Search Results (670)

Multi-objective evolutionary algorithms (MOEAs) have emerged as powerful optimization tools for addressing the complex, often conflicting goals present in modern waste disposal systems. This review explores recent advances and practical applications of MOEAs in key areas, including waste collection routing, waste-to-energy (WTE) systems, and facility location and allocation. Real-world case studies from cities like Braga, Lisbon, Uppsala, and Cyprus demonstrate how MOEAs can enhance operational efficiency, boost energy recovery, and reduce environmental impacts. While these algorithms offer significant advantages, challenges remain in computational complexity, adapting to dynamic environments, and integrating with emerging technologies. Future research directions highlight the potential of combining MOEAs with machine learning and real-time data to create more flexible and responsive waste management strategies. By leveraging these advancements, MOEAs can play a pivotal role in developing sustainable, efficient, and adaptive waste disposal systems capable of meeting the growing demands of urbanization and stricter environmental regulations. Full article

On-farm emergency slaughter (OFES) is employed when cattle are unfit for transport but still suitable for human consumption, thereby ensuring animal welfare and reducing food waste. This study analysed OFES patterns in Northern Italy, where a large cattle population is housed but information on the practice is rarely analysed. A total of 12,052 OFES cases from 2021 to 2023 were analysed. Most involved female cattle (94%) from dairy farms (79%). Locomotor disorders were the leading reason (70%), particularly trauma and fractures, followed by recumbency (13%) and calving-related issues (10%). Post-mortem findings showed limbs and joints as the most frequent condemnation sites (36%), often linked to trauma. A significant reduction in OFES cases occurred over time, mainly due to fewer recumbency and calving issues, likely reflecting stricter eligibility criteria introduced in 2022. Weekly variations, with peaks on Mondays and lows on Saturdays, suggest that logistical constraints may sometimes influence OFES promptness. These findings suggest that on-farm management and animal handling could be improved further to reduce welfare risks and carcass waste. Due to the lack of standardised data collection and regulatory harmonisation, a multi-country investigation could improve our understanding of this topic and inform best practice. Full article

Geopolymers, as an eco-friendly alternative construction material to ordinary Portland cement (OPC), exhibit superior performance in soil stabilization. However, their inherent brittleness limits engineering applications. To address this, polyethylene terephthalate (PET) fibers can be incorporated into a one-part geopolymer (OPG) binder to enhance ductility while promoting plastic waste recycling. However, the evaluation of ductile behavior of OPG-stabilized soil with PET fiber normally demands extensive laboratory and field experiments. Leveraging artificial intelligence, a predictive model can be developed for this purpose. In this study, data were collected from compressive and tensile tests performed on the OPG-stabilized soil with PET fiber. Four deep learning neural network models, namely ANN, BPNN, CNN, and LSTM, were then used to construct prediction models. The input parameters in the model included the fly ash (FA) dosage, dosage and length of the PET fiber, and the Curing Time. Results revealed that the LSTM model had the best performance in predicting the three ductile properties (i.e., the compressive strength index [UCS], strain energy index [CSE], and tensile strength index [TES]). The SHAP and 2D-PDP methods were further used to verify the rationality of the LSTM model. It is found that the Curing Time was the most important factor for the strength and ductile behavior. The appropriate addition of PET fiber of a certain length had a positive impact on the ductility index. Thus, for the OPG-stabilized soil, the optimal dosage and length of PET fiber were found to be 1.5% and 9 mm, respectively. Additionally, there was a synergistic effect between FA and PET on the ductility metric. This research provides theoretical support for the application of geopolymer and PET fiber in enhancing the ductility of the stabilized soil. Full article

