Search Results (16,631)

This paper presents a comprehensive study of the mechanical properties of lime-based mortar in an acidic environment, employing both experimental analysis and machine learning to model techniques. Despite the extensive use of lime-based mortar in construction, particularly for the strengthening of structures as externally bonded materials, its behavior under acidic conditions remains poorly understood in the literature. This study aims to address this gap by investigating the mechanical performance of lime-based mortar under prolonged exposure to acidic environments, laying the groundwork for further research in this critical area. In the experimental phase, a commercial hydraulic lime-based mortar was subjected to varying environmental conditions, including acidic solution immersion with a pH of 3.0, distilled water immersion, and dry storage. Subsequently, the specimens were tested under flexure following exposure durations of 1000, 3000, and 5000 h. In the modeling phase, the extreme gradient boosting (XGBoost) algorithm was deployed to predict the mechanical properties of the lime-based mortar by 1000, 3000, and 5000 h of exposure. Using the experimental data, the machine learning models were trained to capture the complex relationships between the stress-displacement curve (as the output) and various environmental and mechanical properties, including density, corrosion, moisture, and exposure duration (as input features). The predictive models demonstrated remarkable accuracy and generalization (using a 4-fold cross-validation approach) capabilities (R² = 0.984 and RMSE = 0.116, for testing dataset), offering a reliable tool for estimating the mortar’s behavior over extended periods in an acidic environment. The comparative analysis demonstrated that mortar samples exposed to an acidic environment reached peak values at 3000 h of exposure, followed by a decrease in the mechanical properties with prolonged acidic exposure. In contrast, specimens exposed to distilled water and dry conditions exhibited an earlier onset of strength increase, indicating different material responses under varying environmental conditions. Full article

This strategic review examines the pivotal role of sustainable methodologies in battery recycling and the recovery of critical minerals from waste batteries, emphasizing the need to address existing technical and environmental challenges. Through a systematic analysis, it explores the application of green organic solvents in mineral processing, advocating for establishing eco-friendly techniques aimed at clipping waste and boosting resource utilization. The escalating demand for and shortage of essential minerals including copper, cobalt, lithium, and nickel are comprehensively analyzed and forecasted for 2023, 2030, and 2040. Traditional extraction techniques, including hydrometallurgical, pyrometallurgical, and bio-metallurgical processes, are efficient but pose substantial environmental hazards and contribute to resource scarcity. The concept of green extraction arises as a crucial step towards ecological conservation, integrating sustainable practices to lessen the environmental footprint of mineral extraction. The advancement of green organic solvents, notably ionic liquids and deep eutectic solvents, is examined, highlighting their attributes of minimal toxicity, biodegradability, and superior efficacy, thus presenting great potential in transforming the sector. The emergence of organic solvents such as palm oil, 1-octanol, and Span 80 is recognized, with advantageous low solubility and adaptability to varying temperatures. Kinetic (mainly temperature) data of different deep eutectic solvents are extracted from previous studies and computed with machine learning techniques. The coefficient of determination and mean squared error reveal the accuracy of experimental and computed data. In essence, this study seeks to inspire ongoing efforts to navigate impediments, embrace technological advancements including artificial intelligence, and foster an ethos of environmental stewardship in the sustainable extraction and recycling of critical metals from waste batteries. Full article

Insecticide use in agriculture has significantly increased over the past decades, reaching 774 thousand metric tons in 2022. This widespread reliance on chemical insecticides has substantial economic, environmental, and human health consequences, highlighting the urgent need for sustainable pest management strategies. Early detection, insect monitoring, and population forecasting through Artificial Intelligence (AI)-based methods, can enable swift responsiveness, allowing for reduced but more effective insecticide use, mitigating traditional labor-intensive and error prone solutions. The main challenge is creating AI models that perform with speed and accuracy, enabling immediate farmer action. This study highlights the innovating potential of such an approach, focusing on the detection and prediction of black aphids under state-of-the-art Deep Learning (DL) models. A dataset of 220 sticky paper images was captured. The detection system employs a YOLOv10 DL model that achieved an accuracy of 89.1% (mAP₅₀). For insect population prediction, random forests, gradient boosting, LSTM, and the ARIMA, ARIMAX, and SARIMAX models were evaluated. The ARIMAX model performed best with a Mean Square Error (MSE) of 75.61, corresponding to an average deviation of 8.61 insects per day between predicted and actual insect counts. For the visualization of the detection results, the DL model was embedded to a mobile application. This holistic approach supports early intervention strategies and sustainable pest management while offering a scalable solution for smart-agriculture environments. Full article

