Deep learning image analysis for filamentous fungi taxonomic classification: Dealing with small datasets with class imbalance and hierarchical grouping.

Deep learning applications in taxonomic classification for animals and plants from images have become popular, while those for microorganisms are still lagging behind. Our study investigated the potential of deep learning for the taxonomic classification of hundreds of filamentous fungi from colony images, which is typically a task that requires specialized knowledge. We isolated soil fungi, annotated their taxonomy using standard molecular barcode techniques, and took images of the fungal colonies grown in petri dishes (n = 606). We applied a convolutional neural network with multiple training approaches and model architectures to deal with some common issues in ecological datasets: small amounts of data, class imbalance, and hierarchically structured grouping. Model performance was overall low, mainly due to the relatively small dataset, class imbalance, and the high morphological plasticity exhibited by fungal colonies. However, our approach indicates that morphological features like color, patchiness, and colony extension rate could be used for the recognition of fungal colonies at higher taxonomic ranks (i.e. phylum, class, and order). Model explanation implies that image recognition characters appear at different positions within the colony (e.g. outer or inner hyphae) depending on the taxonomic resolution. Our study suggests the potential of deep learning applications for a better understanding of the taxonomy and ecology of filamentous fungi amenable to axenic culturing. Meanwhile, our study also highlights some technical challenges in deep learning image analysis in ecology, highlighting that the domain of applicability of these methods needs to be carefully considered.

On the relationship between water adsorption and surface chemistry in soda-lime silicate glasses.

Understanding how the surface structure affects the bioactivity and degradation rate of the glass is one of the primary challenges in developing new bioactive materials. Here, classical and reactive molecular dynamics simulations are used to investigate the relationship between local surface chemistry and local adsorption energies of water on three soda-lime silicate glasses.  The compositions of the glasses, (SiO2)(65-x)(CaO)35(Na2O)x with x = 5, 10, and 15, were chosen for their bioactive properties. Analysis of the glass surface structure, compared to the bulk structure, showed that the surface is rich in modifiers and non-bridging oxygen atoms, which were correlated with local adsorption energies. The reactivity of the glasses is found to increase with higher Na2O content, attributed to elevated Na cations and undercoordinated species at the glass surfaces. The current work provides insights into the relationship between the surface structure, chemistry, and properties in these bioactive glasses and offers a step toward their rational design.

Antibacterial Activity of Ethanol Extract from Australian Finger Lime.

Australian finger lime (Citrus australasica L.) has become increasingly popular due to its potent antioxidant capacity and health-promoting benefits. This study aimed to determine the chemical composition, antibacterial characteristics, and mechanism of finger lime extract. The finger lime extracts were obtained from the fruit of the Australian finger lime by the ethanol extraction method. The antibacterial activity of the extract was examined by detecting the minimum inhibitory concentration (MIC) for two Gram-positive and four Gram-negative bacterial strains in vitro, as well as by assessing variations in the number of bacteria for Candidatus Liberibacter asiaticus (CLas) in vivo. GC-MS analysis was used to identify the antibacterial compounds of the extract. The antibacterial mechanisms were investigated by assessing cell permeability and membrane integrity, and the bacterial morphology was examined using scanning electron microscopy. The extract demonstrated significant antibacterial activity against Staphylococcus aureus, Bacillus subtilis, and Gram-negative bacterial species, such as Escherichia coli, Agrobacterium tumefaciens, Xanthomonas campestris, Xanthomonas citri, and CLas. Among the six strains evaluated in vitro, B. subtilis showed the highest susceptibility to the antimicrobial effects of finger lime extract. The minimum inhibitory concentration (MIC) of the extract against the tested microorganisms varied between 500 and 1000 µg/mL. In addition, the extract was proven effective in suppressing CLas in vivo, as indicated by the lower CLas titers in the treated leaves compared to the control. A total of 360 compounds, including carbohydrates (31.159%), organic acid (30.909%), alcohols (13.380%), polyphenols (5.660%), esters (3.796%), and alkaloids (0.612%), were identified in the extract. We predicted that the primary bioactive compounds responsible for the antibacterial effects of the extract were quinic acid and other polyphenols, as well as alkaloids. The morphology of the tested microbes was altered and damaged, leading to lysis of the cell wall, cell content leakage, and cell death. Based on the results, ethanol extracts from finger lime may be a fitting substitute for synthetic bactericides in food and plant protection.

