinking, authenticity, and clout—using textual analysis with the Linguistic Inquiry Word Count software. Our findings reveal significant impression management gaps for tone, clout, and analytics dimensions throughout the entire research sample, confirming a more pronounced use of impression management techniques in sustainability reporting. Despite increasing regulatory pressure during the study period, the calculated gaps did not show signs of decreasing. The results further indicate a high likelihood of greenwashing in the Services and Manufacturing sectors, and a lower likelihood in the Utilities sector. Such gaps risk misleading stakeholders by shaping perceptions that diverge from a company’s actual sustainability practices. Our findings suggest that greenwashing can be effectively detected through textual analysis of disclosures, particularly when conducting comparative studies between different reports. Building on these results, we argue that targeted reporting standards, advanced assurance methodologies, and clearer boundaries for impression management are essential to curbing greenwashing and strengthening the integrity of sustainability communication. Full article

We present a novel approach for the synthesis of crystalline zinc oxide (ZnO) nanopowders based on the direct interaction of high-power microwave radiation with a zinc wire in atmospheric air. The process utilizes a localized microwave-induced plasma to rapidly vaporize the metal, followed by oxidation and condensation, resulting in the deposition of ZnO nanostructures on glass substrates. Plasma diagnostics confirmed the generation of a plasma in local thermodynamic equilibrium (LTE), characterized by high electron temperatures. Optical emission spectroscopy highlighted atomic species such as ZnI, ZnII, OI, OII, and NI, as well as molecular species including OH, N₂ and O₂. The spectral fingerprint of N₂ molecules reveals the presence of high energy electrons, while the persistent occurrence of OI and OII emission lines throughout the plasma spectrum reveals that ZnO formation is mainly driven by the continuous dissociation of molecular oxygen. High crystallinity and chemical purity of the synthesized ZnO nanoparticles were confirmed through SEM, TEM, XRD, FTIR, and EDX characterization. The resulting nanorods exhibit a rod-like morphology, with diameters ranging from 12 nm to 63 nm and lengths between 58 nm and 354 nm. This low-cost, high-yield method offers a scalable and efficient route for metal oxide nanomaterial fabrication via direct metal–microwave coupling, providing a promising alternative to conventional physical and chemical synthesis techniques. Full article

Background: A considerable proportion of breast cancer (BC) patients experience chemotherapy-related cognitive impairment (CRCI) and other symptoms even after the completion of treatment. The persistence of CRCI throughout the oncological process highlights the need for routine assessment of its severity, impact on quality of life, and the effectiveness of interventions aimed at addressing it. Objectives: To analyse the effectiveness of cognitive stimulation interventions on CRCI in BC patients and to identify the characteristics of such interventions, including the most appropriate timing for their implementation, the most suitable techniques, and their duration. Methodology: A systematic review and meta-analysis were conducted in accordance with PRISMA guidelines. Randomized controlled trials published between 1 January 2020 and 31 December 2024 were searched across three electronic databases. Studies involving cognitive stimulation interventions for the management of CRCI in BC patients were included. Results: A total of 12 eligible studies were identified for the systematic review and 10 for the meta-analysis. The review revealed a wide range of cognitive stimulation interventions, differing in techniques, duration, format, and timing of implementation. Group-based therapies lasting between 6 and 12 weeks predominated, with cognitive outcomes primarily assessed using the FACT-Cog scale. The meta-analysis demonstrated a moderate positive effect of cognitive stimulation interventions on cognitive functioning in BC patients (d = 0.59), although not statistically significant (p = 0.07), and showed high heterogeneity across studies (I² = 93%). Conclusions: Cognitive stimulation interventions show potential benefits in improving cognitive functioning in BC patients following chemotherapy. However, the high methodological heterogeneity limits the strength of the evidence. Further research is needed to develop standardized and personalized early intervention protocols. Full article

Background: Antimicrobial resistance (AMR) is a major public health concern. Urinary tract infections (UTIs) account for up to 85–90% of community-acquired cases. The COVID-19 pandemic disrupted healthcare access and may have influenced resistance patterns. In this context, we retrospectively evaluated the antibiotic resistance dynamics of various bacterial strains isolated between 2019 and 2023 in a hospital unit; Methods: A total of 8217 clinical specimens (urine, wound secretions, sputum, pharyngeal exudate, nasal exudate, tracheal secretions, vaginal and cervical secretions, puncture fluids, purulent secretions, blood, ear secretions, eye secretions) were processed using standard microbiological techniques. Pathogen identification and susceptibility testing were performed with the VITEK 2 Compact system, following CLSI guidelines. Results: Following the analysis of 8217 clinical samples collected over a five-year period (2019–2023), a total of 2900 microorganisms were isolated and identified. Among these, the most frequently encountered were E. coli strains, with 1204 isolates. Urine cultures represented 71.3% of all processed samples. Out of these 5860 urine cultures, 1530 (26%) were positive. The resistance of E. coli strains to ampicillin (48–55.2%), trimethoprim/sulfamethoxazole (22.9–34%), and ciprofloxacin (21.4–31.5%) remained high throughout the period. ESBL-producing strains peaked at 17.6% in 2020, with multidrug resistance rates ranging from 14% to 22.4%. Conclusions: E. coli strains displayed persistently high resistance to ampicillin, trimethoprim/sulfamethoxazole, and ciprofloxacin, with peaks in ESBL production and multidrug resistance during the COVID-19 pandemic. These trends underscore the importance of continuous surveillance and antibiotic stewardship, with direct implications for empirical UTI therapy and broader strategies to mitigate the public health impact of antimicrobial resistance. Full article

