Search Results (150)

The current state of the art research, and latest engineering technology application in the synthesis of the aragonite crystalline phase of calcium carbonate is presented here. Aragonite crystalline products are highly valuable in selected industries, such as medical and personal care, and in food additives using MgCl₂ as a chemical inducer. The outcome of this literature review provides the outlook of the available research whitespace opportunity in optimizing the current process parameters and in ensuring that sustainable and economically feasible continuous production of aragonite products could be achieved. One of the major improvements proposed in this study is to investigate the methods of synthesizing aragonite crystalline particles using a continuous mineral carbonation reactor system and optimizing the operating parameters. An experimental design was established to identify all the main effects to maximize aragonite production. The three main effects investigated are the effect of feedstock or reactant concentration, the effect of reaction temperature, and the effect of reaction time towards aragonite yield in the final products synthesized. An optimized operating parameter for maximum aragonite yield at 95% purity was proposed at the reaction temperature T of 90 °C, reaction time t of 10 min, and feedstock ratio Mg-to-Ca of 0.4. Subsequently, the continuous reactor system was designed, operated, and tested for at least 50 h operation, where the lime CaO(s) feed was successfully converted into aragonite products with purity between 75 and 81%. The properties and quality of the aragonite produced were analytically characterized from the following laboratory methods which include the thermalgravimetric analysis, TGA; X-Ray Diffraction, XRD; scanning electron microscopy, SEM; and induction coupled plasma, ICP. TGA mass balance after decomposition suggests that 44% of the mass balance represents the weight of CO₂ sequestered in the aragonite crystalline carbonates. Hence, the aragonite crystalline carbonates can be labeled as a green product which sequesters 0.44 kg of CO₂ per 1 kg of precipitated aragonite products synthesized. Interestingly, SEM microscopy characterization results revealed that the aragonite precipitate has a physical morphology of needle-like shape with a good aspect ratio (length/diameter) AR of between 8.67 micron and 11.35 micron. The properties were found to be suitable for paper making fillers, medical, personal care, and food additive applications. Full article

The black pine (Pinus nigra Arn.) is among the most preferred tree species for afforestation in Türkiye. This study aims to examine the effects of afforestation carried out in 1968, 1973, 1985, 1996, and 2002 on soil properties, especially soil organic carbon (SOC) and nitrogen (N) in a semi-arid region of Türkiye. Soil texture, electrical conductivity (EC), reaction (pH), Cation Exchange Capacity (CEC), lime (CaCO₃) concentration, N, and inorganic-organic C contents were determined for each afforestation site. Although afforestation significantly increases SOC and TN stocks, the stand’s age did not affect the dynamics of the SOC stocks. But early stages of afforestation increased N stocks by more than 500%–600% compared to older ones. Our results show that afforestation combined with soil preparation increases the SOC and N contents, and soil tilling without plantation accelerates this process in the initial stages of afforestation. Rather than planting only one tree species, a plantation that mixes broadleaves and conifers with other annual and perennial plants may be more suitable for long-term C sequestration and the use of assisted natural succession in the revegetation of degraded arid and semi-arid regions as an alternative to large-scale afforestation should be paid more attention in the future. Full article

The early detection of diabetes risk and effective management of patient data are critical for avoiding serious consequences and improving treatment success. This research describes a two-part architecture that combines an energy-efficient wireless communication technology with an interpretable deep learning model for diabetes categorization. In Phase 1, a unique wireless communication model is created to assure the accurate transfer of real-time patient data from wearable devices to medical centers. Using Lagrange optimization, the model identifies the best transmission distance and power needs, lowering energy usage while preserving communication dependability. This contribution is especially essential since effective data transport is a necessary condition for continuous monitoring in large-scale healthcare systems. In Phase 2, the transmitted multimodal clinical, genetic, and lifestyle data are evaluated using a one-dimensional Convolutional Neural Network (1D-CNN) with Bayesian hyperparameter tuning. The model beat traditional deep learning architectures like LSTM and GRU. To improve interpretability and clinical acceptance, SHAP and LIME were used to find global and patient-specific predictors. This approach tackles technological and medicinal difficulties by integrating energy-efficient wireless communication with interpretable predictive modeling. The system ensures dependable data transfer, strong predictive performance, and transparent decision support, boosting trust in AI-assisted healthcare and enabling individualized diabetes control. Full article