Effects of Lime Powder on the Properties of Portland Cement-Sulphoaluminate Cement Composite System at Low Temperature.

In order to reduce the risk of early freezing damage to cement-based materials in winter construction, lime powder was used to improve the properties of the Portland cement-sulphoaluminate cement (PC-CSA) composite system at low temperatures. In this study, the effects of lime powder dosage on the properties of a PC-CSA blended system with two proportions (PC:CSA = 9:1 and 7:3) at -10 °C were investigated, and the mechanisms of improvement were revealed. The results showed that the compressive strength of the PC-CSA composite system was effectively improved, and the setting time was shortened by the addition of lime powder. Lime powder could effectively act as an early heating source in the PC-CSA composite system, as the maximum temperature of samples exposed to sub-zero temperatures was increased and the time before dropping to 0 °C was prolonged by the addition of lime powder. The extra CH generated by the hydration of lime powder provided an added hydration path for C4A3S¯, which accelerated the formation of AFt at each stage. Frozen water as well as the early frost damage were effectively decreased by lime powder because of the faster consumption of free water at an early stage. The modification of the hydration products also contributed to the denseness of the microstructure.

Antibacterial Activity for Synthesized Coumarin Derivatives and a Coumarin Component of Lime Peel (Citrus aurantifolia).

In this study, we investigated the antibacterial activity of the coumarin component isolated from lime peel and coumarin derivatives synthesized using various techniques against eight types of food-poisoning bacteria. The minimum inhibitory concentration (MIC) for the 3b [5,7-dihydroxy-4-trifluoromethylcoumarin] derivative was measured as 1.5 mM in Bacillus cereus, Micrococcus luteus, Listeria monocytogenes, and Staphylococcus aureus subsp. aureus; that for the 3c [7-hydroxy-4-trifluoromethylcoumarin] derivative was 1.7 mM in Enterococcus facium; and that for the 3n [dicoumarol] derivative was 1.2 mM in L. monocytogenes. These results confirmed that coumarin derivatives with CF3 and OH substituents had enhanced antibacterial activity.

Milk vetch returning combined with lime materials alleviates soil cadmium contamination and improves rice quality in soil-rice system.

Milk vetch (Astragalus sinicus L.) returning and lime materials is employed as an effective strategy for remediating cadmium (Cd)-contaminated paddy fields. However, the combined effects of them on alleviating Cd pollution and the underlying mechanisms remain poorly explored. Therefore, this study investigated the impact of these combined treatments on soil properties, iron oxides, iron plaque, mineral elements, and amino acids through a field experiment. The following treatments were employed: lime (LM), limestone (LS), milk vetch (MV), MV + LM (MVLM), and MV + LS (MVLS), and a control (CK) group with no materials. Results demonstrated that treatments significantly decreased soil available Cd by 19.40-32.55 %, 10.20-39.58 %, and 25.36-40.66 % at tillering, filling, and maturing stages compared to CK, respectively. Moreover, exchangeable Cd was transformed into more stable fractions. Compared with individual treatments, MVLM and MVLS treatments further decreased available Cd and exchangeable Cd. Overall, Cd in brown rice was reduced by 18.97-77.39 % compared with CK. And the Cd in iron plaque decreased by 14.12-31.14 %, 24.65-61.60 %, 2.6-38.28 % across three stages. Furthermore, soil pH, dissolved organic carbon, and cation exchange capacity increased, along with 0.22-62.09 % and 0.57-10.66 % increases in free and amorphous iron oxide contents at all stages, respectively. Compared with lime alone, the integration of MV returning facilitated increased formation of Fed, Feo and enhanced the antagonistic effect among grain Ca with Cd; Additionally, it increased AAs in brown rice, improving rice quality and potentially reducing Cd transport. Mantel tests and Partial least squares path modeling revealed a significant positive correlation between Cd in IP and rice Cd uptake and a significant negative correlation between available Cd, Fed and Feo. These findings provide valuable insights into the mechanisms involved in mitigating soil Cd bioavailability using integrated approaches with MV returning and lime materials.

