Search Results (17,562)

As academic institutions expand, the proliferation of laboratories dealing with hazardous chemicals has risen. While the physicochemical characterization equipment employed in these academic chemical laboratories is widely recognized, its usage presents a notable risk to researchers at various levels. This paper presents a simplified approach for evaluating the effects of the implementation of prevention investments in regard to working with nanomaterials on a lab scale. The evaluation is based on modeling the benefits (avoided accident costs) and costs (safety training), as opposed to an alternative (not investing in safety training). Each scenario analyzed in the economic evaluation reflects a different level of risk. The novelty of this study lies in its objective to provide an economic assessment of the benefits and returns from safety investments—specifically training—in a chemical laboratory, using a framework that integrates qualitative insights to explore and define the context alongside quantitative data derived from a cost–benefit analysis. The Net Present Value (NPV) was evaluated. The results of the cost–benefit analysis demonstrated that the benefits exceed the cost of the investment. The findings from the sensitivity analysis highlight the significant influence of insurance benefits on safety investments in the specific case study. In this case study, the deterministic analysis yielded a Net Present Value (NPV) of €280,414.67, which aligns closely with the probabilistic results. The probabilistic NPV indicates 90% confidence that the investment will yield a positive NPV ranging from €283,053 to €337,356. The cost–benefit analysis results demonstrate that the benefits outweigh the costs, showing that with an 87% training success rate, this investment would generate benefits of approximately €6328 by preventing accidents in this study. To the best of the researchers’ knowledge, this is the first study to evaluate the influence of safety investment through an economic evaluation of laboratory accidents with small-angle X-ray scattering during the physicochemical characterization process of engineered nanomaterials. The proposed approach and framework are relevant not only to academic settings but also to industry. Full article

Reducing environmental impacts and energy consumption in construction is increasingly important, prompting the use of renewable, ecological, and cost-effective materials. This research investigates an ecological building material combining clay and ground reed fibers, offering a promising alternative to conventional resources. A composite made of 50% clay and 50% ground reed was developed to study the influence of fiber size after grinding, as reed is typically used in its unprocessed form. Initial analyses included a physico-chemical characterization of both clay and reed. Thermal performance was then evaluated under steady-state and transient conditions to assess heat storage, heat transfer, and the material’s thermal inertia. The results showed a thermal conductivity of 0.38 W/m·K and an estimated 50% energy savings compared to clay alone, demonstrating the composite’s enhanced insulation capacity. Mechanical tests revealed compressive strengths of 2.48 MPa and flexural strengths of 0.79 MPa, with no significant effect from fiber size. The composite is lighter and more insulating than traditional clay blocks, indicating potential for reduced heating demand and improved indoor comfort. This study confirms the feasibility of incorporating ground reed fibers into clay-based composites to produce more sustainable building materials, supporting the transition toward energy-efficient and environmentally responsible construction practices. Full article

Direct seeding is considered a versatile and cost-effective approach to forest regeneration; however, its broader application is limited by low seedling survival rates and species-specific regeneration requirements, which often necessitate site preparation. We investigated the emergence, survival, and growth of Korean ash (Fraxinus chinensis subsp. rhynchophylla (Hance) A.E.Murray) seedlings regenerated by direct seeding over six years following two site preparation treatments—scarification and mixing—to determine appropriate site preparation methods for direct seeding and to assess the effects of site preparation treatments on soil, understory vegetation, and seedling growth. Additionally, the seed germination, shoot and root lengths, and biomass of the seedlings were investigated over 50 days in a growth chamber using soils from each site preparation treatment to examine early-stage growth responses. Both scarification and mixing treatments enhanced seed germination and seedling establishment. Seedling emergence rates were similar between treatments; however, the seedling mortality and the height and coverage of competing understory vegetation were significantly greater at the scarification treatment than at the mixing treatment during the first year (p < 0.05). Both treatments reduced minimum winter soil temperatures during the first two years, with frost heaving identified as a primary cause of early seedling mortality. From the second year onward, seedling growth was significantly greater in the mixing treatment (p < 0.05), which also more effectively suppressed competing vegetation. A shallow depth mixing treatment (<5 cm) is recommended for direct seeding of Korean ash, as it reduces frost heaving damage and facilitates seedling survival and growth by minimizing understory competition. Full article

