Search Results (157)

Spent coffee grounds (SCG) are a widely available agro-industrial residue rich in carbon and phenolic compounds, presenting significant potential for biotechnological valorization. This study evaluated the use of SCG as a suitable substrate for fungal laccase production and the application of the resulting fermented biomass (RFB), a mixture of fermented SCG and fungal biomass as a biosorbent for textile dye removal. Two fungal strains, namely Lentinus crinitus UCP 1206 and Trametes sp. UCP 1244, were evaluated in both submerged (SmF) and solid-state fermentation (SSF) using SCG. L. crinitus showed superior performance in SSF, reaching 14.62 U/g of laccase activity. Factorial design revealed that a lower SCG amount (5 g) and higher moisture (80%) and temperature (30 °C ± 0.2) favored enzyme production. Fourier transform infrared (FTIR) spectroscopy and scanning electron microscopy (SEM) analyses confirmed significant structural degradation of SCG after fermentation, especially in SSF. Furthermore, SCG and RFB were chemically activated and evaluated as biosorbents. The activated carbon from SCG (ACSCG) and RFB (ACRFB) exhibited high removal efficiencies for Remazol dyes, comparable to commercial activated carbon. These findings highlight the potential of SCG as a low-cost, sustainable resource for enzyme production and wastewater treatment, contributing to circular bioeconomy strategies. Full article

Naive Bayes (NB) classifiers are widely used for their simplicity, computational efficiency, and interpretability. However, their predictive performance can degrade significantly in real-world settings where the conditional independence assumption is often violated. More complex NB variants address this issue but typically introduce structural complexity or require explicit dependency modeling, limiting their scalability and transparency. This study proposes two lightweight ensemble-based extensions—randomized feature naive Bayes (RF-NB) and randomized feature bootstrapped naive Bayes (RFB-NB)—designed to enhance robustness and predictive stability without altering the underlying NB model. By integrating randomized feature selection and bootstrap resampling, these methods implicitly reduce feature dependence and noise-induced variance. Evaluation across twenty real-world datasets spanning medical, financial, and industrial domains demonstrates that RFB-NB consistently outperformed classical NB, RF-NB, and k-nearest neighbor in several cases. Although random forest achieved higher average accuracy overall, RFB-NB demonstrated comparable accuracy with notably lower variance and improved predictive stability specifically in datasets characterized by high noise levels, large dimensionality, or significant class imbalance. These findings underscore the practical and complementary advantages of RFB-NB in challenging classification scenarios. Full article

Transitioning to sustainable energy systems is crucial for reducing greenhouse gas (GHG) emissions, especially in remote industrial operations where diesel generators remain the dominant power source. This study examines the feasibility of integrating a redox flow battery (RFB) storage system to optimize wind energy utilization at the Raglan mining site in northern Canada, with the goal of reducing diesel dependency, enhancing grid stability, and improving energy security. To evaluate the effectiveness of this hybrid system, a MATLAB R2024b-based simulation model was developed, incorporating wind energy forecasting, load demand analysis, and economic feasibility assessments across multiple storage and wind penetration scenarios. Results indicate that deploying 12 additional E-115 wind turbines combined with a 20 MW/160 MWh redox flow battery system could lead to diesel savings of up to 63.98%, reducing CO₂ emissions by 68,000 tonnes annually. However, the study also highlights a key economic challenge: the high Levelized Cost of Storage (LCOS) of CAD (Canadian dollars) 7831/MWh, which remains a barrier to large-scale implementation. For the scenario with high diesel economy, the LCOS was found to be CAD 6110/MWh, and the corresponding LCOE was CAD 590/MWh. While RFB integration improves system reliability, its economic viability depends on key factors, including reductions in electrolyte costs, advancements in operational efficiency, and supportive policy frameworks. This study presents a comprehensive methodology for evaluating energy storage in off-grid industrial sites and identifies key challenges in scaling up renewable energy adoption for remote mining operations. Full article