Discarded substrates from soilless hydroponic horticulture as potential amendments for metal-contaminated soils.

Soil contamination with metals is a major threat for the environment and public health since most metals are toxic to humans and to non-human biota, even at low concentrations. Thus, new sustainable remediation approaches are currently needed to immobilize metals in soils to decrease their mobility and bioavailability. In this work, we explore the application of discarded substrates from hydroponic cultivation, namely coconut shell and a mixture of coconut shell and pine bark, for immobilization of metals (Cd, Cr, Ni, Cu, Pb, Hg, Sb and As) in a naturally contaminated soil from a mining region in Portugal. The immobilization capacity of substrates (added to the soil at 5% mass ratio) was assessed both individually and also combined with other traditional agriculture soil additives (limestone and gypsum, at 2% mass ratio) and nanoparticles of zero-valent iron (nZVI) at 1-3% mass ratio. The overall results obtained after a 30-d incubation showed that the discarded substrates are a viable, economic, and environmental-friendly solution for metal remediation in soils, with the capacity of immobilization ranging from 20 to 91% for the metals and metalloids studied. Furthermore, they showed the capacity to reduce the soil toxicity (EC50 ∼ 6000 mg/L) to non-toxic levels (EC50 > 10000 mg/L) to the bacteria Aliivrio fischeri.

Study on fresh and hardened state properties of eco-friendly foamed concrete incorporating waste soda-lime glass.

Improper waste management is causing global environmental problems. Waste glass may have adverse impacts on the ecosystem. While a substantial amount of soda-lime glass bottle (SGB) undergoes recycling to create new glass items, a significant volume still ends up in landfills. Therefore, the aim of this study was to explore the potential use of SGB in foamed concrete (FC) production as an aggregate replacement. SGB was substituted for sand in different weight fractions, ranging from 5 to 50%. The fresh state, mechanical, thermal, pore structure, and transport properties were examined. The findings showed a significant enhancement in the FC's mechanical properties when SGB replaced 20% of sand. The compressive, flexural, and splitting tensile strengths exhibited a rise of up to 17.7, 39.4, and 43.8%, respectively. The findings also demonstrated that the addition of SGB improved the thermal conductivity, sorptivity, water absorption, and porosity. The scanning electron microscopy analysis indicated that the inclusion of 20% SGB caused a substantial decrease in void diameter and enhanced its uniformity. A comparison was made between the experimental data and predictions of the mechanical properties using various models of international standards, such as IS 456, ACI 318, NZS-3101, EC-02, AS 3600, and CEB-FIB, along with several references in the literature. The findings implied a strong correlation between the strength properties. The outcomes of this research offer valuable insights into both the possible advantages and constraints of using SGB in FC. Furthermore, this extensive laboratory investigation may serve as a guideline for future study and aid in the advancement of greener and more environmentally friendly FC alternatives.

Effect of Thermoactivated Recycled Cement, Hardened Cement Powder and Hydrated Lime on the Compressive Strength of Mortars.

Thermoactivated recycled cement (RC) is a growing area of research and development in the cement industry. The approach represents a reversible process of cement hydration in which dehydrated compounds with similar characteristics to cement are obtained by means of thermal activation. To avoid CO2 emissions during the production of such RC, this study assesses the possibility of replacing ordinary Portland cement (OPC) with hardened cement powder (HCP) prepared with different proportions of hydrated lime (HL), relying on a second pozzolanic reaction, and compares it with RC mortars. Due to the thermal activation of HCP, the compressive strength increases by 11.5%. The addition of 8% HL produced an important increase in strength from 28 days to 90 days by 12.8%, although without surpassing the strength values of mortar produced only with HCP or with RC. The compressive strength results suggest the existence of a secondary pozzolanic reaction when using HCP from a cement paste source, but such a pozzolanic reaction was fully perceived in XRD patterns when using concrete as parent material, unlike cement paste, possibly due to large crystalline sand peaks that could have hindered the effective identification of smaller crystalline peaks.