Background: The management of ovarian cysts in postmenopausal women is still a diagnostic dilemma. Although ultrasound is the diagnostic cornerstone of the initial assessment, it is limited by its interpretation in cases without clear morphological features of malignancy. Objectives: The aim of this study was to assess whether the addition of grayscale ultrasound features with inflammatory markers including the neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) can improve diagnostic accuracy in the identification of malignant ovarian lesions as compared to benign cysts in postmenopausal women. Methods: A total of 103 surgically removed adnexal masses were examined retrospectively. Ultrasound morphology was categorized to either simple or complex while NLR and PLR were calculated from preoperative full blood counts. The reference standard was histopathology. Results: Of the 103 cysts taken out, 74 cysts (71.8%) were benign while 29 cysts (28.2%) were malignant. Complex morphology was shown by all malignant lesions. NLR values in malignancy vs. benignancy showed a mean NLR of 4.96 ± 2.3 in the malignant cases, while it was 2.56 ± 1.2 in the benign cases (p < 0.001). In a similar fashion, the PLR was 198.4 ± 45.1 in malignant compared to 134.2 ± 32.7 in benign cases (p < 0.001). In the group of complex cysts (n = 52), NLR and PLR were compared to differentiate between malignant and benign lesions. In logistic regression, complex morphology was an independent predictor of malignancy, while NLR showed a positive, non-significant trend; PLR was not independently associated. Conclusions: Use of NLR and PLR in combination with grayscale ultrasonographic morphology improves the diagnostic characterization of postmenopausal women with adnexal masses. This easy, cost-effective method might aid in better triage and surgery planning. Full article

Background: Accurate route planning is a core challenge in logistics, particularly for small- and medium-sized enterprises that lack access to costly geospatial tools. This study explores whether usable distance matrices and routing outputs can be generated solely from geographic coordinates without relying on full map-based infrastructure. Methods: A dataset of over 5000 Hungarian postal locations was used to evaluate five models: Haversine-based scaling with circuity, linear regression, second- and third-degree polynomial regressions, and a trained artificial neural network. Models were tested on the full dataset, and three example routes representing short, medium, and long distances. Both statistical accuracy and route-level performance were assessed, including a practical optimization task. Results: Statistical models maintained internal consistency, but systematically overestimated longer distances. The ANN model provided significantly better accuracy across all scales and produced routes more consistent with map-based paths. A new evaluation method was introduced to directly compare routing outputs. Conclusions: Practical route planning can be achieved without GIS services. ML-based estimators offer a cost-effective alternative, with potential for further improvement using larger datasets, additional input features, and the integration of travel time prediction. This approach bridges the gap between simplified approximations and commercial routing systems. Full article

The Economic Dispatch Problem (EDP) plays a critical role in power system operations by trying to allocate power generation across multiple units at minimal cost while satisfying complex operational constraints. Traditional optimization techniques struggle with the non-convexities introduced by factors such as valve-point effects, prohibited operating zones, and spinning reserve requirements. While metaheuristics methods have shown promise, they often suffer from convergence issues and constraint-handling limitations. In this study, we introduce a novel application of Group Relative Policy Optimization (GRPO), a reinforcement learning framework that extends Proximal Policy Optimization by integrating group-based learning and relative performance assessments. The proposed GRPO approach incorporates smart initialization, adaptive exploration, and elite-guided updates tailored to the EDP’s structure. Our method consistently produces high-quality, feasible solutions with faster convergence compared to state-of-the-art metaheuristics and learning-based methods. For instance, in the case of the 15-unit system, GRPO achieved the best cost of USD 32,421.67/h with full constraint satisfaction in just 4.24 s, surpassing many previous solutions. The algorithm also demonstrates excellent scalability, generalizability, and stability across larger-scale systems without requiring parameter retuning. These results highlight GRPO’s potential as a robust and efficient tool for real-time energy scheduling in smart grid environments. Full article