Recent tailing dam failures in Brazil have been attributed to liquefaction. Chemical stabilization offers a promising solution to enhance the strength and stiffness of tailings and mitigate liquefaction potential. This study investigated the mechanical and microstructural behavior of gold mine tailings (GMTs) stabilized using (i) an alkali-activated binder composed of sugar cane bagasse ash (SCBA), hydrated eggshell lime (HEL), and sodium hydroxide (NaOH) and (ii) Portland cement (PC). Drained and undrained triaxial shear tests and scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM-EDS) analyses were performed. Specimens stabilized with Portland cement exhibited a strong strain-softening behavior and the highest strength, with 5.3 MPa under 200 kPa confining pressure compared to 2.3 MPa for alkali-activated samples and 740 kPa for untreated GMTs. The addition of either binder also increased both the peak effective friction angle and the critical state stress ratio, confirming an enhanced shear strength. SEM-EDS analyses confirmed the formation of cementitious reaction products, explaining these improvements. This research validates both binders as viable solutions for tailing stabilization, with the novel alkali-activated binder offering a sustainable alternative for large-scale applications. Full article

The internet’s evolution as a global communication nexus has enabled unprecedented connectivity, allowing users to share information, media, and personal updates across social platforms. However, these platforms also amplify risks such as cyberbullying, cyberstalking, and other forms of online abuse. Cyberbullying, in particular, causes significant psychological harm, disproportionately affecting young users and females. This work leverages recent advances in Natural Language Processing (NLP) to design a robust and privacy-preserving framework for detecting abusive language on social media. The proposed approach integrates ensemble federated learning (EFL) and transfer learning (TL), combined with differential privacy (DP), to safeguard user data by enabling decentralized training without direct exposure of raw content. To enhance transparency, Explainable AI (XAI) methods, such as Local Interpretable Model-agnostic Explanations (LIME), are employed to clarify model decisions and build stakeholder trust. Experiments on a balanced benchmark dataset demonstrate strong performance, achieving 98.19% baseline accuracy and 96.37% with FL and DP respectively. While these results confirm the promise of the framework, we acknowledge that performance may differ under naturally imbalanced, noisy, and large-scale real-world settings. Overall, this study introduces a comprehensive framework that balances accuracy, privacy, and interpretability, offering a step toward safer and more accountable social networks. Full article

Accurate classification of defects in the semiconductor manufacturing process is critical for improving yield and ensuring quality. While previous works have mainly focused on improving classification accuracy, we propose a model that can simultaneously assess accuracy, prediction confidence, and interpretability in wafer defect classification. To solve the class imbalance problem, we used a weighted cross-entropy loss function and convolutional neural network–based model to achieve a high accuracy of 97.8% on the test dataset and applied a temperature-scaling technique to enhance confidence. Furthermore, by simultaneously employing local interpretable model-agnostic explanations and gradient-weighted class activation mapping, the rationale for the predictions of the model was visualized, allowing users to understand the decision-making process of the model from various perspectives. This research can provide a direction for the next generation of intelligent quality management systems by enhancing the applicability of the proposed model in actual semiconductor production sites through explainable predictions. Full article