Background: With 11 million sepsis-related deaths worldwide, the development of tools for early prediction of sepsis onset in hospitalized patients is a global health priority. We developed a machine learning algorithm, capable of detecting the early onset of sepsis in all hospital departments. Methods: Predictors of sepsis from 45,127 patients from all departments of Valenciennes Hospital (France) were retrospectively collected for training. The binary classifier SEPSI Score for sepsis prediction was constructed using a gradient boosted trees approach, and assessed on the study dataset of 5270 patient stays, including 121 sepsis cases (2.3%). Finally, the performance of the model and its ability to detect early sepsis onset were evaluated and compared with existing sepsis scoring systems. Results: The mean positive predictive value of the SEPSI Score was 0.610 compared to 0.174 for the SOFA (Sepsis-related Organ Failure Assessment) score. The mean area under the precision–recall curve was 0.738 for SEPSI Score versus 0.174 for the most efficient score (SOFA). High sensitivity (0.845) and specificity (0.987) were also reported for SEPSI Score. The model was more accurate than all tested scores, up to 3 hours before sepsis onset. Half of sepsis cases were detected by the model at least 48 hours before their medically confirmed onset. Conclusions: The SEPSI Score model accurately predicted the early onset of sepsis, with performance exceeding existing scoring systems. It could be a valuable predictive tool in all hospital departments, allowing early management of sepsis patients. Its impact on associated morbidity-mortality needs to be further assessed. Full article

Recently, researchers have been committed to boosting the environmental friendliness and functional performance of multifunctional additives. In this study, an eco-friendly methylbenzotriazole-amide derivative (MeBz-2-C18) was designed and synthesized, with ethylamine serving as the linkage between methylbenzotriazole and the oleoyl chain. The structure of MeBz-2-C18 was characterized by nuclear magnetic resonance (NMR), high-resolution mass spectrometry (HR-MS), Fourier-transform infrared spectroscopy (FT-IR), and thermogravimetric analysis (TGA). Subsequently, the storage stability and tribological behavior of MeBz-2-C18 and the commercial benzotriazole oleamide salt (T406) were comparatively evaluated. The covalently-bonded MeBz-2-C18 exhibits superior thermal stability, along with boosted storage stability and tribological performance in the synthetic base oil. Specifically, 0.5 wt.% addition of MeBz-2-C18 and T406 can reduce the average wear scar diameter (ave. WSD) by 21.6% and 13.9%, respectively. To further explore the micro-mechanism, the electrostatic potential (ESP) and worn surfaces were analyzed with scanning electron microscope-energy dispersive spectrometer (SEM–EDS), X-ray photoelectron spectroscopy (XPS), and density functional theory (DFT) calculations. The results show that MeBz-2-C18 possesses stronger adsorption on the metal surface, and its amide bond preferentially breaks during friction. This reduces the interfacial shear force and promotes the film formation of iron oxides, thus resulting in superior tribological performance. Full article