The application of thermal barrier coatings (TBCs) for protecting mechanical components is widespread, particularly in high-temperature environments, such as gas turbines and aero-engines. Ensuring the integrity of these coatings throughout their service life is essential, as their degradation can lead to delamination, ultimately compromising the underlying component. It has been demonstrated that monitoring the thermal diffusivity value over time allows the monitoring of degradation of the coatings. Common thermographic techniques like pulsed and lock-in thermography have been used so far. However, to enhance both the signal-to-noise ratio (SNR) and the accuracy of thermal property measurements, new active thermography techniques have been developed. These methods rely on optimized excitation schemes combined with advanced signal processing strategies. In this work, we first introduce the pulse-compression thermography approach, which employs pseudo-noise modulated excitation to monitor and estimate the thermal diffusivity of the coating layers. Full article

Artificial intelligence (AI) is rapidly redefining both computer science and cybersecurity by enabling more intelligent, scalable, and privacy-conscious systems. While most prior surveys treat these fields in isolation, this paper provides a unified review of 256 peer-reviewed publications to bridge that gap. We examine how emerging AI paradigms, such as explainable AI (XAI), AI-augmented software development, and federated learning, are shaping technological progress across both domains. In computer science, AI is increasingly embedded throughout the software development lifecycle to boost productivity, improve testing reliability, and automate decision making. In cybersecurity, AI drives advances in real-time threat detection and adaptive defense. Our synthesis highlights powerful cross-cutting findings, including shared challenges such as algorithmic bias, interpretability gaps, and high computational costs, as well as empirical evidence that AI-enabled defenses can reduce successful breaches by up to 30%. Explainability is identified as a cornerstone for trust and bias mitigation, while privacy-preserving techniques, including federated learning and local differential privacy, emerge as essential safeguards in decentralized environments such as the Internet of Things (IoT) and healthcare. Despite transformative progress, we emphasize persistent limitations in fairness, adversarial robustness, and the sustainability of large-scale model training. By integrating perspectives from two traditionally siloed disciplines, this review delivers a unified framework that not only maps current advances and limitations but also provides a foundation for building more resilient, ethical, and trustworthy AI systems. Full article

In light of the increasing focus on global climate change and environmental issues, countries around the world are collaboratively working towards the establishment of a low-carbon economy (LCE). As the most populous developing nation, China is proactively advocating for low-carbon economic development as a means to achieve sustainable growth. Nevertheless, the efficiency of the low-carbon economy (LCEE) exhibits considerable variation across different regions within China. This article seeks to explore the regional disparities in LCEE throughout the country and to identify the factors that contribute to these variations. Firstly, this paper examines the advancements in LCEE research, concentrating on an analysis of 30 Chinese provinces. Employing the Multi-directional Efficiency Analysis (MEA) framework alongside the global Malmquist (GM) index, this study evaluates the efficiency of the low-carbon economy across the 30 provinces from 2010 to 2021. Secondly, by integrating spatial autocorrelation analysis techniques, the research encompasses a multifaceted examination, including spatiotemporal analysis, regional disparities, driving factors, and potential for improvement. The findings indicate significant discrepancies in LCEE among various provinces in China. Notably, LCEE tends to be higher in the eastern coastal regions, attributed to their advanced economic development, whereas the western inland areas generally exhibit lower efficiency levels due to comparatively limited economic progress. Thirdly, LCEE exhibits significant spatial heterogeneity, with clear high–high and low–low clustering patterns, revealing systemic coordination gaps between eastern coastal and central/western regions. Fourthly, from the decomposition results of the global Malmquist index, it can be seen that efficiency change (EC) is less than 1 and technology change (TC) is greater than 1, which promotes the improvement of LCEE. Technical efficiency is the main factor affecting the improvement of LCEE. Full article

This study introduces a novel approach to correlation-based feature selection and dimensionality reduction in high-dimensional data structures. To this end, a customized scoring function is proposed, designed as a dual-objective structure that simultaneously maximizes the correlation with the target variable while penalizing redundant information among features. The method is built upon three main components: correlation-based preliminary assessment, feature selection via the tailored scoring function, and integration of the selection results into a t-SNE visualization guided by Rel/Red ratios. Initially, features are ranked according to their Pearson correlation with the target, and then redundancy is assessed through pairwise correlations among features. A priority scheme is defined using a scoring function composed of relevance and redundancy components. To enhance the selection process, an optimization framework based on stochastic differential equations (SDEs) is introduced. Throughout this process, feature weights are updated using both gradient information and diffusion dynamics, enabling the identification of subsets that maximize overall correlation. In the final stage, the t-SNE dimensionality reduction technique is applied with weights derived from the Rel/Red scores. In conclusion, this study redefines the feature selection process by integrating correlation-maximizing objectives with stochastic modeling. The proposed approach offers a more comprehensive and effective alternative to conventional methods, particularly in terms of explainability, interpretability, and generalizability. The method demonstrates strong potential for application in advanced machine learning systems, such as credit scoring, and in broader dimensionality reduction tasks. Full article

