Search Results (16,613)

This study aimed to isolate, characterize, and evaluate bacterial consortia capable of degrading the diamide insecticide cyantraniliprole in aqueous systems and to assess their bioremediation potential under environmentally relevant conditions. Four bacterial consortia, each comprising six isolates, demonstrated significant growth in mineral media containing cyantraniliprole as the sole carbon source, and the isolates were identified using conventional microbiological techniques in combination with MALDI-TOF-MS analysis. The bacterial consortia were enriched from pesticide-contaminated environments and systematically evaluated using microbiological, physiological, and analytical approaches to determine their degradation potential and environmental adaptability. The degradation performance of the consortia was systematically assessed under varying environmental parameters, including temperature, pH, salinity, and incubation time, with optimal degradation observed at 30–35 °C, pH 7.0–8.0, 0.5–5.0% NaCl, and 11 days of incubation at 150 rpm using an initial cyantraniliprole concentration of 50 mg/L. Biodegradation efficiency was further evaluated using DCPIP reduction assays, alongside measurements of biofilm formation and biomass production, indicating enhanced metabolic activity and adaptive responses under pesticide-induced stress. The consortia also exhibited the capacity to degrade structurally related diamide pesticides, including flubendiamide, chlorantraniliprole, cyclaniliprole, and fluchlordiniliprole, suggesting broad-spectrum biodegradation potential. Their performance was further validated in a simulated water microcosm system designed to mimic environmentally relevant contamination scenarios. In simulated contaminated water (60 mg/L cyantraniliprole), bacterial inoculants standardized to 10⁷ CFU/mL achieved substantial degradation after 20 days of incubation at 30 °C, as confirmed by HPLC analysis, with the six-strain consortium (T4), comprising Bacillus subtilis subsp. subtilis AZFS3, Bacillus pumilus AZFS5, Bacillus mojavensis AZFS15, Bacillus paramycoides AZFS18, Pseudomonas aeruginosa KZFS4, and Alcaligenes aquatilis KZFS11, demonstrating the highest removal efficiency (98.27%) and reducing the pesticide concentration to 1.00 mg/L, followed by consortium T3 (96.72%), which consisted of Bacillus subtilis Ht1, Bacillus subtilis Ht2, Bacillus mojavensis Ht3, Pseudomonas aeruginosa Ht4, Pseudomonas aeruginosa Ht5, and Pseudomonas aeruginosa Ht6. Residue analysis and predictive bioinformatic assessment further supported the biodegradation capacity of the selected bacterial communities and suggested the formation of simpler transformation products. Overall, the investigated bacterial consortia exhibited high degradation efficiency and environmental adaptability, highlighting their potential as effective and eco-friendly agents for the bioremediation of cyantraniliprole-contaminated water systems Full article

Climate change has increased the frequency of extreme weather events, posing a major threat to the sustainable development of agriculture and farmers’ welfare. Based on provincial meteorological data and China Family Panel Studies (CFPS) data from 2014 to 2022, this study systematically investigates the impact of climate shocks on farmers’ welfare, heterogeneity characteristics, and the buffering role of off-farm employment, using a two-way fixed-effect model. The results show that climate shocks significantly reduce farmers’ welfare, with greater welfare losses in northern regions, major grain-producing areas, and plain areas. Extreme low temperatures, extreme high temperatures, and drought are the three dominant climate hazards. In response to climate shocks, off-farm employment effectively buffers welfare losses. This study clarifies the logic of changes in farmers’ welfare and livelihood adaptation mechanisms under climate change, providing micro-empirical support for improving differentiated climate adaptation policies, strengthening agricultural risk management systems, enhancing agricultural system resilience, and promoting high-quality and sustainable agricultural development. However, constrained by the matching precision between micro-level data and meteorological indicators, future research should further refine the measurement of climate shock exposure at the individual farmer level. Full article