An explainable AI-based blood cell classification using optimized convolutional neural network.

White blood cells (WBCs) are a vital component of the immune system. The efficient and precise classification of WBCs is crucial for medical professionals to diagnose diseases accurately. This study presents an enhanced convolutional neural network (CNN) for detecting blood cells with the help of various image pre-processing techniques. Various image pre-processing techniques, such as padding, thresholding, erosion, dilation, and masking, are utilized to minimize noise and improve feature enhancement. Additionally, performance is further enhanced by experimenting with various architectural structures and hyperparameters to optimize the proposed model. A comparative evaluation is conducted to compare the performance of the proposed model with three transfer learning models, including Inception V3, MobileNetV2, and DenseNet201.The results indicate that the proposed model outperforms existing models, achieving a testing accuracy of 99.12%, precision of 99%, and F1-score of 99%. In addition, We utilized SHAP (Shapley Additive explanations) and LIME (Local Interpretable Model-agnostic Explanations) techniques in our study to improve the interpretability of the proposed model, providing valuable insights into how the model makes decisions. Furthermore, the proposed model has been further explained using the Grad-CAM and Grad-CAM++ techniques, which is a class-discriminative localization approach, to improve trust and transparency. Grad-CAM++ performed slightly better than Grad-CAM in identifying the predicted area's location. Finally, the most efficient model has been integrated into an end-to-end (E2E) system, accessible through both web and Android platforms for medical professionals to classify blood cell.

Enhancing Hydraulic Lime Mortar with Metakaolin: A Study on Improving Restoration Materials for Historic Buildings.

This study investigates the enhancement of hydraulic lime mortar (HLM) using varying contents of metakaolin (MK) to improve its application in the restoration of historic buildings. Samples from historic structures were analyzed, and the effects of different MK contents on the physical and mechanical properties of HLM were examined. The reaction mechanism and microstructural changes were evaluated using XRD and SEM analysis. The results indicated that increasing MK levels in HLM led to a decrease in fluidity, with fluidity reducing by 4.8% at 12% MK. The addition of MK increased water consumption for standard consistency by 5.4% and shortened the final setting time by 10.2%. MK consumption promoted secondary hydration, enhancing compressive strength by up to 98.1% and flexural strength by up to 55.1%, and increasing bonding strength by 26.9%. The density of HLM improved with MK addition, slightly reducing moisture content by 4.5% and water absorption by 4.6%, while the water vapor transmission properties decreased by 50.9%, indicating reduced porosity. The elastic modulus of the mortar increased significantly from 2.19 GPa to 7.88 GPa with the addition of MK, enhancing rigidity and crack resistance. The optimal blend for restoration materials was found to be 9.0% MK and 25.0% heavy calcium carbonate and was characterized by moderate mechanical strength, enhanced early strength, commendable permeability, minimal risk of cracking, and ease of application. This blend is highly suitable for the rehabilitation of historic structures.

Explainable AI for Interpretation of Ovarian Tumor Classification Using Enhanced ResNet50.

