Search Results (9,312)

Glycosaminoglycans (GAGs) are essential regulators of numerous biological processes through their interactions with growth factors, chemokines, cytokines, and enzymes. Their structural diversity and heterogeneity, however, limit reproducibility and translational use, as native GAGs are typically obtained from animal-derived sources with notable batch-to-batch variability. To overcome these challenges, a wide range of GAG mimetics has been developed with the aim of replicating or modulating the biological functions of native GAGs while offering improved structural definition, accessibility, and therapeutic potential. Polysaccharide-based GAG mimetics, including derivatives of heparan sulfate, hyaluronan, dextran, and other natural glycans, represent one major strategy, whereas non-saccharide-based mimetics provide alternative scaffolds with enhanced stability and selectivity. Both approaches have yielded compounds that serve as valuable tools for dissecting GAG/protein interactions and as candidates for therapeutic development. Biomedical applications of GAG mimetics span diverse areas such as cancer, cardiovascular and inflammatory diseases, bone and cartilage regeneration, wound healing, and infectious diseases. This mini-review summarizes key developments in the design and synthesis of GAG mimetics, highlights their potential biomedical applications, and discusses current challenges and future perspectives in advancing them toward clinical translation. Full article

The integrity of natural gas pipelines will decrease with an increase in operating time, thus causing pipeline leaks and accidents. However, it is challenging to improve the precision and automation of existing sensors to raise leak prediction and classification precision. Therefore, based on deep learning, a 1D convolutional neural network (CNN) incorporating the channel attention mechanism is proposed for recognizing and classifying the type of natural gas pipeline leakage. Firstly, the data reconstruction of the leaked acoustic signals, which have been classified by energy modes, is performed by feature augmentation and Bessel filtering. Subsequently, a lightweight CNN is proposed, and an attention mechanism is introduced to optimize the model performance. The results show that the training performance of the network with the attention mechanism is superior to that of the original network and the network with batch normalization. The attention mechanism network is then used to train the leakage signals with different features of engineering parameters. Finally, the test accuracy achieves 97.81%, validating the effectiveness of the proposed method for identifying and classifying natural gas leaks. It presents new ideas for the implementation of deep learning in the natural gas and chemical industries. Full article

The current paper investigated the potential of oleaginous fungus Rhizopus oryzae B97 for lipid accumulation under varying process variables. The fungal strain was isolated from bread mold and analyzed for its potential to grow on sludge with simultaneous production of microbial lipids. The sludge sample was sourced from the wastewater treatment plant located in Sector I-9, Islamabad. The effects of various process variables, such as pH, temperature, carbon and nitrogen sources, and shaking, on lipid accumulation, cell dry weight (CDW), chemical oxygen demand (COD), and volatile solids (VS) removal were investigated. It was found that glucose and yeast promoted the maximum lipid accumulation. At the same time, the fungal biomass reached its maximum value of up to 64% at 30 °C and at pH 4 (CDW: 28 g/L). These process conditions also improved the sludge treatment efficiency, achieving 68% COD and 55% VS removal in 168 h. FTIR analysis of the accumulated lipids indicated strong characteristic peaks of functional groups associated with fatty acids. The GC-MS analysis confirmed the production of essential FAMEs required in biodiesel production from the corresponding fatty acids, such as oleic acid, palmitic acid, stearic acid, and erucic acid. Operation in a continuous-shaking aerobic batch reactor (CSABR) system under optimum conditions further improved the process efficiency. Overall, the results indicated the competent potential of oleaginous fungus Rhizopus oryzae B97 for lipid-based biofuel production through fatty acid transesterification. Full article

