Lime Stabilization of Tropical Soil for Resilient Pavements: Mechanical, Microscopic, and Mineralogical Characteristics.

Lime stabilization is a sustainable technique due to its use of local materials, increased durability, reduced maintenance, and improved resistance to water action. This paper examines the impact of lime stabilization on the mechanical, microscopic, and mineralogical properties of a tropical soil. Two types of lime, calcitic and dolomitic, were tested at 3% and 5% by weight. Compressive, indirect tensile and flexural test results and statistical analysis revealed that calcitic lime mixtures had higher strength and stiffness, whereas dolomitic lime mixtures exhibited greater deformability with higher tensile strain at break. Scanning electron microscopy indicated that the soil's porous matrix closed within 7 days for both lime types due to flocculation, with increased matrix interlocking over time. The calcitic lime mixture developed a more closed matrix compared to the dolomitic lime, which showed weaker cementing. X-ray diffraction analysis indicated higher consumption of clay minerals and a notable reduction in calcium hydroxide peaks in the lime-treated soils. The study concludes that calcitic lime provides better pavement performance for stabilizing the soil, enhancing its engineering properties while also being sustainable by reducing the need for raw material extraction and improving resilience to climate-related issues such as floods.

A hybrid transformer and attention based recurrent neural network for robust and interpretable sentiment analysis of tweets.

Sentiment analysis is a pivotal tool in understanding public opinion, consumer behavior, and social trends, underpinning applications ranging from market research to political analysis. However, existing sentiment analysis models frequently encounter challenges related to linguistic diversity, model generalizability, explainability, and limited availability of labeled datasets. To address these shortcomings, we propose the Transformer and Attention-based Bidirectional LSTM for Sentiment Analysis (TRABSA) model, a novel hybrid sentiment analysis framework that integrates transformer-based architecture, attention mechanism, and recurrent neural networks like BiLSTM. The TRABSA model leverages the powerful RoBERTa-based transformer model for initial feature extraction, capturing complex linguistic nuances from a vast corpus of tweets. This is followed by an attention mechanism that highlights the most informative parts of the text, enhancing the model's focus on critical sentiment-bearing elements. Finally, the BiLSTM networks process these refined features, capturing temporal dependencies and improving the overall sentiment classification into positive, neutral, and negative classes. Leveraging the latest RoBERTa-based transformer model trained on a vast corpus of 124M tweets, our research bridges existing gaps in sentiment analysis benchmarks, ensuring state-of-the-art accuracy and relevance. Furthermore, we contribute to data diversity by augmenting existing datasets with 411,885 tweets from 32 English-speaking countries and 7,500 tweets from various US states. This study also compares six word-embedding techniques, identifying the most robust preprocessing and embedding methodologies crucial for accurate sentiment analysis and model performance. We meticulously label tweets into positive, neutral, and negative classes using three distinct lexicon-based approaches and select the best one, ensuring optimal sentiment analysis outcomes and model efficacy. Here, we demonstrate that the TRABSA model outperforms the current seven traditional machine learning models, four stacking models, and four hybrid deep learning models, yielding notable gain in accuracy (94%) and effectiveness with a macro average precision of 94%, recall of 93%, and F1-score of 94%. Our further evaluation involves two extended and four external datasets, demonstrating the model's consistent superiority, robustness, and generalizability across diverse contexts and datasets. Finally, by conducting a thorough study with SHAP and LIME explainable visualization approaches, we offer insights into the interpretability of the TRABSA model, improving comprehension and confidence in the model's predictions. Our study results make it easier to analyze how citizens respond to resources and events during pandemics since they are integrated into a decision-support system. Applications of this system provide essential assistance for efficient pandemic management, such as resource planning, crowd control, policy formation, vaccination tactics, and quick reaction programs.

Moisture buffering and mould growth characteristics of naturally ventilated lime plastered houses.