To improve the flow mass transfer inside the electrodes and the efficiency of an all-iron redox flow battery, a semi-solid all-iron redox flow battery is presented experimentally. A slurry electrode is designed to replace the traditional porous electrode. Moreover, the effects of an additional external magnetic field are further investigated in the semi-solid battery experiment. The results show that the mass transfer of the slurry in the battery flow channel and the prolonged discharge time are significantly affected by the additional external magnetic fields. In addition, a three-dimensional model of the semi-solid all-iron redox flow battery is presented in detail, and it is verified to be reliable by experimental data. The simulation results show that the ion concentration distributions in the battery become more uniform with the increase in the flow rate and the initial concentration. Furthermore, it is also found that the size of the flow channel influences the mass transfer efficiency of the slurry. After optimizing the flow channel, it is found that when the flow channel length of the slurry inlet and outlet section is 2 cm, the operating efficiency of the semi-solid battery shows an increasing trend. This work provides comprehensive insight into the improvement of the performances of flow batteries, which will be conducive to the practical application of flow batteries. Full article

The copper redox flow battery (CuRFB) stands out as a promising hybrid redox flow battery technology, offering significant advantages in electrolyte stability. Within the CuBER H-2020 project framework, this study addresses critical phenomena such as electrodeposition at the negative electrode during charging and copper crossover through the membrane, which influence capacity fading. A comprehensive two-dimensional physicochemical model of the CuRFB cell was developed using COMSOL Multiphysics, providing insights into the distribution of electroactive materials over time. The model was validated against experimental cycling data, demonstrating a Root Mean Square Error (RMSE) of 0.0212 in voltage estimation. Least-squares parameter estimation, utilizing Bound Optimization by Quadratic Approximation, was conducted to determine active material diffusivities and electron transfer coefficients. The results indicate that higher current densities and lower flow rates lead to increased copper deposition near the inlet, significantly impacting the battery’s State of Health (SoH). These findings highlight the importance of considering fluid dynamics and ion concentration distribution to improve battery performance and longevity. The study’s insights are crucial for optimizing and scaling up CuRFB operations, guiding potential cell-scale-up strategies into stack-level configurations. Full article

The 2,6-bis(pyrazol-1-yl)pyridine (bpp) ligand family is widely recognized for its versatile coordination abilities and broad functionalization potential. This review examines bpp and its modifications at the pyridine ring’s 4-position, focusing on their influence on magnetic, optical, and electronic properties. Key applications discussed include spin crossover (SCO), single-ion and single-molecule magnetism (SIM and SMM), luminescence, redox flow batteries (RFBs), and photonic devices. We provide a comprehensive overview of ligand modifications involving carboxylates, extended aromatic systems, radicals, and redox-active units such as tetrathiafulvalene (TTF), alongside supramolecular architectures. The review highlights fundamental design principles, particularly the role of substituents in tuning the SCO behavior, photophysical properties, and self-assembly into functional nanostructures. Notable advancements include SCO-driven conductivity modulation, reversible luminescent switching, and amphiphilic bpp-based vesicles for multicolor emission. By analyzing the interplay between ligand structure and magnetic, optical, and electronic functions, we provide insights into the potential of bpp derivatives for advanced materials design. This review presents recent experimental and theoretical developments, offering a foundation for future exploration of bpp-based compounds in multifunctional devices. Full article