This study investigated the preference behaviour of 24 four-week-old weaned piglets under different lighting conditions (0 lux with 0 Kelvin vs. 80 lux with 3000 Kelvin vs. 6500 Kelvin). Two trials with 12 piglets each were conducted over five weeks in a room with four interconnected pens, allowing free movement between the pens. Pens A and B were nearly dark (~0 lux), while pen C (80 lux, 3000 Kelvin) and pen D (80 lux, 6500 Kelvin) were illuminated. On three days in weeks 1, 3 and 5, behaviour (lying, eating and activity) was recorded using video observations and a 5 min time sampling method. Cleanliness was also monitored daily. In the first week, piglets in the first batch preferred the darkened pens, whereas piglets in the second batch preferred illuminated pens, especially when the colour temperature was 3000 Kelvin. By the third week, piglets in the second batch now preferred darker areas. In the fifth week, the piglets spent more time in the dark in the mornings and evenings but showed no preference for colour temperature. The darkened pens remained mostly clean, whereas pen D, which had a light colour temperature of 6500 Kelvin, was the most soiled. The results show that piglet behaviour changes with age and the time of day, suggesting that lighting concepts can be adapted to improve both animal welfare and pen hygiene. Full article

The discharge of dye-laden effluents remains an environmental challenge since conventional treatments remove color but not the organic load. This study systematically compared anodic oxidation (AO), electro-Fenton (EF), and photoelectro-Fenton (PEF) processes for three representative industrial dyes, such as Coriasol Red CB, Brown RBH, and Blue VT, and their ternary mixture, using boron-doped diamond (BDD) and Ti/IrO₂–SnO₂–Sb₂O₅ (MMO) anodes. Experiments were conducted in a batch reactor with 50 mM Na₂SO₄ at pH = 3.0 and current densities of 20–60 mA cm⁻². Kinetic analysis showed that AO-BDD was most effective at low pollutant loads, EF-BDD became superior at medium loads due to efficient H₂O₂ electrogeneration, and PEF-MMO dominated at higher loads by fast UVA photolysis of surface Fe(OH)²⁺ complexes. In a ternary mixture of 120 mg L⁻¹ of dyes, EF-BDD and PEF-MMO achieved >98% decolorization in 22–23 min with pseudo-first-order rate constants of 0.111–0.136 min⁻¹, whereas AO processes remained slower. COD assays revealed partial mineralization of 60–80%, with EF-BDD providing the most consistent reduction and PEF-MMO minimizing treatment time. These findings confirm that decolorization overestimates efficiency, and electrode selection must be tailored to dye structure and effluent composition. Process selection rules allow us to conclude that EF-BDD is the best robust dark option, and PEF-MMO, when UVA is available, offers practical guidelines for cost-effective electrochemical treatment of textile wastewater. Full article

Image-based geo-localization is a challenging task that aims to determine the geographic location of a ground-level query image captured by an Unmanned Ground Vehicle (UGV) by matching it to geo-tagged nadir-view (top-down) images from an Unmanned Aerial Vehicle (UAV) stored in a reference database. The challenge comes from the perspective inconsistency between matched objects. In this work, we propose a novel metric learning scheme for hard exemplar mining to improve the performance of cross-view geo-localization. Specifically, we introduce a Dynamic Online Cross-Batch (DOCB) hard exemplar mining scheme that solves the problem of the lack of hard exemplars in mini-batches in the middle and late stages of training, which leads to training stagnation. It mines cross-batch hard negative exemplars according to the current network state and reloads them into the network to make the gradient of negative exemplars participating in back-propagation. Since the feature representation of cross-batch negative examples adapts to the current network state, the triplet loss calculation becomes more accurate. Compared with methods only considering the gradient of anchors and positives, adding the gradient of negative exemplars helps us to obtain the correct gradient direction. Therefore, our DOCB scheme can better guide the network to learn valuable metric information. Moreover, we design a simple Siamese-like network called multi-scale feature aggregation (MSFA), which can generate multi-scale feature aggregation by learning and fusing multiple local spatial embeddings. The experimental results demonstrate that our DOCB scheme and MSFA network achieve an accuracy of 95.78% on the CVUSA dataset and 86.34% on the CVACT_val dataset, which outperforms those of other existing methods in the field. Full article

