Search Results (246)

Wild edible fruit plants are integral to the cultural, nutritional, medicinal, and economic practices of Indigenous Isan communities in Roi Et Province, northeastern Thailand, a region characterized by plateau and lowland topography and a tropical monsoon climate. This study aimed to document the diversity, traditional uses, phenology, and conservation status of these species to inform sustainable management and conservation efforts. Field surveys and ethnobotanical interviews with 200 informants (100 men, 100 women; random ages) were conducted across 20 local communities to identify species diversity and usage patterns, while phenological observations and conservation assessments were performed to understand reproductive cycles and species vulnerability between January and December 2023. A total of 68 species from 32 families were recorded, with peak flowering in March–April and fruiting in May–June. Analyses of Species Use Value (0.19–0.48) and Relative Frequency of Citation (0.15–0.44) identified key species with significant roles in food security and traditional medicine. Uvaria rufa had the highest SUV (0.48) and RFC (0.44). Informant consensus on medicinal applications was strong for ailments such as gastrointestinal and lymphatic disorders. Economically important species were also identified, with some contributing notable income through local trade. Conservation proposed one species as Critically Endangered and several others as Vulnerable. The results highlight the need for integrated conservation strategies, including community-based initiatives and recognition of Other Effective area-based Conservation Measures (OECMs), to ensure the preservation of biodiversity, traditional knowledge, and local livelihoods. Full article

The Aedes aegypti mosquito, vector of dengue, is a major public health threat in the Caribbean. In Guadeloupe, where dengue outbreaks occur frequently, traditional plant-based remedies are part of the local heritage but remain poorly documented. This study aimed to evaluate the anti-mosquito potential of 38 Guadeloupean plants through an ethnobotanical survey. A semi-structured online questionnaire was conducted over five months, targeting the plant knowledge of residents. Inclusion/exclusion criteria were applied to identify and validate relevant species. Ethnobotanical indices such as Frequency of Citation (FC), Fidelity Level (FL), and Relative Frequency of Citation (RFC) were calculated. Out of the 38 surveyed plants, 22 were confirmed for their traditional anti-mosquito uses. The most cited species included Cymbopogon citratus (93.3%), Artocarpus altilis (25%), and Pimenta racemosa (18.3%). Comparative analysis with existing literature showed that 12 of these plants had not been previously reported for vector control. This highlights the value of ethnobotanical approaches for discovering alternative, eco-friendly vector control options and the importance of preserving traditional knowledge. The study reveals both the high potential of Guadeloupean flora and the risk of cultural erosion, supporting further research into the bioactive compounds of the most cited species. Full article

The Bignoniaceae family encompasses numerous species of ecological, medicinal, and cultural significance, yet its ethnobotanical value remains underexplored in many regions of Thailand. This study investigates the diversity, phenology, cultural relevance, and traditional uses of Bignoniaceae species in Maha Sarakham Province, Northeastern Thailand. Through semi-structured interviews with 260 local informants across 13 districts—alongside field observations and herbarium voucher collections—we documented 27 species across 21 genera. These integrated methods enabled the identification of key culturally significant species and provided insights into their traditional uses. Phenological data revealed clear seasonal patterns in flowering and fruiting, aligned with the regional climatic cycle. Quantitative ethnobotanical indices—including Species Use Value (SUV), Genera Use Value (GUV), Relative Frequency of Citation (RFC), Cultural Importance Index (CI), and Cultural Food Significance Index (CFSI)—were employed to evaluate species significance. Results indicate that species such as Dolichandrone serrulata, D. spathacea, and Oroxylum indicum hold high cultural and practical value, particularly in traditional medicine, spiritual practices, and local landscaping. These findings underscore the critical role of Bignoniaceae in sustaining biocultural diversity and emphasize the urgency of preserving traditional botanical knowledge amid environmental and socio-economic change. Moreover, the insights contribute to broader efforts in cultural heritage preservation and biodiversity conservation across tropical and subtropical regions. Full article

Hyperspectral imaging has been increasingly used in mining for detailed mineral characterization and enhanced ore–waste discrimination, which is essential for optimizing resource extraction. However, the full deployment of this technology still faces challenges due to the variability of field conditions and the spectral complexity inherent in real-world mining environments. In this study, we compare the performance of two approaches for ore–waste discrimination in both laboratory and actual mine site conditions: (i) a data-driven feature extraction (FE) method and (ii) a knowledge-based mineral mapping method. Rock samples, including ore and waste from an open-pit gold mine, were obtained and scanned using a hyperspectral imaging system under laboratory conditions. The FE method, which quantifies the frequency absorption peaks at different wavelengths for a given rock sample, was used to train three discriminative models using the random forest classifier (RFC), support vector classification (SVC), and K-nearest neighbor classifier (KNNC) algorithms, with RFC achieving the highest performance with an F1-score of 0.95 for the laboratory data. The mineral mapping method, which quantifies the presence of pyrite, calcite, and potassium feldspar based on prior geochemical analysis, yielded an F1-score of 0.78 for the ore class using the RFC algorithm. In the next step, the performance of the developed discriminative models was tested using hyperspectral data of two muck piles scanned in the open-pit gold mine. The results demonstrated the robustness of the mineral mapping method under field conditions compared to the FE method. These results highlight hyperspectral imaging as a valuable tool for improving ore-sorting efficiency in mining operations. Full article