The strategic goal of the transition to a low-carbon economy in Russia requires the active integration of forest-climatic projects into regional sustainable development strategies, especially for areas with high agricultural pressure such as the central forest-steppe of the European part of the Russian Federation. The region contains over 18 million hectares of forest land, which is approximately 2.1% of the area of Russian forests, and intensive agricultural development increases the need for innovative approaches to restoring forest ecosystems. The work uses indicators of the state forest register, data on 18 reforestation projects and 22 afforestation projects, and the results of forecasting the dynamics of greenhouse gas absorption until 2030. It is estimated that by 2030, the sequestration potential of the forests of the central forest-steppe can be increased by 28–30%, which will neutralize up to 12% of emissions from industrial enterprises in the region. In the paper, to unify the assessment, it is proposed to use the carbon intensity factor of investment costs, which, in a number of implemented projects, ranged from 1.2 to 2.7 RUB/1 kg CO₂ eq., reflecting the cost of achieving one ton of absorbed CO₂ equivalent. At ratios above 1, the economic value of the carbon units created exceeds investment costs by at least 20%. Environmental–economic modeling showed that with an increase in the forest cover of the region by 1% (180 thousand hectares), the annual absorption of CO₂ increases by approximately 0.9–1.1 million tons, and the increase in potential income from the sale of carbon units could amount to 1.6–2.2 billion RUB per year at the current price of 1.8–2 RUB/kg CO₂-eq. The use of an integral criterion of environmental and economic efficiency helps increase the transparency and investment-attractiveness of forest-climatic projects, as well as the effective integration of natural and climatic solutions into long-term strategies for the sustainable development of the Central Forest-Steppe of Russia. Full article

Objective. This randomized controlled trial evaluated the efficacy of lavender–neroli oil aromatherapy in managing dental anxiety and pain in children undergoing inferior alveolar nerve block (IANB) anesthesia. Methods. Fifty-four children aged 6–11 years were randomly assigned to either a control group or an aromatherapy group. Children in the control group were asked to wear a regular scented-free nitrous oxide mask. Children in the control group were asked to wear a regular scented-free nitrous oxide mask. Children in the intervention group inhaled lavender–neroli oil via a nitrous oxide nasal mask for 5 min before and during IANB administration. Anxiety and pain levels were assessed pre-and post-treatment using the Facial Image Scale (FIS), Face–Legs–Activity–Cry–Consolability (FLACC) scale, and vital signs (heart rate, blood pressure, oxygen saturation). The collected data were statistically analyzed using SPSS software 20. The Mann–Whitney U test was used for analyzing FIS results, and the independent T test and T Paired test were used for analyzing heart rate, blood pressure, and oxygen saturation results. Results. Results demonstrated significantly lower anxiety, heart rate, blood pressure, and pain scores in the aromatherapy group compared to the control group (p < 0.05), with no significant change in oxygen saturation. Conclusions. Lavender–neroli aromatherapy is a safe, low-cost, and effective adjunct to reduce anxiety and discomfort during pediatric dental anesthesia. Full article

Intravenous push (IVP) administration of controlled substances in hospital settings presents operational challenges related to medication waste, documentation, and diversion risk. This multi-site observational study aimed to quantify the pharmacy workforce time and associated costs linked to IVP waste management across a 16-hospital health system in Southwest Florida. Data were collected from over 4400 controlled substance transactions involving fentanyl, midazolam, hydromorphone, morphine, ketamine, and lorazepam. Methods included automated transaction analysis, manual chart reviews, and software-based compliance case evaluations. Results indicated patterns of partial dose waste, particularly for midazolam (85.2%) and hydromorphone (78.8%), and identified opportunities where documentation efforts could be further optimized through automation. Manual review of 333 incidents required an average of 6 min and 43 s per case, extrapolating to over 496 h of quarterly pharmacy labor or nearly 1985 h annually. Software-based case reviews added another 32 h per quarter or 130 h annually. Additionally, waste receptacle systems incurred over USD 1.1 million in capital costs and USD 322,500 in annual maintenance, with technician labor contributing further operational burden. These findings underscore the resource demands of IVP waste management and support the need for standardized dosing, enhanced documentation workflows, and pharmacy-led interventions to improve efficiency and reduce diversion risk. Full article