This paper presents a primary side model predictive control (MPC) strategy for wireless power transfer (WPT) based charging of light electric vehicle (LEVs). A battery simulator develops a model to accurately reproduce constant-current (CC) charging profile from Open Ciruit Voltage (OCV) and State of Charge (SoC) parameters of the battery. This model forms the foundation of the predictive control design, allowing accurate prediction of the charging trajectory while avoiding reliance on secondary-side feedback signals. The WPT system employs a phase-shifted full-bridge (PSFB) inverter with S-S compensation, where the primary-side controller regulates the secondary-side charging current using only primary-side current measurements. In contrast to conventional secondary side control, which is tuned around nominal coupling, requires explicit feedback, and degrades under coil misalignment and parameter variations, the proposed MPC leverages integrated system and battery models to predict future states and optimally adjust the phase shift for robust charging operation. Simulation and experimental validation on a real-time LEV charging prototype under aligned, lateral, and angular misalignment conditions demonstrate significant reduction in current-settling time compared to fixed-gain proportional-integral (PI) and known adaptive feedback controllers for same system, with lower RMS current and reduced current spikes at the battery. On the embedded controller, the proposed MPC executes within approximately 1 µs per 85 kHz PWM cycle, corresponding to less than 10% CPU utilization, confirming its practical real-time feasibility. Full article

The 3-30-300 rule, proposed by Cecil Konijnendijk, is oriented towards the design of greener cities. However, subsequent literature has revealed some application limits due to overly simple definitions (visibility of 3 trees), fixed thresholds (30% tree cover) and theoretical distances (300 m to the park) that do not consider ecological quality, real green area proximity and possible socio-demographic differences. The present research attempts to overcome these limitations through the elaboration of a scalable composite index that, starting from the original rule, integrates ecological, infrastructural and population variables to give a more robust measure of the availability and usability of urban green. The index was tested in the study area of the urban centre of Ferrara (Italy). Three sub-indices were calculated for each building: Indicator 3—Visibility (I3), Indicator 30—Tree cover (I30), and Indicator 300—Green area proximity (I300). Once normalized and weighted, the three indicators were aggregated into a composite index conceived as a scalable and replicable framework adaptable to diverse urban settings. By spatially integrating population data, the methodology explicitly embeds the distributional dimension of climate justice, supporting evidence-based adaptation strategies and equitable urban regeneration policies. Moving beyond the binary logic of the original 3-30-300 rule, the approach provides an operational decision-support tool to detect intra-urban inequalities, to address just green transitions and to monitor urban greening interventions over time. Full article

In underwater navigation, MUVs risk damage from obstacles and equipment. Effective damage management supports timely decisions and maximizes functionality recovery. Existing approaches can be roughly categorized into rule-based reasoning, case-based reasoning and expert systems. However, the primary limitation of the existing approaches is their inability to adapt to dynamically changing scenarios. In this paper, an auxiliary decision-making system (ADMS) for manned underwater vehicle (MUV) damage management based on deep reinforcement learning (DRL) is proposed to address the problem of cabin flooding. This system is designed to provide auxiliary decision-making in emergency situations and help preserve MUV vitality. Furthermore, a comprehensive States–Actions cluster encompassing various damage management measures for real damage scenarios is constructed and digitized. Moreover, several novel reward functions are developed to ensure the DRL model obtains a safe strategy with ADMS operations. Finally, the MUV buoyancy and stability vitality evaluation criteria are defined and analyzed. The simulation results show that the auxiliary decision-making measures given by the ADMS in the damage state are effective and rational. The evaluation criterion for buoyancy vitality can exceed 38%, while the criterion for stability vitality can surpass 92%, with an optimal value exceeding 99%. Full article