Lime plaster is a sustainable building material that can be an effective passive cooling strategy. The moisture buffering quality of lime causes adsorption and desorption of moisture which moderates the indoor relative humidity. Its vapour permeability is also influential in moisture transfer across the building envelope. Lime plaster also has a self-healing quality which prevents the formation of inner cracks. Moreover, its strength increases with time leading to a longer life span. In old structures, an important function is the breathability of ceilings and walls. Hence, it is often used in conservation projects where it improves the appearance and durability of old buildings. Often organic additives employed to impart certain qualities to the lime mortar/plaster led to mould growth. Mould growth degrades indoor air quality, and the occupant health is compromised. To avoid mould-related problems, it is necessary to understand the behaviour of lime plaster with respect to the indoor relative humidity and surface moisture content. This paper focuses on the hygrothermal performance of lime plaster in naturally ventilated residential spaces. Surveys were carried out in 45 traditional buildings in Ahmedabad in India with measurements of ambient variables, such as temperature, relative humidity, wall moisture content, etc. The mould growth patterns of these spaces are related to the measured variables and wall characteristics. Hygrothermal simulations of some spaces were also carried out to observe the moisture buffering of lime plaster. Experimental observations were then compared to simulation results to see if the predictions of the hygrothermal models were realistic.

Development and Characterization of Lime-Based Mortars Modified with Graphene Nanoplatelets.

Materials for the conservation of cultural heritage must meet specific demands, such as high durability, service life, and compatibility with other materials used in the original building structures. Due to their low permeability to water and water vapor and their high rigidity, the use of Portland cement (PC) mortars, despite their high mechanical resistance and durability, does not represent an appropriate solution for the repair of historic masonry and structures. Their incompatibility with the original materials used in the past, often on a lime basis, is therefore a serious deficiency for their application. On the other hand, lime-based mortars, compared to PC-based materials, are more susceptible to mechanical stress, but they possess high porosity, a high water vapor transmission rate, and moderate liquid water transport. This study aims at the development of two types of lime-based mortars, calcium lime (CL) and hydraulic lime (HL). The modification of mortars was conducted with a carbon-based nanoadditive and graphene nanoplatelets (GNs) in three dosages: 0.1%, 0.3%, and 0.5% of the binder weight. The enhancement of CL mortars by GNs greatly increased mechanical strength and affected heat transport characteristics, while other characteristics such as porosity, water absorption, and drying rate remained almost similar. The application of GNs to HL not only enhanced the strength of mortars but also decreased their porosity, influenced pore size distribution, and other dependent characteristics. It can be concluded that the use of graphene nanoplatelets as an additive of lime-based composites can be considered a promising method to reinforce and functionalize these composite materials. The improved mechanical resistance while maintaining other properties may be favorable in view of the increasing requirements of building materials and may prolong the life span of building constructions.

Performance Study of Stabilized Recycled Aggregate Base Material with Two-Gray Components.

This article studies the practical road performance of recycled materials from construction waste, relying on the paving test section of the supporting project for the Qingdao Cross-Sea Bridge. The research focuses on the construction technology and road performance of using recycled construction waste materials in urban road sub-base construction. Through indoor tests such as sieving and unconfined compressive strength tests, relevant technical indicators were obtained and analyzed. Additionally, periodic core sampling, compaction tests, and rebound deflection tests were conducted on-site according to relevant standards to thoroughly investigate the specific effects of using construction waste in practice and to analyze and evaluate the actual feasibility of the materials for road use. The results indicate that the particle gradation of the construction mix in the test section aligns well with the target gradation, and the dosage of the mixing agent meets the design requirements. The 7-day unconfined compressive strength already satisfied the technical requirements for heavy and extremely heavy traffic on highways as specified in the "Technical Specifications for Construction of Highway Pavement Subbase" (JTG/T F20-2015), with the 14-day strength generally reaching 7 MPa. Core sampling revealed good aggregate gradation, smooth and straight profiles, and the thickness and strength of all parts meet the specifications. The compaction levels met the testing requirements, the surface deflection values showed a decreasing trend, and the deformation resistance was good, consistent with the general development patterns of semi-rigid sub-bases.

Exploring online public survey lifestyle datasets with statistical analysis, machine learning and semantic ontology.