Emerging pollutants, such as N-acetyl-para-aminophenol, pose significant challenges to environmental sustainability, and Bi₂Fe₂O₂ (BFO) nanomaterials are an emerging class of piezoelectric materials. This study presents a novel piezoelectric-driven Fenton system based on Bi₂Fe₄O₉ nanosheets for the efficient degradation of organic pollutants. BFO nanosheets with varying thicknesses were synthesized, and their piezoelectric properties were confirmed through piezoresponse force microscopy and heavy metal ion reduction experiments. The piezoelectric electrons generated within the BFO nanosheets facilitate the in situ production of hydrogen peroxide, which in turn drives the Fenton-like reaction. Furthermore, the piezoelectric electrons enhance the redox cycling of iron in the Fenton process, boosting the overall catalytic efficiency. The energy band structure of BFO nanosheets is well-suited for this process, enabling efficient hydrogen peroxide generation and promoting Fe³⁺ reduction. The findings demonstrate that thinner BFO nanosheets exhibit superior piezoelectric activity, leading to enhanced catalytic performance. Additionally, the incorporation of gold nanodots onto BFO nanosheets further boosts their piezocatalytic efficiency, particularly in the reduction of Cr (VI). The system exhibited robust oxidative capacity, stability, and recyclability, with reactive oxygen species (ROS) verified via electron paramagnetic resonance spectroscopy. Overall, BFO nanosheets, with their optimal energy band structure, self-supplied hydrogen peroxide, and enhanced Fe³⁺ reduction, represent a promising, sustainable solution for advanced oxidation processes in wastewater treatment and other applications. Full article

Carbon neutrality is an important goal for addressing global warming. It can be achieved by increasing carbon storage and reducing carbon emissions. Vegetation plays a key role in storing carbon, but it is often lost or damaged, especially in areas affected by desertification. Therefore, restoring vegetation in these areas is crucial. Using advanced techniques to improve ecosystem structure can support ecological processes, and enhance soil and environmental conditions, encourage vegetation growth, and boost carbon storage effectively. This study focuses on optimizing Ecological Spatial Networks (ESNs) for revitalization and regional development, employing advanced techniques such as the MCR model for corridor construction, spatial analysis, and Gephi for mapping topological attributes. Various ecological and topological metrics were used to evaluate network performance, while the EFCT model was applied to optimize the ESN and maximize carbon sinks. In the Thal Desert, ecological source patches (ESPs) were divided into four modularity levels (15.6% to 49.54%) and five communities. The northeastern and southwestern regions showed higher ecological functionality but lower connectivity, while the central region exhibited the reverse. To enhance the ESN structure, 27 patches and 51 corridors were added to 76 existing patches, including 56 forest and 20 water/wetland patches, using the EFCT model. The optimized ESN resulted in a 14.97% improvement in carbon sink capacity compared to the unoptimized structure, primarily due to better functioning of forest and wetland areas. Enhanced connectivity between components contributed to a more resilient and stable ESN, supporting both ecological sustainability and carbon sequestration. Full article

This study presents an integrated approach combining the Hardy Cross method and a gradient boosting (GB) optimization model to enhance ventilation systems in underground mines, with a specific application at the Jabal Sayid mine in Saudi Arabia. The Hardy Cross method addresses variations in airflow resistance caused by obstacles within ventilation pathways, enabling accurate predictions of the flow distribution across the network. The GB model complements this by optimizing fan placement, pressure control, and airflow intensity to achieve reduced energy consumption and improved efficiency. The results demonstrate significant improvements in fan efficiency, optimized energy usage, and enhanced ventilation effectiveness, achieving a 31.24% reduction in electricity consumption. This study bridges deterministic and machine learning methodologies, offering a novel framework for the real-time optimization of underground mine ventilation systems. By combining the Hardy Cross method with GB, the proposed approach outperforms traditional techniques in predicting and optimizing airflow distribution under dynamic conditions. Full article

