Search Results (20,031)

To improve collision risk prediction in high-traffic coastal waters and support real-time decision-making in maritime navigation, this study proposes a regional collision risk prediction system integrating the Computed Distance at Collision (CDC) method with an Adaptive Neuro-Fuzzy Inference System (ANFIS). Unlike Distance at Closest Point of Approach (DCPA), which depends on the position of Global Positioning System (GPS) antennas, Computed Distance at Collision (CDC) directly reflects the actual hull shape and potential collision point. This enables a more realistic assessment of collision risk by accounting for the hull geometry and boundary conditions specific to different ship types. The system was designed and validated using ship motion simulations involving bulk and container ships across varying speeds and crossing angles. The CDC method was used to define collision, almost-collision, and near-collision situations based on geometric and hydrodynamic criteria. Subsequently, the FIS–CDC model was constructed using the ANFIS by learning patterns in collision time and distance under each condition. A total of four input variables—ship speed, crossing angle, remaining time, and remaining distance—were used to infer the collision risk index (CRI), allowing for a more nuanced and vessel-specific assessment than traditional CPA-based indicators. Simulation results show that the time to collision decreases with higher speeds and increases with wider crossing angles. The bulk carrier exhibited a wider collision-prone angle range and a greater sensitivity to speed changes than the container ship, highlighting differences in maneuverability and risk response. The proposed system demonstrated real-time applicability and accurate risk differentiation across scenarios. This research contributes to enhancing situational awareness and proactive risk mitigation in Maritime Autonomous Surface Ship (MASS) and Vessel Traffic System (VTS) environments. Future work will focus on real-time CDC optimization and extending the model to accommodate diverse ship types and encounter geometries. Full article

The siphon effect between airports has amplified the polarization in passenger throughput, undermining the balanced development and sustainability of airport clusters. The airport siphon effect occurs when one airport attracts a disproportionate share of passengers, concentrating traffic at the expense of others, which affects the overall resilience of the entire airport cluster. To address this issue, this study proposes a siphon index, expands the range of ground transportation options for passengers, and establishes a zero-siphon model to assess the impact of siphoning on the resiliency of airport clusters. Using this framework, four major airport clusters in China were selected as research subjects, with regional aviation accessibility serving as a measure of resilience. The results showed that among the four airport clusters, the siphon effect is most pronounced in the Guangzhou region. To explore the implications of this effect further, three airport disruption scenarios were simulated to assess the resilience of the Pearl River Delta airport cluster. The results indicated that the intensity and timing of disruptive events significantly affect airport cluster resilience, with hub airports being particularly sensitive. This study analyzes the risks associated with excessive route concentration, providing policymakers with critical insights to enhance the sustainability, equity, and resilience of airport clusters. The proposed strategies facilitate coordinated infrastructure development, optimized air–ground intermodal connectivity, and risk mitigation. These measures contribute to building more sustainable and adaptive aviation networks in rapidly urbanizing regions. Full article

Objective: To develop and validate a ResNet-50-based deep learning model for automatic detection of osteomyelitis (DFO) in plain radiographs of patients with diabetic foot ulcers (DFUs). Research Design and Methods: This retrospective study included 168 patients with type one or type two diabetes and clinical suspicion of DFO confirmed via a surgical bone biopsy. An experienced clinician and a pretrained ResNet-50 model independently interpreted the radiographs. The model was developed using Python-based frameworks with ChatGPT assistance for coding. The diagnostic performance was assessed against the histopathological findings, calculating sensitivity, specificity, the positive predictive value (PPV), the negative predictive value (NPV), and the likelihood ratios. Agreement between the AI model and the clinician was evaluated using Cohen’s kappa coefficient. Results: The AI model demonstrated high sensitivity (92.8%) and PPV (0.97), but low-level specificity (4.4%). The clinician showed 90.2% sensitivity and 37.8% specificity. The Cohen’s kappa coefficient between the AI model and the clinician was −0.105 (p = 0.117), indicating weak agreement. Both the methods tended to classify many cases as DFO-positive, with 81.5% agreement in the positive cases. Conclusions: This study demonstrates the potential of IA to support the radiographic diagnosis of DFO using a ResNet-50-based deep learning model. AI-assisted radiographic interpretation could enhance early DFO detection, particularly in high-prevalence settings. However, further validation is necessary to improve its specificity and assess its utility in primary care. Full article