CO₂ flooding has become a strategic tool for enhanced oil recovery and reservoir management in mature fields. This technique, however, is rarely utilized in asphaltenic crude oil systems, due to the likely occurrence of high asphaltene precipitation. The effect of asphaltene concentrations and CO₂ injection pressures has mostly been the focus of studies in determining asphaltene precipitation rates. However, asphaltene precipitation is not the only direct factor to be considered in predicting the extent of damage in an asphaltenic crude oil system. In this study, a compositional reservoir simulation was conducted using Eclipse 300 to investigate the injection pressure at which asphaltene-induced formation damage can be avoided during both miscible and immiscible CO₂ flooding in an asphaltenic crude system. Simulation results indicate that asphaltene-induced permeability reduction exceeded 35% in most affected zones, with a corresponding drop in injectivity of 28%. Cumulative oil recovery improved by 19% compared to base cases without CO₂ injection, achieving peak recovery after approximately 4200 days of simulation time. As CO₂ was injected below the Minimum Miscibility Pressure (MMP) of 2079.2 psi, a significantly lower asphaltene precipitation was observed near the injector. This could be attributed to the stripping of lighter hydrocarbon components (C2–C7+) occurring in the transition zone at the gas–oil interface. Injecting CO₂ at pressures above the MMP resulted in precipitation occurring throughout the entire reservoir at 3200 psia and 1000 bbl/day injection rates. An increase in the injection rate at pressures above the MMP increased the rate of precipitation. However, a further increase in the injection rate from 1000 bbl/day to 4200 bbl/day resulted in a decrease in asphaltene deposition. The pressure drop in the water phase caused by pore throat increase demonstrated that water injection was effective in removing asphaltene deposits and restoring permeability. This work provides critical insights into optimizing CO₂ injection strategies to enhance oil recovery while minimizing asphaltene-induced formation damage in heavy oil reservoirs. Full article

The miR156 family, crucial for phase transition and stress responses in plants, remains functionally uncharacterized in the ecologically and commercially important gymnosperm Larix kaempferi. This study systematically investigated L. kaempferi miR156 through phylogenetic analysis, structural prediction, expression profiling during somatic embryogenesis, and heterologous functional validation in Arabidopsis. Four MIR156 family members (LkMIR156s) were identified in Larix kaempferi, each with a characteristic stem-loop structure and highly conserved mature sequences. Computational predictions indicated that these LkMIR156s target four LkSPL family genes (LkSPL1, LkSPL2, LkSPL3, and LkSPL9). qRT-PCR analysis showed that mature LkmiR156s expression remained relatively low during early embryonic development but was significantly upregulated at the cotyledonary stage (21–42 days). Precursor transcript levels peaked earlier (around 28 days) than those of the mature LkmiR156, which remained highly expressed throughout cotyledonary embryo development. This sustained high expression coincided with cotyledon morphogenesis and embryonic dormancy. Functional validation via heterologous overexpression of LkMIR156b1 in Arabidopsis resulted in increased rosette leaf numbers (42.86% ± 6.19%) and individual leaf area (54.90% ± 6.86%), phenotypically consistent with the established role of miR156 in growth regulation. This study reveals the temporal expression dynamics of LkmiR156s during L. kaempferi somatic embryogenesis and its coordinated expression patterns with cotyledon development and embryonic dormancy. The functional conservation of the miR156-SPL module was confirmed in a model plant, providing key molecular insights into the developmental regulatory network of conifers. These findings offer potential strategies for optimizing somatic embryogenesis techniques in conifer species. Full article

Waterline extraction is a key step in applying the Waterline Detection Method (WDM) to Digital Elevation Model (DEM) generation. Cloud interference remains a major challenge for achieving high-quality extraction of waterlines. This study developed an image filtering method termed “Cloud Coverage in Region of Interest” (CCROI). By integrating the CCROI method with the Otsu algorithm and noise smoothing techniques, this study enabled high-quality batch and automated extraction of waterlines within the Google Earth Engine (GEE) platform. Using the WDM, DEMs were established to evaluate recent geomorphological changes in the estuarine tidal flats of the abandoned Diaokou Course (ETFADC). The results confirm that the erosional trend of the ETFADC has persisted throughout nearly 50 years of natural adjustment. In areas distant from oil extraction zones, erosion dominates the high-tide zone, while accretion prevails in the low-tide zone, indicating a slope-flattening process. However, in areas near the oil extraction zone, tree-shaped embankments have acted to inhibit erosion rather than exacerbate it, with strong accretion even occurring in wave-sheltered areas. By enhancing the quality of the selected images and reducing the waterline false detection rate, the CCROI method demonstrates significant potential for time-series studies of small regions. Full article