Deep learning architectures like ResNet and Inception have produced accurate predictions for classifying benign and malignant tumors in the healthcare domain. This enables healthcare institutions to make data-driven decisions and potentially enable early detection of malignancy by employing computer-vision-based deep learning algorithms. These CNN algorithms, in addition to requiring huge amounts of data, can identify higher- and lower-level features that are significant while classifying tumors into benign or malignant. However, the existing literature is limited in terms of the explainability of the resultant classification, and identifying the exact features that are of importance, which is essential in the decision-making process for healthcare practitioners. Thus, the motivation of this work is to implement a custom classifier on the ovarian tumor dataset, which exhibits high classification performance and subsequently interpret the classification results qualitatively, using various Explainable AI methods, to identify which pixels or regions of interest are given highest importance by the model for classification. The dataset comprises CT scanned images of ovarian tumors taken from to the axial, saggital and coronal planes. State-of-the-art architectures, including a modified ResNet50 derived from the standard pre-trained ResNet50, are implemented in the paper. When compared to the existing state-of-the-art techniques, the proposed modified ResNet50 exhibited a classification accuracy of 97.5 % on the test dataset without increasing the the complexity of the architecture. The results then were carried for interpretation using several explainable AI techniques. The results show that the shape and localized nature of the tumors play important roles for qualitatively determining the ability of the tumor to metastasize and thereafter to be classified as benign or malignant.

Addition of lime juice and NaCl to minced seafood may stimulate the expression of Listeria monocytogenes virulence, adhesion, and stress response genes.

Listeria monocytogenes is a ubiquitous opportunistic bacterium responsible for deadly listeriosis outbreaks. This pathogen has been recognized as a significant food-borne pathogen in seafood products. The present study aimed to investigate the transcript levels of virulence, adhesion, and stress response genes of L. monocytogenes upon exposure to sublethal levels of lime juice and NaCl in shrimp matrix. For this purpose, minced and broth shrimp samples (control, 2% NaCl, 5% NaCl, 25 µL/mL lime, and 50 µL/mL lime, as well as 2% NaCl+25 µL/mL lime) were inoculated with approximately 107 CFU/g or ml of L. monocytogenes, and subsequently, the samples were stored at 12°C or 37°C. For the minced samples, the transcription of one stress-related (sigB), two adhesion (imo1634 and imo1847), and four virulence (hly, prf, intA, and plc) genes was assessed by RT-qPCR after different storage times (0 and 48 h). Results showed that the transcript levels of sigB, imo1847, and imo1634 genes increased with increasing storage temperatures of shrimp broth (12°C to 37°C). At the beginning, the transcription of the studied genes decreased in all treatments of minced shrimp; however, after 48 h of storage at 12°C, the transcript levels of hly, prf, imo1847, imo1634, and intA genes were significantly upregulated up to 0.5-9 log2 fold-change in all treatments compared to the control group (p < .05). These results highlight that the survived L. monocytogenes after exposure to moderate salt content or lime juice could represent enhanced virulence and adhesion capabilities, posing a significant public health risk.

Phylogenetic and Pathogenic Evidence Reveals Novel Host-Pathogen Interactions between Species of Lasiodiplodia and Citrus latifolia Dieback Disease in Southern Mexico.

Mexico ranks second in the world for Persian lime (Citrus latifolia) exports, making it the principal citrus exporter within the national citrus industry, exporting over 600,000 tons per year. However, diseases are the main factor reducing production, resulting in significant economic losses. Among these diseases, fungal diseases like dieback, caused by species of Lasiodiplodia, are an emerging issue in Persian lime. Symptoms include gummosis, twig and branch dieback, cankers, the necrosis of bark and wood, fruit mummification, and tree decline. The aim of this study was to investigate the occurrence and pathogenicity of the fungal species associated with twig and branch dieback, cankers, and decline of Persian lime trees in southern Mexico, and to elucidate the current status of the Lasiodiplodia species causing the disease in Mexico. During June, July, and August of 2023, a total of the 9229 Persian lime trees were inspected across 230 hectares of Persian lime orchards in southern Mexico, and symptoms of the disease were detected in 48.78% of the trees. Branches from 30 of these Persian lime trees were collected. Fungal isolates were obtained, resulting in a collection of 40 strains. The isolates were characterized molecularly and phylogenetically through the partial regions of four loci: the internal transcribed spacer region (ITS), the ß-tubulin gene (tub2), the translation elongation factor 1-alpha gene (tef1-α), and the DNA-directed RNA polymerase II second largest subunit (rpb2). Additionally, pathogenicity was assessed, successfully completing Koch's postulates on both detached Persian lime branches and certified 18-month-old Persian lime plants. Through multilocus molecular phylogenetic identification, pathogenicity, and virulence tests, five species were identified as causal agents: L. iraniensis, L. lignicola, L. mexicanensis, L. pseudotheobromae, and L. theobromae. This study demonstrates that in southern Mexico, at least five species of the genus Lasiodiplodia are responsible for dieback in Persian lime. Additionally, this is the first report of L. lignicola and L. mexicanensis as causal agents of the disease in citrus, indicating novel host interactions between species of Lasiodiplodia and C. latifolia.

