Search Results (39)

To address issues existing in current multi-media pollution assessment, such as data mismatch, parameter conflicts, and inadequate characterization of nonlinear effects, this study developed a multi-factor synergistic assessment methodological system encompassing “data preprocessing-parameter calibration-damage quantification-model coupling”. A three-stage parameter calibration system of “inheritance-linkage-sensitivity screening” was established to achieve cross-media parameter synergy; an Environmental Damage Entropy (EDE) model was constructed based on information entropy to quantify the nonlinear coupled damage of multiple factors; and the optimal governance threshold was determined by combining the coupling theory of marginal damage and governance cost. Taking a multi-media pollution incident (atmosphere-soil-surface water-groundwater) caused by a chemical plant explosion as a case study, pollution chain identification, damage quantification, ecological risk cascading effect analysis, and health risk assessment were conducted. The results show that this method can accurately identify key pollution pathways. Based on the calculation of Environmental Damage Entropy (EDE = 0.604) and the synergy coefficient (δ = 1.32), the comprehensive damage value was quantified as 8.21 million yuan. Additionally, the threshold exceedance characteristics of various media were identified, reflecting the cumulative and lagging nature of ecological risk cascading effects. The method proposed in this study can accurately identify key pollution pathways and quantify comprehensive damage as well as ecological risks, providing scientific support for the allocation of multi-media pollution governance responsibilities and precise prevention and control. Full article

Static cold storage (SCS) remains the most widely used method of liver graft preservation due to its simplicity, accessibility, and reduced cost in transplantation practice. Since the invention of the University of Wisconsin (UW) solution, several alternative preservation solutions—including histidine–tryptophan–ketoglutarate (HTK), Celsior, and more recently IGL-1 and IGL-2—have been formulated to optimize cellular and vascular protection during cold ischemia. More recently, the introduction of dynamic perfusion techniques, such as hypothermic oxygenated perfusion (HOPE) and normothermic machine perfusion (NMP), approximately fifteen years ago, has further enhanced transplantation protocols, being applied either alone or in combination with traditional SCS to ensure optimal graft preservation prior to implantation. Despite these technological advances, achieving fully effective dynamic perfusion remains a key challenge for improving outcomes in vulnerable grafts, particularly steatotic or marginal livers. This review details how Polyethylene Glycol 35 (PEG35)-based solutions activate multiple cytoprotective pathways during SCS, including AMP-activated protein kinase (AMPK), nitric oxide (NO) production, and the antioxidant transcription factor Nrf2. We propose that these molecular mechanisms serve as a form of preconditioning that is synergistically leveraged by HOPE to preserve mitochondrial function, endothelial glycocalyx integrity, and microvascular homeostasis. Furthermore, the oncotic and rheological properties of PEG35 reduce perfusate viscosity, mitigating shear stress and microcirculatory damage during dynamic perfusion—effects that are further enhanced by NO- and AMPK-mediated protection initiated during the SCS phase. This integrated approach provides a strong rationale for combining PEG35-mediated SCS with HOPE, particularly for grafts with high susceptibility to ischemia–reperfusion injury, such as fatty livers. Finally, we highlight emerging avenues in graft preservation, including the design of unified perfusion solutions that optimize endothelial, mitochondrial, and redox protection, with the potential to improve post-transplant outcomes and extend applicability to other solid organ grafts. Full article