Main roads are usually equipped with traffic flow monitoring devices in the road network to record the traffic flow data of the main roads in real time. Three complex scenarios, i.e., Y-junctions, multi-lane merging, and signalized intersections, are considered in this paper by developing a novel modeling system that leverages only historical main-road data to reconstruct branch-road volumes and identify pivotal time points where instantaneous observations enable robust inference of period-aggregate traffic volumes. Four mathematical models (I–IV) are built using the data given in appendix, with performance quantified via error metrics (RMSE, MAE, MAPE) and stability indices (perturbation sensitivity index, structure similarity score). Finally, the significant traffic flow change points are further identified by the PELT algorithm. Full article

The habenula (Hb) of the epithalamus is formed of the medial (MHb) and lateral (LHb) parts. The improper functioning of the Hb may lead to depression and anxiety. The glutamate excitotoxicity is accompanied by astroglia reactivity and leads to the damage of nervous system structures. The aim of the study was to assess the influence of monosodium glutamate (MSG) administrated subcutaneously to rats in doses of 2 g/kg b.w. (I) and 4 g/kg b.w. (II), on astroglia in the MHb and LHb. Based on immunohistochemical reactions, the morphology, number of astrocytes immunoreactive for glial fibrillary acidic protein (GFAP-IR) and S100β protein (S100β-IR), and their surface area, perimeter, number and length of processes, and cytoplasmic-nuclear immunostaining intensity for the studied proteins were assessed. In the MHb of animals receiving MSG, especially at a high dose, hypertrophy and an increase in the number of GFAP-IR and S100β-IR cells were demonstrated. In the LHb, only hypertrophy of processes in S100β-positive glia was observed. The immunostaining intensity increased in GFAP-IR glia and decreased in S100β-IR cells only in animals from group I. The results revealed that astroglia respond to MSG depending on its dose and the Hb part. This different behavior of glia may indicate their different sensitivity and resistance to damaging factors. Full article

The Complementary Relationship (CR) principle of evapotranspiration provides an efficient approach for estimating actual evapotranspiration (ET_a), owing to its simplified computation and effectiveness in utilizing meteorological factors. Accurate estimation of actual evapotranspiration (ET_a) is crucial for understanding surface energy and water cycles, especially in permafrost regions. This study aims to evaluate the applicability of two Complementary Relationship (CR)-based methods—Bouchet’s in 1963 and Brutsaert’s in 2015—for estimating ETa on the Qinghai–Tibetan Plateau (QTP), using observations from Eddy Covariance (EC) systems. The potential evapotranspiration (ET_p) was calculated using the Penman equation with two wind functions: the Rome wind function and the Monin–Obukhov Similarity Theory (MOST). The comparison revealed that Bouchet’s method underestimated ET_a during frozen soil periods and overestimated it during thawed periods. In contrast, Brutsaert’s method combined with the MOST yielded the lowest RMSE values (0.67–0.70 mm/day) and the highest correlation coefficients (r > 0.85), indicating superior performance. Sensitivity analysis showed that net radiation (Rn) had the strongest influence on ET_a, with a daily sensitivity coefficient of up to 1.35. This study highlights the improved accuracy and reliability of Brutsaert’s CR method in cold alpine environments, underscoring the importance of considering freeze–thaw dynamics in ET modeling. Future research should incorporate seasonal calibration of key parameters (e.g., ε) to further reduce uncertainty. Full article

Natural fiber-based biocomposites are rapidly gaining traction in sustainable manufacturing. However, their supply chain (SC) designs at the feedstock pre-processing stage often lack robust multicriteria decision-making evaluations, which can impact downstream processes and final product quality. This case study proposes a sustainability-driven multicriteria decision-making framework for selecting pre-processing equipment configurations within a hemp-based biocomposite SC. Using a cradle-to-gate system boundary, four alternative configurations combining balers (square vs. round) and hammer mills (full-screen vs. half-screen) are evaluated. The analytical network process (ANP) model is used to evaluate alternative SC configurations while capturing the interdependencies among environmental, economic, social, and technical sustainability criteria. These criteria are further refined with the inclusion of sub-criteria, resulting in a list of 11 key performance indicators (KPIs). To evaluate ranking robustness, a non-linear programming (NLP)-based sensitivity model is developed, which minimizes the weight perturbations required to trigger rank reversals, using an IPOPT solver. The results indicated that the Half-Round setup provides the most balanced sustainability performance, while Full-Square performs best in economic and environmental terms but ranks lower socially and technically. Also, the ranking was most sensitive to the weight of the system reliability and product quality criteria, with up to a 100% shift being required to change the top choice under the ANP model, indicating strong robustness. Overall, the proposed framework enables decision-makers to incorporate uncertainty, interdependencies, and sustainability-related KPIs into the early-stage SC design of bio-based composite materials. Full article