Microbial fuel cells (MFCs) represent a sustainable alternative for wastewater treatment by simultaneously removing organic pollutants and generating energy. In this work, ceramic membranes were fabricated from low-cost locally available clays and tested as separators in MFCs. The systems achieved chemical oxygen demand (COD) removal efficiencies of up to 95%, comparable to those obtained with conventional Nafion membranes. In terms of energy performance, the ceramic membranes maintained open-circuit voltages of 0.80 ± 0.05 V during batch operation with voltage generation cycles ranging from 6 to 3 days, and delivered power densities between 140 and 180 mW/m² under closed-circuit conditions. These values were very similar to those obtained with Nafion. The ceramic membranes maintained consistent COD removal performance during successive batch feeding cycles, confirming their stability under repeated operation. Overall, these results highlight the potential of ceramic materials as cost-effective and robust alternatives for large-scale wastewater treatment using MFC technology. Full article

Healthcare systems worldwide have faced unprecedented pressure during crises such as the COVID-19 pandemic, exposing limits in managing scarce hospital resources. Many predictive models remain static, unable to adapt to new variants, shifting conditions, or diverse patient populations. This work proposes a dynamic prioritization framework that recalculates severity scores in batch mode when new factors appear and applies them instantly through a streaming pipeline to incoming patients. Unlike approaches focused only on fixed mortality or severity risks, our model integrates dual datasets (survivors and non-survivors) to refine feature selection and weighting, enhancing robustness. Built on a big data infrastructure (Spark/Databricks), it ensures scalability and responsiveness, even with millions of records. Experimental results confirm the effectiveness of this architecture: The artificial neural network (ANN) achieved 98.7% accuracy, with higher precision and recall than traditional models, while random forest and logistic regression also showed strong AUC values. Additional tests, including temporal validation and real-time latency simulation, demonstrated both stability over time and feasibility for deployment in near-real-world conditions. By combining adaptability, robustness, and scalability, the proposed framework offers a methodological contribution to healthcare analytics, supporting fair and effective hospitalization prioritization during pandemics and other public health emergencies. Full article

The growing demand for sustainable solutions in the plastics industry has highlighted the need to reintroduce post-industrial polymer waste into high-performance applications. This study focuses on the mechanical recycling of automotive scraps containing variable proportions of polycarbonate (PC), acrylonitrile–butadiene–styrene (ABS), and a commercial PC/ABS blend. After determining the composition of two representative batches, a screening of seven commercial compatibilizers and impact modifiers was performed to improve impact strength. Among them, an ethylene–methyl acrylate–glycidyl methacrylate (E-MA-GMA) terpolymer was identified as the most effective additive. Its influence was further investigated through a mixture design approach, varying the composition of the three polymer phases and the additive content (0–10 wt.%). The resulting response surface model revealed a significant increase in impact resistance in PC-rich formulations with increasing E-MA-GMA content, while ABS and PC/ABS showed more complex trends. Rheological, mechanical, and thermal analyses supported the observed behavior, suggesting improved matrix compatibility and reduced degradation during processing. The proposed model enables the prediction of impact performance across a wide range of compositions, offering a practical tool for the optimization of recycled blends. These findings support the potential of targeted compatibilization strategies for closed-loop recycling in the automotive sector. Full article

Labor shortages in seedling nurseries, particularly in manual inspection and replanting, hinder operational efficiency despite advancements in automation. This study aims to develop a cost-effective, GPU-free machine vision system to automate the detection of deficient seedlings in plug trays, specifically for small-scale nursery operations. The proposed Deficiency Detection and Replanting Positioning (DDRP) machine integrates low-cost components including an Intel RealSense Depth Camera D435, Raspberry Pi 4B, stepper motors, and a programmable logic controller (PLC). It utilizes OpenCV’s Haar cascade algorithm, HSV color space conversion, and Otsu thresholding to enable real-time image processing without GPU acceleration. The proposed Deficiency Detection and Replanting Positioning (DDRP) machine integrates low-cost components including an Intel RealSense Depth Camera D435, Raspberry Pi 4B, stepper motors, and a programmable logic controller (PLC). It utilizes OpenCV’s Haar cascade algorithm, HSV color space conversion, and Otsu thresholding to enable real-time image processing without GPU acceleration. Under controlled laboratory conditions, the DDRP-Machine achieved high detection accuracy (96.0–98.7%) and precision rates (82.14–83.78%). Benchmarking against deep-learning models such as YOLOv5x and Mask R-CNN showed comparable performance, while requiring only one-third to one-fifth of the cost and avoiding complex infrastructure. The Batch Detection (BD) mode significantly reduced processing time compared to Continuous Detection (CD), enhancing real-time applicability. The DDRP-Machine demonstrates strong potential to improve seedling inspection efficiency and reduce labor dependency in nursery operations. Its modular design and minimal hardware requirements make it a practical and scalable solution for resource-limited environments. This study offers a viable pathway for small-scale farms to adopt intelligent automation without the financial burden of high-end AI systems. Future enhancements, adaptive lighting and self-learning capabilities, will further improve field robustness and including broaden its applicability across diverse nursery conditions. Full article