This article examines the effects of climate change on the 32 million inhabitants of the Megalopolis of Central Mexico (MCM), which is threatened by chaotic urbanization, land-use changes, the deforestation of the Forest of Water by organized crime, unsustainable agriculture, and biodiversity loss. Expensive hydraulic management extracting water from deep aquifers, long pipes exploiting water from neighboring states, and sewage discharged outside the endorheic basin result in expensive pumping costs and air pollution. This mismanagement has increased water scarcity. The overexploitation of aquifers and the pollution by toxic industrial and domestic sewage mixed with rainfall has increased the ground subsidence, damaging urban infrastructure and flooding marginal neighborhoods with toxic sewage. A system approach, satellite data, and participative research methodology were used to explore potential water scarcity and weakened water security for 32 million inhabitants. An alternative nature-based approach involves recovering the Forest of Water (FW) with IWRM, including the management of Natural Protected Areas, the rainfall recharge of aquifers, and cleaning domestic sewage inside the valley where the MCM is found. This involves recovering groundwater, reducing the overexploitation of aquifers, and limiting floods. Citizen participation in treating domestic wastewater with eco-techniques, rainfall collection, and purification filters improves water availability, while the greening of urban areas limits the risk of climate disasters. The government is repairing the broken drinking water supply and drainage systems affected by multiple earthquakes. Adaptation to water scarcity and climate risks requires the recognition of unpaid female domestic activities and the role of indigenous people in protecting the Forest of Water with the involvement of three state authorities. A digital platform for water security, urban planning, citizen audits against water authority corruption, and aquifer recharge through nature-based solutions provided by the System of Natural Protected Areas, Biological and Hydrological Corridors [SAMBA] are improving livelihoods for the MCM’s inhabitants and marginal neighborhoods, with greater equity and safety. Full article

The operation of modern power networks is increasingly exposed to overlapping climate extremes and volatile system conditions, making it essential to adopt scheduling approaches that are resilient as well as economical. In this study, a two-stage stochastic formulation is advanced, where indicators of system adaptability are embedded directly into the optimization process. The objective integrates standard operating expenses—generation, reserve allocation, imports, responsive demand, and fuel resources—with a Conditional Value-at-Risk component that reflects exposure to rare but damaging contingencies, such as extreme heat, severe cold, drought-related hydro scarcity, solar output suppression from wildfire smoke, and supply chain interruptions. Key adaptability dimensions, including storage cycling depth, activation speed of demand response, and resource ramping behavior, are modeled through nonlinear operational constraints. A stylized test system of 30 interconnected areas with a 46 GW demand peak is employed, with more than 2000 climate-informed scenarios compressed to 240 using distribution-preserving reduction techniques. The results indicate that incorporating risk-sensitive policies reduces expected unserved demand by more than 80% during compound disruptions, while the increase in cost remains within 12–15% of baseline planning. Pronounced spatiotemporal differences emerge: evening reserve margins fall below 6% without adaptability provisions, yet risk-adjusted scheduling sustains 10–12% margins. Transmission utilization curves further show that CVaR-based dispatch prevents extreme flows, though modest renewable curtailment arises in outer zones. Moreover, adaptability provisions promote shallower storage cycles, maintain an emergency reserve of 2–3 GWh, and accelerate the mobilization of demand-side response by over 25 min in high-stress cases. These findings confirm that combining stochastic uncertainty modeling with explicit adaptability metrics yields measurable gains in reliability, providing a structured direction for resilient system design under escalating multi-hazard risks. Full article

In winter, stay cable sheaths are prone to icing, which increases cable loads and poses a falling-ice hazard upon thawing. While manual and chemical de-icing are common methods, their safety and cost drawbacks make robotic de-icing a promising alternative. Robotic de-icing offers a promising alternative. However, to protect the sheath from damage, the de-icing blade is designed to minimize contact with its surface. Consequently, a thin layer of residual ice is often left behind, which reduces the surface friction coefficient and complicates the climbing process. This study evaluates the climbing performance of a self-manufactured cable-climbing mechanism through laboratory tests and dynamic simulations (ADAMS). A physical prototype was built, and dynamic simulations of the cable-climbing mechanism were conducted using Automated Dynamic Analysis of Mechanical Systems (ADAMS) software. The preliminary validation results demonstrate that the mechanism is capable of maintaining stable climbing under extreme conditions, including a friction coefficient of 0.12 to reflect thin-ice variability and indicated stable climbing even at μ = 0.12), a vertical inclination of 90°, and a load of 12 kg, confirming the design’s validity. Furthermore, we analyzed key parameters. A lower friction coefficient requires a higher clamping force and adversely affects the climbing speed due to increased slip. Similarly, an increased payload elevates the mechanism’s deflection angle, spring force, and wheel torque, which in turn reduces the climbing speed. Cable inclination has a complex effect: deflection decreases with slope, yet clamping force peaks near 70°, showing a bell-shaped trend. This peak requirement dictated the damping spring selection, which was given a safety margin. This ensures safe operation and acceleration at all other angles. Limitations: The present results constitute a feasibility validation under controlled laboratory conditions and rigid-support simulations. The long-term effects of residual ice and field performance remain to be confirmed in planned field trials. Full article