We propose a dynamic monitoring and precision fertilization decision system for agricultural soil nutrients, integrating UAV remote sensing and GIS technologies to address the limitations of traditional soil nutrient assessment methods. The proposed method combines multi-source data fusion, including hyperspectral and multispectral UAV imagery with ground sensor data, to achieve high-resolution spatial and spectral analysis of soil nutrients. Real-time data processing algorithms enable rapid updates of soil nutrient status, while a time-series dynamic model captures seasonal variations and crop growth stage influences, improving prediction accuracy (RMSE reductions of 43–70% for nitrogen, phosphorus, and potassium compared to conventional laboratory-based methods and satellite NDVI approaches). The experimental validation compared the proposed system against two conventional approaches: (1) laboratory soil testing with standardized fertilization recommendations and (2) satellite NDVI-based fertilization. Field trials across three distinct agroecological zones demonstrated that the proposed system reduced fertilizer inputs by 18–27% while increasing crop yields by 4–11%, outperforming both conventional methods. Furthermore, an intelligent fertilization decision model generates tailored fertilization plans by analyzing real-time soil conditions, crop demands, and climate factors, with continuous learning enhancing its precision over time. The system also incorporates GIS-based visualization tools, providing intuitive spatial representations of nutrient distributions and interactive functionalities for detailed insights. Our approach significantly advances precision agriculture by automating the entire workflow from data collection to decision-making, reducing resource waste and optimizing crop yields. The integration of UAV remote sensing, dynamic modeling, and machine learning distinguishes this work from conventional static systems, offering a scalable and adaptive framework for sustainable farming practices. Full article

The aim of the article is to highlight the increasing environmental burden on aquatic ecosystems in Slovakia due to continuous pollution from municipal, industrial and agricultural sources. Laboratory analyses have shown alarming exceedance of the limit values of contaminants, with nitrate nitrogen (NO₃⁻) reaching 5.8 mg/L compared to the set limit of 2.5 mg/L and phosphorus concentrations exceeding the permissible values by a factor of five, thereby escalating the risk of eutrophication and loss of ecological stability of the aquatic ecosystem. The accumulation of heavy metals is also a problem—lead (Pb) concentrations reach up to 9.7 μg/L, which exceeds the safe limit by a factor of ten. Despite the measures implemented, such as scum barriers, there is continuous contamination of the aquatic environment, with illegal waste dumps and uncontrolled runoff of agrochemicals playing a significant role. The research results underline the critical need for a more effective environmental policy and more rigorous monitoring of toxic substances in real time. These findings highlight not only the urgency of more effective environmental policy and stricter real-time monitoring of toxic substances, but also the necessity of integrating environmental logistics into the design of sustainable solutions. Logistical approaches including the optimization of waste collection, coordination of stakeholders and creation of infrastructural conditions can significantly contribute to reducing environmental burdens and ensure the continuity of environmental management in ecologically sensitive areas. Full article

Background: The differential effects of post-exercise rehydration beverages on inflammatory processes and organ protection remain incompletely characterized. This study investigated how beverages with distinct compositions influence urinary biomarkers following endurance exercise. Methods: In a randomized crossover design, eight trained male runners performed 6000 m pace running followed by consumption of 500 mL of either: water (Drink 1), hypotonic sports drink (Drink 2, 200 mOsm/L), oral rehydration solution (Drink 3, 270 mOsm/L), or modified hypotonic formulation (Drink 4, 200 mOsm/L). After 60 min, participants completed a 1000 m time trial. Urine samples were collected at baseline, post-6000 m, and post-1000 m for analysis of biochemical parameters and inflammatory cytokines. Results: No significant differences in 1000 m performance were observed between trials. Drink 3 significantly reduced creatinine and uric acid excretion compared to other beverages (p < 0.05), suggesting decreased waste product elimination. Creatinine-corrected intestinal fatty acid-binding protein values were lower with Drinks 2 and 3, indicating potential intestinal protection. Notably, Drink 4 showed modest but significant enhancement of IL-4 excretion (p < 0.05, ηp² = 0.347), demonstrating beverage-specific modulation of anti-inflammatory cytokines with moderate effect sizes. Conclusions: Different beverage formulations exert distinct effects on waste product elimination, intestinal organ damage markers, and inflammatory cytokine profiles. These findings suggest that beverage selection should be tailored to specific recovery priorities and training contexts. Full article