Informal urban settlements grow through incremental and adaptive processes, yet the temporal logic through which their access networks emerge, endure, and consolidate has received relatively little systematic attention. This paper examines the configurational development of the access network in Likoni, Mombasa, where rapid informal urbanisation has transformed an area containing only sparse footpaths into a dense urban network over two decades. Using historical satellite imagery, the study mapped five temporal states of access network for 2006, 2011, 2016, 2021, and 2026. The study utilises Space Syntax angular segment analysis. The analysis combines measures of angular connectivity, segment length, global and local integration, global and local choice, intelligibility, and synergy. The study aims to address three main questions: whether early informal footpaths persisted as the structural basis of later development of access network, whether subsequent growth strengthened local or global accessibility, and whether densification improved the overall configurational accessibility and legibility of the system as a whole. The results indicate that a finer-grained and more locally integrated network was produced through subdivision, densification, and the multiplication of short connecting segments. However, the gains were uneven across scales. Global integration and choice remained concentrated along a limited set of inherited and edge-related corridors, while local integration and local choice spread more widely through the settlement. The paper argues that the development of Likoni is a process of selective consolidation. Early footpaths became a persistent movement skeleton, forming the subsequent major paths of the later stages of the settlement. Later growth intensified local accessibility—albeit, as demonstrated through Space Syntax analysis rather than direct observation of movement—without necessarily producing notable improvements in global integration or whole-system configurational intelligibility. This finding adds a temporal and syntactic dimension to the understanding of informal morphogenesis. Full article

Spinal cord stimulation (SCS) is an established therapy for chronic pain, yet treatment response remains highly variable and patient selection largely empirical. The identification of biomarkers with the potential to predict and monitor therapeutic response is therefore critical for advancing toward precision neuromodulation. This study provides a structured narrative synthesis of current evidence on biomarkers in SCS, focusing on their predictive and monitoring roles and their translational potential. Available studies were analysed across electrophysiological, neuroimaging, autonomic, and molecular domains and conceptually organized into predictive biomarkers—reflecting baseline biological states associated with treatment susceptibility—and monitoring biomarkers, capturing physiological and molecular adaptations following stimulation. Among predictive approaches, intraoperative electroencephalography (EEG) and resting-state functional magnetic resonance imaging (rs-fMRI) have shown promising but exploratory discriminative performance. However, EEG findings are derived from intraoperative settings, limiting their applicability to pre-implantation patient selection. In contrast, monitoring biomarkers—including heart rate variability, metabolic imaging, and immunological parameters—provide objective measures of treatment-induced changes but do not currently support predictive use. Molecular and genomic biomarkers, while mechanistically informative, remain exploratory and lack validated clinical utility. A central limitation of the field is the fragmentation of biomarker research, with most studies evaluating single modalities in isolation. To address this gap, we propose a translational framework integrating predictive and monitoring biomarkers through a two-stage model combining baseline stratification with longitudinal response assessment. Although biomarker research in SCS is rapidly evolving, its clinical application remains limited. The development of multimodal, validated biomarker strategies may support improved patient selection and more objective evaluation of treatment response, enabling a transition toward mechanism-based neuromodulation. Full article

Managed aquifer recharge (MAR) is increasingly being realized as an important approach to improve water security in arid and semi-arid environments where there is a low amount of surface water and high climatic variability. This paper introduces a unified approach to the process of locating appropriate MAR locations and estimating recharge potential in Central Kazakhstan through a multi-criteria analysis using geographic information systems (GIS) and hydrogeological field exploration, water balance modelling. Remote sensing datasets and evapotranspiration (ET) analyses were conducted for the 2014–2024 period, while field investigations, infiltration tests, and hydrochemical sampling were performed during the 2025 field campaign. The suitability testing was preliminarily performed in the Google Earth Engine (GEE; Google LLC, Mountain View, CA, USA) environment as a weighted overlay test with the combination of terrain, vegetation, hydrological, and land cover parameters. According to the suitability map obtained and patterns of activity in agricultural activities, eleven candidate sites were identified, out of which eight were found to be suitable after hydrochemical analysis. The Nesterov and Boldyrev techniques of field-based infiltration tests produced a range of 0.05 to 1.42 m/day of hydraulic conductivity. Water balance analysis shows that the total amount of water that could potentially be added to groundwater recharge is about 40.2 million m³/year and that the effective amount of water could be recharged is about 11.0 million m³/year, which is limited by the infiltration processes. This means that about 27 percent of the available water is added into ground water recharge, which is a significant boost to the original estimates. The assessment of the storage capacity of the aquifers indicates that at all locations, the pore space is much greater than the recharge volumes that have been calculated and, therefore, storage is not a limiting factor in the implementation of MAR. It is estimated that the potential MAR rates range between 174 and 5282 m³/day depending on local hydrogeological conditions. The suggested method offers a powerful and generalizable site selection and measurement framework of MAR in arid areas with limited data. The findings highlight the significance of combining remote sensing, field measurements, and process-based modeling to aid sustainable groundwater management and climate adaptation strategies. Full article