To investigate the influence patterns and underlying mechanisms of solid waste-derived Nano-Micro-Powder (NMP) materials on asphalt performance, this study selected nano-sized silica fume (a typical industrial solid waste) along with conventionally used hydrated lime and cement powders as representative modifiers. Based on material type, dosage, and particle size, the high-temperature rheological properties, low-temperature rheological behavior, and nano-scale mechanical characteristics of NMP-modified asphalt were systematically evaluated through dynamic shear frequency tests, Multiple Stress Creep Recovery (MSCR) tests, Bending Beam Rheometer (BBR) tests, and Atomic Force Microscopy (AFM) measurements. Additionally, the grey relational analysis method was employed to quantify the impact of key nanoparticle characteristics on modified asphalt performance. The results demonstrate the following: (1) With increasing NMP dosage and decreasing particle size, the complex modulus (G*) of modified asphalt increases significantly, while the creep recovery rate (R) rises and non-recoverable creep compliance (J_nr) decreases. The creep stiffness slope (m-value) diminishes under low-temperature conditions. (2) Among different NMP types, silica fume-modified asphalt exhibits the highest G*, R, and m-value parameters. (3) At the nanoscale, adhesion force, modulus, and surface roughness all increase with higher NMP dosage and smaller particle size. Silica fume demonstrates superior performance in these nano-mechanical properties compared to hydrated lime and cement powders. (4) Grey relational analysis reveals that specific surface area shows the strongest correlation with the overall performance of NMP-modified asphalt. Full article

Owing to its excellent crack resistance and durability, High-Performance Fiber-Reinforced Concrete (HPFRC) has been extensively applied in engineering structures exposed to extreme loading conditions. The Mode I dynamic fracture strength of HPFRC under high-strain-rate conditions exhibits significant strain-rate sensitivity and nonlinear response characteristics. However, existing experimental methods for strength measurement are limited by high costs and the absence of standardized testing protocols. Meanwhile, conventional data-driven models for strength prediction struggle to achieve both high-precision prediction and physical interpretability. To address this, this study introduces a dynamic fracture strength prediction method based on a feature-weighted linear ensemble (FWL) mechanism. A comprehensive database comprising 161 sets of high-strain-rate test data on HPFRC fracture strength was first constructed. Key modeling variables were then identified through correlation analysis and an error-driven feature selection approach. Subsequently, six representative machine learning models (KNN, RF, SVR, LGBM, XGBoost, MLPNN) were employed as base learners to construct two types of ensemble models, FWL and Voting, enabling a systematic comparison of their performance. Finally, the predictive mechanisms of the models were analyzed for interpretability at both global and local scales using SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) methods. The results demonstrate that the FWL model achieved optimal predictive performance on the test set (R² = 0.908, RMSE = 2.632), significantly outperforming both individual models and the conventional ensemble method. Interpretability analysis revealed that strain rate and fiber volume fraction are the primary factors influencing dynamic fracture strength, with strain rate demonstrating a highly nonlinear response mechanism across different ranges. The integrated prediction framework developed in this study offers the combined advantages of high accuracy, robustness, and interpretability, providing a novel and effective approach for predicting the fracture behavior of HPFRC under high-strain-rate conditions. Full article

This study investigates the production and economic feasibility of biochar for smallholder and family farms in Central Amazonia, with potential implications for other tropical regions. The costs of construction of a prototype mobile kiln and biochar production were evaluated, using small-sized biomass from acai (Euterpe oleracea Mart.) agro-industrial residues as feedstock. The biochar produced was characterised in terms of its liming capacity (calcium carbonate equivalence, CaCO_3eq), nutrient content via organic fertilisation methods, and ash analysis by ICP-OES. Field trials with cowpea assessed economic outcomes, as well scenarios of fractional biochar application and cost comparison between biochar production in the prototype kiln and a traditional earth-brick kiln. The prototype kiln showed production costs of USD 0.87–2.06 kg⁻¹, whereas traditional kiln significantly reduced costs (USD 0.03–0.08 kg⁻¹). Biochar application alone increased cowpea revenue by 34%, while combining biochar and lime raised cowpea revenues by up to 84.6%. Owing to high input costs and the low value of the crop, the control treatment generated greater net revenue compared to treatments using lime alone. Moreover, biochar produced in traditional kilns provided a 94% increase in net revenue compared to liming. The estimated externalities indicated that carbon credits represented the most significant potential source of income (USD 2217 ha⁻¹). Finally, fractional biochar application in ten years can retain over 97% of soil carbon content, demonstrating potential for sustainable agriculture and carbon sequestration and a potential further motivation for farmers if integrated into carbon markets. Public policies and technological adaptations are essential for facilitating biochar adoption by small-scale tropical farmers. Full article