Machine predictive maintenance plays a critical role in reducing unplanned downtime, lowering maintenance costs, and improving operational reliability by enabling the early detection and classification of potential failures. Artificial intelligence (AI) enhances these capabilities through advanced algorithms that can analyze complex sensor data with high accuracy and adaptability. This study introduces an explainable AI framework for failure detection and classification using symbolic expressions (SEs) derived from a genetic programming symbolic classifier (GPSC). Due to the imbalanced nature and wide variable ranges in the original dataset, we applied scaling/normalization and oversampling techniques to generate multiple balanced dataset variations. Each variation was used to train the GPSC with five-fold cross-validation, and optimal hyperparameters were selected using a Random Hyperparameter Value Search (RHVS) method. However, as the initial Threshold-Based Voting Ensembles (TBVEs) built from SEs did not achieve a satisfactory performance for all classes, a meta-dataset was developed from the outputs of the obtained SEs. For each class, a meta-dataset was preprocessed, balanced, and used to train a Random Forest Classifier (RFC) with hyperparameter tuning via RandomizedSearchCV. For each class, a TBVE was then constructed from the saved RFC models. The resulting ensemble demonstrated a near-perfect performance for failure detection and classification in most classes (0, 1, 3, and 5), although Classes 2 and 4 achieved a lower performance, which could be attributed to an extremely low number of samples and a hard-to-detect type of failure. Overall, the proposed method presents a robust and explainable AI solution for predictive maintenance, combining symbolic learning with ensemble-based meta-modeling. Full article

Consumer scrutiny of fat content in foods is becoming a notable trend in health concerns. This study aims to develop a novel functional low-fat goat feta cheese by utilizing polydextrose (PDX) and inulin as dietary fiber-based fat replacers to improve its overall characteristics. The physicochemical and textural properties, along with consumer acceptance, of the feta cheese were evaluated across three fat levels (full-fat [FFC], reduced-fat [RFC], low-fat [LFC]) and three fibers: PDX, inulin, and their combination. The intercorrelation of all characteristics was assessed through principal component analysis and Pearson’s correlation. Fat reduction significantly altered the cheese’s visual properties, increasing lightness and the total color difference, which inversely correlated with a* and b* values. Lower-fat cheeses exhibited decreased pH and increased lactic acid, with salinity playing a crucial role in both lactic acid development and texture. Under Scanning Electron Microscopy (SEM), PDX yielded a cheese matrix with a finer pore structure than inulin or the combined fibers. Lower-fat cheeses exhibited greater hardness, with PDX resulting in the highest hardness among the fiber treatments. Crucially, the RFC with PDX was as well-received by consumers as the FFC. These findings not only empower goat farmers and cheese entrepreneurs to increase their product value for niche market but also contribute to sustainability by providing a healthier food option for functional benefits. Full article

Soil structure has an important role in storing and transporting substances, providing natural habitats for soil microorganisms, and allowing chemical reactions in the soil. A complex investigation on factors affecting soil structure characteristics under herbaceous (H), deciduous (D), mixed (M), and coniferous (SP—Scots Pine and NS—Norway Spruce) vegetation was conducted at three experimental stations—Gabra, Govedartsi, and Igralishte, located correspondingly in the Lozenska, Rila, and Maleshevska Mountains in South-West Bulgaria. The data set obtained includes soil structure indicators and physical, physicochemical, chemical, mineralogical, and microbiological parameters of the A and AC horizons of 11 soil profiles. Under different vegetation conditions, soil structure indicators respond differently depending on climatic conditions and basic soil properties. Regarding the plant available water capacity (PAWC), air capacity (AC), and water-stable aggregates (WSAs), the surface soil layers have an optimal structure in Gabra (H, D), Govedartsi (H, SP, NS), and Igralishte (H). The values for the relative field capacity (RFC < 0.6) showed that the studied soils were water-limited. The WSAs correlated with SOC in Gabra, while in Govedartsi and Igralishte, the WSAs correlated with the β-glucosidase known to hydrolyze organic carbon compounds in soil. The information obtained is important for soil quality monitoring under climatic and anthropogenic changes. Full article