Background: Giant retroperitoneal schwannomas with vertebral body erosion are exceedingly rare, and the decision regarding spinal instrumentation following tumor resection remains controversial in the absence of established guidelines. A 25% vertebral body involvement threshold has been proposed as an indication for fixation, yet this criterion does not account for bone quality or the potential biological adaptation of bone to chronic mechanical loading. Case Presentation: A 56-year-old man presented with bilateral gluteal pain and urinary urgency secondary to a giant retroperitoneal lumbar schwannoma (97 × 67 mm) with 36.6% erosion of the L5 vertebral body, confirmed by CT-guided biopsy (S100+, SOX10+, Ki-67 < 5%). Despite erosion exceeding the proposed instrumentation threshold, complete tumor resection was performed via an anterior laparotomic approach without spinal fixation, based on the absence of clinical or radiological signs of instability and the integrity of the intervertebral disc and posterior ligamentous complex. Intraoperative neurophysiological monitoring guided sacrifice of the tumor-origin root. The postoperative course was uneventful, with complete resolution of symptoms and no new complaints or neurological deficits at one-year follow-up. Conclusions: Post-hoc Hounsfield Unit measurements on pre-operative CT demonstrated markedly elevated bone density at the eroded L5 vertebral body (480 HU) compared with the adjacent L4 vertebra (317 HU), consistent with compensatory sclerosis induced by chronic mechanical compression. Pre-operative HU assessment may represent a valuable, readily available adjunct to anatomical erosion criteria in the surgical decision-making process for giant schwannomas with vertebral body involvement. Full article

This study investigates the “competence–performance gap” between cognitive ability (measured by the WISC-IV) and actual adaptive performance (measured by the ABAS-II) in children with autism spectrum disorder (ASD), and examines the moderating role of family environment, specifically parental education levels. We applied Latent Profile Analysis (LPA) to cross-sectional data from 3246 children with ASD (aged 6–16 years). The analysis identified three distinct cognitive–adaptive subgroups: the Balanced High-Functioning group (33%), the Classic Mismatch group (44%), and the Cognitively Vulnerable group (23%). Notably, the Classic Mismatch group was characterized by adaptive performance that significantly trailed cognitive potential. Multinomial logistic regression revealed that maternal education—but not paternal education—significantly predicted a child’s likelihood of being in the “Balanced High-Functioning” group. This moderating effect was especially pronounced during the school-age years. These findings highlight the critical role of environmental factors in the translation of intellectual potential into practical social adaptive functioning, providing theoretical support for targeted family-based interventions. Full article