(1) Background: The effectiveness of RF ablation for PVI depends on the lesion location and size to achieve continuous and durable lesion lines. AI and LSI are widely accepted lesion metrics for guiding the ablation procedure. LI dynamics is another parameter that guides PVI and does not rely on input variables. Limited data are available on a direct comparison between lesion metrics. Our study aims to compare RF application durations and influencing factors during index-guided (AI and LSI) and LI-guided approaches by predicting lesion metrics using machine learning. (2) Methods: While the coefficients in AI and LSI formulas are not disclosed, we trained custom machine-learning models based on Random Forest and Gradient Boosting Regressors to predict AI and LSI metrics for LI-guided ablations. (3) Results: The median RF application durations differed significantly between the lesion metrics, with 7.32, 19.91, and 11.92 s for AI-, LSI-, and LI-guided procedures, respectively. Mean CF was found to be an important predictor of RF application duration for the AI- and LSI-guided approaches. (4) Conclusions: Depending on the metric used, the significant differences in RF application durations suggest that an AI-guide approach may allow for shorter RF application durations, followed by LSI-guided and LI-guided procedures. Further studies are needed to evaluate the safety and efficacy of these results in a clinical setting. Full article

The thin-film lithium niobate platform, which is emerging as a promising photonic integration platform, currently lacks a polarization-insensitive grating coupler (GC), a crucial component for polarization-independent fiber interfaces. This limitation restricts its use in many applications, such as polarization-insensitive modulation systems and polarization management. In this study, we propose a polarization-insensitive nonuniform GC, achieved by intersecting optimal TE- and TM-mode grating periods. Based on our simulation results, the proposed design delivers a coupling efficiency (CE) of 80% for TE and 78.5% for TM polarization, with a polarization-dependent loss of less than 0.14 dB at a wavelength of 1550 nm. The inserted gold layer, i.e., that inside the substrate layer, boosts the CEs of the optimal TE- and TM-mode GC by about 50%, resulting in a highly efficient, polarization-insensitive solution. This advancement enables on-chip polarization diversity applications on the thin-film lithium niobate platform. We also investigate the fabrication and alignment tolerances of the proposed design to ensure robust performance under practical conditions. Full article

The aquafeed industry evolved alongside fish farming, utilizing scientific and technological advancements to incorporate a variety of feed additives, supplements, and alternative ingredients in the nutrition and feeding of fish in aquaculture. These advances played a significant role in improving the production, health, and welfare of farmed fish. Recent research in Brazil highlighted the importance of using fish feed additives, such as vitamins, minerals, and amino acids, to ensure that farmed fish receive all the necessary nutrients for growth and health. Functional additives can enhance the immune system, boosting disease resistance and promoting the overall health of fish. Antimicrobial and antiparasitic additives help prevent and treat infections and infestations, reducing the occurrence of disease outbreaks. Additionally, some additives improve feed digestibility, leading to better nutrient absorption and reduced feed requirements. Overall, nutritional strategies are essential for optimizing fish farming practices in Brazil and globally, promoting fish health and sustainability in the industry. This review emphasizes the significance of certain additives, supplements, and ingredients strategically incorporated into experimental feeds for research in Brazilian fish farming. It also underscores the necessity for ongoing research. There is a noticeable trend towards developing more sustainable and efficient feeds, which is essential for the future of sustainable aquaculture. The goal is to minimize environmental impacts while maintaining economic viability in aquaculture operations. Full article

Industry 4.0 (I4.0) represents a transformative approach, integrating technology, production methods, and information and communication technology to enhance industrial value creation. A central I4.0 goal in the energy domain is improving energy efficiency to boost industrial competitiveness and profitability. Given that induction motors account for nearly two-thirds of industrial electrical energy consumption, optimizing their efficiency is crucial. Energy management systems (EMSs) need real-time data to assess motor efficiency, enabling prompt identification and replacement of inefficient motors with alternatives that have optimal efficiency class and rated power for specific applications. This paper introduces a novel non-intrusive method for estimating the load and efficiency of induction motors without disrupting their operation. To reach that goal, the proposed method optimizes the parameters of a set of relationships between output power, input power, and losses with the motor speed, minimizing the error in the estimates. It requires only input electrical power and motor speed measurements to set the model parameters and estimates the load and efficiency using either speed or input power measurements. The experimental results demonstrate that the proposed method, with a mean overall error of less than 3.5% in estimating output power and efficiency, outperforms conventional methods. Full article

