Search Results (9)

Background and Objectives: Alzheimer’s disease (AD) has become a serious health problem. Agomelatine (Ago) is a neuroprotective antidepressant. This study aimed to assess how Ago influences behavioral outcomes in AD-like rat model. Materials and Methods: Forty-eight Wistar albino rats were allocated into four groups: Control (C), Alzheimer’s disease-like model (AD), Alzheimer’s disease-like model treated with Ago (ADAgo), and Ago alone (Ago). Physiological saline was injected intrahippocampally in C and Ago animals, whereas Aβ peptide was delivered similarly in AD and ADAgo rats. On day 15, 0.9% NaCl was administered to the C and AD groups, and Agomelatine (1 mg/kg/day) was infused into ADAgo and Ago rats via osmotic pumps for 30 days. Behavioral functions were evaluated using Open Field (OF), Forced Swim (FST), and Morris Water Maze (MWM) tests. Brain tissues were examined histopathologically. Neuritin, Nestin, DCX, NeuN, BDNF, MASH1, MT1, and MT2 transcripts were quantified by real-time PCR. Statistical analyses were performed in R 4.3.1, with p < 0.05 deemed significant. Results: In the FST, swimming, climbing, immobility time, and mobility percentage differed significantly among groups (p < 0.05). In the MWM, AD rats exhibited impaired learning and memory that was ameliorated by Ago treatment (p < 0.05). DCX expression decreased in AD rats but was elevated by Ago (p < 0.05). Nestin levels differed significantly between control and AD animals; MT1 expression varied between control and AD cohorts; and MT2 transcript levels were significantly lower in AD, ADAgo, and Ago groups compared to C (all p < 0.05). Conclusions: Ago exhibits antidepressant-like activity in this experimental AD model and may enhance cognitive function via mechanisms beyond synaptic plasticity and neurogenesis. Full article

Recent advances in sensor technology, data acquisition, and signal processing have enabled the development of data-driven structural health monitoring (SHM) strategies, offering a powerful alternative or complement to traditional model-based approaches. These approaches rely on damage-sensitive features (DSFs) extracted from vibration measurements. This study introduces an innovative, unsupervised learning framework leveraging transmissibility functions (TFs) as DSFs due to their local sensitivity to changes in dynamic behavior and their ability to operate without requiring input excitation measurements—an advantage in civil engineering applications where such data are often difficult to obtain. The novelty lies in the use of sequential sensor pairings based on structural connectivity to construct TFs that maximize damage sensitivity, combined with one-class classification algorithms for automatic damage detection and a damage index for spatial localization within sensor resolution. The method is evaluated through numerical simulations with noise-contaminated data and experimental tests on a masonry arch bridge model subjected to progressive damage. The numerical study shows detection accuracy above 90% with one-class support vector machine (OCSVM) and correct localization across all damage scenarios. Experimental findings further confirm the proposed approach’s localization capability, especially as damage severity increases, aligning well with observed damage progression. These results demonstrate the method’s practical potential for real-world SHM applications. Full article

Progressive deterioration and accumulated damage due to overloading, extreme events, and fatigue necessitate the continuous monitoring of civil infrastructure to ensure serviceability and safety. With advances in sensor technology, data-driven structural health monitoring (SHM) strategies, particularly artificial neural networks (ANNs), have gained prominence for analyzing large datasets and identifying complex patterns. Among these, autoencoders (AEs), a specialized class of ANNs, are well-suited for unsupervised learning tasks, enabling dimensionality reduction and feature extraction. This study employs transmissibility functions (TFs) as training samples for the AE. TFs are directly derived from response measurements without the need to measure input and exhibit local sensitivity to changes in dynamic properties, making them an efficient feature for structural assessment. The reconstruction errors in TFs, quantifying the deviation between the original and AE-reconstructed data, are leveraged as damage-sensitive features for classification using a one-class support vector machine (OC-SVM). The proposed methodology is validated through numerical simulations with noise-contaminated data representing various damage scenarios in a shear-building model, as well as experimental tests on a masonry arch bridge model subjected to progressive damage. Numerical investigations demonstrate improved detection accuracy and robustness of the procedure through the incorporation of nonlinear encoding into the dimensionality reduction process, compared to the classical principal component analysis method.. Experimental results confirm the framework’s effectiveness in detecting and localizing damage using unlabeled field data. Full article

Gas–liquid mass transfer is a major issue during various bioprocesses, particularly in processes such as syngas fermentation (SNF). Since SNF involves the movement of gases into the fermentation broth, there is always a rate-limiting step that reduces process efficiency. Improving this process could lead to increased efficiency, higher production of ethanol, and reduced energy consumption. One way to improve fluid transfer between gas and liquid is by incorporating nanoparticles (NPs) into the liquid phase. This entry describes recent advances in using NPs to improve gas–liquid mass transfer during SNF. The entry also describes the basics of SNF and the impact of NPs on the process and suggests areas for future research. For example, carbon nanotubes have been found to elevate the available surface area needed for gas–liquid transfer, thus improving the process efficiency. Another area is the use of NPs as carriers for enzymes involved in syngas fermentation. Full article

Higher heating values (HHV) is a very useful parameter for assessing the design and large-scale operation of biomass-driven energy systems. HHV is conventionally measured experimentally with an adiabatic oxygen bomb calorimeter. This procedure is often time-consuming and expensive. Furthermore, limited access to the required facilities is the main bottleneck for researchers. Empirical linear and nonlinear models have initially been proposed to address these concerns. However, most of the models showed discrepancies with experimental results. Data-driven machine learning (ML) methods have also been adopted for HHV predictions due to their suitability for nonlinear problems. However, most ML correlations are based on proximate or ultimate analysis. In addition, the models are only applicable to either the originator biomass or one specific type. To address these shortcomings, a total of 227 biomass datasets based on four classes of biomass, including agricultural residue, industrial waste, energy crop, and woody biomass, were employed to develop and verify three different ML models, namely artificial neural network (ANN), decision tree (DT) and random forest (RF). The model incorporates proximate and ultimate analysis data and biomass as input features. RF model is identified as the most reliable because of its lowest mean absolute error (MAE) of 1.01 and mean squared error (MSE) of 1.87. The study findings can be used to predict HHV accurately without performing experiments. Full article