Maintaining good indoor air quality (IAQ) is essential for student health, cognitive performance, and overall well-being. Traditional ventilation strategies, particularly constant air volume systems and manual window operation, often fail to maintain optimal IAQ while simultaneously increasing building energy consumption. In response, smart ventilation systems have emerged as a promising alternative capable of dynamically modulating airflow based on occupancy patterns and real-time pollutant levels. This study presents a systematic review of fourteen carefully selected peer-reviewed studies (2015–2025) that represent the most recent and methodologically robust research on smart ventilation applications in school environments across diverse climatic conditions. The selected studies encompass experimental, simulation-based, and hybrid methodologies, and classify control strategies into demand-controlled, temperature-adaptive, occupancy-based, AI-enhanced, and building management system (BMS)-integrated approaches. Collectively, the findings demonstrate measurable improvements in IAQ indicators (e.g., carbon dioxide (CO₂), particulate matter (PM_2.5), ozone (O₃), and volatile organic compounds (VOCs)) and significant energy savings, in some cases exceeding 60%, while also identifying system vulnerabilities such as fault sensitivity, short monitoring durations, and limited long-term validation. Importantly, the review reveals critical geographic and climatic research gaps, particularly in hot–arid regions where ventilation-related cooling demand is substantial, as well as limited long-term assessments in cold climates. Furthermore, although smart ventilation systems perform effectively under controlled conditions, insufficient real-world verification, user interaction analysis, and climate-specific optimization constrain broader implementation. Addressing these gaps through climate-dependent performance evaluation and long-term operational studies is essential to unlocking the full potential of smart ventilation systems in delivering healthier, energy-efficient classrooms. Full article

Objectives: This study evaluated the utility of adaptive complex signal average (ACSA) diffusion-weighted imaging (DWI) specifically in breath-hold (BH) liver imaging, with a focus on signal intensity (SI) improvement, intrahepatic signal homogeneity, and apparent diffusion coefficient (ADC) behavior, and compared these findings with conventional non-ACSA DWI and free-breathing (FB) ACSA DWI. Methods: This retrospective study included 62 patients (mean age, 67.8 ± 13.6 years; 27 women) who underwent liver MRI with both FB and BH DWI on a 3-T system. Non-ACSA images were generated using conventional magnitude reconstruction, and ACSA images were reconstructed from identical raw data. SI, signal-to-noise ratio (SNR) and ADC were measured in the left lateral segment and right hepatic lobe. The signal intensity difference ratio (SIDR) between ACSA and non-ACSA, signal intensity ratio (SIR) and ADC ratio between right lobe and lateral segment were calculated. Results: In both FB and BH imaging, SI and SNR in both liver regions were significantly higher on ACSA DWI than on non-ACSA DWI (p < 0.01). ADC values were significantly lower with ACSA. SIDR was significantly higher in the left lateral segment (p < 0.01), indicating greater SI improvement in motion-prone regions. SIR and ADC ratios between lobes were significantly smaller with ACSA in both respiratory conditions (p < 0.01). FB-ACSA showed smaller SIR than BH-ACSA, while ADC ratios did not differ. Conclusions: ACSA DWI significantly improves SI, intrahepatic uniformity, and ADC reliability even under BH liver imaging. BH ACSA DWI may represent a potentially useful application complementary to FB ACSA DWI, supporting its consideration as a post-processing strategy for improving qualitative and quantitative liver DWI in future investigations. Full article

This review paper presents a comprehensive synthesis of current scientific knowledge on the integration of low-emission technologies into airport operational models. Attention is also given to the role of artificial intelligence techniques in predicting environmental risks, optimizing energy system design, and enhancing operational safety. The primary objective of the study is to evaluate the synergy between renewable energy sources (solar and wind energy) and emerging propulsion technologies in aviation (hydrogen and electrification) from the perspective of safety and operational stability. The methodology is based on a systematic review of 78 scientific studies identified in the Scopus and Web of Science databases. The analysis identifies critical technical and operational barriers, including electromagnetic interference caused by wind turbines, optical hazards associated with photovoltaic systems, and stability challenges in airport microgrids under peak loads resulting from the charging of electric aircraft. Particular attention is given to the safety of hydrogen infrastructure, where findings from the literature indicate the need to revise separation distances and highlight the potential reduction of airport stand capacity by 5% to 16%. The study synthesizes these findings into a strategic framework for “Smart Green Airports”, proposing solutions such as adaptive infrastructure design, the deployment of predictive models based on artificial intelligence, and the implementation of inherently safe energy storage systems. The paper concludes that achieving airport energy self-sufficiency while maintaining the integrity of flight operations is feasible only through the holistic integration of technical measures, simulation-based planning, and strict compliance with updated safety regulations. Full article