Ginger is an important spice crop in the north-eastern region of India. Rhizome rot, also called soft rot, is one of the most devastating diseases found in ginger that causes yield losses of up to 100% under favourable conditions. Initially, the disease symptoms appear as a light yellowing of the leaf tips that gradually spreads down to the leaf blade of lower leaves and the leaf sheath along the margin. Under favourable environmental conditions, the disease spreads rapidly, potentially causing significant crop damage. The pathogen can infect at any stage of crop growth, and under favourable environmental conditions, the disease spreads rapidly, failing the crop. Current research emphasises mitigating the losses caused by the devastating disease by using management strategies and biocontrol agents (BCAs). Results revealed that the average highest percent rhizome germination, lowest mean disease incidence, lowest mean disease severity index, lowest coefficient of disease index value, highest rhizome yield and benefit–cost ratio were recorded with Trichoderma harzianum (10 g/kg of rhizomes) + soil application of T. harzianum-enriched well-decomposed farm yard manure (3 kg of T. harzianum mixed with 100 kg FYM at 10–15 days before sowing) + soil drenching with T. harzianum at the rate 10 kg/ha, compared to the untreated control. Furthermore, soil chemical properties such as pH, electrical conductivity, soil organic carbon, total available nitrogen, total available phosphorus, and total available potassium play critical roles in rhizome rot disease severity. BCAs can suppress the phytopathogenic fungi and modulate different functions in plants. Full article

During natural disasters, social media platforms, such as X (formerly Twitter), become a valuable source of real-time information, with eyewitnesses and affected individuals posting messages about the produced damage and the victims. Although this information can be used to streamline the intervention process of local authorities and to achieve a better distribution of available resources, manually annotating these messages is often infeasible due to time and cost constraints. To address this challenge, we explore the use of semi-supervised learning, a technique that leverages both labeled and unlabeled data, to enhance neural models for disaster tweet classification. Specifically, we investigate state-of-the-art semi-supervised learning models and focus on co-training, a less-explored approach in recent years. Moreover, we propose a novel hybrid co-training architecture, Multihead Average Pseudo-Margin, which obtains state-of-the-art results on several classification tasks. Our approach extends the advantages of the voting mechanism from Multihead Co-Training by using the Average Pseudo-Margin (APM) score to improve the quality of the pseudo-labels and self-adaptive confidence thresholds for improving imbalanced classification. Our method achieves up to 7.98% accuracy improvement in low-data scenarios and 2.84% improvement when using the entire labeled dataset, reaching 89.55% accuracy on the Humanitarian task and 91.23% on the Informative task. These results demonstrate the potential of our approach in addressing the critical need for automated disaster tweet classification. We made our code publicly available for future research. Full article