Plastic pollution in marine environments is a globally recognized concern that poses ecological and economic threats. While 80% of plastic originates from land, 20% comes from sea-based sources like shipping and fishing. Comprehensive assessments of fishing-related plastics are limited but crucial for mitigation. This study analyzed the distribution and temporal evolution of three fishing-related items (EPS fish boxes, fragments, and buoys) along the Bocassette spit in the northern Adriatic Sea, a region with high fishing and aquaculture activity. UAV monitoring (November 2019, June/October 2020) and structured interviews with Po Delta fishermen were conducted. The collected debris was mainly EPS, with boxes (54.8%) and fragments (39.6%). Fishermen showed strong awareness of degradation, identifying plastic as the primary litter type and reporting gear loss. Litter concentrated in active dunes and the southern sector indicates human and riverine influence. Persistent items (61%) at higher elevations suggest longer residence times. Mapped EPS boxes could generate billions of micro-particles (e.g., ~10¹³). The results reveal a complex interaction between natural processes and human activities in litter distribution. This highlights the need for integrated management strategies, like improved waste management, targeted cleanup, and community involvement, to reduce long-term impacts on vulnerable coastal ecosystems. Full article

Automated quality assurance is gaining popularity across application areas; however, automatization for monitoring and control of product quality in waste processing is still in its infancy. At the same time, research on this topic is scattered, limiting efficient implementation of already developed strategies and technologies across research and application areas. To this end, the current work describes a scoping review conducted to systematically map available sensor-based quality assurance technologies and research based on the PRISMA-ScR framework. Additionally, the current state of research and potential automatization strategies are described in the context of construction and demolition waste processing. The results show 31 different sensor types extracted from a collection of 364 works, which have varied popularity depending on the application. However, visual imaging and spectroscopy sensors in particular seem to be popular overall. Only five works describing quality control system implementation were found, of which three describe varying manufacturing applications. Most works found describe proof-of-concept quality prediction systems on a laboratory scale. Compared to other application areas, works regarding construction and demolition waste processing indicate that the area seems to be especially behind in terms of implementing visual imaging at higher technology readiness levels. Moreover, given the importance of reliable and detailed data on material quality to transform the construction sector into a sustainable one, future research on quality monitoring and control systems could therefore focus on the implementation on higher technology readiness levels and the inclusion of detailed descriptions on how these systems have been verified. Full article

The resource utilization potential and environmental impact of fly ash from municipal solid waste incinerators (MSWIs) have attracted wide attention. In this study, four MSWIs in Hangzhou, Zhejiang Province were selected to systematically evaluate the effects of different incinerator types and flue gas deacidification processes on fly ash’s oxide and heavy metal components and their temporal changes as well as conduct risk assessment. The results showed that the contents of MgO, Al₂O₃, SiO₂, and Fe₂O₃ in the grate furnace fly ash were significantly lower than those in the fluidized bed fly ash, but the compressive strength of its fly ash was high. Chemicals added during the flue gas deacidification process such as CaO and NaHCO₃ significantly affected the contents of CaO and Na₂O. In addition, heavy metals such as Cu, Mn, Cr, and Ni were mainly distributed in the fluidized bed fly ash, while heavy metals such as Pb and Cd were mainly collected in the grate furnace fly ash. The concentrations of various components in the fly ash fluctuated but were not significant under different time dimensions. Risk assessment indicated that heavy metals such as Cd, Pb, and Sb posed a high risk. This study is expected to provide theoretical support for the safe management and resource utilization of fly ash. Full article