This study aims to develop an optimal underwriting strategy for affordable (H1 and M1) and premium (L1 and M2) electric vehicles (EVs), balancing financial risk and sustainability commitments. The research is motivated by regulatory pressures, risk management needs, and sustainability goals, necessitating an adaptation of traditional underwriting models. The study employs a modified Delphi method with industry experts to identify key risk factors, including accident risk, repair costs, battery safety, driver behavior, and PCAF carbon impact. A sensitivity analysis was conducted to examine premium adjustments under different risk scenarios, categorizing EVs into four risk segments: Low-Risk, Low-Carbon (L1); Medium-Risk, Low-Carbon (M1); Medium-Risk, High-Carbon (M2); and High-Risk, High-Carbon (H1). Findings indicate that premium EVs (L1 and M2) exhibit lower volatility in underwriting costs, benefiting from advanced safety features, lower accident rates, and reduced carbon attribution penalties. Conversely, budget EVs (H1 and M1) experience higher premium fluctuations due to greater accident risks, costly repairs, and higher carbon costs under PCAF implementation. The worst-case scenario showed a 14.5% premium increase, while the best-case scenario led to a 10.5% premium reduction. The study recommends prioritizing premium EVs for insurance coverage due to their lower underwriting risks and carbon efficiency. For budget EVs, insurers should implement selective underwriting based on safety features, driver risk profiling, and energy efficiency. Additionally, incentive-based pricing such as telematics discounts, green repair incentives, and low-carbon charging rewards can mitigate financial risks and align with net-zero insurance commitments. This research provides a structured framework for insurers to optimize EV underwriting while ensuring long-term profitability and regulatory compliance. Full article

Soil respiration (Rt), consisting of heterotrophic (Rh) and autotrophic respiration (Ra), plays a vital role in terrestrial carbon cycling and is sensitive to soil temperature and moisture. In dryland agriculture, plastic film mulching (PM) is widely used to regulate soil hydrothermal conditions, but its effects on Rt components and their temperature sensitivity (Q₁₀) across regions remain unclear. A two-year field study was conducted at two rain-fed maize sites: Anding (warmer, semi-arid) and Yuzhong (colder, drier). PM significantly increased Rt, Rh, and Ra, especially Ra, due to enhanced root biomass and improved microclimate. Yield increased by 33.6–165%. Peak respiration occurred earlier in Anding, aligned with maize growth and soil temperature. PM reduced Q₁₀ of Rt and Ra in Anding, but only Ra in Yuzhong. Rh Q₁₀ remained stable, indicating microbial respiration was less sensitive to temperature changes. Structural equation modeling revealed that Rt and Ra were mainly driven by soil temperature and root biomass, while Rh was more influenced by microbial biomass carbon (MBC) and dissolved organic carbon (DOC). Despite increased CO₂ emissions, PM improved carbon emission efficiency (CEE), particularly in Yuzhong (+67%). The application of PM is recommended to enhance yield while optimizing carbon efficiency in dryland farming systems. Full article

Background and Aim: Artificial intelligence (AI) is among the most promising innovations for transforming nursing education, making it more interactive, personalized, and competency-based. However, its integration also raises significant ethical and practical concerns. This scoping review aims to analyze and summarize key studies on the application of AI in university-level nursing education, focusing on its benefits, challenges, and future prospects. Methods: A scoping review was conducted using the Population, Concept, and Context (PCC) framework, targeting nursing students and educators in academic settings. A comprehensive search was carried out across the PubMed, Scopus, and Web of Science databases. Only peer-reviewed original studies published in English were included. Two researchers independently screened the studies, resolving any disagreements through team discussion. Data were synthesized narratively. Results: Of the 569 articles initially identified, 11 original studies met the inclusion criteria. The findings indicate that AI-based tools—such as virtual simulators and ChatGPT—can enhance students’ learning experiences, communication skills, and clinical preparedness. Nonetheless, several challenges were identified, including increased simulation-related anxiety, potential misuse, and ethical concerns related to data quality, privacy, and academic integrity. Conclusions: AI offers significant opportunities to enhance nursing education; however, its implementation must be approached with critical awareness and responsibility. It is essential that students develop both digital competencies and ethical sensitivity to fully leverage AI’s potential while ensuring high-quality education and responsible nursing practice. Full article