This research makes a strong focus on improving fluid dynamics inside the reservoir after stimulation for enhancing oil and gas well performance, particularly in terms of increasing the Gas–oil ratio (GOR) and injectivity leading to a better productivity index (PI). Advanced stimulation operation using new formulated emulsified acid treatment greatly improves the reservoir permeability, allowing for better fluid movement and less formation damage. This, in turn, results in injectivity increases of at least 2.5 times and, in some situations, up to five times the original rate, which is critical for sustaining reservoir pressure and ensuring effective hydrocarbon recovery. The emulsified acid outperforms typical 15% HCl treatments in terms of dissolving and corrosion rates, as it is tuned for the reservoir’s pressure, temperature, permeability, and porosity. This dual-phase technology increases injectivity by five times while limiting the environmental and material consequences associated with spent and waste acid quantities. Field trials reveal significant improvements in injection pressure and a marked reduction in circulation pressure during stimulation, underscoring the treatment’s efficient penetration within the rock pores to enhance oil flow and sweep. This increase in performance is linked to the creation of the wormholing impact of the emulsified acid, resulting in improved fluid dynamics and optimized reservoir efficiency, as shown by the enhanced gas–oil ratio (GOR) in the four mentioned cases. A critical component of attaining such improvements is the capacity to effectively analyze and forecast reservoir behavior prior to executing the stimulation in real life. Engineers can accurately forecast injectivity gains and improve fluid injection tactics by constructing an advanced predictive model with low error margins, decreasing the need for time-consuming and costly trial-and-error approaches. Importantly, the research utilizes sophisticated neural network modeling to forecast stimulation results with minimal inaccuracies. This predictive ability not only diminishes the dependence on expensive and prolonged trial-and-error methods but also enables the proactive enhancement of treatment designs, thereby increasing efficiency and cost-effectiveness. This modeling approach based on several operational and reservoir factors, combines real-time field data, historical well performance records, and fluid flow simulations to verify that the expected results closely match the actual field outcomes. A well-calibrated prediction model not only reduces uncertainty but also improves decision making, allowing operators to create stimulation treatments based on unique reservoir features while minimizing unnecessary costs. Furthermore, enhancing fluid dynamics through precise modeling helps to improve GOR management by keeping gas output within appropriate limits while optimizing liquid hydrocarbon recovery. Finally, by employing data-driven modeling tools, oil and gas operators can considerably improve reservoir performance, streamline operational efficiency, and achieve long-term production growth through optimal resource usage. This paper highlights a new approach to optimizing reservoir productivity, aligning with global efforts to minimize environmental impacts in oil recovery processes. The use of real-time monitoring has boosted the study by enabling for exact measurement of post-injectivity performance and oil flow rates, hence proving the efficacy of these advanced stimulation approaches. The study offers unique insights into unconventional reservoir growth by combining numerical modeling, real-world data, and novel treatment methodologies. The aim is to investigate novel simulation methodology, advanced computational tools, and data-driven strategies for improving the predictability, reservoir performance, fluid behavior, and sustainability of heavy oil recovery operations. Full article

In this paper, we study the decision-making and coordination problem of a two-tier fresh food supply chain consisting of a supplier and a retailer. Considering the influencing factors of consumers’ information preference, freshness, and misrepresentation, we construct a centralized decision-making model and a decentralized decision-making Stackelberg game model. We also analyze the changes in the equilibrium solution of the supply chain before and after the input of blockchain technology, identify the conditions for the investment in blockchain technology, and design a “cost-sharing + benefit-sharing” combination contract for the coordination of the blockchain. The results are as follows: Firstly, under decentralized decision-making, if the fresh supplier misreports the freshness of the product, it will mislead the retailer to increase the order quantity, and its own profit will rise. Therefore, the fresh supplier has the motivation to misreport freshness. However, the backlog of fresh products will eventually damage the retailer’s profit, and the overall profit of the supply chain will also be damaged. Therefore, the increase in the profit of the fresh supplier is at the expense of the overall interests and stability of the supply chain. Second, when the investment cost of blockchain technology is within a certain threshold, it is feasible to invest in blockchain technology. Consumers’ preference for traceable fresh products will encourage the fresh supply chain to improve the level of information traceability and increase investment in blockchain technology. Finally, there are double marginal effects in the fresh supply chain under decentralized decision-making. The combined contract of “cost-sharing + revenue-sharing” can coordinate the overall revenue of the supply chain to the level of centralized decision-making. When the contract parameters meet certain conditions, Pareto improvement in revenue can be achieved for all parties involved in the fresh supply chain. The willingness of retailers to invest in blockchain technology will change with the change in contract parameters. When the proportion of retailers’ costs and the proportion of shared income are higher, the level of retailers’ investment in blockchain technology will decrease. Therefore, the interests of supply chain members need to be balanced in the process of contract coordination. Full article