Redox Flow Batteries (RFBs) are promising large-scale Energy Storage Systems, which support the integration of renewable energies into the current electric grid. Emerging chemistries for electrolytes, such as Polyoxometalates (POMs), are being studied. POMs have attracted great interest because of their reversible multi-electron transfers and the possibility of tuning their electrochemical properties. Recently, the cobalt-containing Keggin-type species [CoW₁₂O₄₀]⁶⁻ (CoW₁₂) has been successfully implemented in a symmetric RFB, and its further implementation calls for new materials for the membrane to enhance its cell performance. In this work, different types of ion exchange membranes (Nafion™-NR212, FAPQ-330 and Amphion™) were tested. The electrolyte uptake, swelling, conductivity and permeability of the membranes in the CoW₁₂ electrolyte, as well as a detailed cell performance study, are reported herein. Better performance results ascribed to the robustness, efficiency and energy density of the system were found for Nafion™-NR212, with 88.5% energy efficiency, 98.9% capacity retention and 3.1 Wh L⁻¹ over 100 cycles at 20 mA cm⁻². FAPQ-330 and Amphion membranes showed large capacity fade (up to 0.2%/cycle). Crossover and the low conductivity of these membranes in the mild pH conditions of the electrolyte were revealed to be responsible for the reduced cell performance. Full article

This study optimizes the 3D extrusion printing parameters—water-to-flour ratio (X₁), temperature (X₂), and printing speed (X₃)—for raw (RFB) and extruded (EFB) dehulled Andean fava bean flours to maximize print quality and minimize structural defects. A 2³ central composite design combined with response surface methodology (RSM) was used to identify the optimal conditions for achieving geometric precision, surface homogeneity, and textural stability. Physicochemical analyses showed that extrusion cooking substantially modified the composition and rheology of the flour. Compared with RFB, EFB exhibited lower protein and fiber contents, a higher proportion of digestible carbohydrates, and reduced rheological parameters (τ₀, K, G′, G″), which facilitated printing. The evaluation of different parameter combinations revealed notable differences between the two flours, with X₁ and X₂ exerting the greatest influence on print quality. For RFB, the highest desirability (0.853) was achieved at X₁ = 0.806, X₂ = 23.18 °C, and X₃ = 2470.5 mm/min, yielding more uniform and firmer printed structures. In contrast, EFB reached a desirability of 0.844 at X₁ = 1.66 °C, X₂ = 56.82 °C, and X₃ = 1505.43 mm/min, indicating its outstanding geometric accuracy and robustness. In conclusion, raw flour requires higher hydration and lower temperatures to prevent excessive viscosity. In contrast, extruded flour benefits from low water and high temperatures to achieve stable structures and firm textures. These findings demonstrate the feasibility of using Andean fava bean flour in 3D food printing to create nutrient-dense, functional foods with improved printability. This work offers practical applications for developing personalized foods—such as customized meals for individuals with specific dietary requirements—while contributing to sustainable and secure food production. Future research should address long-term storage, post-printing drying methods, and scaling production. Full article

Redox flow batteries (RFBs) are known for their exceptional attributes, including remarkable energy efficiency of up to 80%, an extended lifespan, safe operation, low environmental contamination concerns, sustainable recyclability, and easy scalability. One of their standout characteristics is the separation of electrolytes into two distinct tanks, isolating them from the electrochemical stack. This unique design allows for the separate design of energy capacity and power, offering a significantly higher level of adaptability and modularity compared to traditional technologies like lithium batteries. RFBs are also an improved technology for storing renewable energy in small or remote communities, benefiting from larger storage capacity, lower maintenance requirements, longer life, and more flexibility in scaling the battery system. However, flow batteries also have disadvantages compared to other energy storage technologies, including a lower energy density and the potential use of expensive or scarce materials. Despite these limitations, the potential benefits of flow batteries in terms of scalability, long cycle life, and cost effectiveness make them a key strategic technology for progressing to net zero. Specifically, in Australia, RFBs are good candidates for storing the increasingly large amount of energy generated from green sources such as photovoltaic panels and wind turbines. Additionally, the geographical distribution of the population around Australia makes large central energy storage economically and logistically difficult, but RFBs can offer a more locally tailored approach to overcome this. This review examines the status of RFBs and the viability of this technology for use in Australia. Full article