The most prevalent growth of Candida cells is based on biofilm development, which causes the intensification of antifungal resistance against a large range of chemicals. Nanoparticles can be synthesized using green methods via various biological extracts and reducing agents to control Candida biofilms. This study aims to compare copper oxide nanoparticles (CuONPs) synthesized through chemical methods and those synthesized using Cinnamomum verum-based green methods against Candida infections and their biofilms isolated from Iraqi patients, with the potential to improve treatment outcomes. The physical and chemical properties of these nanoparticles were characterized using Fourier-transform infrared spectroscopy (FT-IR,) scanning electron microscopy (SEM), transmission electron microscopy (TEM), atomic force microscopy (AFM) and X-ray diffraction (XRD). Four strains of Candida were isolated and characterized from Iraqi patients in Tikrit Hospital and selected based on their ability to form biofilm on polystyrene microplates. The activity of green-synthesized CuONPs using cinnamon extract was compared with both undoped and doped (Fe, Sn) chemically synthesized CuONPs. Four pathogenic Candida strains (Candida glabrata, Candida lusitaniae, Candida albicans, and Candida tropicalis) were isolated from Iraqi patients, demonstrating high biofilm formation capabilities. Chemically and green-synthesized CuONPs from Cinnamomum verum showed comparable significant antiplanktonic and antibiofilm activities against all strains. Doped CuONPs with iron or tin demonstrated lower minimum inhibitory concentration (MIC) values, indicating stronger antibacterial activity, but exhibited weaker anti-adhesive properties compared to other nanoparticles. The antiadhesive activity revealed that C. albicans strain seems to produce the most resistant biofilms while C. glabrata strain seems to be more resistant towards the doped CuONPs. Moreover, C. tropicalis was the most sensitive to all the CuONPs. Remarkably, at a concentration of 100 µg/mL, all CuONPs were effective in eradicating preformed biofilms by 47–66%. The findings suggest that CuONPs could be effective in controlling biofilm formation by Candida species resistant to treatment in healthcare settings. Full article

Background: Amid growing concerns about climate crisis and its psychological toll, understanding how people find meaning through their connection to nature is increasingly important. The first aim of this study is to examine the association between Nature Relatedness (NR) and Meaning in Life (MIL). The second aim is to investigate if gender moderates this association and to explore how Jewish traditions influence gender differences in this relationship. Methods: A multi-methods design was employed. Participants were recruited through purposive sampling of prominent Jewish community figures, followed by snowball sampling via informant referrals. Thirty-five participants completed the Meaning in Life Questionnaire (MLQ) and the NR Scale. Two questions provided qualitative insights into participants’ personal interpretations and culturally grounded meanings of NR and MIL in the context of climate change and Jewish traditions. Results: Hierarchical multiple regression analyses assessed the main effects of NR and gender, as well as their interaction, on MLQ subscales. NR positively correlated with the MLQ Search dimension, indicating that individuals with stronger NR actively seek meaning in life. Gender moderated this relationship: NR did not correlate with MLQ Presence overall, but higher NR was linked to greater MIL presence among female participants. Thematic analysis of qualitative responses revealed gender-based variations and emphasized the role of Jewish teachings in connecting NR to cultural and religious practices. Conclusions: The findings point to the importance of cultural, religious, and gender factors in shaping the relationship between NR and MIL in a time of climate change crisis, offering implications for positive mental health research and culturally sensitive interventions. Full article