Underground cast-in-place pipes (CIPP, Diameter of 2’–5’) are used to transport water for the Phoenix, AZ area. These pipes have developed leaks due to their age and changes in the environment, resulting in a significant waste of water. Currently, leaks can only be identified when water pools above ground occur and are then manually confirmed through the inside of the pipe, requiring the shutdown of the water system. However, many leaks may not develop a puddle of water, making them even harder to identify. The primary objective of this research was to develop an inspection method utilizing drone-based infrared imagery to remotely and non-invasively sense thermal signatures of abnormal soil moisture underneath urban surface treatments caused by the leakage of water pipelines during the regular operation of water transportation. During the field tests, five known leak sites were evaluated using an intensive experimental procedure that involved conducting multiple flights at each test site and a stringent filtration process for the measured temperature data. A detectable thermal signal was observed at four of the five known leak sites, and these abnormal thermal signals directly overlapped with the location of the known leaks provided by the utility company. A strong correlation between ground temperature and shading before sunset was observed in the temperature data collected at night. Thus, a shadow and solar energy model was implemented to estimate the position of shadows and energy flux at given times based on the elevation of the surrounding structures. Data fusion between the metrics of shadow time, solar energy, and the temperature profile was utilized to filter the existing points of interest further. When shadows and solar energy were considered, the final detection rate of drone-based infrared imaging was determined to be 60%. Full article

This study creates and tests a machine learning model that can predict fuel use and emissions (NO_x, CO₂, CO, HC, PN) from a marine internal combustion engine when it is running normally. The model learned from data collected from conventional diesel fuel experiments. Subsequently, we evaluated its ability to transfer by employing the parameters associated with waste cooking oil (WCO) biodiesel and its 60/40 diesel mixture. The machine learning model demonstrated exceptional proficiency in forecasting diesel mode (R² > 0.95), effectively encapsulating both long-term trends and short-term fluctuations in fuel consumption and emissions across various load regimes. Upon the incorporation of WCO data, the model maintained its capacity to identify trends; however, it persistently overestimated emissions of CO, HC, and PN. This discrepancy arose primarily from the differing chemical composition of the fuel, particularly in terms of oxygen content and density. A significant correlation existed between indicators of incomplete combustion and the utilization of fuel. Nonetheless, NO_x exhibited an inverse relationship with indicators of combustion efficiency. The findings indicate that the model possesses the capability to estimate emissions in real time, requiring only a modest amount of additional training to operate effectively with alternative fuels. This approach significantly diminishes the necessity for prolonged experimental endeavors, rendering it an invaluable asset for the formulation of fuel strategies and initiatives aimed at mitigating carbon emissions in maritime operations. Full article

Construction costs have increased significantly since the COVID-19 pandemic due to supply chain disruption, labour shortages, and construction material price hikes. The market is increasingly demanding innovative construction methods that can save construction costs, reduce construction time, and minimise waste and carbon emission. The prefabrication system has been used for years in industrial construction, resulting in better performance in regard to structure stability, the control of wastage, and the optimisation of construction time and cost. In addition, prefabrication has had a positive contribution on resource utilisation in the construction industry. There are various types of prefabricated wall systems. However, the majority of comparative studies have focused on comparing each prefabrication wall system against the conventional construction system, while limited research has been conducted to compare different prefabrication structures. This study examined four prominent prefabricated wall systems, i.e., precast walls, tilt-up walls, prefabricated steel-frame walls, and on-site-cut steel-frame walls, to determine which one is more suitable for the construction of industrial buildings to minimise cost, time delay, and labourer utilisation on construction sites, as well as to enhance structure durability, construction efficiency, and sustainability. One primary case project and five additional projects were included in this study. For the primary case project, data were collected and analysed; for example, a subcontractor cost comparison for supply and installation was conducted, and shop drawings, construction procedures, timelines, and site photos were collected. For the additional five projects, the overall cost data were compared. The main research finding of this study is that factory-made precast walls and tilt-up wall panels require similar construction time. However, on average, tilt-up prefabrication construction can reduce the cost by around 23.55%. It was also found that prefabricated frame walls provide cost and time savings of around 39% and 10.5%, respectively. These findings can provide architects, developers, builders, suppliers, regulators, and other stakeholders with a comprehensive insight into selecting a method of wall construction that can achieve greater efficiency, cost savings, and environmental sustainability in the construction of industrial and commercial buildings. Full article