This study investigates the adsorption behavior of natural sepiolite for the removal of cadmium (Cd²⁺), copper (Cu²⁺), and nickel (Ni²⁺) ions from aqueous solutions under batch conditions. The sepiolite was extensively characterized prior to adsorption experiments. Mineralogical analysis confirmed the presence of crystalline sepiolite, while DTG-TGA revealed thermal stability with distinct weight loss linked to surface and structural water. BET analysis indicated a high surface area of 194 m²/g and a mesoporous structure favorable for adsorption. Batch experiments evaluated the effects of contact time, pH, adsorbent dosage, and initial metal concentration. Adsorption was highly pH-dependent, with maximum removal near-neutral pH values. Higher adsorbent dosages reduced in a lower adsorption capacity per unit mass, primarily because the fixed amount of solute was distributed over a larger number of available sites, leading to unsaturation of the adsorbent surface and possible particle agglomeration. Isotherm modeling revealed that the Langmuir model provided the best fit, indicating monolayer adsorption with maximum adsorption capacities of 15.95 mg/g for Cd(II), 37.31 mg/g for Cu(II), and 17.83 mg/g for Ni(II). Langmuir constants indicated favorable interactions. Kinetics showed rapid adsorption within the first hour, reaching equilibrium at 240 min through surface adsorption and intraparticle diffusion. Cu(II) exhibited the fastest uptake, while Ni(II) adsorbed more slowly, suggesting differences in diffusion rates among the metal ions. Desorption using 0.1 N HCl achieved over 80% efficiency for all metals, confirming sepiolite reusability. Overall, raw sepiolite is an effective, low-cost adsorbent for removing potentially toxic elements from water. Full article

This paper introduces TRIDENT-DE, a novel ensemble-based variant of Differential Evolution (DE) designed to tackle complex continuous global optimization problems. The algorithm leverages three complementary trial vector generation strategies best/1/bin, current-to-best/1/bin, and pbest/1/bin executed within a self-adaptive framework that employs jDE parameter control. To prevent stagnation and premature convergence, TRIDENT-DE incorporates adaptive micro-restart mechanisms, which periodically reinitialize a fraction of the population around the elite solution using Gaussian perturbations, thereby sustaining exploration even in rugged landscapes. Additionally, the algorithm integrates a greedy line-refinement operator that accelerates convergence by projecting candidate solutions along promising base-to-trial directions. These mechanisms are coordinated within a mini-batch update scheme, enabling aggressive iteration cycles while preserving diversity in the population. Experimental results across a diverse set of benchmark problems, including molecular potential energy surfaces and engineering design tasks, show that TRIDENT-DE consistently outperforms or matches state-of-the-art optimizers in terms of both best-found and mean performance. The findings highlight the potential of multi-operator, restart-aware DE frameworks as a powerful approach to advancing the state of the art in global optimization. Full article