Background: Neurosurgery demands exceptional precision due to the brain’s complex and delicate structures, necessitating precise targeting of pathological targets. Achieving optimal outcomes depends on the surgeon’s ability to accurately differentiate between healthy and pathological tissues during operations. Raman spectroscopy (RS) has emerged as a promising innovation, offering real-time, in vivo non-invasive biochemical tissue characterization. This literature review evaluates the current research on RS applications in intraoperative neurosurgery, emphasizing its potential to enhance surgical precision and patient outcomes. Methods: Following PRISMA guidelines, a comprehensive systematic review was conducted using PubMed to extract relevant peer-reviewed articles. The inclusion criteria focused on original research discussing real-time RS applications with human tissue samples in or near the operating room, excluding retrospective studies, reviews, non-human research, and other non-relevant publications. Results: Our findings demonstrate that RS significantly improves tumor margin delineation, with handheld devices achieving high sensitivity and specificity. Stimulated Raman Histology (SRH) provides rapid, high-resolution tissue images comparable to traditional histopathology but with reduced time to diagnosis. Additionally, RS shows promise in identifying tumor types and grades, aiding precise surgical decision-making. RS techniques have been particularly beneficial in enhancing the accuracy of glioma surgeries, where distinguishing between tumor and healthy tissue is critical. By providing real-time molecular data, RS aids neurosurgeons in maximizing the extent of resection (EOR) while minimizing damage to normal brain tissue, potentially improving patient outcomes and reducing recurrence rates. Conclusions: This review underscores the transformative potential of RS in neurosurgery, advocating for continued innovation and research to fully realize its benefits. Despite its substantial potential, further research is needed to validate RS’s clinical utility and cost-effectiveness. Full article

Manufactured sand (MS) is a promising alternative aggregate to quartz sand (QS) in ultra-high-performance concrete (UHPC) in the preparation of ultra-high-performance manufactured sand concrete (UHPMC), which possesses the characteristics of high strength, low cost, and environmental friendliness. In this study, the effects of variable compositional characteristics including the water–binder ratio, the stone powder (SP) content, and the MS replacement ratio on the mechanical and flexural strength of UHPMC were compared and analyzed based on response surface methodology (RSM). Meanwhile, the damage characteristics of UHPMC during compressive and flexural stress were monitored and evaluated using acoustic emission (AE) technology. The results reveal that the compressive and flexural strengths of UHPMC are both negatively correlated with the water–binder ratio, while they are positively correlated with the MS replacement rate. They tend to firstly increase and subsequently decrease with the increase in the stone powder content. In the load–displacement curve of concrete with a high MS replacement ratio and a low water–binder ratio, the slope in the elastic stage is steeper, the stiffness is higher, and the bending toughness and ductility are also better. The specimens with a 10% to 0% stone powder content present a steeper elastic phase slope, a slightly higher stiffness, and superior ductility. The specimens with a low MS replacement ratio and a high water–binder ratio display earlier cracking and weaker resistance, and the destruction process is complex and very unstable. The damage mode analysis based on RA-AF shows that an increase in the MS replacement ratio and a decrease in the water–binder ratio can both reduce the tensile cracking of UHPMC specimens under a four-point bending test. Although 10% stone powder can marginally slow down crack growth, the failure mode is not significantly affected. Full article

The role of critical infrastructures in maintaining the functioning of the economy and society and ensuring national security, particularly their durability in delivering essential services during crises, including natural disasters such as earthquakes, is critical. This work introduces an analytical methodology to quantify potential earthquake damage to power stations and evaluate the cost-effectiveness of measures to enhance their seismic resistance. By employing fragility curves and probabilistic risk analyses, this approach provides a structured framework for the comprehensive assessment of risks and the identification of economically practical mitigation strategies. A detailed examination of strategies to protect critical power station components against seismic activity is presented, revealing that a minor investment relative to the overall project budget for earthquake-proofing measures is economically effective. This investment, representing a marginal fraction of 0.5% of the total project expenditure significantly reduces the seismic risk of power station failure by 36%. Reinforcing essential elements, including switching stations, water treatment facilities, and water tanks, is emphasized to ensure their continued operation during and after an earthquake. This research highlights the critical significance of integrating risk assessment with benefit-to-cost analysis in strategic decision-making processes, supporting the prioritization of investments in infrastructure enhancements. These enhancements promise substantial reductions of risks at minimal costs, thus protecting essential services against the impacts of natural disasters. This research contributes to state-of-the-art research in critical infrastructures resilience. Full article