This study presents a cost-effective, carbon-negative construction material for affordable housing, developed entirely from locally available agricultural wastes: rice husk ash, wood ash, and rice straw—materials often problematic to dispose of in many African regions. Rice husk ash provides high amorphous silica, acting as a strong pozzolanic agent. Wood ash contributes calcium oxide and alkalis to serve as a reactive binder, while rice straw functions as a lightweight organic filler, enhancing thermal insulation and indoor climate comfort. These materials undergo natural pozzolanic reactions with water, eliminating the need for Portland cement—a major global source of anthropogenic CO₂ emissions (~900 kg CO₂/ton cement). This process is inherently carbon-negative, not only avoiding emissions from cement production but also capturing atmospheric CO₂ during lime carbonation in the hardening phase. Field trials in Kenya confirmed the composite’s sufficient structural strength for low-cost housing, with added benefits including termite resistance and suitability for unskilled laborers. In a collaboration between the University of Tartu and Kenyatta University, a semi-automatic mixing and casting system was developed, enabling fast, low-labor construction of full-scale houses. This innovation aligns with Kenya’s Big Four development agenda and supports sustainable rural development, post-disaster reconstruction, and climate mitigation through scalable, eco-friendly building solutions. Full article

Fogging on glass poses a severe challenge in daily life, potentially even becoming life-threatening during driving and surgery; therefore there is a need for antifog surface structures. Fabricating superhydrophilic surfaces has been one of the major solutions to the challenge. Conventional direct thermal annealing glass in a furnace at 900 K for 2 h led to superhydrophicity but failed to produce superhydrophilicity on quartz. Meanwhile, it degraded transmission and was low throughput. This study developed a programmed fast plasma treatment of planar soda-lime glass and quartz in air, applied for only a few seconds, that was able to fabricate superhydrophilic surfaces. The process led to a 0° contact angle without sacrificing transmission, a result unreported before. The plasma treatment covered a whole 30 × 30 cm² substrate in only approximately 5 s, resulting in superhydrophilicity, which has rarely been reported before. This simple yet controllable process has great potential for further scale-up and practical applications. Full article

This article presents an interpretable framework for customer segmentation and churn risk detection, integrating fuzzy clustering, explainable AI (XAI), and strategic scoring. The process begins with Fuzzy C-Means (FCM) applied to normalized RFM indicators (Recency, Frequency, Monetary), which were then mapped to a 2-tuple linguistic scale to enhance semantic interpretability. Cluster memberships and centroids were analyzed to identify distinct behavioral patterns. An XGBoost classifier was trained to validate the coherence of the fuzzy segments, while SHAP and LIME provided global and local explanations for the classification decisions. Following segmentation, an AHP-based strategic score was computed for each customer, using weights derived from pairwise comparisons reflecting organizational priorities. These scores were also translated into the 2-tuple domain, reinforcing interpretability. The model then identified customers at risk of disengagement, defined by a combination of low Recency, high Frequency and Monetary values, and a low AHP score. Based on Recency thresholds, customers are classified as Active, Latent, or Probable Churn. A second XGBoost model was applied to predict this risk level, with SHAP used to explain its predictive behavior. Overall, the proposed framework integrated fuzzy logic, semantic representation, and explainable AI to support actionable, transparent, and human-centered customer analytics. Full article