Lifestyle diseases significantly contribute to the global health burden, with lifestyle factors playing a crucial role in the development of depression. The COVID-19 pandemic has intensified many determinants of depression. This study aimed to identify lifestyle and demographic factors associated with depression symptoms among Indians during the pandemic, focusing on a sample from Kolkata, India. An online public survey was conducted, gathering data from 1,834 participants (with 1,767 retained post-cleaning) over three months via social media and email. The survey consisted of 44 questions and was distributed anonymously to ensure privacy. Data were analyzed using statistical methods and machine learning, with principal component analysis (PCA) and analysis of variance (ANOVA) employed for feature selection. K-means clustering divided the pre-processed dataset into five clusters, and a support vector machine (SVM) with a linear kernel achieved 96% accuracy in a multi-class classification problem. The Local Interpretable Model-agnostic Explanations (LIME) algorithm provided local explanations for the SVM model predictions. Additionally, an OWL (web ontology language) ontology facilitated the semantic representation and reasoning of the survey data. The study highlighted a pipeline for collecting, analyzing, and representing data from online public surveys during the pandemic. The identified factors were correlated with depressive symptoms, illustrating the significant influence of lifestyle and demographic variables on mental health. The online survey method proved advantageous for data collection, visualization, and cost-effectiveness while maintaining anonymity and reducing bias. Challenges included reaching the target population, addressing language barriers, ensuring digital literacy, and mitigating dishonest responses and sampling errors. In conclusion, lifestyle and demographic factors significantly impact depression during the COVID-19 pandemic. The study's methodology offers valuable insights into addressing mental health challenges through scalable online surveys, aiding in the understanding and mitigation of depression risk factors.

Virtual cleaning of sooty mural hyperspectral images using the LIME model and improved dark channel prior.

Murals, as important carriers of cultural heritage and historical records, showcase artistic, aesthetic, social, and political significance. In ancient times, religious activities such as burning incense and candles in temples led to many murals being polluted by soot, causing them to darken, lose details, and, in severe cases, completely blacken. As a result, the development of efficient virtual cleaning methods has become a key strategy for addressing this issue. In this study, we use synthetic true colour and false colour images in different bands of the hyperspectral spectrum, and use a guided filter fusion technique to fuse these two images into a new image of the sooty mural. Through analyzing the histograms and colour distribution scatterplots of the synthetic sooty mural images, we observed significant similarities to low-luminance images. To enhance the synthesized murals, we applied the LIME model. In addition, comparisons of the histograms and colour distribution scatterplots of the enhanced sooty mural images with those of haze images revealed notable similarities. Therefore, we applied the dark channel prior algorithm to remove soot from the mural images. Considering that soot particles are larger than haze particles, we introduced guided filtering to refine the transmission map and created a nonlinear transformation function to enhance its details. In terms of both visual perception and quantitative analysis, the proposed method significantly outperforms previous methods in the virtual cleaning of sooty murals. This technology can not only restore the colours and details of murals but also provide new clues for subsequent mural studies, allowing people to once again appreciate the true beauty of the murals.

Revealing the interplay of dissolved organic matters variation with microbial symbiotic network in lime-treated sludge landscaping.

Lime pretreatment is commonly used for sludge hygienization. Appropriate lime dosage is crucial for achieving both sludge stabilization (lime dosage >0.2 g/g-TS) and promoting plant and soil health during subsequent landscaping (lime dosage <0.8 g/g-TS). While much research has been conducted on sludge lime treatment, few studies have examined the effects of lime dosing on integrating sludge stabilization and plant growth promotion during landscaping. In this study, we investigated microbial dynamics and dissolved organic matter (DOM) transformation during sludge landscaping with five lime dosage gradients (0, 0.2, 0.4, 0.6, 0.8 g lime/g-TS) over 90 days. Our results showed that a lime dosage of 0.4 g/g-TS is the lower threshold for achieving waste activated sludge (WAS) stabilization during landscaping, leading to maximum humic substance formation and minimal phytotoxicity. Specifically, at 0.4 g/g-TS lime dosage, protein degradation and decarboxylation-induced humification were significantly enhanced. The predominant microbial genera shifted from Aromatoleum to Exiguobacterium and Romboutsia (both affiliated with the phylum Firmicutes). Reactomics analysis further indicated that a 0.4 g/g-TS lime dosage promoted the hydrolysis of proteins (lyase reactions on C-C, C-O, and C-N bonds), amino acid metabolism, and decarboxylation-induced humification (e.g., C1H2O2, C2H4O2, C5H4O2, C6H4O2). The co-occurrence network analysis suggested that the phyla Firmicutes, Proteobacteria, and Bacteroidetes were key players in DOM transformation. This study provides an in-depth understanding of microbe-mediated DOM transformation during sludge landscaping and identifies the optimal lime dosage for improving sludge landscaping efficiency.