In a European context in which the objectives of climate neutrality and digitalization appear fundamental, the work analyzes the relationships between the price of the main stock market indices and the most representative variables such as carbon emissions, digitalization, use of renewable energy, research and development expenses, environmental taxes, and all economic and management activities aimed at reducing or eliminating any form of pollution. The analysis was developed through three different regressions involving the long period 1995–2020 and the short period 2017–2020. The results show how increasing carbon emissions and environmental taxes positively impact stock indices. The former is linked to an increase in production and, therefore, economic growth, and the latter encourages sustainability. Taxes on transport and energy in the long term generate higher costs, which damage profitability and negatively impact the performance of stock indices. Finally, in the short term, implementing environmental protection measures or the sustainable management of resources may lead to higher operating costs for the companies involved. These cost increases can negatively impact profit margins and reduce the value of companies. These results, therefore, show us how environmental sustainability has a significant impact on European stock markets; consequently, the relevant regulations and policies should also consider the economic and managerial impacts that companies implement to achieve their objectives of the Green Deal. Full article

Modern electrical power systems integrate renewable generation, with solar generation being one of the pioneers worldwide. In Latin America, the greatest potential and development of solar generation is found in Chile through the National Electric System. However, its energy matrix faces a crisis of drought and reduction of emissions that limits hydroelectric generation and involves the definitive withdrawal of coal generation. The dispatch of these plants is carried out by the system operator, who uses a simplified mechanism, called “economic merit list” and which does not reflect the real costs of the plants to the damage of the operating and marginal cost of the system. This inefficient dispatch scheme fails to optimize the availability of stored gas and its use over time. Therefore, a real-time redispatch model is proposed that minimizes the operation cost function of the power plants, integrating the variable generation cost as a polynomial function of the net specific fuel consumption, adding gas volume stock restrictions and water reservoirs. In addition, the redispatch model uses an innovative “maximum dispatch power” restriction, which depends on the demand associated with the automatic load disconnection scheme due to low frequency. Finally, by testing real simulation cases, the redispatch model manages to optimize the operation and dispatch costs of power plants, allowing the technical barriers of the market to be broken down with the aim of integrating ancillary services in the short term, using the power reserves in primary (PFC), secondary (SCF), and tertiary (TCF) frequency control. Full article

In this paper, a comprehensive probabilistic framework is proposed and adopted to perform seismic reliability and risk analysis of existing link slab (LS) bridges, representing a widely diffused structural typology within the infrastructural networks of many countries worldwide. Unlike classic risk analysis methods, innovative fragility functions are used in this work to retrieve more specific and detailed information on the possible failure modes, without limiting the analysis to the global failure conditions but also considering several intermediate damage scenarios (including one or more damage mechanisms), and providing insights on the numerosity of elements involved within a given damage scenario. Reliability analyses are performed on a set of LS bridges with different geometries (total lengths and pier heights) designed according to the Italian codes enforced in the 1970s. Accurate numerical models are developed in OpenSees and Multiple-Stripe nonlinear time–history analyses are carried out to build proper demand models, from which fragility functions are determined according to two limit states: damage onset and near-collapse. Mean annual rates of exceeding are thus estimated through the convolution between the hazard and the fragility. The results shed light on the main failure mechanisms characterizing this bridge typology, highlighting how different levels of risk (hence safety margins) can be associated with failure scenarios that differ in terms of elements/mechanisms involved and damage extension. Such a higher level of detail in the risk analysis may be useful to better quantify post-earthquake consequences (e.g., costs and losses) and define more tailored retrofit interventions. A comparison of the reliability levels associated with bridges of the same class with different geometries is finally presented. Full article