This paper presents a novel batch Forward Osmosis (FO) process for hydropower generation. It focuses on analyzing the parameters needed to make the proposed osmotic power plant implementable with currently available technology. Starting from the solution–diffusion model and using flow and mass balance equations, the equations that describe the behavior of the system over time are obtained. Membrane orientation, concentration polarization, reverse solute flux, and membrane fouling are not considered. The equations for calculating the operation time for the charging and discharging stages are obtained. Also, an equation for calculating the required membrane area to make the duration of the two stages the same is obtained. The results indicate that a volume of approximately $30.4 {\mathrm{m}}^3$ discharging through a $0.84 \mathrm{i}\mathrm{n}\mathrm{c}\mathrm{h}$ diameter outflow jet towards a turbine could generate an energy of $25 \mathrm{k}\mathrm{w}·\mathrm{h}.$ The discharging stage would take $12 \mathrm{h}$, and with a membrane with a water permeability constant $A_m=1.763·{10}^{-12} \mathrm{m}/(\mathrm{s}·\mathrm{P}\mathrm{a})$, the charging stage would require a membrane superficial area ${Ar}_m=1·{10}^4 {\mathrm{m}}^2$ to have the same duration. The proposed osmotic power plant, whose working principle is based on volume change over time, contrary to pressure retarded osmosis, whose working principle requires expending energy to extract energy from the salinity gradient, could deliver greater net produced energy with comparatively lower operational costs as it does not require high-pressure pumps or energy recovery devices as are required in pressure-retarded osmosis. The use of several tanks that charge and discharge alternatively can make the system generate energy as if it were a continuous process. Full article

Dynamic technological changes and the variability of market requirements pose significant challenges for modern manufacturing companies in the effective development and implementation of new technological solutions. The aim of the research was to develop an integrated approach covering all key stages of implementation—from formulating technological solutions, through selecting and evaluating variants, to preparing and managing production processes—under the conditions of a medium-sized manufacturing company specializing in the batch production of steel constructions. The analysis was based on an interdisciplinary approach, combining methods of creative design of new technological solutions, including Blue Ocean Strategy, value proposition design, and QFD methodology, with analytical approaches that include multi-criteria evaluation of solution variants, technical preparation of production, as well as the organization and management of production processes in modified organizational conditions. This approach enabled a comprehensive assessment of the developed solutions, taking into account both their operational potential and practical feasibility in realistic implementation conditions, through the use of case studies and simulations to validate the results. The results of the research indicate that integrating methods for creating new solutions with analytical assessment and simulation tools leads to a more precise and data-driven approach to process design, enabling better decision-making based on thorough analysis and predictive modeling. Furthermore, this approach allows for a significant reduction in the risk of implementation failure through early identification of potential problems. The conclusion of the study confirms that a comprehensive and interdisciplinary approach to the implementation of new technologies ensures better alignment with customer demands, reduces production downtime, and enhances product optimization and resource utilization, which are critical factors in building a sustainable competitive advantage for manufacturing companies. The proposed approach enables more deliberate design and organization of manufacturing processes, supporting their flexible adaptation to changing market and technological conditions. Full article

The aim of this study was to determine the effects of low-temperature pretreatment of microalgae (Tetraselmis subcordiformis (Wille) Butcher) and cyanobacteria (Limnospira platensis (Gomont) Ciferri et Tiboni) using solidified carbon dioxide (SCO₂) on the progression of methane fermentation. The experiment was carried out under batch conditions with six process variants that differed in the volumetric ratio of SCO₂ to the biomass tested. Changes in organic matter solubility, anaerobic digestion kinetics and overall CH₄ production performance were analysed. The results showed that pretreatment effectively increased the solubility of organic compounds, especially in the case of L. platensis biomass, where the highest increases in soluble sTOC (up to 21.6%) and sCOD (up to 14.3%) were observed. CH₄ yield in the most efficient variant (SCO₂:biomass = 1:2.5) increased to 354 ± 16 mL CH₄/gVS for T. subcordiformis and 403 ± 18 mL CH₄/gVS for L. platensis, respectively. Despite the apparently less favourable physicochemical parameters of the biomass for anaerobic digestion, L. platensis showed a higher susceptibility to digestion and better kinetic indicators for methane fermentation. The results indicate that the efficiency of anaerobic biodegradation of biomass depends not only on the chemical composition but also on the cellular structure and physicochemical interactions during pretreatment. The use of SCO₂ as a disintegrant could be an effective, energy-saving method to increase the fermentation efficiency of photosynthetic microorganisms in biowaste management. Full article