XAI-Augmented Voting Ensemble Models for Heart Disease Prediction: A SHAP and LIME-Based Approach.

Ensemble Learning (EL) has been used for almost ten years to classify heart diseases, but it is still difficult to grasp how the "black boxes", or non-interpretable models, behave inside. Predicting heart disease is crucial to healthcare, since it allows for prompt diagnosis and treatment of the patient's true state. Nonetheless, it is still difficult to forecast illness with any degree of accuracy. In this study, we have suggested a framework for the prediction of heart disease based on Explainable artificial intelligence (XAI)-based hybrid Ensemble Learning (EL) models, such as LightBoost and XGBoost algorithms. The main goals are to build predictive models and apply SHAP (SHapley Additive expPlanations) and LIME (Local Interpretable Model-agnostic Explanations) analysis to improve the interpretability of the models. We carefully construct our systems and test different hybrid ensemble learning algorithms to determine which model is best for heart disease prediction (HDP). The approach promotes interpretability and transparency when examining these widespread health issues. By combining hybrid Ensemble learning models with XAI, the important factors and risk signals that underpin the co-occurrence of heart disease are made visible. The accuracy, precision, and recall of such models were used to evaluate their efficacy. This study highlights how crucial it is for healthcare models to be transparent and recommends the inclusion of XAI to improve interpretability and medical decisionmaking.

Utilizing customized CNN for brain tumor prediction with explainable AI.

Timely diagnosis of brain tumors using MRI and its potential impact on patient survival are critical issues addressed in this study. Traditional DL models often lack transparency, leading to skepticism among medical experts owing to their "black box" nature. This study addresses this gap by presenting an innovative approach for brain tumor detection. It utilizes a customized Convolutional Neural Network (CNN) model empowered by three advanced explainable artificial intelligence (XAI) techniques: Shapley Additive Explana-tions (SHAP), Local Interpretable Model-agnostic Explanations (LIME), and Gradient-weighted Class Activation Mapping (Grad-CAM). The study utilized the BR35H dataset, which includes 3060 brain MRI images encompassing both tumorous and non-tumorous cases. The proposed model achieved a remarkable training accuracy of 100 % and validation accuracy of 98.67 %. Precision, recall, and F1 score metrics demonstrated exceptional performance at 98.50 %, confirming the accuracy of the model in tumor detection. Detailed result analysis, including a confusion matrix, comparison with existing models, and generalizability tests on other datasets, establishes the superiority of the proposed approach and sets a new benchmark for accuracy. By integrating a customized CNN model with XAI techniques, this research enhances trust in AI-driven medical diagnostics and offers a promising pathway for early tumor detection and potentially life-saving interventions.

Evaluating the Role of Mortar Composition on the Cyclic Behavior of Unreinforced Masonry Shear Walls.

The mechanical behavior of unreinforced masonry (URM) shear walls under in-plane cyclic loading is crucial for assessing their seismic performance. Although masonry structures have been extensively studied, the specific influence of varying lime content in cement-lime mortars on the cyclic behavior of URM walls has not been adequately explored. This study addresses this gap by experimentally evaluating the effects of three mortar mixes with increasing lime content, 1:0:5, 1:1:6, and 1:2:9 (cement:lime:sand, by volume), on the cyclic performance of brick URM walls. Nine single-leaf wall specimens 900 mm × 900 mm were constructed and subjected to combined vertical compression and horizontal cyclic loading. Key parameters such as drift capacity, stiffness degradation, and energy dissipation were measured. The results indicated that the inclusion of lime leads to a moderate improvement in drift capacity and ductility of the walls, with the 1:1:6 mix showing the highest lateral capacity (0.55 MPa), drift at cracking (0.08%), and drift at peak capacity (0.31%). Stiffness degradation and energy dissipation were found to be comparable across all mortar types. These findings suggest that partial substitution of cement with lime can enhance certain aspects of masonry performance. Further research is recommended to optimize mortar compositions for unreinforced masonry applications.

Influence of Accelerated Carbonation on the Physico-Mechanical Properties of Natural Fiber-Reinforced Lime Mortars.