Circular design, material properties, service life and cradle-to-cradle carbon footprint of lime-based building materials.

The massive extraction of virgin raw materials has substantially intensified the focus on circular economy of building materials. As a Cradle-to-Cradle service life and circular approach for lime-based construction materials (LBCM) is lacking, the present study evaluates the environmental impact and feasibility of creating a fully recycled second-life render (SL) by designing a closed-loop upcycling process for first-life renders (FL). To achieve this, a second-life binder was thermally activated (900, 1000, 1100, 1200 °C), while its microstructure, compressive strength, and thermal conductivity were investigated. SL had up to 33 % open porosity (FL 29 %), its compressive strength ranged from 2.5 to 3.4 MPa (FL 4.4 MPa) and the thermal conductivity from 1.002 to 1.107 W/mK (FL 1.231 W/mK). Resistance of SL and FL against sulfate attack was found to be equivalent, measured based on the recent RILEM TC 271-ASC recommendation. The environmental impact indicators integrating material properties and durability confirm that the second life-render can reduce CO2 emissions up to 55 %. The present research provides insights into unlocking essential sustainability gains through circular practices in the life-cycle of LBCM.

Recycling and reuse of waste agricultural plastics with hydrothermal pretreatment and low-temperature pyrolysis method.

Recycling and reuse of agricultural plastics is an urgent worldwide issue. In this work, it is shown that low-density polyethylene (PE) typically used in mulch films can be converted into high-capacity P and N adsorbents through a two-step method that uses hydrothermal pretreatment (180 °C, 24 h) followed by pyrolysis at 500 °C with Ca(OH)2 additive. CaPE@HC500 materials prepared with the proposed two-step method were found to have high adsorption capacities for phosphate (263.6 mg/g) and nitrogen (200.7 mg/g) over wide ranges of pH (3-11). Dynamic adsorption of phosphate by CaPE@HC500 material in a packed-bed had a half-time breakthrough of 210 min indicating the feasibility of continuous systems. Material stability, cost, environmental-friendliness, and recyclability of the CaPE@HC500 material were determined to be superior to literature-proposed Ca-containing adsorbents. The two-step method for converting waste agricultural plastic mulch films into adsorbents is robust and highly-applicable to industrial settings.

Resin-based dental pulp capping restoration enclosing silica and portlandite nanoparticles from natural resources.

Natural-based materials represent green choices for biomedical applications. In this study, resin pulp capping restoration enclosing strengthening silica and bioactive portlandite nanofillers were prepared from industrial wastes. Silica nanoparticles were isolated from rice husk by heat treatment, followed by dissolution/precipitation treatment. Portlandite nanoparticles were prepared by calcination of carbonated lime waste followed by ultrasonic treatment. Both were characterized using x-ray diffraction, energy dispersive x-ray, and transmission electron microscopy. For preparing pulp capping restoration, silica (after silanization) and/or portlandite nanoparticles were mixed with 40/60 weight ratio of bisphenol A-glycidyl methacrylate and triethylene glycol dimethacrylate. Groups A, B, and C enclosing 50 wt.% silica, 25 wt.% silica + 25 wt.% portlandite, and 50 wt.% portlandite, respectively, were prepared. All groups underwent microhardness, compressive strength, calcium release, pH, and apatite forming ability inspection in comparison to mineral trioxide aggregate (MTA) positive control. In comparison to MTA, all experimental groups showed significantly higher compressive strength, group B showed comparable microhardness, and group C showed significantly higher calcium release. Groups B and C showed prominent hydroxyapatite formation. Thus, the preparation of economic, silica-fortified, bioactive pulp capping material from under-utilized agricultural residues (rice husk) and zero-value industrial waste (carbonated lime from sugar industry) could be achieved.