Shiga toxin-producing Escherichia coli (STEC) is a foodborne pathogen and known to reside naturally in cattle. The application of untreated biological soil amendments of animal origin on fresh produce fields results in unique food safety challenges. It is critical to identify farm manure management practices to mitigate pre-harvest pathogen contamination. The objective of this study was to quantify the prevalence and level of STEC in cattle manure in the Mid-Atlantic region of the United States. A total of 161 bovine manure samples were collected from 13 cattle farms between 2016 and 2018. The samples were enriched with non-selective and selective media and quantified following a Most-Probable Number (MPN) assay. Among the recovered STEC isolates, PCR was performed to determine the presence of stx, eae, and rfbE. Clermont PCR was performed to identify phylogenetic groups of isolates. Of the 13 farms, 11 had STEC populations between <1.0 and >5.6 log MPN/g. Farm, humidity, and sampling year significantly (p < 0.05) influenced STEC populations in bovine manure. Of the 108 isolates, 50% were stx+ and 14% eae+. Phylogenetic group analysis revealed that 46% of the isolates belonged to group A, 19% to B1, 7% to B2, and 28% to D. Group D had the highest prevalence of stx+ and eae+ and group B1 had the lowest prevalence. Results suggest STEC geographical distribution in the Mid-Atlantic region is farm-specific, and climatic conditions can be critical for its survival and dissemination. Full article

With the rapid development of machine learning and deep learning technology, cow face detection technology has achieved remarkable results. Traditional contact cattle identification methods are costly; are easy to lose and tamper with; and can lead to a series of security problems, such as untimely disease prevention and control, incorrect traceability of cattle products, and fraudulent insurance claims. In order to solve these problems, this study explores the application of cattle face detection technology in cattle individual detection to improve the accuracy of detection, an approach that is particularly important in smart animal husbandry and animal behavior analysis. In this paper, we propose a novel cow face detection network based on YOLOv7 improvement, named CFR-YOLO. First of all, the method of extracting the features of a cow’s face (including nose, eye corner, and mouth corner) is constructed. Then, we calculate the frame center of gravity and frame size based on these feature points to design the cow face detection CFR-YOLO network model. To optimize the performance of the model, the activation function of FReLU is used instead of the original SiLU activation function, and the CBS module is replaced by the CBF module. The RFB module is introduced in the backbone network; and in the head layer, the CBAM convolutional attention module is introduced. The performance of CFR-YOLO is compared with other mainstream deep learning models (including YOLOv7, YOLOv5, YOLOv4, and SSD) on a self-built cow face dataset. Experiments indicate that the CFR-YOLO model achieves 98.46% accuracy (precision), 97.21% recall (recall), and 96.27% average accuracy (mAP), proving its excellent performance in the field of cow face detection. In addition, comparative analyses with the other four methods show that CFR-YOLO exhibits faster convergence speed while ensuring the same detection accuracy; and its detection accuracy is higher under the condition of the same model convergence speed. These results will be helpful to further develop the cattle identification technique. Full article

Background: A machine learning prognostic mortality scoring system was developed to address challenges in patient selection for clinical trials within the Intensive Care Unit (ICU) environment. The algorithm incorporates Red blood cell Distribution Width (RDW) data and other demographic characteristics to predict ICU mortality alongside existing ICU mortality scoring systems like Simplified Acute Physiology Score (SAPS). Methods: The developed algorithm, defined as a Mixed-effects logistic Random Forest for binary data (MixRFb), integrates a Random Forest (RF) classification with a mixed-effects model for binary outcomes, accounting for repeated measurement data. Performance comparisons were conducted with RF and the proposed MixRFb algorithms based solely on SAPS scoring, with additional evaluation using a descriptive receiver operating characteristic curve incorporating RDW’s predictive mortality ability. Results: MixRFb, incorporating RDW and other covariates, outperforms the SAPS-based variant, achieving an area under the curve of 0.882 compared to 0.814. Age and RDW were identified as the most significant predictors of ICU mortality, as reported by the variable importance plot analysis. Conclusions: The MixRFb algorithm demonstrates superior efficacy in predicting in-hospital mortality and identifies age and RDW as primary predictors. Implementation of this algorithm could facilitate patient selection for clinical trials, thereby improving trial outcomes and strengthening ethical standards. Future research should focus on enriching algorithm robustness, expanding its applicability across diverse clinical settings and patient demographics, and integrating additional predictive markers to improve patient selection capabilities. Full article