Lime mortars are considered the most compatible material for monuments and historic buildings, and they are widely used in restoration works. A key factor determining the mechanical and physical properties of lime mortars is carbonation, which provides strength and hardness. This paper indicates the properties gained in lime mortars produced by Ca(OH)2 and CaO reinforced with different bio-fibers (hemp and lavender) when exposed to the natural environment and in accelerated carbonation. At 90 and 180 days of manufacture, the mechanical and physical properties of the produced composites have been tested. The results show that the carbonation reaction works faster in the case of hot lime mortars, increasing their compressive strength by up to 3.5 times. Hemp-reinforced mortars led to an enhancement in strength by up to 30%, highlighting the significance of bio-fibers in facilitating CO2 diffusion. This was also verified by the thermogravimetric analysis and the determination of the carbon content of the samples. Optimal mechanical properties were observed in mixtures containing quicklime and hemp fibers when conditioned with 3% CO2 at the tested ages.

Enhancing the Interpretability of Malaria and Typhoid Diagnosis with Explainable AI and Large Language Models.

Malaria and Typhoid fever are prevalent diseases in tropical regions, and both are exacerbated by unclear protocols, drug resistance, and environmental factors. Prompt and accurate diagnosis is crucial to improve accessibility and reduce mortality rates. Traditional diagnosis methods cannot effectively capture the complexities of these diseases due to the presence of similar symptoms. Although machine learning (ML) models offer accurate predictions, they operate as "black boxes" with non-interpretable decision-making processes, making it challenging for healthcare providers to comprehend how the conclusions are reached. This study employs explainable AI (XAI) models such as Local Interpretable Model-agnostic Explanations (LIME), and Large Language Models (LLMs) like GPT to clarify diagnostic results for healthcare workers, building trust and transparency in medical diagnostics by describing which symptoms had the greatest impact on the model's decisions and providing clear, understandable explanations. The models were implemented on Google Colab and Visual Studio Code because of their rich libraries and extensions. Results showed that the Random Forest model outperformed the other tested models; in addition, important features were identified with the LIME plots while ChatGPT 3.5 had a comparative advantage over other LLMs. The study integrates RF, LIME, and GPT in building a mobile app to enhance the interpretability and transparency in malaria and typhoid diagnosis system. Despite its promising results, the system's performance is constrained by the quality of the dataset. Additionally, while LIME and GPT improve transparency, they may introduce complexities in real-time deployment due to computational demands and the need for internet service to maintain relevance and accuracy. The findings suggest that AI-driven diagnostic systems can significantly enhance healthcare delivery in environments with limited resources, and future works can explore the applicability of this framework to other medical conditions and datasets.

The Elimination of Viroids through In Vitro Thermotherapy and a Meristem Tip Culture from a New Limonime Hybrid (Citrus x limon var. limon (L.) Burm. f. x Citrus latifolia var. latifolia).

Viruses and viroids pose a significant challenge in citriculture, and their control is crucial for plant health. This study evaluated the effectiveness of in vitro thermotherapy combined with a meristem tip culture for eliminating citrus exocortis viroid (CEVd) and hop stunt viroid (HSVd) from a new limonime hybrid (Citrus x limon var. limon x Citrus latifolia var. latifolia). The elimination success was confirmed by RT-PCR assays. The in vitro elimination rate for CEVd during the shoot proliferation stage (43%) was higher than for HSVd (21%). Accordingly, in the subsequent rooting stage, the in vitro elimination rate for CEVd (50%) was higher than for HSVd (33%). Successful CEVd and HSVd eradication at a 100% rate was confirmed in the ex vitro acclimatized plants in the greenhouse. The study also established an efficient micropropagation protocol. The optimal treatment for in vitro shoot induction was 0.5-2 mg L-1 benzyladenine (BA) + 0.5 mg L-1 gibberellic acid (GA3) + 0.25 mg L-1 naphthalene acetic acid (NAA), while for shoot elongation, it was 0.5 mg L-1 BA + 0.5 mg L-1 kinetin (KIN) + 0.5 mg L-1 GA3 + 0.25 mg L-1 NAA. Rooting was best promoted by 1 mg L-1 NAA. This study provides valuable insights for the mass production of viroid-free propagation material in this new lemon x lime hybrid, contributing to the conservation of genetic resources in citrus breeding programs through the combined application of in vitro thermotherapy and an in vitro meristem tip culture, a novel and highlighted achievement reported for the first time in this study.