Optimizing healthcare resources in neurodegenerative diseases requires balancing diagnostic performance with cost constraints. We introduce AHN-BudgetNet—a tiered, cost-aware assessment framework for Parkinson’s disease motor severity prediction—evaluated on 1387 simulated PPMI subjects via patient-level GroupKFold validation. Our analysis tested seven tier combinations encompassing demographic, self-reported, and clinical features. The baseline (T0) yields AUC = 0.65 (95% CI [0.629, 0.681]) at no cost. Self-assessments (T1) alone achieved an AUC = 0.69 (95% CI [0.643, 0.733]) at USD 75, with an efficiency of 1.07. The combined T0 + T1 set reached AUC = 0.75 (95% CI [0.729, 0.772]) at USD 75, with efficiency 1.43. T2 alone obtained AUC = 0.53 (95% CI [0.517, 0.542]) at USD 300 and efficiency 0.07. The full T0+T1+T2 set achieved the highest performance—AUC = 0.76 (95% CI [0.735, 0.774])—at USD 375, with efficiency 0.54, reflecting diminishing returns beyond T1. High-cost tiers (T3/T4) could not be empirically validated due to over 88% missing data, emphasizing the value of accessible assessments. Gaussian Mixture on Tier 0 features yielded a silhouette score of 0.54, compared to 0.53 for K-means, confirming that patient-reported outcomes can support clinical stratification. Our results underpin evidence-based resource allocation: budgets USD ≤75 prioritize T1, while budgets USD ≤375 justify a comprehensive assessment. This confirms that structured tier prioritization supports robust, resource-efficient diagnosis in resource-limited clinical environments. Full article

Lubricating oils are utilised in equipment and machinery to reduce friction and enhance material utilisation. The utilisation of oil leads to an increase in its thickness and density over time. Current methods for assessing oil life are slow, expensive, and complex, and often only applicable in laboratory settings and unsuitable for real-time or field use. This leads to unexpected equipment failures, unnecessary oil changes, and economic and environmental losses. A comprehensive review of the extant literature revealed no studies and no national or international patents on neural network algorithm-based oil life modelling and classification using green sensors. In order to address this research gap, this study, for the first time in the literature, provides a green conductivity sensor with high-accuracy prediction of oil life by integrating real-time field measurements and artificial neural networks. This design is based on analysing resistance change using a relatively low-cost, three-dimensional, eco-friendly sensor. The sensor is characterised by its simplicity, speed, precision, instantaneous measurement capability, and user-friendliness. The MLP and LVQ algorithms took as input the resistance values measured in two different oil types (diesel, bench oil) after 5–30 h of use. Depending on their degradation levels, they classified the oils as ‘diesel’ or ‘bench oil’ with 99.77% and 100% accuracy. This study encompasses a sensing system with a sensitivity of 50 µS/cm, demonstrating the proposed methodologies’ efficacy. A next-generation decision support system that will perform oil life determination in real time and with excellent efficiency has been introduced into the literature. The components of the sensor structure under scrutiny in this study are conducive to the creation of zero waste, in addition to being environmentally friendly and biocompatible. The developed three-dimensional green sensor simultaneously detects physical (resistance change) and chemical (oxidation-induced polar group formation) degradation by measuring oil conductivity and resistance changes. Measurements were conducted on simulated contaminated samples in a laboratory environment and on real diesel, gasoline, and industrial oil samples. Thanks to its simplicity, rapid applicability, and low cost, the proposed method enables real-time data collection and decision-making in industrial maintenance processes, contributing to the development of predictive maintenance strategies. It also supports environmental sustainability by preventing unnecessary oil changes and reducing waste. Full article

The use of Reclaimed Asphalt Pavement (RAP) in asphalt mixtures offers environmental and economic advantages by reducing reliance on virgin aggregates and minimizing construction waste. However, the aged binder in RAP increases mixture stiffness, which can compromise fatigue resistance. This systematic review evaluates the influence of RAP content on fatigue performance compared to conventional mixtures, with a focus on the Indirect Tensile Test (IDT) as the primary assessment method. Following the parameters of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, five studies published between 2014 and 2024 were identified through searches in Web of Science, ScienceDirect, ASCE, and Scopus. Study quality was assessed using the Cochrane Risk of Bias tool. The results indicate that although RAP enhances rutting resistance, higher contents (>30%) often lead to reduced fatigue performance due to binder hardening and reduced mixture flexibility. The incorporation of rejuvenators—such as heavy paraffinic extracts—and modifiers, including high-modulus agents, polymers, and epoxy binders, can partially restore aged binder properties and improve performance. Sustainable innovations, such as lignin-based industrial by-products and warm-mix asphalt technologies, show promise in balancing mechanical performance with reduced environmental impact. Variability in material sources, modification strategies, and test protocols limits direct comparability among studies, underscoring the need for standardized evaluation frameworks. Overall, this review highlights that optimizing RAP content and selecting effective rejuvenation or modification strategies are essential for achieving durable, cost-effective, and environmentally responsible asphalt pavements. Future research should integrate advanced laboratory methods with performance-based design to enable high RAP utilization without compromising fatigue resistance. Full article