Across much of the Arctic, climate warming has reduced the extent of thicker and more persistent sea ice and increased the prevalence of thinner first-year ice. Thin first-year landfast sea ice is ecologically important because reduced ice thickness can increase light transmission to the ice–water interface, while the associated brine conditions, including salinity and permeability, can strongly influence algal biomass accumulation and photophysiology. This thin (0.24–0.55 m), short-lived, seasonal, first-year landfast sea ice already dominates Nuup Kangerlua fjord, southwest Greenland, making it a useful natural example of ice conditions that may become more common in parts of the future Arctic. We focused on late February–early March because this period captures the seasonal transition from very low winter irradiance toward increasing spring light, when sea ice algal communities begin photosynthetic acclimation prior to the main bloom period. Using this site as an example of future Arctic-like conditions, we investigated chlorophyll a (Chl a) concentration and the photobiology of sea ice algal communities during five sampling events between 2017 and 2022. The vertical distribution of Chl a concentration and photobiological parameters measured with variable chlorophyll fluorescence differed between years, as did Chl a concentrations, with integrated biomass ranging from 0.08 to 0.78 mg Chl a m⁻². Direct under-ice PAR measurements showed transmittance values ranging from 0.013 to 0.29. Bottom-ice communities were acclimated to relatively high light intensities, with E_k often exceeding 200 µmol photons m⁻² s⁻¹, and we detected no clear evidence of photoinhibition in the fluorescence data. Boosted regression tree models identified brine salinity as the main predictor of both Chl a concentration, explaining 42.0% of the variation, and, Φ_{PSII_max}, the maximum dark-adapted photosynthetic efficiency, explaining 86.1% of the variation. Both parameters decreased exponentially with increasing sea ice brine salinity (p < 0.0001), indicating that higher brine salinity was associated with reduced algal biomass and lower photosynthetic efficiency. These results show that short-lived first-year landfast sea ice can support physiologically active sea ice algal communities despite relatively low biomass, and suggest that algal performance in this ice type was more strongly associated with brine salinity during the late-winter to early spring sampling period, while light availability also varied substantially among years. As thin and short-lived sea ice conditions become more common in parts of the Arctic, this habitat may represent an increasingly important, though temporally variable, component of Arctic marine primary production. Full article

The mining industry faces complex operational hazards that necessitate systematic risk assessments to enable proactive accident prevention. While Large Language Models (LLMs) offer significant potential for the automated generation of safety measures, the limited availability of domain-specific terminology and high-quality labelled safety data (in low-resource environments) hinders their direct application. This study investigates and optimises data augmentation strategies to fine-tune LLMs to generate accurate, context-sensitive safety measures from structured coal mine risk records. The study systematically explored four experimental configurations, leveraging the Mistral-7B-Instruct model in conjunction with Quantised Low-Rank Adaptation (QLoRA) for efficient fine-tuning. These configurations comprised: (i) a baseline without augmentation, (ii) input-side lexical augmentation, (iii) output-side multi-reference augmentation, and (iv) a combined strategy. Performance was measured using BLEU, ROUGE, METEOR, and BERTScore metrics, along with statistical significance testing and qualitative analyses. The results show that, compared to other strategies, the input-side data augmentation strategy performs better. The findings indicate that input-side data augmentation yields significant improvements; this strategy increased the BERTScore (F1) from 0.360 to 0.530 and the BLEU score from 16.02 to 29.50 compared to the baseline model. In contrast, output-side multi-reference augmentation contributed to greater learning uncertainty and a consequent decline in performance. Statistical and qualitative analyses confirm that increasing input variety minimises model overfitting and enables the model to generate consistent, applicable, domain-specific safety measures. The proposed methodology provides a highly scalable solution for automated risk management in high-risk industrial environments, such as mining, offering a reliable, data-driven decision-support mechanism that minimises the limitations of manual review. Full article