We investigated the application of artificial intelligence (AI) technology for the inspection of semiconductor process equipment to address key issues such as low production efficiency and high equipment failure rates. The semiconductor industry, being central to modern technology, requires complex and precise processes where even minor defects lead to product failures, negatively impacting yield and increasing costs. Traditional inspection methods are not adequate for modern high-precision, high-efficiency production demands. By integrating advanced AI technologies, such as machine learning, deep learning, and pattern recognition, large volumes of experimental data are collected and analyzed to optimize process parameters, enhance stability, and improve product yield. By using AI, the identification and classification of defects are automated to predict potential equipment failures and reduce downtime and overall costs. By combining AI with automated optical inspection (AOI) systems, a widely used defect detection tool has been developed for semiconductor manufacturing. However, under complex conditions, AOI systems are prone to producing false positives, resulting in overkill rates above 20%. This wastes perfect products and increases the cost due to the need for manual re-inspection, hindering production efficiency. This study aims to improve wafer inspection accuracy using AI technology and reduce false alarms and overkill rates. By developing intelligent detection models, the system automatically filters out false defects and minimizes manual intervention, boosting inspection efficiency. We explored how AI is used to analyze inspection data to identify process issues and optimize workflows. The results contribute to the reduction in labor and time costs, improving equipment performance, and significantly benefitting semiconductor production management. The AI-driven method can be applied to other manufacturing processes to enhance efficiency and product quality and support the sustainable growth of the semiconductor industry. Full article

This study presents the design, fabrication, and experimental validation of a two-finger robotic gripper featuring a 135° V-shaped fingertip profile tailored for lightweight waste collection in laboratory-scale environmental robotics. The gripper was developed with a strong emphasis on cost-effectiveness and manufacturability, utilizing a desktop 3D printer and off-the-shelf servomotors. A four-bar linkage mechanism enables parallel jaw motion and ensures stable surface contact during grasping, achieving a maximum opening range of 71.5 mm to accommodate common cylindrical objects. To validate structural integrity, finite element analysis (FEA) was conducted under a 0.6 kg load, yielding a safety factor of 3.5 and a peak von Mises stress of 12.75 MPa—well below the material yield limit of PLA. Experimental testing demonstrated grasp success rates of up to 80 percent for typical waste items, including bottles, disposable cups, and plastic bags. While the gripper performs reliably with rigid and semi-rigid objects, further improvements are needed for handling highly deformable materials such as thin films or soft bags. The proposed design offers significant advantages in terms of rapid prototyping (a print time of approximately 10 h), modularity, and low manufacturing cost (with an estimated in-house material cost of USD 20 to 40). It provides a practical and accessible solution for small-scale robotic waste-collection tasks and serves as a foundation for future developments in affordable, application-specific grippers. Full article

Environmental pollution remains a significant challenge in animal production. The “ideal protein” concept refers to an amino acid profile that precisely meets the animal’s nutritional requirements, optimizing nutrient utilization and minimizing waste excretion. This study applied untargeted metabolomics to explore metabolic changes induced by limiting AA. Two experimental diets were used in 47-day-old growing rabbits: Met+ (with a methionine level balanced to its optimal utilization) and Met− (with a methionine level that was clearly limiting). A total of 68 blood samples were taken for untargeted metabolomics analysis and 88 were taken for targeted plasmatic urea nitrogen analysis, collected at 08:00 (in ad libitum feeding animals) and 21:00 (after a feeding event in 10 h fasting animals). Our results revealed that both sampling time and diet (at each time point) exerted a significant modulatory influence on the metabolome. Interestingly, the difference between the metabolomes obtained with the different diets was less pronounced at 08:00, likely due to the caecotrophy effect, compared to 21:00, when higher intake and lower caecotrophy frequency were observed. This study identifies pseudourine, citric acid, pantothenic acid, and enterolactone sulfate as promising metabolites that could be targeted in order to refine the ideal protein concept, thus improving nutrient efficiency and reducing the environmental impact of animal production. Full article