Background: Port congestion contributes significantly to operational inefficiency and environmental impact in maritime logistics. With tightening EU regulations such as the Emissions Trading System (EU ETS) and FuelEU Maritime, understanding and mitigating the economic and environmental effects of vessel delays is increasingly critical. This study focuses on a single bulk cargo pier at Constanta Port (Romania), which has experienced substantial traffic fluctuations since 2021, and examines operational and environmental performance through a queuing-theoretic lens. Methods: The authors have applied an M/G/1/∞/FIFO/∞ queuing model to vessel traffic and service time data from 2021–2023, supplemented by Monte Carlo simulations to capture variability in maneuvering and service durations. Environmental impact was quantified in CO₂ emissions using standard fuel-based emission factors, and a Cold Ironing scenario was modeled to assess potential mitigation benefits. Economic implications were estimated through operational cost modeling and conversion of CO₂ emissions into equivalent EU ETS carbon costs. Results: The analysis revealed high berth utilization rates across all years, with substantial variability in waiting times and queue lengths. Congestion was associated with considerable CO₂ emissions, which, when expressed in monetary terms under prevailing EU ETS prices, represent a significant financial burden. The Cold Ironing scenario demonstrated a substantial reduction in at-berth emissions and corresponding cost savings, underscoring its potential as a viable mitigation strategy. Conclusions: Results confirm that operational congestion at the studied berth imposes substantial environmental and financial burdens. The analysis supports targeted interventions such as Just-In-Time arrivals, optimized berth scheduling, and Cold Ironing adoption. Recommendations are most applicable to single-berth bulk cargo operations; future research should extend the approach to multi-berth configurations and incorporate additional operational constraints for broader generalizability. Full article

This study presents a comprehensive multi-criteria decision-making (MCDM) framework for evaluating and selecting optimal non-destructive testing (NDT) techniques for oil and gas facility maintenance operations. This research used a Fuzzy Analytic Hierarchy Process (FAHP) integrated with multiple MCDM methods to assess eight NDT techniques including radiographic testing, ultrasonic testing, and thermographic testing. The evaluation framework incorporated seven technical criteria and seven economic criteria. The FAHP results revealed spatial resolution (0.175) as the most critical technical criterion, followed by depth penetration (0.155) and defect characterization (0.143). For economic criteria, downtime costs (0.210) and operational costs (0.190) emerged as the most significant factors. This study used TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), PROMETHEE (Preference Ranking Organization Method for Enrichment of Evaluations), and VIKOR (VIseKriterijumska Optimizacija I Kompromisno Resenje) methods to rank NDT techniques, with results consolidated using the CRITIC (CRiteria Importance Through Intercriteria Correlation) method. The final techno-economic analysis identified radiographic testing as the most suitable NDT method with a score of 0.665, followed by acoustic emission testing at 0.537. Visual testing ranked lowest with a score of 0.214. This research demonstrates the effectiveness of combining fuzzy logic with multiple MCDM approaches for NDT method selection in offshore welding operations. Full article

In probabilistic risk assessment (PRA), the fracture limit of fuel cladding tubes under loss-of-coolant accident conditions plays a critical role in determining the core damage, highlighting the need for accurate modeling of cladding tube fracture behavior. However, for high-burnup cladding tubes, it is often infeasible to conduct extensive experiments due to limited material availability, high costs, and technical constraints. These limitations make it difficult to acquire sufficient data, leading to substantial epistemic uncertainty in fracture modeling. To enhance the realism of PRA results under such constraints, it is essential to develop methods that can effectively reduce epistemic uncertainty using limited experimental data. In this study, we propose a Bayesian approach for designing experimental conditions based on a widely applicable information criterion (WAIC) in order to effectively reduce the uncertainty in the prediction of fuel cladding tube fracture with limited data. We conduct numerical experiments to evaluate the effectiveness of the proposed method in comparison with conventional approaches based on empirical loss and functional variance. Two cases are considered: one where the true and predictive models share the same mathematical structure (Case 1) and one where they differ (Case 2). In Case 1, the empirical loss-based design performs best when the number of added data points is fewer than approximately 10. In Case 2, the WAIC-based design consistently achieves the lowest Bayes generalization loss, demonstrating superior robustness in situations where the true model is unknown. These results indicate that the proposed method enables more informative experimental designs on average and contributes to the effective reduction in epistemic uncertainty in practical applications. Full article