To counter the consequences of climate change on both planetary and human health, a greater adoption of sustainable behaviors is required. In this context, two factors emerge as potentially crucial: Readiness to Change (RTC) and environmental sensitivity. The study aimed to investigate the interaction between these two constructs and their impact on the engagement of pro-environmental behaviors and levels of eco-anxiety, in order to assess potential differences in behavioral and affective factors that may support the improvement of sustainable habits. Data were anonymously collected online from 947 participants. A Random Forest Clustering (RFC) analysis was performed as well as Analysis of Variance (ANOVA) to explore differences between the identified clusters in terms of sustainable behaviors and eco-anxiety. The RFC revealed the presence of seven distinct clusters and highlighted that environmental sensitivity plays a key role in defining them. Moreover, the findings showed that high RTC combined with high environmental sensitivity is associated with greater engagement in pro-environmental behaviors and higher levels of eco-anxiety. These results represent a promising groundwork for the development of both future studies in this field of research and targeted educational and awareness programs addressing the climate crisis. Full article

This study characterises low-grade copper ore tailings from a conventional flotation circuit to evaluate their feasibility for further processing. A suite of advanced analytical techniques, such as X-ray fluorescence (XRF), inductively coupled plasma (ICP), X-ray diffraction (XRD), and the quantitative evaluation of minerals by scanning electron microscopy (QEMSCAN), was employed to assess the elemental, chemical, and mineralogical composition of the tailings. Chalcopyrite was identified as the dominant copper-bearing mineral phase, predominantly locked within iron oxides and silicate gangue minerals. The QEMSCAN results showed that chalcopyrite was only partially liberated, which highlights the complex mineral intergrowths that hinder efficient recovery. Based on the mineralogical characteristics, the applicability of various processing techniques, including conventional froth flotation, advanced flotation methods [including Hydrofloat^TM, Jameson, and the Reflux Flotation Cell (RFC)], magnetic separation, and gravity separation, was evaluated. Overall, this study indicates that incorporating HydroFloat™, the Jameson Cell, and the RFC into the flotation circuit could greatly improve copper recovery from tailings. This study also identified rare earth elements (REEs) as potential by-products of copper recovery, so it is an additional opportunity for resource recovery. This paper contributes to sustainable mining practices and resource optimization by highlighting the characteristics and recovery of valuable minerals from tailings. Full article

This study explores the ethnobotanical significance of plant species used in the Heet Sip Song (Twelve Monthly Merit-Making) ceremonies in Roi Et Province, Northeastern Thailand. A total of 80 plant species across 73 genera and 42 families were documented. The findings reveal that plants play multifaceted roles in ceremonial life, serving both symbolic and practical purposes rooted in spiritual belief systems and seasonal agricultural cycles. Quantitative analyses using Cultural Significance Index (CSI), Species Use Value (SUV), Genera Use Value (GUV), and Relative Frequency of Citation (RFC) highlighted the prominence of key species such as Oryza sativa, Musa acuminata, and Saccharum officinarum in ritual contexts. While staple crops dominate in frequency and cultural value, less commonly cited wild species fulfill specialized functions, reflecting deep local ecological knowledge. The integration of ritual and plant use promotes biodiversity conservation by maintaining plant populations and reinforcing sustainable harvesting practices. These results emphasize the vital role of traditional knowledge in conserving both biological and cultural diversity. As environmental pressures increase, this study underscores the importance of supporting community-led conservation efforts that honor indigenous practices and their contributions to ecological resilience. Full article

The work presented in this paper discusses the steps taken to design, implement, and test a mechatronic test stand that uses historical wind power data to generate thermal power that could be used by small-to-medium consumers. The work also pertains to usage in areas where large wind turbines could not be installed due to space restrictions, such as highly populated areas. A rotor flux control (RFC) speed-controlled 2.2 kW AC motor was used to simulate the action of a wind turbine on a 6 cm³ hydraulic pump. The setup allows for a small form factor and a much lighter turbine to be installed. The paper describes the schematic, installation, usage, and initial results obtained using a hydraulic test stand developed by the authors. The initial work allowed us to obtain different temperatures of the hydraulic oil, up to 60 °C, over a period of 30 min, for various pressures and flow rates, thus confirming that the system is functional overall. Further work will elaborate on the effect of different wind patterns on the setup, as well as provide an in-depth study on a use case for the system. Full article