Interpretable machine learning for dermatological disease detection: Bridging the gap between accuracy and explainability.

Research on disease detection by leveraging machine learning techniques has been under significant focus. The use of machine learning techniques is important to detect critical diseases promptly and provide the appropriate treatment. Disease detection is a vital and sensitive task and while machine learning models may provide a robust solution, they can come across as complex and unintuitive. Therefore, it is important to gauge a better understanding of the predictions and trust the results. This paper takes up the crucial task of skin disease detection and introduces a hybrid machine learning model combining SVM and XGBoost for the detection task. The proposed model outperformed the existing machine learning models - Support Vector Machine (SVM), decision tree, and XGBoost with an accuracy of 99.26%. The increased accuracy is essential for detecting skin disease due to the similarity in the symptoms which make it challenging to differentiate between the different conditions. In order to foster trust and gain insights into the results we turn to the promising field of Explainable Artificial Intelligence (XAI). We explore two such frameworks for local as well as global explanations for these machine learning models namely, SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME).

Enhancing the properties of swelling soils with lime, fly ash, and expanded polystyrene -A review.

This paper discusses efforts made by past researchers to steady the expansive (problematic) soils using mechanical and chemical techniques - specifically with EPS beads, lime and fly ash. Administering swelling of problematic soils is critical for civil engineers to prevent structural distress. This paper summarizes studies on reduction of swelling potential using EPS, lime and fly ash individually. Chemical stabilization with lime and fly ash are conventional methods for expansive soil stabilization, with known merits and demerits. This paper explores the suitability of different materials under various conditions and stabilization mechanisms, including cation exchange, flocculation, and pozzolanic reactions. The degree of stabilization is influenced by various factors such as the type and amount of additives, soil mineralogy, curing temperature, moisture content during molding, and the presence of nano-silica, organic matter, and sulfates. Additionally, expanded polystyrene (EPS) improves structural integrity by compressing when surrounded clay swells, reducing overall swelling. Thus, EPS addresses limitations of chemicals by mechanical means. Combining EPS, lime and fly ash creates a customized system promoting efficient, long-lasting, cost-effective and eco-friendly soil stabilization. Chemicals address EPS limitations like poor stabilization. This paper benefits civil engineers seeking to control expansive soil swelling and prevent structural distress. It indicates potential of an EPS-lime-fly ash system and concludes by identifying research gaps for further work on such combinatorial stabilizer systems.

Evaluation of Terpene Decomposition in Kaffir Lime Juice during Storage Using Gas Chromatography-Mass Spectrometry and Proton Transfer Reaction-Mass Spectrometry.

Kaffir lime juice, often treated as production waste, can be a good source of terpenes. These compounds undergo various decomposition processes under the influence of external factors, especially during transportation and storage. In this paper, it was possible to monitor changes in the terpene profile of kaffir lime juice under different storage conditions, namely, 4 °C, 20 °C, and 35 °C. The identification of key decomposition products was achieved using gas chromatography-mass spectrometry (GC-MS) and a data mining protocol. It was followed by tracing those products in different storage conditions using a high-throughput proton transfer reaction mass spectrometry (PTR-MS) approach. Based on our findings, degradation pathways were presented, showing that the main products resulting from storage are p-cymene, p-cymenene, terpinene-4-ol, and α-terpineol. It was shown that conversion to p-cymenene occurs after 5 days of storage. Terpinene-4-ol and α-terpineol were found to be the final products of the conversion at all temperatures. Changes in the composition of terpenes are important from the point of view of their bioactive properties.