A lime–sand island–reef formation has a dual structure consisting of an overlying loose or weakly consolidated coral sand (CS) layer and an underlying reef limestone layer. The coral sand layer is the sole carrier of the underground freshwater lens in the lime–sand island–reef, and it differs in terms of its hydraulic properties from common terrigenous quartz sand (QS). This study investigated the mechanism of freshwater lens formation, dominated by solute dispersion, combining multi-scale experiments and numerical simulations (GMS) to reveal the control mechanisms behind the dispersion properties of coral sand and their role in freshwater lens formation. Firstly, the dispersion test and microscopic characterization revealed the key differences in coral sand in terms of its roundness, roughness, particle charge, and surface hydrophilicity. Accordingly, a hierarchical conversion model for the coral sand–quartz sand coefficient of dispersion (COD) was established (R² > 0.99). Further, combining this with numerical simulation in GMS revealed that the response pattern of the coefficient of dispersion to key parameters of freshwater lens development is as follows: freshwater appearance time > steady-state freshwater body thickness > steady-state freshwater reserve > lens stabilization time. These results clarify the development mechanism and formation process behind freshwater lenses on island reefs, from the micro to the macro scale, and provide a scientific basis for optimizing the protection of freshwater resources in coral islands and guiding the construction of artificial islands. Full article

Sustained reductions in terrestrial water storage (TWS) have been observed globally using Gravity Recovery and Climate Experiment (GRACE) satellite data since 2002. However, the underlying mechanisms remain incompletely understood due to limited record lengths and data discontinuity. Recently, explainable artificial intelligence (XAI) has provided robust tools for unveiling dynamics in complex Earth systems. In this study, we employed a deep learning technique (Long Short-Term Memory network, LSTM) to reconstruct global TWS dynamics, filling gaps in the GRACE record. We then utilized the Local Interpretable Model-agnostic Explanations (LIME) method to uncover the underlying mechanisms driving observed TWS reductions. Our results reveal a consistent decline in the global mean TWS over the past 22 years (2002–2024), primarily influenced by precipitation (17.7%), temperature (16.0%), and evapotranspiration (10.8%). Seasonally, the global average of TWS peaks in April and reaches a minimum in October, mirroring the pattern of snow water equivalent with approximately a one-month lag. Furthermore, TWS variations exhibit significant differences across latitudes and are driven by distinct factors. The largest declines in TWS occur predominantly in high latitudes, driven by rising temperatures and significant snow/ice variability. Mid-latitude regions have experienced considerable TWS losses, influenced by a combination of precipitation, temperature, air pressure, and runoff. In contrast, most low-latitude regions show an increase in TWS, which the model attributes mainly to increased precipitation. Notably, TWS losses are concentrated in coastal areas, snow- and ice-covered regions, and areas experiencing rapid temperature increases, highlighting climate change impacts. This study offers a comprehensive framework for exploring TWS variations using XAI and provides valuable insights into the mechanisms driving TWS changes on a global scale. Full article

The semiconductor industry currently lacks a robust, holistic method for detecting parameter drifts in wide-bandgap (WBG) manufacturing, where conventional fault detection and classification (FDC) practices often rely on static thresholds or isolated data modalities. Such legacy approaches cannot fully capture the intricate, multi-modal shifts that either gradually erode product quality or trigger abrupt process disruptions. To surmount these challenges, we present M2D2 (Multi-Machine and Multi-Modal Drift Detection), an end-to-end framework that integrates data preprocessing, baseline modeling, short- and long-term drift detection, interpretability, and a drift-aware federated paradigm. By leveraging self-supervised or unsupervised learning, M2D2 constructs a resilient baseline of nominal behavior across numeric, textual, and categorical features, thereby facilitating the early detection of both rapid spikes and slow-onset deviations. An interpretability layer—using attention visualization or SHAP/LIME—delineates which sensors, logs, or batch identifiers precipitate each drift alert, accelerating root-cause analysis. An active learning loop dynamically refines threshold settings and model parameters in response to real-time feedback, reducing false positives while adapting to evolving production conditions. Crucially, M2D2’s drift-aware federated learning mechanism reweights local updates based on each site’s drift severity, preserving global model integrity at scale. The key scientific breakthrough of this work lies in combining advanced multi-modal processing, short- and long-term anomaly detection, transparent model explainability, and an adaptive federated infrastructure—all within a single, coherent framework. Evaluations of real WBG fabrication data confirm that M2D2 substantially improves drift detection accuracy, broadens anomaly coverage, and offers a transparent, scalable solution for next-generation semiconductor manufacturing. Full article