Developing data-driven models for spatiotemporal hydrological prediction presents challenges in managing complexity, capturing fine spatial and temporal resolution, and ensuring model resilience across diverse regions. This study introduces an innovative surrogate deep learning (SDL) architecture designed to predict daily soil moisture (DSM) and daily actual evapotranspiration (DAE) by integrating climate data and geophysical insights, with a focus on mountainous areas such as the Adige catchment. The proposed framework aims to enhance the parameter-calibration quality. The process begins by mapping the statistical characteristics of DAE and DSM across the whole region using an unsupervised fusion technique. Model accuracy is assessed by comparing the similarity of Fuzzy C-Means (FCM) clusters before and after fusion, providing a metric for feature reduction. A data transformation technique using Gradient Boosting Regression (GBR) is then applied to each homogeneous subregion identified by the Random Forest classifier (RFC), based on elevation parameters (Wflow_dem). Furthermore, Kernel density estimation is used to ensure the reproducibility of the RFC-GBR process across large-scale applications. A comparative analysis is conducted across multiple SDL architectures, including LSTM, GRU, TCN, and ConvLSTM, over 50 epochs to better evaluate the beneficial effect of the transformed parameters on model performance and accuracy. Results indicate that adjusted parameter calibration improves model performance in all cases, with better alignment to Wflow ground truth during both wet and dry periods. The proposed model increases the accuracy by 20% to 42% when using simpler SDL models like LSTM and GRU, even with fewer epochs. Full article

As essential components for communication, network protocol programs are highly security-critical, making it crucial to identify their vulnerabilities. Fuzzing is one of the most popular software vulnerability discovery techniques, being highly efficient and having low false-positive rates. However, current network protocol fuzzing is hindered by the coarse-grained and missing state annotations in programs. The current solutions primarily rely on the manual modification of programs, which is inefficient and prone to omissions. In this paper, we propose StatePre, a novel state-handling method for stateful network protocol programs, which leverages large language model (LLM) code- and text-understanding capabilities to analyze request for comments (RFC)-defined state knowledge and optimize the state handling of programs for fuzzing. StatePre automatically refines coarse-grained state annotations and complements missing state annotations in programs to ensure precise state tracking and fuzzing effectiveness. We implement a prototype of StatePre. The evaluation shows that programs modified with StatePre, with fine-grained and comprehensive state annotations, achieve better fuzzing efficiency, higher code coverage, and improved crash detection compared to those not modified with StatePre. Moreover, StatePre demonstrates good scalability, thus is applicable to various network protocol programs. Full article

Particle Swarm Optimization (PSO) has been extensively applied to optimization tasks in various domains, including image segmentation. In this work, we present a clustering-based segmentation algorithm that employs a multidimensional variant of PSO. Unlike conventional methods that require a predefined number of segments, our approach automatically selects an optimal segmentation granularity based on specified similarity criteria. This strategy effectively isolates brain tumors by incorporating both grayscale intensity and spatial information across multiple MRI modalities, allowing the method to be reliably tuned using a limited amount of training data. We further demonstrate how integrating these initial segmentations with a random forest classifier (RFC) enhances segmentation precision. Using MRI data from the RSNA-ASNR-MICCAI brain tumor segmentation (BraTS) challenge, our method achieves robust results with reduced reliance on extensive labeled datasets, offering a more efficient path toward accurate, clinically relevant tumor segmentation. Full article

Oil-in-water emulsions (O/W emulsions) are generally used to lubricate the cold rolling process of low-carbon steel. In addition to the obvious advantages of efficient lubrication and cooling of the process, there are also some disadvantages, mainly related to emulsion bath maintenance, subsequent production steps and waste disposal. In some application areas, Oil-Free Lubricants (OFL’s) have been shown to be at least equally effective in decreasing friction and wear as conventional oil-based lubricants, while resulting in benefits related to waste disposal. In 2023, a project named “Transfer of aqueous oil free lubricants into steel cold rolling practice” (acronym ‘RollOilFreeII’) began, with it receiving funding from the Research Fund for Coal and Steel (RFCS). This project aims at an industrial application of Oil-Free Lubricants in the steel cold rolling process. The project builds on the work of the ‘RollOilFree’ project (also carried out in the RFCS-framework). This article briefly recapitulates the findings in the RollOilFree project and describes the objectives, benefits, activities and first results of the RollOilFreeII project. Notably, a pilot mill trial at high speed has been carried out, showing a good performance of the investigated OFLs. Back-calculated friction values were equal to, or even slightly lower than, reference O/W emulsions. The strip cleanliness with OFLs is much better than it is with the reference O/W emulsions. Only for a very thin product, as is the case in tinplate rolling, does the direct application of a conventional O/W dispersion (a high-particle-sized O/W emulsion) give a better performance than the investigated OFLs. Further development of OFLs should focus on this aspect. Full article