Freshwater is in limited supply, and the growing population further contributes to its scarcity. The effective treatment of wastewater is essential now more than ever, because waterborne infections significantly contribute to global deaths, and millions of people are deprived of safe drinking water. Current wastewater treatment technologies include preliminary, primary, secondary, and tertiary treatments, which are effective in removing several contaminants; however, contaminants in the nanoscale range are often difficult to eliminate using these steps. Some of these include organic and inorganic pollutants, pharmaceuticals, pathogens and contaminants of emerging concern. The use of nanomaterials is a promising solution to this problem. Nanoparticles have unique properties allowing them to efficiently remove residual contaminants while being cost-effective and environmentally friendly. In this review, the need for novel developments in nanotechnology for wastewater treatment is discussed, as well as key nanomaterials and their corresponding target contaminants, which they are effective against. The nanomaterials of focus in this review are carbon nanotubes, graphene-based nanosheets, fullerenes, silver nanoparticles, copper nanoparticles and iron nanoparticles. Finally, the challenges and prospects of nanoparticle utilisation in the context of wastewater treatment are presented. Full article

The remarkable adsorption capacity of graphene-derived materials has prompted their examination in composite materials suitable for deployment in treatment of contaminated waters. In this study, crosslinked calcium alginate–graphene oxide beads were prepared and activated by exposure to pH 4 by using 0.1M HCl. The activated beads were investigated as novel adsorbents for the removal of organic pollutants (methylene blue dye and the pharmaceuticals famotidine and diclofenac) with a range of physicochemical properties. The effects of initial pollutant concentration, temperature, pH, and adsorbent dose were investigated, and kinetic models were examined for fit to the data. The maximum adsorption capacities q_max obtained were 1334, 35.50 and 36.35 mg g⁻¹ for the uptake of methylene blue, famotidine and diclofenac, respectively. The equilibrium adsorption had an alignment with Langmuir isotherms, while the kinetics were most accurately modelled using pseudo- first-order and second order models according to the regression analysis. Thermodynamic parameters such as ΔG°, ΔH° and ΔS° were calculated and the adsorption process was determined to be exothermic and spontaneous. Full article

The composition of challenging matrices must be fully understood in order to determine the impact of the matrix and to establish suitable treatment methods. Rendering condensate wastewater is a complex matrix which is understudied. It is produced when the vapour from rendering facilities (heat processing of slaughterhouse waste material) is cooled as a liquid for discharge. This study offers a full physicochemical characterisation of rendering condensate wastewater and its potential for valorisation via production of viable by-products. A study of seasonal variation of levels of dissolved oxygen, chemical oxygen demand, total nitrogen and ammonia was carried out on the wastewater. The results show that the wastewater was high strength all year-round, with a chemical oxygen demand of 10,813 ± 427 mg/L and high concentrations of total Kjeldahl nitrogen (1745 ± 90 mg/L), ammonia (887 ± 21 mg/L), crude protein (10,911 ± 563 mg/L), total phosphorous (51 ± 1 mg/L), fat and oil (11,363 ± 934 mg/L), total suspended solids (336 ± 73 mg/L) and total dissolved solids (4397 ± 405 mg/L). This characterisation demonstrates the requirement for adequate treatment of the condensate before releasing it to the environment. While there is a reasonably constant flow rate and dissolved oxygen level throughout the year, higher chemical oxygen demand, total nitrogen and ammonia levels were found in the warmer summer months. From this study, rendering condensate slaughterhouse wastewater is shown to have potential for production of marketable goods. These products may include ammonium sulphate fertilizer, protein supplements for animal feeds and recovery of acetic acid calcium hydroxyapatite, thus enhancing both the financial and environmental sustainability of slaughterhouse operations. This work demonstrates a valuable assessment of a complex wastewater, while taking advantage of on-site access to samples and process data to inform the potential for wastewater reuse. Full article

Whiskey distillery waste streams consisting of pot ale (liquid residue) and spent grain (solid residue) are high strength organic wastes and suitable feedstock for anaerobic digestion (AD) from both economic and environmental stand points. Anaerobic digestion of pot ale and pot ale/spent grain mixtures (with mixing ratios of 1:1, 1:3, and 1:5 by wet weight) was performed after implementation of a novel hybrid pre-treatment (combined chemical and mechanical) in order to modify lignocellulosic structure and ultimately enhance digestion yield. Lignin, hemicellulose, and cellulose fractions were determined before and after chemical pre-treatment. Effects of different inoculum rates (10–30–50% on wet basis) and beating times (0–7.5–15 min) on anaerobic digestion of pot ale alone and of pot ale/spent grain mixtures were investigated in lab scale batch mode with a major focus of optimising biogas yield by using response surface methodology (RSM) in Design Expert Software. The highest biogas yields of 629 ± 8.5 mL/g vs. (51.3% CH₄) and 360 ± 10 mL/g vs. (55.0 ± 0.4) with anaerobic digestion of pot ale alone and spent grain mix after 1M NaOH and 7.5 min beating pre-treatments with 50% inoculum ratio respectively. The optimum digestion conditions to maximise the biogas quality and quantity were predicted as 10 and 13 min beating times and 32 and 38 °C digestion temperatures for anaerobic digestion of pot ale alone and spent grain mix respectively. Full article