Background/Objetives: Antimicrobial Resistance (AMR) is a multifaceted issue that the World Health Organization (WHO) identifies as one of the primary threats to global health for humans, animals, and the environment. In Colombia, AMR has been extensively studied at the hospital level; however, there are limited environmental studies, particularly concerning leachates from landfills. The objective of this study was to identify and determine the genetic relationships, as well as the sensitivity profiles to antibiotics and heavy metals, of ten Pseudomonas mendocina isolates from a leachate pond. Methods: Identification was conducted using MALDI-TOF (Matrix-Assisted Laser Desorption/Ionization Time-of-Flight), while genotyping was performed via rep-PCR. Antibiotic susceptibility to β-lactams, aminoglycosides, and quinolones was assessed using the Kirby-Bauer method. Additionally, sensitivity profile to heavy metals was evaluated using the broth microdilution technique. Results: Rep-PCR analysis indicated that 60% (n = 6/10) of the isolates exhibited a clonal relationship. Sensitivity testing revealed that 30% (n = 3/10) of the isolates displayed reduced sensitivity to aminoglycosides and β-lactams. Finally, the broth microdilution showed that 90% (n = 9/10) of the isolates were tolerant to copper sulfate. Conclusions: These results provide evidence that landfill leachates may serve as a potential reservoir for bacteria harboring antimicrobial resistance determinants. Full article

In this study, we evaluated the spatial variability in droplet deposition in herbicide applications using a remotely piloted aircraft (RPA) in pasture areas. The investigation was conducted in a square grid (50.0 m × 50.0 m), with 121 sampling points, at two operational flight heights (3.0 and 4.0 m). Droplet deposition was quantified using the fluorescent dye rhodamine B, and the droplet spectrum was characterised using water-sensitive paper tags. Geostatistical analysis was implemented to characterise spatial dependence, complemented by multivariate statistical analysis. Droplet deposition ranged from 1.01 to 9.02 and 1.10–6.10 μL cm⁻² at 3.0 and 4.0 m flight heights, respectively, with the coefficients of variation between 19.72 and 23.06% for droplet spectrum parameters. All droplet spectrum parameters exhibited a moderate to strong spatial dependence (relative nugget effect ≤75%) and a predominance of adjustment to the exponential model, with spatial dependence indices ranging from 12.55 to 47.49% between the two flight heights. Significant positive correlations were observed between droplet deposition and droplet spectrum parameters (r = 0.60–0.79 at 3.0 m; r = 0.37–0.66 at 4.0 m), with the correlation magnitude decreasing as the operational flight height increased. Cross-validation indices demonstrated acceptable accuracy in spatial prediction, with a mean estimation error ranging from −0.030 to 0.044 and a root mean square error ranging from 0.81 to 2.25 across parameters and flight heights. Principal component analysis explained 99.14 and 85.72% of the total variation at 3.0 and 4.0 m flight heights, respectively. The methodological integration of geostatistics and multivariate statistics provides a comprehensive understanding of the spatial variability in droplet deposition, with relevant implications for the optimisation of phytosanitary applications performed using RPAs. Full article

Deep marine shale is the primary carrier of shale gas resources in Southwestern China. Because the occurrence and gas content of methane vary with burial conditions, understanding the microscopic mechanism of methane occurrence in deep marine shale is critical for effective shale gas exploitation. The temperature and pressure conditions in deep shale exceed the operating limits of experimental equipment; thus, few studies have discussed the microscopic occurrence mechanism of shale gas in deep marine shale. This study applies molecular simulation technology to reveal the methane’s microscopic occurrence mechanism, particularly the main controlling factor of adsorbed methane in deep marine shale. Two types of simulation models are also proposed. The Grand Canonical Monte Carlo (GCMC) method is used to simulate the adsorption behavior of methane molecules in these two models. The results indicate that the isosteric adsorption heat of methane in both models is below 42 kJ/mol, suggesting that methane adsorption in deep shale is physical adsorption. Adsorbed methane concentrates on the pore wall surface and forms a double-layer adsorption. Furthermore, adsorbed methane can transition to single-layer adsorption if the pore size is less than 1.6 nm. The total adsorption capacity increases with rising pressure, although the growth rate decreases. Excess adsorption capacity is highly sensitive to pressure and can become negative at high pressures. Methane adsorption capacity is determined by pore size and adsorption potential, while accommodation space and adsorption potential are influenced by pore size and mineral type. Under deep marine shale reservoir burial conditions, with burial depth deepening, the effect of temperature on shale gas occurrence is weaker than pressure. Higher temperatures inhibit shale gas occurrence, and high pressure enhances shale gas preservation. Smaller pores facilitate the occurrence of adsorbed methane, and larger pores have larger total methane adsorption capacity. Deep marine shale with high formation pressure and high clay mineral content is conducive to the microscopic accumulation of shale gas in deep marine shale reservoirs. This study discusses the microscopic occurrence state of deep marine shale gas and provides a reference for the exploration and development of deep shale gas. Full article