Novel neuropharmacological activity of citrus lime (Citrus aurantifolia): A standardized lime peel supplement enhances non-rapid eye movement sleep by activating the GABA type A receptor.

Polyphenols have been well-established to exert sedative-hypnotic effects in psychopharmacology. Lime (Citrus aurantifolia) peel is rich in biologically active polyphenols; however, the effects of lime peel extract on sleep have not yet been demonstrated. A comparison was conducted in mice, between the sleep-promoting effects of a standardized lime peel supplement (SLPS) and a well-known hypnotic drug, zolpidem, and its hypnotic mechanism was investigated using in vivo and in vitro assays. The effects of SLPS on sleep were assessed using a pentobarbital-induced sleep test and sleep architecture analysis based on recording electroencephalograms and electromyograms. Additionally, a GABAA receptor binding assay, electrophysiological measurements, and in vivo animal models were used to elucidate the hypnotic mechanism. SLPS (200 and 400 mg/kg) was found to significantly decrease sleep latency and increase the amount of non-rapid eye movement sleep without altering delta activity. The hypnotic effects of SLPS were attributed to its flavonoid-rich ethyl acetate fraction. SLPS had a binding affinity to the GABA-binding site of the GABAA receptor and directly activated the GABAA receptors. The hypnotic effects and GABAA receptor activity of SLPS were completely blocked by bicuculline, a competitive antagonist of the GABAA receptor, in both in vitro and in vivo assays. To the best of our knowledge, this study is the first to demonstrate the hypnotic effects of SLPS, which acts via the GABA-binding site of the GABAA receptor. Our results suggest that lime peel, a by-product abundantly generated during juice processing, can potentially be used as a novel sedative-hypnotic.

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.

Enhancing antioxidant properties of lime juice powder through polyelectrolyte microparticles of chitosan-alginate: Formulation, characterization and stability study.

Lime (Citrus aurantifolia) juice was reported to contain ascorbic acid (AA) and flavonoids, which has bioactivity as antioxidants. To develop an antioxidant product, improving its stability is necessary due to the perishable characteristics of compounds in lime. Therefore, the formulation of polyelectrolyte microparticles using chitosan and alginate was conducted to overcome the weaknesses. This study aims to evaluate the effect of various chitosan, alginate, and lime juice powder (LJP) concentrations on the physical characteristics and antioxidant activity of LJP encapsulated in chitosan-alginate microparticles (CALM). Microparticles with various concentrations of chitosan and alginate were prepared by ionic gelation method using CaCl2 as a crosslinker. The microparticles were evaluated for its physical properties and its antioxidant activity using 2-2-diphenyl-1-picrylhydrazyl reagent. A one-way ANOVA test and Tukey's honest significant difference post hoc were used to determine the effect of LJP amount on the antioxidant activity. The highest AA content in CALM was 0.14 mg/100 mg, with a % encapsulation efficiency of 18.38% ± 0.02%. Antioxidant activity tests revealed that LJP possessed the strong antioxidant activity with an IC50 value of 32.59 µg/mL, whereas IC50 values of the microparticles ranged from 24.79 ± 0.03 µg/mL to 39.96 ± 0.07 µg/mL. During storage, the IC50 of LJP decreased from 32.59 ± 0.13 µg/mL to 65.53 ± 0.03 µg/mL, whereas the IC50 of microparticles remained stable. This study concluded that the chitosan-alginate polyelectrolyte microparticle formulation can improve and protect LJP's antioxidant activity.

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.

Laboratory investigation of the swelling pressure of bentonite with quicklime and hydrated lime using ASTM-4546-96 and constant volume methods.

The swelling soils, also known as expansive soils, increase in volume due to an increase in moisture content. The settlement of expansive soils could be the main reason for considerable damage to roads, highways, structures, irrigation channel covers, and the protective shell of tunnels that use bentonite for wall stability. Therefore, it is important to determine the amount of swelling pressure in expansive soils. This research uses two laboratory swelling test methods with constant volume (CVS) and ASTM-4546-96 standard, the swelling pressure of lime-stabilized bentonite soil has been estimated. Based on the key findings of this study, the swelling pressure values of pure bentonite samples tested using the ASTM-4546-96 method, compared to the constant volume swelling test, show an approximately 170% increase.

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.