Polymer-based aqueous redox flow batteries (RFBs) are attracting increasing attention as a promising next-generation energy storage technology due to their potential for low cost and environmental friendliness. The search for new redox-active organic compounds for incorporation into polymer materials is ongoing, with anolyte-type compounds in high demand. In response to this need, we have synthesized and tested a range of new water-soluble redox-active s-tetrazine derivatives, including both low molecular weight compounds and polymers with different architectures. S-tetrazines are some of the smallest organic molecules that can undergo a reversible two-electron reduction in protic media, making them a promising candidate for anolyte applications. We have successfully modified linear polyacrylic acid and poly(N-isopropylacrylamide-co-acrylic acid) microgels with pendent 1,2,4,5-tetrazine groups. Electrochemical testing has shown that the new tetrazine-containing monomers and, importantly, the water-soluble redox polymers, both linear and microgel, demonstrate the chemical reversibility of the reduction process in an aqueous solution containing acetate buffer. This expands the range of water-soluble anodic materials suitable for water-based organic RFBs. The reduction potential value can be adjusted by changing the substituents in the tetrazine core. It is also worth noting that the choice of electrode material plays an important role in the kinetics of the tetrazine reaction: the use of carbon electrodes is particularly beneficial. Full article

In the process of steel strip production, the accuracy of defect detection remains a challenge due to the diversity of defect types, complex backgrounds, and noise interference. To improve the effectiveness of surface defect detection in steel strips, we propose an enhanced detection model known as YOLOv8-BSPB. First, we propose a novel pooling layer module, SCRD, which replaces max pooling with average pooling. This module introduces the receptive field block (RFB) and deformable convolutional network version 4 (DCNv4) to obtain learnable offsets, allowing convolutional kernels to flexibly move and deform on the input feature map, thus, more effectively extracting multi-scale features. Second, we integrate a polarized self-attention (PSA) mechanism to improve the model’s feature representation and enhance its ability to focus on relevant information. Additionally, we incorporate the BAM attention mechanism after the C2f module to strengthen the model’s feature selection capabilities. A bidirectional feature pyramid network is introduced at the neck of the model to improve feature transmission efficiency. Finally, the WIoU loss function is employed to accelerate the model’s convergence speed and enhance regression accuracy. Experimental results on the NEU-DET dataset demonstrate that the improved model achieves a classification accuracy of 81.3%, an increase of 4.9% over the baseline, with a mean average precision of 86.9%. The model has a parameter count of 5.5 M and operates at 103.1 FPS. To validate the model’s effectiveness, we conducted tests on the Kaggle steel strip dataset and our custom dataset, where the average accuracy improved by 2.3% and 5.5%, respectively. The experimental results indicate that the model meets the requirements for real-time, lightweight, and portable deployment. Full article

The applicability of Hybrid Energy Storage Systems (HESSs) has been shown in multiple application fields, such as Charging Stations (CSs), grid services, and microgrids. HESSs consist of an integration of two or more single Energy Storage Systems (ESSs) to combine the benefits of each ESS and improve the overall system performance. In this work, we propose a novel power management controller called the Hybrid Controller for the efficient HESS’s charging and discharging, considering the State of Charge (SoC) of the HESS and the dynamic supply and load. The Hybrid Controller optimises the use of the HESS, i.e., minimises the amount of energy drawn from and discharged to the grid, thus utilising and prioritising the provided Photovoltaic (PV) power. The performance of our proposal was assessed via simulation using various evaluation metrics, i.e., Autarky, charge/discharge cycle, and Self-Consumption (SC), where we defined 24 scenarios in different locations in Germany. Full article