Net ecosystem productivity (NEP) is an extremely important flux for terrestrial ecosystems, indicating the value of net ecosystem exchange (NEE) between terrestrial ecosystems and the atmosphere, excluding carbon fluxes from disturbances. Leveraging flux network NEE annual measurements, this study focuses on upscaling the tower-based NEP to a global 250 m resolution dataset with flux site distribution considerations. Firstly, the data augmentation method was presented to address issues related to the uneven spatial distribution of flux sites. Secondly, a random forest model was developed for NEP estimation using the optimized tower-based NEP and remotely sensed and meteorological gridded sample sets, giving an R² value of 0.73 and an RMSE value of 149.83 gC m⁻² yr⁻¹. Finally, a global NEP product at a 250 m resolution was generated (2001–2022, average 13.79 PgC yr⁻¹) and evaluated. In summary, we present a solution to the overestimation of global NEP by data-driven methods, producing a long-time-series, high-resolution NEP dataset that is more comparable to atmospheric inversion results. This dataset enhances comparability with atmospheric inversion results, thereby boosting our confidence in conducting a consistency analysis of terrestrial carbon sinks across different methods within the framework. Full article

Podophyllotoxin is a natural compound belonging to the lignan family and is well-known for its great antitumor activity. However, it shows several limitations, such as severe side effects and some pharmacokinetics problems, including low water solubility, which hinders its application as an anticancer agent. Over the past few years, antitumor research has been focused on developing nanotechnology-based medicines or nanomedicines which allow researchers to improve the pharmacokinetic properties of anticancer compounds. Following this trend, podophyllotoxin nanoconjugates have been obtained to overcome its biopharmaceutical drawbacks and to enhance its antitumor properties. The objective of this review is to highlight the advances made over the past few years (2017–2023) regarding the inclusion of podophyllotoxin in different nanosystems. Among the huge variety of nanoconjugates of diverse nature, drug delivery systems bearing podophyllotoxin as cytotoxic payload are organic nanoparticles mainly based on polymer carriers, micelles, and liposomes. Along with the description of their pharmacological properties as antitumorals and the advantages compared to the free drug in terms of biocompatibility, solubility, and selectivity, we also provide insight into the synthetic procedures developed to obtain those podophyllotoxin-nanocarriers. Typical procedures in this regard are self-assembly techniques, nanoprecipitations, or ionic gelation methods among others. This comprehensive perspective aims to enlighten the medicinal chemistry community about the tendencies followed in the design of new podophyllotoxin-based drug delivery systems, their features and applications. Full article

Red grapes often suffer from postharvest diseases like blue mold and black mold caused by Penicillium expansum and Aspergillus niger. Biological control using beneficial yeasts and bacteria is an effective method to manage these diseases. Rhodotorula sp. and Bacillus sp. are effective microorganisms for the control of postharvest diseases of red grapes. This study combined two yeast strains (Rhodotorula graminis and Rhodotorula babjevae) and two bacterial strains (Bacillus licheniformis and Bacillus velezensis) to investigate their biological control effects on major postharvest diseases of red grapes and explore the underlying physiological mechanisms. Research showed that compound microorganism W3 outperformed the others; it reduced spore germination and germ tube growth of P. expansum and A. niger, while its volatiles further inhibited pathogen growth. Additionally, the treatment enhanced the antioxidant capacity of grapes and increased resistance to pathogens by boosting peroxidase activities, superoxide dismutase, catalase and ascorbate peroxidase, phenylalanine ammonolyase, and polyphenol oxidase. Furthermore, the combined treatment increased the activity and accumulation of antifungal compounds such as total phenols and flavonoids, thereby improving disease resistance and reducing decay. Therefore, composite microorganisms combining various antagonistic strains may offer a viable substitute for tackling postharvest diseases in red grapes. Full article